effectiveness of marketing strategy research paper

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Open Access

Peer-reviewed

Research Article

Determinants of content marketing effectiveness: Conceptual framework and empirical findings from a managerial perspective

Roles Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Health and Nursing, Katholische Stiftungshochschule München, Munich, Germany

• Clemens Koob

• Published: April 1, 2021
• https://doi.org/10.1371/journal.pone.0249457
• Reader Comments

Content marketing has gained momentum around the world and is steadily gaining importance in the marketing mix of organizations. Nevertheless, it has received comparatively little attention from the scientific community. In particular, there is very little knowledge about the effectiveness, optimal design and implementation of content marketing. In this study, the authors conceptualize content marketing as a set of activities that are embedded in and contingent on the specific organizational context. Based on this framework, the authors empirically investigate the context features determining content marketing effectiveness from a managerial perspective, using primary data collected from senior marketers in 263 organizations from various sectors and across different size categories, conducting multiple regression analysis. The empirical results indicate that clarity and commitment regarding content marketing strategy and a content production in line with the organization’s target groups’ content needs as well as normative journalistic quality criteria are context factors associated with higher content marketing effectiveness. The outcomes also reveal that regularly measuring content marketing performance and using the data obtained as guidance for improving content offerings positively influence content marketing effectiveness, as do structural specialization and specialization-enabling processes and systems. The insights provided in this study could offer important theoretical contributions for research on content marketing and its effectiveness and may help practitioners to optimize the design and implementation of content marketing initiatives.

Citation: Koob C (2021) Determinants of content marketing effectiveness: Conceptual framework and empirical findings from a managerial perspective. PLoS ONE 16(4): e0249457. https://doi.org/10.1371/journal.pone.0249457

Editor: Jarosław Jankowski, West Pomeranian University of Technology, POLAND

Received: June 23, 2020; Accepted: March 18, 2021; Published: April 1, 2021

Copyright: © 2021 Clemens Koob. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The author received no specific funding for this work.

Competing interests: The author has declared that no competing interests exist.

Introduction

In times when consumers are becoming increasingly skeptical of traditional advertising, organizations need, more than ever, effective alternatives to traditional marketing communications. In these circumstances, content marketing (CM) has gained momentum around the world and is steadily gaining importance in the marketing mix of organizations, complementing traditional marketing instruments [e.g., 1 ]. CM investments have increased substantially. In the German-speaking area, for example, investments have risen from € 4.4b in 2010 to € 9.4b in 2019 and are forecast to grow further to € 12.5b by 2023 [ 2 ].

Content marketing refers to the creation and distribution of relevant, valuable brand-related content to current or prospective customers or other target groups (e.g. jobseekers, employees or investors) via digital platforms or print media to drive strategic business objectives [ 3 – 5 ]. Unlike traditional advertising, which typically denotes a form of communication designed to persuade or even push target groups to take some action, now or in the future [ 6 ], content marketing focuses on adding value to their lives, for instance by educating them, helping them solve problems, entertaining them or supporting them make well-informed decisions. Thus, content marketing is based on the social exchange theoretical principle that an organization’s delivery of valuable content to a target group will see it rewarding the organization in exchange with positive attitudes (e.g. brand trust) or behaviors (e.g. brand related interactions).

However, despite content marketing’s growing importance, it has received comparatively little attention from the scientific community [ 3 , 5 ]. So far, research has primarily focused on definitions and conceptualizations of content marketing [e.g. 3 , 5 , 7 , 8 ] and potential consumer- and firm-based consequences. Besides, there is a limited number of exploratory analyses and investigations about the effectiveness of content marketing that focus on specific sectors and types of media. Wang et al. [ 4 ], e.g., found CM effectiveness in the B2B domain to depend on the frequency of customers’ content consumption. Taiminen and Ranaweera [ 9 ] identified specific helpful brand actions, i.e. approaching content marketing with a problem-solving orientation, as increasing the effectiveness of B2B content marketing. With respect to consumers and branded social content, Ashley and Tuten [ 10 ] identified frequent updates, incentives for participation, as well as experiential, image and exclusivity messages to be associated with effectiveness. Chwialkowska’s study [ 11 ] revealed that customer-centric as opposed to brand-centric social content is more effective. Also, Liu and colleagues [ 12 ] provided evidence that short video clips can be effective to drive usage of other branded online content. However, apart from such rather focused studies, we have very little overall knowledge about the effectiveness of content marketing. In particular, and as Hollebeek and Macky [ 3 ] noted, still “little is known regarding its optimal design and implementation”. The question “what are the key factors for effectiveness” has long been an important theme in the marketing communications literature, but academic understanding regarding the determinants of content marketing effectiveness lags behind to date [ 3 ], generating an important knowledge gap that we address in this paper.

To investigate this gap, we conceptualize content marketing from an activity-based perspective. In line with the activity-based perspective of marketing [ 13 , 14 ], we propose to view content marketing as a set of specific activities, comprising content marketing strategizing, content production, content distribution, content promotion, performance measurement and content marketing organization. Referring to the concept of embeddedness [ 15 , 16 ], we further assume that these content marketing activities are rooted in and contingent on the specific organizational context, and that particular context features are potential determinants of content marketing effectiveness. Based on this framework, we will empirically investigate the features driving content marketing effectiveness.

Our contribution is as follows: As far as we know, the determinants of content marketing effectiveness have not yet been empirically investigated from a broader perspective. We therefore first provide a theoretical framework for analyzing content marketing effectiveness. Second, we offer empirical insights that could help marketers to potentially improve the design and implementation of their content marketing initiatives, which researchers have called for [ 3 , 5 ]. Third and in doing so, we might help to move the research on content marketing effectiveness beyond the prevailing anecdotal to an evidence-based level. Fourth, for scholars, this research could offer a platform for further studies into the drivers of content marketing effectiveness. Taken together, these advances could extend current academic and managerial discussions of how to achieve effective marketing communications.

Theoretical framework and derivation of hypotheses

Any empirical investigation of the determinants of content marketing effectiveness requires a proper conceptualization of CM effectiveness. Hence, the next section proposes such a conceptualization. After that, we propose that content marketing activities take place in an organizational context [ 15 , 16 ] affecting their effectiveness. Context refers to the specific intra-organizational circumstances, environments and constellations of forces shaping the character of the content marketing activities and their outcome [ 17 ]. We outline the potentially relevant context dimensions, being content marketing strategizing, content production, content distribution, content promotion, content marketing performance measurement, and content marketing organization, respectively.

Content marketing effectiveness

Based on a literature review ([ 3 – 5 , 8 , 18 – 23 ], see S1 Appendix for details), content marketing activities can be seen as effective if they trigger superior levels of cognitive, emotional and behavioral customer engagement at the appropriate points throughout the customer journey, strengthen customers’ brand trust and induce favorable brand attitudes, and increase customers’ perceived value of a brand, leading to more favorable responses to the brand and its communications, and thus helping the focal organization reach its strategic business objectives.

CM effectiveness and CM strategizing

Porter and McLaughlin [ 15 ] conclude that there is no universally agreed-upon set of components that comprise the relevant organizational context dimensions. However, they point to the strategizing context to be one of them, i.e. the constellations under which strategizing in the sense of ‘doing of strategy’ unfolds [ 15 ]. Strategy research supports the idea that strategic clarity is one aspect of the strategizing context that plays a key role regarding effectiveness since it gives direction and provides orientation [ 24 , 25 ]. This is also in line with goal setting theory which posits that specific and well-defined challenging goals lead to higher performance [ 26 ]. Strategy research also suggests strategy commitment, which can be defined as the extent to which managers and employees comprehend and support the goals and objectives of a strategy [ 27 , 28 ], as an essential aspect, as it is known to affect strategy supportive behavior. We assume these two factors to be pivotal for content marketing effectiveness, too. In the content marketing domain, strategizing comprises, e.g., the crafting of a content marketing mission and vision, the definition of objectives, the identification and prioritization of target groups, the specification of the unique value an organization is looking to provide through its content, the clarification of key stories to be communicated, or decisions regarding the platforms that will be used to disseminate content [e.g., 5 ]. A clearly defined content marketing strategy that is communicated and understood within the organization might positively influence CM effectiveness, because it allows to select those CM projects which promise a high strategy contribution. In case commitment to a content marketing strategy is high, all managers and employees might show vigor, get engaged and take personal responsibility for the successful realization of the content marketing initiative. Thus, we expect:

• Hypothesis 1 : Content marketing is more effective when organizations have a stronger CM strategizing context characterized by strategic clarity and commitment .

CM effectiveness and content production

Furthermore, we suggest a strong content production context will be positively related to CM effectiveness. By this, we refer to content production environments in which high quality content can be created [ 5 ]. The necessity to create and provide quality content is widely acknowledged in the CM literature [e.g. 5 ], as it is assumed that quality content is more likely to be interacted with. However, this raises the question of what constitutes quality content. Uses-and-gratifications-theory supports the idea that people seek out media that satisfy their needs and lead to gratification [ 29 , 30 ]. From this perspective, consumers may select content for functional (e.g. learning about brands, self-education), hedonic (e.g. entertainment, diversion, relaxation) or authenticity motives (e.g. identity construction, self-assurance) [ 3 , 30 ]. In addition to that, research proposes that ‘quality content’ not only has to meet consumers’ subjective standards, but also certain objective specifications or normative principles. The criteria mentioned in the literature typically include aspects like timeliness, objectivity, accuracy, or diversity of viewpoints [ 31 – 36 ]. Hence, we believe:

• Hypothesis 2 : A strong content production context , characterized by efforts to optimize customer-perceived content value and to adhere to normative quality criteria should be associated with higher content marketing effectiveness .

CM effectiveness and content distribution

We assume a specific content distribution context will also be positively related to CM effectiveness. The content distribution context refers to the conditions under which content is distributed and particularly includes the media platforms (e.g. customer magazines, digital magazines, blogs, podcasts, social media, chatbots etc.) used [ 3 , 5 , 8 ]. Research generally supports the idea that communications efforts using multiple media platforms are more effective than initiatives using only a single medium [e.g. 37 , 38 ]. According to Voorveld et al. [ 39 ], two psychological processes play a role in explaining these effects. First, forward encoding implies that the exposure to content in the first medium primes interest in the content in the second medium, which in turn stimulates deeper processing and easier encoding of the second content piece, resulting in multiple content retrieval cues and higher effectiveness. Second, multiple source perception refers to the effect that consumers perceive cross-media communications as more expensive, leading to the belief that the communicating brand has to be popular and successful, also resulting in more positive communications results. Furthermore, benefits from combining multiple media distribution platforms might arise from accompanying prospects and customers with the appropriate content platforms at the different points in their consideration and buying processes [ 40 ]. On the other hand, it could be argued that investment in too many media distribution properties might attenuate the power of communications, because it prevents an organization from focusing its resources on the most suitable platforms [ 38 ]. Reactance theory also suggests that communication across multiple media platforms could unfold negative consequences as customers might associate a brands omnipresence at various platforms with increasing pressure from the firm’s communications attempts which could be perceived as obtrusive [ 41 ]. Based on these considerations we believe:

• Hypothesis 3a : Content marketing is more effective , when the content distribution context is characterized by the usage of an intermediate number of media platforms .

Content marketers continue to watch out for new opportunities to reach customers and, over time, have shifted content distribution budgets away from print media such as customer magazines to digital media such as digital magazines, blogs, social media and the like [ 2 ]. The question is whether and to what extent this shift is beneficial for improving CM effectiveness. Communications theory implies that for effective communication, the sender should match the channel that the receiver prefers [ 42 ]. Based on this recommended practice of media matching, organizations ought to be cognizant of customers’ media platform preferences as well as actual media use and adjust their channel choices accordingly. With regard to media preferences, research has repeatedly revealed a high level of consumer conservatism, indicating that established media channels, especially print media, retain favored attributes such as trust, high perceived value, intimacy or visual power, whereas digital media are, e.g., more strongly associated with speed, convenience and efficiency [ 42 , 43 ]. Considering media use, two models predict different relationships between new and established media. The displacement model assumes increases in new media use will go along with declines in the use of established media (e.g. due to functional advantages of new media or limited time budgets [ 44 , 45 ]). The complementary model hypothesizes new media usage has no or even a positive effect on established media use within a content domain, as people “interested in procuring information in a particular content area expose themselves to a multitude of media outlets to optimize the information on that particular content area” [ 46 ]. Recent studies [ 45 , 47 ] have provided evidence that adoption of new platforms is reducing the consumption of established media, but that established media will not be fully displaced. Other theoretical accounts also suggest not to neglect print media for digital media. Psychological ownership theory implies that print media, being physical goods, might have a greater capacity to garner an association with the self than digital media, leading to greater value ascribed to them [ 48 ]. Regarding text-based content, educational research points to the fact that reading on paper leads to significantly better content comprehension than reading digitally [ 49 ], possibly due to better spatial mental representation of the content and more visual and tactile cues fostering immediate overview of the content. Consequently, we expect:

• Hypothesis 3b : Content marketing is more effective , when the content distribution context is characterized by a joint deployment of print and digital media platforms .

CM effectiveness and content promotion

Furthermore, we propose the content promotion context is key for CM effectiveness. Content promotion refers to any paid measures an organization takes to draw attention to its content or to stimulate interest in or usage of its content, typically with the help of or on third-party platforms, with the aim of optimizing content reach. Instruments include, amongst others, influencer marketing, social media and search engine advertising, or classic public relations [ 50 ]. Research has repeatedly suggested an attention economy [e.g., 51 ], denoting a world where people are awash in content, and where peoples’ available time and attention spans are limited, creating an environment in which content competes for customers’ time and attention as scarce resources. Under these circumstances, we expect that paid content promotion measures can help to accentuate content and draw attention to potentially relevant and valuable content pieces, so that these pieces can break though the “content clutter” [ 52 ].

Furthermore, the power law of practice and cognitive lock-in theory [ 43 , 53 ] state, that when people practice specific tasks, the repetition of these tasks increases efficiency, which induces familiarity, from which in turn people are inclined to get cognitively locked-in to the respective media environment. Cognitive lock-in thus denotes a condition wherein a consumer has learned how to use a specific media environment, thanks to multiple interactions with it, with the effect that more familiarity decreases his propensity to search for and switch to competing media alternatives. Research has demonstrated these effects for websites [ 53 , 54 ], as well as for print media [ 43 ]. We believe this thinking may be applicable for a broad range of media environments and applying it to the content marketing context leads us to believe that if customers are already accustomed to use specific content offerings, they see no need to switch to a new content offering. Under these conditions, paid content promotion measures might help to stimulate customers to try a focal organization’s content offer, potentially breaking up existing and initiating new cognitive lock-in processes, thereby supporting the organization’s attempt to transition customers to its own content offerings. Hence:

• Hypothesis 4 : Content marketing is more effective when organizations have a stronger content promotion context characterized by comprehensive paid content promotion measures .

CM effectiveness and CM performance measurement

We also propose that a strong content marketing performance measurement context within an organization will be positively related to CM effectiveness. Content marketing performance measurement (CMPM) can be defined as establishing metrics related to the organization’s content marketing objectives and measuring and evaluating performance relative to these objectives, for the purpose of providing evidence for effectiveness and efficiency of content marketing activities and optimizing these activities. Previous studies have shown positive performance implications of marketing performance measurement in contexts other than content marketing [e.g. 55 – 57 ]. We believe for four reasons, that this also applies to the content marketing domain. First, the attention-based view of the firm accentuates that one of the key characteristics of measurement systems is their property to focus and direct attention of organizational members to important issues [ 58 ]. By directing minds at what needs to be done, chances increase that it will get done. Thus, we expect, that content marketing performance measurement will get an organization to attend to essential content marketing objectives and activities. We believe that the presence of CMPM activates managers and employees and causes them to achieve coordinated action and to orient their efforts to succeeding on the measured content marketing aspects. Second, previous research [ 59 ] has shown that producing measurements is not enough to get the organization into acting, but that organizations are also sensitive to what issues are internally discussed. We argue that CMPM sparks discussions about important content marketing issues, which helps to summon attention and resources for acting, ultimately improving content marketing effectiveness. Third, performance measurement usually allows to monitor the performance of marketing activities, be it relative to prior objectives, similar activities in the past, or other benchmarks, lowering uncertainty about the performance of decisions and about whether the decisions were the right ones, which in turn helps to learn and plan marketing activities producing desired outcomes [ 56 ]. We thus expect that CMPM will nurture learning, which in turn will improve content marketing decisions, and thus content marketing effectiveness. Fourth, performance measurement usually includes performance feedback, and previous studies have consistently shown that performance feedback is positively associated with work engagement [ 60 ]. Higher work engagement in turn implies that managers and employees invest more energy into their work roles, leading to superior work outcomes [ 61 ]. Thus, we expect that CMPM energizes organizational actors to act in desired ways to meet the organization’s goals. Hence:

• Hypothesis 5 : Content marketing is more effective when organizations have a stronger content marketing performance measurement context .

CM effectiveness and content marketing organization

Finally, we expect a strong content marketing organization will be positively related to CM effectiveness. Porter and McLaughlin [ 15 ] indicate that organizational structures and processes are one of the major components contextualizing activities within an organization. Research on marketing organization also highlights the importance of organizational structures and processes for marketing performance [ 62 , 63 ]. It is widely acknowledged in the marketing literature, that organizations face dynamic and complex marketing communications environments, e.g. in terms of the development and transformation of technology and media or consumer behavior evolving at an increasingly rapid pace [ 6 ]. Under these conditions, specialization and autonomy seem to be favorable characteristics of organizational structure [ 64 ]. Specialization denotes the level to which activities in the organization are differentiated into unique elements, while autonomy refers to the level to which employees have control in executing those activities. Organizations high in specialization and autonomy have a high share of specialist employees who direct their efforts to a clearly defined set of activities, and as experts with specialized knowledge in their particular work areas, they enjoy substantial autonomy to determine the best approach to carry-out their tasks [ 65 ]. According to prior research, the combination of specialization and autonomy enables an organization to assign tasks to those employees who are best able to perform them, it enhances the organization’s knowledge base, and it promotes the development of innovative ideas and solutions [ 62 , 63 , 66 ]. However, research has also indicated that specialized organizational structures with high degrees of autonomy need the support of adequate processes and systems to function properly [ 62 ].

The application of this thinking to content marketing leads us to two considerations: First, we believe that, also in this domain, structural specialization coupled with autonomy could be beneficial. It could allow an organization to assign content marketing tasks to managers and employees that are best prepared to tackle them. Further, specialization could enhance an organization’s content marketing knowledge base, foster the development of innovative content marketing ideas and solutions and enable the organization to quickly respond to upcoming communication needs. An example for such a structure could be a dedicated content marketing unit with a high share of task- and skill-specialized content marketing experts that have control over how they organize their work and that have significant autonomy in making decisions. Second, we assume that an increase in content marketing specialization and autonomy within an organization also demands processes and information technology systems with a proper fit [ 67 ]. We believe that processes and systems are required that enable and support interaction and collaboration between content marketing specialists, between content marketing experts and further marketing functions, and also between content marketing experts and other relevant organizational entities. To sum up, we posit:

• Hypothesis 6 : Content marketing is more effective when organizations have a stronger content marketing organization .

Fig 1 provides a summary of the proposed theoretical framework.

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https://doi.org/10.1371/journal.pone.0249457.g001

Data collection and sample

We gathered data from organizations with over 250 employees in the German-speaking area, that is Germany, Switzerland, and Austria. Regarding industry characteristics, organizations from all sectors in line with the business registers of the three countries, comprising a broad range of industrial, services, finance and trade sectors, were eligible to take part in the investigation. We targeted medium- and large-sized organizations because they are more likely to employ complex marketing practices such as content marketing. All data were collected using an online survey with the sample drawn from an online panel provider. There is profound evidence from prior research that online panel data is capable of delivering high-quality data outcomes [ 68 ]. Porter et al. [ 68 ] recommend using online panel data particularly for studies requiring access to specific populations. Referring to this guidance, online panel data and the online panel provider Norstat were deliberately chosen for this study, because it required access to the very specific population of senior marketing or communications directors, and people in equivalent positions, responsible for the respective firms’ content marketing activities, as key informants, with the online panel provider being capable of recruiting this hard-to-reach sample. The aforementioned group of managers was identified as key informants because they are organizational members who can provide reliable data on the organizations’ content marketing activities and effectiveness. Data collection was carried out in accordance with further recommendations compiled from the literature by Porter et al. [ 68 ] regarding participant recruitment, selection and information and data quality measures. We captured participants’ managerial positions and involvement in content marketing activities in a screener survey to verify key informant appropriateness and reduce potential key informant bias, used attention checks and applied lower and upper limits of survey completion time to ensure high-quality responses, and captured IP addresses to control for potential multiple responses from the same managers.

Before carrying out the study, the University Ethics Review Board regulations indicated that a research ethics review was not required. Reasons for this decision are that the investigation does not include any manipulations or vulnerable groups, and participants were guaranteed that their data is treated anonymously. Moreover, the data has been collected consistent with the ethical guidelines of the Academy of Marketing Science and in accordance with the EU General Data Protection Regulation. All participants provided informed consent by clicking on the link to start the study, participation was completely voluntary, and only data from participants were used who fully completed the study.

In total, data collection yielded 319 responses. The sample comprised 53 managers from organizations that do not apply content marketing practices and 3 executives that failed to pass the aforementioned data quality checks. We therefore eliminated those respondents from the sample. Hence, the final sample comprised the answers from 263 organizations.

The characteristics of respondents were in line with our expectations of key informants. We were successful in getting senior-level marketing and communications executives as respondents: 131 were board members such as CMOs, 56 were marketing vice presidents or directors, 38 were corporate communications vice presidents or directors, 36 were vice presidents or directors of a dedicated content marketing unit, and the remaining 2 were senior executives in other marketing communications functions. Of the 263 organizations in our sample, 125 were from the services sector, 67 from the industrial sector, 51 from the finance sector, and 20 from the trade sector. Regarding size, 69 organizations had between 250 and 499 employees, 58 had 500 to 999 employees, 72 had between 1,000 and 4,999 employees, and 64 employed a workforce of 5,000 or more people.

For collecting data, we relied on a structured questionnaire. Whenever possible, we used measures from previous research and modified them for our study. All questions were asked in German language. The measures of the main variables are displayed in the table in S1 Table .

Dependent variable.

Content marketing effectiveness (CMEFFECT) . To capture the degree of achieved content marketing effectiveness, we asked senior marketing and communications executives for their evaluations. For assessing attained customer engagement as aspect of content marketing effectiveness, we adapted three items from the consumer brand engagement scale which was developed by Hollebeek et al. [ 69 ]. These questions capture the managerial assessment of the extent to which focal content marketing activities foster positive brand-related cognitive, affective and conative activity, i.e. consumers’ brand processing, affection, and activation. To assess content marketing’s effects on brand attitudes and perceived brand value as further aspects of content marketing effectiveness, we adapted four perceptual items drawn from Sirdeshmukh et al. [ 70 ] and Sengupta and Johar [ 71 ]. These questions capture the managerial assessment of the degree to which the respective organization’s content marketing activities trigger brand trust in terms of credibility (expectancy that a promise made by the brand can be relied upon) and benevolence (confidence in the brand motives) and contribute to favorable brand evaluations. Responses to all items of content marketing effectiveness were given on 5-point agreement scales (1 = strongly disagree and 5 = strongly agree). An exploratory factor analysis delivered a one-factor solution; thus, we averaged all items to calculate the overall index of content marketing effectiveness. Cronbach’s alpha coefficient for content marketing effectiveness was .88, exceeding the recommended minimum of .70, indicating a very good reliability [ 72 ].

Independent variables.

Content marketing strategizing context (CMSTRAT) . The content marketing strategizing context was assessed using a four-item scale that measured whether the organization had a defined, comprehensible, long-term content marketing strategy and to what extent managers and employees support the strategic direction. The items for strategic clarity and strategy commitment were adapted from related scales developed by Bates et al. [ 73 ] and Noble and Mokwa [ 74 ]. Responses were given on 5-point agreement scales (1 = strongly disagree and 5 = strongly agree).

Content production context (CPROD) . We assessed the content production context using a three-item scale. The items rest on previous research by Hollebeek and Macky [ 3 ], Urban and Schweiger [ 35 ] and Chen and colleagues [ 75 ] and include an organization’s efforts to optimize customer-perceived content value, to adhere to normative content quality criteria, and to plan and create content systematically. Responses were given on 5-point agreement scales (1 = strongly disagree and 5 = strongly agree).

Content distribution context / intermediate number of media platforms (CDIST1) . In line with previous research by Kabadayi and colleagues [ 76 ], we used a single item to measure the number of media platforms the organizations used for content distribution purposes. We presented our respondents with the following seven media platform alternatives and asked them to mark the ones used by their organizations: customer magazines or newspapers, corporate books, company reports, owned digital media (websites, apps, newsletters, blogs), organic social media, paid social media and emerging platforms (e.g. chatbots, voice assistants). We developed this list on the basis of a review of the academic and trade literature combined with prestudy interviews of content marketing executives. Although we intended the list to be comprehensive, we asked respondents with media platforms not included in the list to add those platforms in a space that was provided. The measure of platform number was simply the number of platforms that each organization used. The range on this item was 1 to 7 platforms. Based on this item, we calculated our measure so that the usage of the intermediate number of four media platforms was assigned the maximum value 4, while lower or higher number of platforms used were assigned values in the range between 1 and 3.

Content distribution context / joint deployment of print and digital media platforms (CDIST2) . To operationalize the joint deployment of print and digital media platforms in content distribution, we asked respondents–as done in prior research [ 77 ]–how much of their content distribution budgets their organizations were allocating to print or digital media platforms, respectively, with the percentages summing up to 100 percent. We used this information to construct the joint deployment score for each organization and assigned values between zero (print or digital only) and fifty (balanced budget shares) to reflect joint platform usage.

Content promotion context (CPROM) . To measure the weight organizations attached to content promotion, respondents were requested to state the share of overall content marketing investments that their organizations allocated to content promotion measures. We adapted this approach from Fam and Yang [ 77 ] because marketing executives are usually sensitive to budget information, hence they would feel more comfortable in providing the relative weight of content promotion budgets rather than an absolute figure, leading to more accurate data. The range on this item was 0 to 100 percent.

Content marketing performance measurement context (CMPERME) . We assessed the CM performance measurement context using a three-item scale. The items rest on previous research by O’Sullivan and colleagues [ 55 ] and Mintz and Currim [ 56 ]. They capture content marketing performance measurement frequency regarding deployed print and digital content platforms as well as actual performance measurement data use in terms of the employment of data as guidance for continuously improving content offerings. Responses were given on 5-point agreement scales (1 = strongly disagree and 5 = strongly agree).

Content marketing organization (CMORG) . To capture structural specialization and autonomy in the content marketing domain and specialization-enabling processes and systems, we used four questions based on prior research by Olson et al. [ 63 ], Walker and Ruekert [ 66 ], Barclay [ 78 ] and Škrinjar and Trkman [ 79 ]. These questions capture the presence of dedicated content marketing units, task- and skill-specialized, autonomous content marketing experts, and processes and information technology systems that enable collaboration of specialized staff and units. Responses were given on 5-point agreement scales (1 = strongly disagree and 5 = strongly agree).

Control variables.

In addition to the above variables, we considered control variables in our analyses. We followed recommendations for control variable use in the literature that suggest a focused use of controls to not unnecessarily loose available degrees of freedom and statistical power [ 80 , 81 ]. We also opted for a focused approach to avoid increase in questionnaire length, because this commonly leads to higher response burden [ 82 ], which is associated with lower response rates and more response biases. First, we included organizational size (SIZE) as a control variable. Size is established to potentially confound marketing practices [ 83 ] and organizational performance measures [ 84 ]. For example, compared to larger organizations, smaller organizations were found to be more informal with regard to marketing planning and to use fewer ways to measure performance [ 83 ]. Thus, organizational size may relate to an organization’s content marketing activities and CM effectiveness. Organizational size was measured by asking the key informants for the number of full-time employees, referring to four size categories. Three dummy variables were used, concerning organizations with 500 to 999, 1,000 to 4,999, and 5,000+ employees, respectively. Organizations with 250–499 employees served as the comparative category. Second, we also controlled for an organization’s sector affiliation (SECTOR) . A dummy-coded variable (0 = industrial sector and 1 = services sectors) was assigned to the participating organizations. The rational for selecting sector affiliation as control was that it is well established that sector characteristics, in particular differences between industry and services, play an important role for organizational behavior and outcomes [ 85 ]. Examples for sector-specific features are legal restrictions, competitive specifics, ethical concerns, or customer specifics [ 86 ]. In content marketing it could, e.g., be that creating attractive, compelling content is harder for organizations in industrial sectors.

Measure validation and analytical approach

Measure validation..

As our data met sample size recommendations [ 87 ], we assessed the validity of our measures using confirmatory factor analysis. The analysis was performed using the lavaan package in R. We estimated a measurement model with the seven reflective constructs in our study (CMSTRAT, CPROD, CDIST1, CDIST2, CPROM, CMPERME and CMORG). Regarding the inclusion of the three single-indicator latent variables (CDIST1, CDIST2, CPROM) in the analysis, we followed the recommendations in the literature [ 88 , 89 ] to fix loadings at “.95 * variance” and to calculate error variance as “sample variance of the indicator * (1 - .85)”, thus separating the single indicators from the latent variables. We used the robust Satorra-Bentler MLM estimator, since the multivariate normality assumption was not met (Mardia Statistics: skew = 41.95, p < .01 and kurtosis = 374.90, p < .01). The results indicate adequate levels of fit (CFI = 0.97, SRMR = 0.04, RMSEA = 0.05, χ 2 /df = 145.5/101), in accordance with the guidelines provided by Hu and Bentler [ 90 ].

We assessed convergent validity of the measures by examining factor loadings. The analysis indicated that all factor loadings are high (ranging from 0.58 to 0.92), in line with the guidelines of Hair et al. [ 91 ], and significant. Cronbach’s alphas of all of the measures range from 0.71 to 0.86, surpassing the acceptable level of 0.70, and composite reliabilities also surpass the acceptable level of 0.60 suggested by Fornell and Larcker [ 92 ]. Average variance extracted (AVE), reflecting the amount of variance in the indicators that is accounted for by the latent construct, is a more conservative estimate of the validity of a measurement model [ 92 ], and was also calculated for each construct. With the exception of CPROD (0.45), the AVE for each construct is greater than the 0.50 level recommended by Fornell and Larcker [ 92 ]. In sum (see table in S2 Table ), these results indicate convergent validity of the measures.

To test for discriminant validity , we calculated the difference between one model, which allowed the correlations between the constructs (with multiple indicators) to be constrained to unity (i.e. perfectly correlated), and another model, which allowed the correlations between the constructs to be free [ 93 ]. This was done for one pair of constructs at a time. For example, in testing CPROD and CMPERME, the chi-square difference test between the two models (χ 2 d (1) = 362.69, p < .001) affirmed the discriminant validity of these constructs. Similar results were obtained for the other chi-square difference tests, indicating discriminant validity.

To assess content marketing effectiveness, we drew on subjective measures . A part of the literature on performance measurement tends to conclude that subjective measures, compared with objective measures, are less appropriate for performance assessments. It has been argued that managers may tend to overrate their organization’s performance [e.g., 94 ], and that using subjective measures can be problematic when explanatory variables of performance are measured using the same informant, as this can implicate common method bias [ 95 ]. However, as done in prior research [ 96 ], we deliberately decided to rely on managers’ subjective evaluations because of the lack of generally accepted and comparable objective content marketing effectiveness indicators. Moreover, Singh et al. [ 96 ] have demonstrated that carefully collected subjective performance measures can yield reliable and valid data. To alleviate common method concerns we first used procedural remedies in line with recommendations provided by Podsakoff et al. [ 95 ]. We divided the questionnaire into various subsections, so respondents were required to pause and carefully read instructions for each set of questions, contributing to the psychological separation of predictor and criterion measures. We relied on different scale types to reduce common scale properties. In addition, we kept items specific and labeled every point on the response scales to minimize item ambiguity. We also guaranteed anonymity to diminish the tendency to respond in a socially desirable manner, and we kept the questionnaire as short as possible to maintain motivation to respond accurately. In addition to these procedural remedies, we used the regression-based marker variable technique proposed by Siemsen et al. [ 97 ] to statistically control for potential method bias. According to this approach, common method bias can be effectively reduced when estimating a regression equation by adding a marker variable that is largely uncorrelated with the substantive variables of interest and suffers from some type of method bias. Hence, we deliberately included impression management , i.e. the conscious attempt to present oneself positively, as a potentially ideal marker variable into our study, based on the expectation that this measure is theoretically unrelated and similarly vulnerable to common method variance relative to other study variables. We measured the impression management form of social desirability via the three-item scale described by Winkler et al. [ 98 ]. Items were on 5-point agreement scales (1 = strongly disagree and 5 = strongly agree). Analysis of our data exhibited no to small bivariate correlations (< .15) of the impression management marker ( IMM ) with the substantive variables of interest, supporting the assumed unrelatedness. Thus, we added the marker variable to our regression analysis, described in more detail below, to control for potential common method bias.

The study variables were on different response scales. Hence, we followed the recommendation from Cohen et al. [ 99 ] to put research findings into common, easily understandable metrics, and used simple linear transformations of the original scale units to convert the scores of all variables into standardized units of 0 to 100 (0, 100 for dichotomous variables), representing the percent of maximum possible (POMP) scores for each scale. This approach simplifies interpretability for example by giving immediate meaning to summary statistics such as means and measures of variability or by facilitating comparisons of scores across constructs.

We used linear multiple regression analysis for hypotheses testing in which all variables entered the regression equation on the same step. With regard to Hypothesis 3a, which predicts that content marketing is more effective when an intermediate number of media platforms is used, we categorized, as described above in the measures section, the originally continuous predictor variable so that an intermediate number of media platforms used was assigned the maximum value. Though such categorization is accompanied by loss of information, this allowed us to investigate whether CM effectiveness at an intermediate number of platforms used was different from when more or less platforms were used without resorting to a quadratic function. We proceeded analogously with regard to the analysis of Hypothesis 3b. Statistical analyses were performed using SPSS Statistics 24.0.0.1 software, reporting adheres to the SAMPL guidelines [ 100 ]. Prior to the main analysis, the assumptions of regression analysis were tested. To check linearity between the dependent and the independent variables, we employed partial residual plots of independent variables [ 101 ]. The plots exhibited only minor deviations from linear relations. Hence, we concluded that there was no major problem with the linearity assumption. Regarding multicollinearity, the highest value of variance-inflation factor was 2.81, and the highest value of the condition index equaled 24.90. Since these values are below the recommended threshold of respectively 10 and 30 [ 72 ], there is no indication for collinearity concerns. A Shapiro-Wilk test of the residuals (W(263) = 0.985, p < .01) found some evidence of nonnormality and a Koenker test (K = 29.97, p < .01) indicated presence of heteroscedasticity in the residuals. We therefore used the generalized information matrix (GIM) test described by King and Roberts [ 102 ] to detect potential model misspecification. Since the value (GIM = 1.375) is below the recommended threshold of 1.5, denoting that robust standard errors are not 1.5 times larger than classic standard errors, there is no indication for misspecification. Hence, we proceeded with our model, and to account for nonnormality and heteroscedasticity, we followed the recommendation of Dudgeon [ 103 ] to use HC3 as robust standard error estimator in our regression. Multiple regression with robust standard errors was carried out using the SPSS macro by Daryanto [ 104 ]. A p-value of < .05 was considered significant.

Descriptive statistics

Table 1 lists the means, standard deviations, correlations, and Cronbach’s alphas of the study variables. In line with expectations, CMEFFECT related positively to CMSTRAT (r = .66, p < .001), to CPROD (r = .68, p < .001), to CMPERME (r = .61, p < .001), and to CMORG (r = .62, p < .001). Notably, CMEFFECT was not correlated with CDIST1, CDIST2, and CPROM.

https://doi.org/10.1371/journal.pone.0249457.t001

Hypothesis testing

Results of the multiple regression analysis with CMEFFECT as dependent variable are presented in Table 2 . The study variables explained a substantial proportion of variance in content marketing effectiveness (R 2 = .61, F(12, 250) = 36.71, p < .001). In Hypothesis 1, we expected that there would be a positive association between a strong content marketing strategizing context, characterized by strategic clarity and strategy commitment, and content marketing effectiveness. The regression coefficient indicates that as we hypothesized, CMSTRAT is significantly and positively associated with CMEFFECT (β = .23, t(250) = 2.94, p < .01). Therefore, the data support Hypothesis 1.

https://doi.org/10.1371/journal.pone.0249457.t002

With regard to Hypothesis 2, we predicted that a strong content production context, characterized by efforts to optimize customer-perceived content value and to adhere to normative quality criteria, should be associated with higher content marketing effectiveness. Results showed that CPROD was positively related to CMEFFECT (β = .37, t(250) = 5.05, p < .001). Thus, Hypothesis 2 cannot be rejected. Hypotheses 3a and 3b predicted that two aspects of content distribution, the usage of an intermediate number of media platforms and a joint deployment of print and digital media platforms, each affect content marketing effectiveness. However, results showed that CDIST1 (β = .01, t(250) = .29, p = .77) and CDIST2 (β = -.02, t(250) = -.50, p = .62) were not significantly related to CMEFFECT. Therefore, Hypotheses 3a and 3b are not supported by our data. Related to Hypothesis 3a, we conducted two exploratory post-hoc analyses to examine whether there might be (a) a linear relationship between the number of content distribution platforms used and content marketing effectiveness, or (b) an inverted U‐shaped relationship between the number of content distribution platforms used and content marketing effectiveness. With regard to (b), we introduced the square of the number of media platforms used as a new variable in the regression model in addition to the number of platforms used. With respect to Hypothesis 3b, we also conducted (a) a post-hoc analysis to test an alternative model that included the potential effect of focusing on print or digital media platforms on content marketing effectiveness, and (b) an analysis testing for a U‐shaped relationship between the share of content distribution budget allocated to digital media platforms and content marketing effectiveness. With regard to (b), we introduced the square of the budget share as a new variable in the regression model in addition to the budget share. However, none of these post-hoc analyses yielded significant effects. In Hypothesis 4, we predicted that there would be a positive relation between a strong content promotion context in terms of paid content promotion budgets and content marketing effectiveness. With respect to this hypothesis, CPROM was not found to have a significant impact on CMEFFECT (β = .02, t(250) = .41, p = .69). Hence, we find no support for Hypothesis 4. To further evaluate the relationship between content promotion and content marketing effectiveness, we conducted an additional exploratory post-hoc analysis. We tested an alternative model that assessed whether the number of content promotion measures is positively related to content marketing effectiveness. The number of measures was also not linked to content marketing effectiveness. Hypothesis 5 stated that content marketing is more effective when organizations have a stronger content marketing performance measurement context. Regarding this Hypothesis, the regression coefficient indicates that CMPERME is significantly and positively associated with CMEFFECT (β = .18, t(250) = 2.69, p < .01). This is the hypothesized outcome, and therefore the data support Hypothesis 5. Furthermore, a specialized content marketing organization with supporting processes and information technology systems (CMORG) was found to have a positive effect on content marketing effectiveness (CMEFFECT) (β = .14, t(250) = 1.97, p < .05), as we hypothesized in Hypothesis 6. Consequently, Hypotheses 6 cannot be rejected.

Finally, we conducted a robustness check of our results by adding the respective organization’s annual content marketing budget to the model. Including this variable into our model did not change our findings, all the variables that were significant remained significant, while the overall annual budget was not significant (β = -.04, t(245) = -0.70, p = .48).

This study examined whether and how the organizational context in which content marketing activities are embedded in determines content marketing effectiveness. We conceptualized and empirically tested a model that proposed that strong content marketing strategizing, content production, content distribution, content promotion, content marketing performance measurement, and structural and processual contexts drive content marketing effectiveness.

Summary of findings and theoretical implications

Considered together, our analysis of the data reveals that context features have a substantial impact on the effectiveness of content marketing activities. Table 3 summarizes the findings.

https://doi.org/10.1371/journal.pone.0249457.t003

Regarding the strategizing context, we found that a well-defined content marketing strategy that is clearly communicated, thoroughly understood by managers and employees, and widely supported within the organization positively influences content marketing effectiveness. The demonstration of this link between strategic clarity and strategy commitment on the one hand and content marketing effectiveness on the other hand adds to the theoretical and empirical elaboration of the determinants of content marketing effectiveness while incorporating insights from strategy research [ 24 , 25 , 27 , 28 ] into the content marketing domain.

In addition, we found that a strong content production context, characterized by the optimization of customer-perceived content value and adherence to normative content quality criteria, has a significant, positive impact on content marketing effectiveness. Our results support the line of reasoning in the uses-and-gratifications- as well as information quality literature [ 29 – 32 ], that providing content aligned with a target group’s subjective judgement of usefulness will increase the likelihood that content is interacted with, in turn positively influencing content marketing effectiveness. While prior content marketing research focused on this argument [e.g., 3 ], we also introduce the compliance with normative content quality criteria (such as diversity of viewpoints or impartiality) as a novel content production context factor that positively influences content marketing effectiveness. From this perspective, the integration of research on journalistic quality in theories about content marketing effectiveness is essential for the progress of knowledge about content marketing effectiveness.

With regard to the content distribution context, we did not find that the usage of an intermediate number of media platforms has a positive influence on content marketing effectiveness. This finding is noteworthy since research on integrated marketing communications generally assumes that using multiple media platforms will increase the effectiveness of communications efforts but that deploying too many media properties will attenuate effectiveness [ 37 , 38 , 40 , 41 ]. One reason for our result could be that the assumption of reactance theory underlying our hypothesis, that, from a certain point, the negative consequences of using an increasing number of media platforms outweigh the positive effects [ 41 ], does not hold. This explanation would be supported by a positive linear association between the number of content distribution platforms used and content marketing effectiveness. However, our post hoc analysis did not provide any evidence for this kind of relationship. Contrary to expectations, we also did not find a positive influence of a joint deployment of print and digital media platforms on content marketing effectiveness. In addition, post hoc analyses showed no significant effects of focusing on print or digital platforms only on CM effectiveness. These findings suggest that there is no general difference in effectiveness between these two kinds of media platforms, a result similar to the conclusion by Kwon and colleagues [ 105 ]. Heterogeneity of preferences theory suggests one interpretation for this [ 41 ], positing that media platform preference is idiosyncratic and that heterogeneity in individual platform preferences influences customer response to content marketing activities. Taking the aforementioned results together, the present study advances research on content marketing effectiveness by suggesting that effectiveness may be less a question of how many or whether print or digital content distribution vehicles are used, but more of utilizing precisely those media platforms that are best aligned with the respective organization’s target groups’ preferences. Following up on this, further research on the effects of using various content distribution platforms on content marketing effectiveness is warranted.

The present study did not find a positive relationship between paid content promotion budgets and content marketing effectiveness. This is not what we expected. However, empirical evidence from the field of advertising effectiveness research suggests an interpretation of the finding that more paid media investments are not always consistent with higher performance. According to respective descriptive knowledge [ 106 ], a metric that determines the level of performance is excess share of voice, defined as a brand’s share of voice minus share of market. Arguably, then, the amount invested in paid content promotion by a brand would have to be related to the total amount invested in paid content promotion in the brand’s category, and to the brand’s market position. Also, the contribution of paid content promotion to content marketing effectiveness could be shaped by the balance between paid promotion and owned content distribution platforms (e.g., [ 107 ]). This research therefore highlights that further work is needed to untangle the conditions under which paid content promotion measures might positively influence content marketing effectiveness.

Our theoretical elaboration and empirical investigation also provided evidence that core elements of the content marketing performance measurement context–regularly measuring the performance of print and digital content platforms and actually using the data obtained as guidance for continuously improving content offerings–positively influence content marketing effectiveness. Though previous research has shown positive performance implications of performance measurement in contexts other than content marketing [e.g., 55 – 57 ], this is the first study to successfully demonstrate this relationship for the content marketing domain. Our research thus expands previous research on CM effectiveness by incorporating performance measurement as a central element of a model of content marketing effectiveness. This finding might also have implications for future research, e.g. regarding the optimal configuration of content marketing performance measurement systems.

Finally, our work extends previous research on content marketing effectiveness by including structural specialization and specialization enabling processes and information technology systems as a new factor that positively influences content marketing effectiveness. The demonstration of the link between organizational structural and processual design elements on the one hand and content marketing effectiveness on the other hand lends support to researchers, such as Lee et al. [ 62 ], who have called for a new perspective of structural marketing, recognizing the importance of using organizational design elements to achieve marketing outcomes.

Overall, the aforementioned findings are important giving the centrality of empirical insights regarding the optimal design and implementation of content marketing initiatives to current academic interest [ 3 , 5 , 8 ].

Management implications

The present study has important implications for practice as well. It clearly identifies four context factors that positively influence content marketing effectiveness. However, it is noteworthy that the strength of relationship between each of these factors and content marketing effectiveness varies. This implies, that managers could, e.g. if necessary due to budget or attention restrictions, prioritize improvements in the content marketing context factors in line with their order of importance for effectiveness as it was found in this study, being (1) content production context, (2) content marketing strategizing context, (3) content marketing performance measurement context and (4) content marketing organization. Nevertheless, efforts to drive improvement in a single context domain are less beneficial than a comprehensive effort to establish strong content marketing context conditions across the entire range of content marketing activities.

In the following sections, we present individual management recommendations, based on the order in which the various context areas in this study were found to be important.

We first advise managers to constitute a strong content production environment. To do so, we encourage content marketing executives to systematically evaluate and optimize customer-perceived content value, which means putting the audience and its needs and wants first while at the same time keeping an eye on the organization’s communications objectives without becoming self-centered. Moreover, our findings provide a powerful argument that organizations should not compromise on the journalistic quality of their content, but instead strive for creating content pieces that stand out regarding journalistic aspects such as narrative perspective, originality, diversity of viewpoints, accuracy, comprehensibility, or compliance with ethical standards.

Our findings also suggest that a strong content marketing strategizing context is associated with higher content marketing effectiveness. In this respect, managers should work towards establishing strategic clarity. To do so, crafting a compelling content marketing purpose and vision, formulating clear content marketing goals and objectives, defining content creation principles and standards, clarifying key stories and main topics, developing customer personas, investing care about what the most appropriate content formats would be for the audiences being targeted, or planning content that is matched to customers’ buying processes would be beneficial for marketers. In addition, our findings suggest that practitioners should pursue strengthening commitment to the content marketing strategy within the organization. Possible measures to enhance comprehension and backing of the content marketing strategy include regularly communicating its core pillars, rigorously and openly addressing areas of concern, explaining strategic decisions, continuously training employees, or fostering strategic conversations (e.g., [ 108 ]).

Third, we highly recommend establishing a strong content marketing performance measurement context because that would quite certainly go along with a higher level of content marketing effectiveness. Establishment of a strong content marketing performance measurement context requires content marketers to shift part of their content marketing budgets from actual content marketing initiatives to measurement and analytic efforts. Doing so would be counterproductive if it did not enhance content marketing effectiveness. Our research supports exactly such a reallocation of resources, demonstrating that it can positively affect content marketing effectiveness.

Fourth, our investigation implies that shaping the structural and processual context of content marketing activities is a central task of managers since a specialized organizational context unfolds positive effects on content marketing effectiveness. One promising way to advance structural specialization is setting up organizational platforms offering shared and specialized working environments, often referred to as brand newsrooms or content factories. Such platforms could include various desks dedicated to specific topics, media, and target groups, teams devoted to strategy, project management, and further service areas such as graphics, video, or analytics, and an editorial board ensuring integration. To unleash agility, these structures should be supported by processes and underlying information technology solutions enabling interaction and collaboration between content marketing specialists as well as integration with further marketing functions and other relevant organizational entities.

Finally, our study questions the current high level of practitioner enthusiasm for focusing on digital content distribution platforms and multichannel communications. In the light of this study’s findings, it seems to be beneficial for organizations to utilize precisely those media platforms and systems that are best aligned with the respective organization’s target groups’ preferences. Caution is also advised regarding practitioner enthusiasm for paid content promotion measures. “Pay to play” measures such as influencer marketing, social media advertising or native ads in editorial environments have been presented as indispensable means to boost content marketing reach and thus improve content marketing effectiveness. However, we do not observe any simple and direct positive effect of content promotion budgets on content marketing effectiveness. As this is one of the first investigations to examine the impact of paid content promotion in the content marketing domain and given that the use and functionality of content promotion measures evolve continuously, our findings are preliminary. Scholars and practitioners need to further explore this emerging field.

Limitations and research directions

As all empirical research, the present investigation has limitations that call for attention in interpreting its findings. First, the data was cross-sectional which prohibits unambiguously interpreting the findings as indicating causality. Still, based on the theoretical argumentation provided above, the directions of causality implied in this study are likely. Future research might try to replicate these relationships via longitudinal or experimental study designs. A second limitation is that, though the study included organizations from various sectors and across different size categories, the sample is rather homogeneous with respect to cultural factors, as all participating organizations were located in Germany, Switzerland or Austria. Hence and given the global nature of content marketing research, scholars could investigate the suggested relationships in other contexts in order to further generalize the current findings. Third, the measurement of content marketing effectiveness is a potential limitation of this investigation, since we relied on subjective ratings rather than objective data. Thus, researchers might validate our findings with objective content marketing performance data. The study builds upon the views of a single key informant in every organization. While the key informant approach is common, relying on multiple informants from each organization might provide an even more balanced view. Besides, as earlier mentioned, the lack of any evidence of effects of the content distribution and content promotion contexts on content marketing effectiveness could be due to the way we framed them in this study. Therefore, other conceptualizations are worth investigating, including considering interactions of these context factors, as each factor’s contribution to content marketing effectiveness might be contingent upon the other. Also, only a limited number of potential confounders could be taken into account in this study. We adjusted for potential effects of firm size and industry, controlled for social desirability, and conducted an additional robustness check of our results that included the respective organization’s annual content marketing budget. In future, researchers could map out the nomological network of the research field in more detail using causal graph analysis [ 81 ], and subsequently conduct studies including further control variables to rule out alternative explanations for the observed relationships. Beyond addressing limitations, this study offers a number of additional directions for prospective research. For example, given that a strong content marketing performance measurement context offers demonstrable benefits, scholars might consider whether certain findings from the general marketing performance measurement field [e.g., 55 , 109 ] also apply to the content marketing domain. Research might, e.g., explicitly take into account whether content marketing performance measurement is comprehensive or selectively focused on particular dimensions, because larger organizations could benefit from more comprehensive and smaller organizations from more focused approaches. Furthermore, future studies may explore the influence of the organizational content marketing context on content marketing effectiveness via structural characteristics other than specialization. Other major structural characteristics, such as centralization, formalization, or modularity, might also exert influence on content marketing effectiveness. Importantly, future research might investigate mediating or moderating variables, such as external environmental effects. Market turbulence, for example, may moderate the value of content marketing context factors. Such investigations could further deepen the understanding of the determinants of content marketing effectiveness.

Supporting information

S1 table. measurement of main variables..

https://doi.org/10.1371/journal.pone.0249457.s001

S2 Table. Factor loadings, composite reliability estimates, average variance extracted.

https://doi.org/10.1371/journal.pone.0249457.s002

S1 File. Dataset of the study.

https://doi.org/10.1371/journal.pone.0249457.s003

S1 Appendix. Literature review on CM effectiveness.

https://doi.org/10.1371/journal.pone.0249457.s004

Acknowledgments

We gratefully acknowledge the valuable comments of Vanessa Haselhoff in the development of earlier drafts of this article.

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Reimagining marketing strategy: driving the debate on grand challenges

Ko de ruyter.

1 King’s College, London, London, UK

Debbie Isobel Keeling

2 University of Sussex, Brighton, UK

Kirk Plangger

Matteo montecchi, maura l. scott.

3 Florida State University, Tallahassee, FL USA

Darren W. Dahl

4 University of British Columbia, Vancouver, Canada

Associated Data

A little less conversation….

A little more action, please. There is no record of Elvis Presley's views on responsible marketing, but his 1968 song, “A Little Less Conversation,” could have been written as a reflection on the global marketing community’s current progress in transforming our field. At the United Nations (UN) General Assembly in 2015, the leaders of 193 nations adopted an ambitious set of 17 global Sustainable Development Goals (SDGs) combatting poverty, inequality, and discrimination. Since then, it has been an imperative for organizations to reimagine their marketing strategy with an eye towards global impact. This is not only a matter of international policy; important shifts in stakeholder views on responsible marketing are also starting to emerge. For example, supply chain partners and end-customers across many industries are increasingly interested in end-of-life cycle initiatives, product-emission rates, product provenance, and transparency of production. These stakeholders are steadily demanding more environmentally-friendly packaging and lower carbon footprints. Further, stakeholders expect human dignity to be respected along this process. Consequently, long-term supply chain strategies are being redefined to acknowledge climate change and human rights issues in strategy formulation and execution.

In turn, marketing scholars have increasingly become concerned with responsible marketing, and although these issues have not always been the focus of our scholarship, it is evident from current work that they are now. There is a growing, rich conversation involving notions of responsibility within marketing in the current scholarship base. The past decade has witnessed an expansion of concepts and empirical evidence regarding the challenges of environmental sustainability, social responsibility, (mental) health and social care, wealth disparities and poverty, nationalism and its impact on global trade, identity loss, and a wide array of unintended consequences of digitization (Hensen et al., 2016 ). As an academic marketing community we are well-placed to lead on relevant change across the social, economic, environmental, and political landscapes; doing so will provide further opportunities for novel contributions to marketing strategy knowledge. Moreover, there is a wider call for societal and political action through purposeful engagement with the world’s grand challenges, thereby inspiring scholars and industry to work together as partners to reimagine the very definition of effective marketing strategy.

Key to successfully transforming marketing strategy is the creation of forward-looking intellectual frameworks, which can serve as springboards for future research that can inform creative and critical scholarship and practice. At this point, marketing scholars are primed to develop sustainable solutions by aligning the interests of principal stakeholders, not just shareholders, and by balancing longer-term and shorter-term benefits. The conversation about reimagining marketing strategy started with a fundamental and paradigmatic shift away from the discipline’s earlier focus on agency and transaction costs. A fruitful lens through which to continue this conversation is the emerging theorizing on stewardship (c.f., Mick et al., 2012 ), which can simultaneously be aligned with sustaining contributions to (or even reimagining) the bottom-line. Furthermore, and in the spirit of stewardship thinking, we recognize that the strength of extant marketing scholarship lies in its knowledge exchange and co-creation with stakeholders. This collaborative approach during the various stages of research design and execution can, and does, bring about meaningful change. It also involves consideration of the interplay between customers/consumers, firms, governmental policies, and society.

We begin by introducing the notion of stewardship as a basis for identifying three complementary principles to guide the continued transformation of marketing strategy (i.e., becoming responsible, respectful, and resilient), which we discuss and integrate with the 17 UN SDGs ( https://sdgs.un.org/goals ). Importantly, we argue that the application of these principles to the grand challenges faced by society today will be an effective way to frame marketing investigations and achieve substantive contributions that meet these challenges. Subsequently, we take stock of the current marketing scholarship through the lens of these three principles by applying them directly to the results of a bibliometric analysis of the marketing literature. We conclude by reflecting on the opportunities for academic practice in marketing with respect to meeting the grand challenges that the world faces.

Responsible, resilient, and respectful principles

Central to stewardship theory is recognizing the importance of balancing personal goals with goals of a larger entity (Hernandez, 2008 ). We feel that stewardship provides a robust basis for reimagining marketing strategy for three reasons. First, if individuals are to assume responsibility to support the greater good, they do so based on the development of an ideological and relational commitment. There is an opportunity for marketing scholars to both identify business practices that can promote collective solutions that benefit both society and the firm, and also quantify benefits to firms and customers of taking a broader collective focus in business practices. This may encourage managers and decision-makers to strive for equilibrium between personal and collective interests. For example, how a store manager values collective welfare (e.g., environmental responsibility) can inspire sales associates to engage in selling green products while managing their sales targets, or can shape how novel product attributes, such as recyclability, biodegradability, and ethical sourcing, can best be promoted. Second, the notion of stewardship implies that people may not fully realize the longer-term consequences of near-term actions. Marketing research on self-control and self-regulation can offer insights into the trade-offs between near-term actions and longer-term consequences of such decisions. This underlines marketing’s unique capacity to conceive solutions that are both resilient and sustainable to collective interests across time; this could involve intergenerational product positioning, and potentially influence environmentally-friendly behaviors across different stakeholders. Third, stewardship affords an equitable distribution of rewards, which indicates the integrity and respect of a shared value approach to contributors to economic and social activity. Marketing’s deep understanding of value can inform facilitation of shared value(s) between stakeholders in multiple domains. This is, perhaps, particularly the case in complex services, which are often characterized by complex power, knowledge, and experiential asymmetries (Keeling et al., 2021 ). Based on this foundation from the stewardship literature, we identify three principles to guide the transformation of marketing strategy in becoming increasingly responsible , resilient, and respectful .

The Responsible principle requires giving voice to all marketing stakeholders for a shared vision of what constitutes a well-balanced and sustainable offering. This principle can be advanced by being approached in a manner that is mutually beneficial to other long-term organizational goals, especially when these offerings challenge conventional thinking or center on short-term benefits. For example, marketing scholars can collaborate with organizations to understand how service firms can adapt to support refugees, and how novel approaches can also strengthen relationships with existing customers. This approach requires extending the focus of scholarly marketing research to include themes that are traditionally not considered to be ‘marketing’, as well as articulating social benefits alongside economic ones. For example, marketing scholarship can make a substantive contribution to public health policy by addressing such issues as how to combat stigmatization in mental health campaigns and how to heighten engagement in health communities among stigmatized patients. Conversely, it also involves taking a fresh look at traditional topics of academic inquiry and revisiting them with a responsibility perspective, in which balancing the needs of individuals and societal concerns are in fact key priorities of the organization. For example, the Responsible Research in Business and Management network encourages research that aligns with this principle ( www.rrbm.network ). Thus, marketing scholarship can help to advance UN SDGs, such as promoting good health and wellbeing (SDG 3), and responsible consumption and production (SDG 12).

The Resilient principle is based on continuous improvement through self and group reflections. Here, the focus is on ensuring and enculturating operational effectiveness and sustainability. This is achieved through establishing world class infrastructure and supply chains, and appropriately harnessing innovation and entrepreneurship. The Covid-19 crisis has exposed the vulnerability of international supply chains, as well as cash and information flows; firms need to develop resilience strategies to deal with this moving forward. Firms are currently revisiting their (ethical) sourcing and procuring (e.g., support of local suppliers), and manufacturing and contactless delivering systems (e.g., Amazon’s last mile concept) to fulfil the changing needs of channel partners and end-consumers. Furthermore, the pandemic-driven surge in peer-to-peer home delivery services (e.g., Instacart, UberEats) has introduced novel dilemmas for firms in terms of product safety, brand management and uniformity, and developing a sustainable workforce. Resilience could also be viewed in terms of marketing’s contribution to alleviating poverty and addressing potential issues associated with climate change, natural resource sustainability, and social instability.

The Respectful principle focuses on enabling different levels of aspiration within a fair society. Equality, diversity, and social inclusion underpin this principle to ensure that vulnerable, disadvantaged, and previously marginalized communities are empowered to make their own meaningful contributions in marketplaces. Mars (a manufacturer of confectionery, pet food, and other food products) revised its advertising code based on the principle of respect, pledging to facilitate casting that ‘ reflects the true diversity of the consumer base that we sell to, as determined by gender, race, sexuality, age, ability, class’ and to portray people as ‘empowered actors and full personalities, rather than using stereotypes ’ (Whiteside, 2021 ). There is a plethora of research themes stemming from the respectful principle, such as implicit gender bias in conversational AI-agents, and rebranding and advertising in times of increased social-political movements (e.g., MeToo, Black Lives Matter). Conversely, uncovering research themes from cases like The Wine Noire, an African American women-owned wine collective organized around an equitable and sustainable supply chain and logistic services for female winemakers and winemakers of color, might inform an agenda of research action.

Mapping the conversation

To further the discussion of the Responsible, Resilient, and Respectful principles, we illustrate current scholarly conversations using a bibliometric approach. This approach organizes the literature by identifying important contributors, contributions, and knowledge structures (Zupic & Čater, 2015 ). Informed by past JAMS editorials, four authors debated and selected keywords relevant to the three principles. 1 We used this curated set of keywords to identify and select articles published in the six leading marketing journals listed in the FT 50 journal ranking. 2 An initial search and article extraction performed on Scopus ( www.scopus.com ) resulted in a sample of 536 articles. We examined each article’s title, keywords, and abstract to determine its relevance to the three principles and retained a final sample of 254 articles.

Annual scientific production (in terms of publications) in our sample has increased substantially over the period considered (1973 to May 2021), exhibiting a compound annual growth of 6.12%. The first production peak is in 1997 with nine articles that broadly examine pro-environmental and pro-social marketing strategies, as well as the impact of these strategies on consumers’ perceptions of firms. The annual scholarly outputs have grown steadily every year since 2011, as evidenced by the 18 articles already published by May 2021. Among the six leading marketing journals we selected, the Journal of the Academy of Marketing Science dominates this literature domain (86 articles), followed by the Journal of Marketing (57 articles) and the Journal of Consumer Psychology (51 articles). We assessed authors’ influence by examining the total number of articles published and citations accumulated by each author. Julie Irwin (McCombs School, University of Texas) is the most prolific author in our sample with a total of seven articles, whereas CB Bhattacharya (Katz Graduate School of Business, University of Pittsburg) leads the citations ranking with 5012 total citations across five publications.

To identify the intellectual structure of this literature domain, we constructed a bibliometric network with VOS Viewer using bibliographic coupling (Fig.  1 3 ). Bibliographic coupling examines similarities between articles in a collection by considering the number of cited references that the articles share (Zupic & Čater, 2015 ). This analysis revealed seven clusters of articles representing distinctive lines of inquiry. We named these clusters to reflect the substantive focus of the scholarly contributions included therein, and then grouped them according to the principles (Table ​ (Table1 1 ).

Bibliometric visualization of the literature according to the Responsible, Resilient, and Respectful principles

Responsible, Respectful, and Resilient principles literature clusters

Notes: a The table includes the top three articles in each cluster by normalized number of citations. The normalization takes into consideration that more recent articles had less time to accumulate citations. The formula is as follows: N o r m a l i z e d - c i t a t i o n s - f o r - a n - a r t i c l e = T o t a l - c i t a t i o n s - o f - a n - a r t i c l e A v e . - c i t a t i o n s - f o r - a l l - a r t i c l e s - p u b l i s h e d - i n - t h e - s a m e - y e a r - i n c l . - i n - s a m p l e   

b Key: JM = Journal of Marketing; JCP = Journal of Consumer Psychology; JCR = Journal of Consumer Research; JMR = Journal of Marketing Research; JAMS = Journal of the Academy of Marketing Science

The conceptual building blocks of the Responsible principle are reflected in Clusters A, B, and C. Articles in the largest cluster (A – Green consumption) examine factors leading to consumer preferences for environmentally-friendly and ethically sourced products, as well as associated persuasion strategies. Taking a broader perspective, contributions in the second largest cluster (B – Responses to Corporate Social Responsibility (CSR) strategies) examine how consumers’ react to firms’ CSR associations. The articles within these clusters are closely connected with the research in Cluster C (Stakeholder relationships) that explores how CSR contributes to corporate reputation among external firm’s stakeholders. In short, despite the extensive conversations regarding corporate responsibility issues, further research needs to focus on how marketing approaches the CSR agenda to encourage even more sustainable behaviors that appeal to a wider range of stakeholders.

The Resilience principle is well represented by Clusters D and E. Cluster D’s (Sustainable marketing strategies) research revolves around green approaches that inoculate marketing from environmental challenges, including enviropreneurialism, corporate environmentalism, and organizational capabilities for resilience. Articles in Cluster E (CSR strategies and firm performance) examine strategic outcomes of CSR investments, including firm market value, firm idiosyncratic risk, and customers’ product and brand evaluations. In sum, the Resilience principle incorporates seminal conceptualizations of sustainability and CSR marketing strategies as drivers of firms’ competitive advantage. However, recent external challenges (e.g., the COVID pandemic) call for a re-examination of these ideas to re-imagine marketing capabilities that will increase the resilience of firms.

The Respectful principle is represented by Clusters F and G. Cluster F’s (Ethical consumption) research concentrates on ethical consumer choices in the context of environmental sustainability, cause-related initiatives, stakeholder collaborations, and other ethical initiatives. Articles in Cluster G (Ethical marketing strategies) includes contributions elucidating the relationship between marketing strategy and CSR initiatives to achieve organizational effectiveness and ethical managerial decision making. As depicted in Fig.  1 , research on the Respectful principle is somewhat more dispersed and often disparate from other conversations. However, research inspired by the Respectful principle has the potential for many substantive future contributions that will shape how organizations interact with diverse, vulnerable, and underrepresented stakeholder groups.

A little more impact please …

Current societal expectations set within the broader context of the UN SDGs, recognition of the individual value of research endeavors (San Francisco Declaration on Research Assessment, DORA, https://sfdora.org/ ), and the move towards wide-scale Open Access of research, mean that the position, nature, and value of academic research in society is being reexamined. This is also the case for business research. For instance, the Association to Advance Collegiate Schools of Business (AACSB, www.aacsb.edu/ ) has expanded its accreditation standards to include ‘engagement and social impact’, a change which is directly tied to the UN SDGs. In parallel, the traditional academic role is changing within Higher Education, as distinct career pathways develop that recognize differing, yet complementary, expertise in research, education, and enterprise. Together, these drivers offer opportunities for further innovation in the field of marketing, integral to which is a change in how marketing scholars and practitioners understand, discuss, and measure research ‘impact’ and how changes in our academic environment offer further channels for development.

With respect to research impact, marketing, as an applied discipline, has consistently examined the ‘fitness’ of research as defined by its relevance and robustness in today’s dynamic environment. The Responsible, Resilient, and Respectful principles that we outline can provide a guide toward articulating impactful contributions to knowledge and practice. As a discipline, marketing is well-placed to develop the opportunities within each of these principles with respect to marketing strategy. We offer Table ​ Table2, 2 , which identifies example research questions that connect each of the stewardship principles to the UN SDGs, as an initial template in framing research impact for marketing strategy moving forward.

Examples of future marketing research questions at the intersection of stewardship principles and United Nations Sustainable Development Goals (UN SDGs)

With respect to the changing academic environment, like many other disciplines, the marketing discipline’s application of impact metrics is in flux and will continue to change in the coming years. The current assessment of output impact based on output levels (typically published journal articles) and using mainly numerical indicators is being challenged (e.g., through institutions committing to the San Francisco Declaration on Research Assessment). Instead, applying the Responsible principle, there is now a demand for broader (e.g., AltMetrics) and more qualitative evaluations of research impact. This demand will drive change in the types of output that articulate how marketing scholars undertake research that generates value for multiple stakeholders. In developing a cohesive narrative of the impact value of scholarly marketing research, there is an opportunity to broaden the definition of impact to include impactful outcomes , in addition to impactful outputs . For example, impactful outcomes due to changes in the marketing strategies that promote electric vehicle adoption will bring a corresponding positive change in the quality of life of consumers by improving air quality. Or consider the example of altering marketing strategies to combat youth obesity by restricting when and where sugary drinks and other junk food can be advertised. At the same time, marketing scholars can also examine the thresholds and timeframes for reasonable expectations about the impact that marketing strategy can achieve.

Furthermore, researchers often navigate the tension between generating research that addresses the need for academic relevance and robustness alongside the need for societal relevance and robustness. Academic relevance (in how contributions and implications are framed) and robustness (in how methodological approaches are framed) are familiar building blocks in the literature. However, due to a broad scope, the marketing discipline has less clarity and consensus on societal relevance and societal robustness . Societal relevance directly asks: ‘What societal challenge does this research contribute to?’ Societal robustness demands not only value for money, but also, more fundamentally, research accessibility and usefulness. An impact framework that integrates outcomes valued by multiple stakeholders can help guide the marketing discipline’s pursuit of high impact research with a societal focus. Such a framework can readily be devised in directly linking the reimagining of marketing strategy to the UN SDGs (such as in Table ​ Table2) 2 ) to enable researchers to articulate the value of their research in terms of mutual relevance, robustness and value.

The Resilient principle calls into question the longevity of what marketing research and practice offers, and how this contributes to sustainable solutions for society. The nature of published content undoubtedly needs to be more diversified to effectively meet the demands of differing audiences. Journal articles are an important means of mobilizing knowledge in academia and for other ‘users’ of research who are able to access such sources. However, journal articles are one part of a wider portfolio of content and services that could fulfil the different needs and purposes of society. Many universities and academics are already diversifying their research portfolios, both in terms of the content produced and services offered (e.g., professional development opportunities directly extending from research), alongside the approaches to communication of outcomes (e.g., podcasts, open access toolkits). This trend will help develop sustainable solutions, especially with respect to the UN SDGs. For example, creating health communications in collaboration with the intended audiences can result in tools that are readily accepted by those audiences, in terms of language, format and content, to bring about the intended impact.

The existing tension is often a simple one: How might we best develop and share proven methods or tools to embed them in practice, and in such a way that this effort is also recognized as a valuable scholarly activity? Companies and communities want to work with academics, but the outcomes they value are not always easily aligned with the outputs valued by academia. This is by no means a new challenge, but the conversation about impact potentially changes the perspective of said challenge. One promising development, in our view, to meet this challenge is the current change in emergent specialist career pathways. These pathways will broaden the way in which academic work is conducted and delivered, thus, impacting traditional research portfolios (i.e., in terms of outputs and outcomes). That is, marketing academics specializing in education are updating pedagogical approaches for future academics and practitioners to aid marketing strategy in coping with global challenges. Those specializing in knowledge exchange are innovating how knowledge about developing marketing strategy is mobilized in multiple formats to reach wider and more diverse user groups. Finally, those specializing in relevant enterprise are driving practical changes in marketing strategy through the commercialization of academic research into valuable products to society. The emergence of these new pathways provides opportunities to not only better address the questions laid out in Table ​ Table2, 2 , but also presents exciting opportunities for academics to further develop new capabilities, for example, their entrepreneurial skills, that complement existing academic skillsets.

More fundamentally, the move towards embracing representatives of a broader society as both co-creators and drivers of the research process is completely changing the conversation between marketing and society. The Respectful principle assumes co-creation. A distinctive strength that marketing strategy scholars bring to the literature is their experience and expertise in working with stakeholders in the field (e.g., consumer groups, nonprofits, companies, governmental agencies). Thus, marketing strategy can leverage these insights to support the development of rigorous research focused on pursuing the grand challenges that are more directly linked to those who are most impacted. The concept here is that outputs and outcomes are not delivered to ‘users’, but rather co-created with stakeholders in society. There are multiple emerging co-creation processes across disciplines and sectors. In healthcare, for example, the principle of respect is embedded within the process of co-production (e.g., https://www.nihr.ac.uk/documents/co-production-in-action-number-three/26382 ). Engaging non-academic partners as co-creators means being respectful of their lived experiences and how it shapes their active creator roles, as well as rebalancing power structures to allow multiple voices to be heard.

At the same time, it is important to respectfully acknowledge and accommodate individual or group heterogeneity in terms of motivation, knowledge and ability to co-create. New approaches (that are to be celebrated in our estimation) involve training non-academic co-creators in research methods. Conversely, in the future, non-academic co-creators can also train academics in this manner. As marketing scholars are aware, the integration of resources in the form of knowledge, skills, experience, enterprise, and networks creates connections and builds awareness between stakeholders to heighten impact. This connectivity is especially valuable where stakeholders have not had an opportunity to meet, discuss, and share ideas previously. Using the UN SDGs, it is possible to identify situations in which stakeholder groups have not had this opportunity, especially groups who are perceived as more vulnerable. An additional benefit of building co-creation opportunities with vulnerable groups is increased transparency and trust between the academic and wider communities. Conflicts can emerge during such collaboration, especially where there has been little previous interaction, but facilitating resolution of this conflict is impactful in its own right. Marketing, with its keen understanding of stakeholder perspectives, can empower groups of stakeholders to move away from normalized or entrenched expert knowledge and solutions. In doing so, groups of co-creation partners can truly deliver outcomes that are very much ‘fit for purpose’ (e.g., in relation to innovative solutions to address aspects of the UN SDGs). We see marketing as a discipline that is well-positioned to take the lead in developing and fostering diverse multi-disciplinary groups to bring about this shift.

Embracing the broader changes in academia, the outcome we seek here is a renewed call for the facilitation of better marketing strategy that will boldly address society’s grand challenges, and contribute to tackling the UN SDGs through responsible, resilient, and respectful research collaborations with stakeholders.

Supplementary Information

Below is the link to the electronic supplementary material.

1 Our selection of keywords included: societal, corporate social responsibility, social responsibility, CSR, sustainability, sustainable, ethics, ethical, cause-related, environmental, stewardship, vulnerable, disenfranchise, equality, diversity, inclusivity, morality, empowerment . This set of keywords allowed us to extract a comprehensive literature sample that delineates the core themes in marketing strategy relevant to the three principles. We acknowledge that this set of keywords is not comprehensively conclusive. Within the context of this editorial, our analysis is intended as a conversation and action starter.

2 Journals included: Journal of Consumer Psychology, Journal of Consumer Research, Journal of Marketing, Journal of Marketing Research, Journal of the Academy of Marketing Science, Marketing Science .

3 To reduce visual complexity and aid interpretation, we set the minimum number of article citations to five and excluded articles without links in the collection. This resulted in a total of 196 articles that were visualized in the figure by normalized number of citations.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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HYPOTHESIS AND THEORY article

Research on the impact of marketing strategy on consumers’ impulsive purchase behavior in livestreaming e-commerce.

• 1 School of Foreign Languages for Business, Guangxi University of Finance and Economics, Nanning, China
• 2 School of Business, Guilin University of Electronic Technology, Guilin, China
• 3 School of Economics, Pakistan Institute of Development Economics, Islamabad, Pakistan

Livestreaming e-commerce has emerged as a highly profitable e-commerce that has revolutionized the retail industry, especially during the COVID-19 pandemic. However, research on livestreaming e-commerce is still in its infancy. This study sheds new light on impulsive purchase behavior in livestreaming e-commerce. Based on stimulus-organism-response (SOR) theory, this study introduces the “People-Product-Place” marketing strategy for livestreaming e-commerce from the perspective of consumer perception and aims to understand the impact of marketing strategy on impulsive purchase behavior in e-commerce livestreaming shopping scenes, and to examine the mediating effect of involvement. The study conducted SEM analysis, in Amos, on 437 response sets from an online anonymous survey. The results show that perceived e- commerce anchor attributes , perceived scarcity , and immersion positively influence impulsive purchase behavior; that “People-Product-Place” marketing strategy is important; and that effective marketing triggers impulsive purchase. Perceived e-commerce anchor attributes, perceived scarcity , and immersion positively influence involvement , which positively influences impulsive purchase. Involvement mediates between perceived e- commerce anchor attributes , perceived scarcity and immersion , and impulsive purchase. These findings guide marketers to improve the profitability of livestreaming e-commerce and provide some references of economic recovery for many other countries that also suffered from the impact of the COVID-19 pandemic.

Introduction

New consumption patterns derived from the Internet, network, and information systems technology have emerged in recent years ( Corcoran and Andrae, 2013 ). These developments have led to changes in individuals’ concepts of consumption and their consumption habits. After the web portal era of Web 1.0 and the social media era of Web 2.0, we have entered the scene media era of Web 3.0 ( Zhang, 2020 ). The emergence of online shopping has greatly improved consumers’ shopping experience ( Helm et al., 2020 ). Online shoppers are not restricted by time, location, or travel/transportation. However, in “traditional” online shopping, shoppers receive information only through images, text, and prerecorded video ( Wongkitrungrueng and Assarut, 2020 ). Thus, in the Web 3.0 era the development of this e-commerce has entered a bottleneck ( Wu Q. et al., 2020 ). Intentionally designed promotional videos and excessively beautified images of online products make it difficult for consumers to obtain true information. This “asymmetry of information” between online consumers and merchants contributes to consumer doubt and distrust in purchase decision-making ( Demaj and Manjani, 2020 ; Lamr and Dostál, 2022 ; Utz et al., 2022 ). Lagging customer consultation services further frustrate the online shopping experience ( Othman et al., 2020 ). Thus, innovation that emphasizes a comprehensively good consumer experience is needed.

China is the largest Internet market in the world ( Akram et al., 2018a ). In 2016, a new online retailing model integrating e-commerce with online livestreaming shopping emerged in China ( Rui and Kang, 2016 ). This marketing model is based on e-commerce, uses livestreaming as a marketing tool ( Ding et al., 2020 ), and provides direct and efficient communication to minimize information asymmetry ( Wongkitrungrueng et al., 2020 ). This enables online shoppers to get a real three-dimensional experience in the virtual network environment and increases adhesion and trust between users, merchants, and platforms ( Li, 2020 ). Professional selection of products, anchor persona, live product display, and real-time interaction is integrated into a retailing model that attracts users to watch, interact, and purchase ( Liu L. et al., 2020 ). The COVID-19 pandemic has driven rapid change in product consumption patterns ( Liu L. et al., 2020 ; Zwanka and Buff, 2021 ). Since 2020, the “home economy” trend has further stimulated the growth of livestreaming e-commerce. In 2020, China’s livestreaming e-commerce market exceeded ¥1.2 trillion, with an annual growth of 197.0% ( IResearch, 2021 ). Livestreaming shopping has become a new engine of economic growth in China ( Ma, 2021 ). According to CNNIC (2022) , there were 1.032 billion Chinese netizens at the end of 2021, and 99.7% of these netizens were mobile device users. By the end of 2020, there were 388 million e-commerce livestreaming users, accounting for 39.2% of the total netizens ( China Live E-Commerce Industry Research Report, 2021 ). A large number of netizens provides a vigorous driving force for the development of e-commerce. The sales volume during the “618” promotion period (1–18 June) of Jingdong livestreaming increased by 161% year-on-year ( CNNIC, 2022 ). The first-hour sales volume on Taobao livestreaming on 1 June 2021 exceeded the whole-day sales volume for the same day in 2020 ( Sina, 2021 ). The Gross Merchandise Value (GMV) of major B2C e-commerce platforms in China during the Double 11 period in 2021 was ¥952.3 billion, to which livestreaming shopping contributed over ¥73 billion ( Syntun, 2021 ).

According to eMarketer’s Global E-Commerce Report, China continued to lead the global e-commerce market in 2021 with 792.5 million digital buyers (33.3% of the global total) and $2.779 trillion e-commerce sales (56.8% of the global total). The e-commerce share of total retail sales in China is the highest compared to other countries. China has become the first country to account for more than 50% of total transactions through e-commerce retail sales ( Ethan Cramer-Flood, Global Ecommerce Update, 2021 ). Live commerce or live video shopping generated sales of $171 million in 2020 in China ( Utsi, 2022 ). Compared to China, the United States and Europe are taking baby steps in the expansion of livestreaming commerce. Amazon and YouTube advanced capabilities of their websites and reviewing consumers’ reaction toward livestream shopping ( Ryan, 2020 ). Livestreaming e-commerce generated $60 billion sales in 2019 globally, but the US market contributed less than $1 billion ( Kharif and Townsend, 2020 ). However, the US market is growing fast, especially in certain products, for instance, apparel, makeup, and alcoholic beverages ( Kharif and Townsend, 2020 ). In the European market, few consumers understand the concept of live video shopping, which is one of the main reasons why live commerce is not as popular as in the Chinese market ( Andersson and Pitz, 2021 ). Live video service providers Zellma and Bambuser 1 suggest that companies in Europe need education on how to apply livestreaming e-retailing into their business, and they are confident that European consumers are ready to embrace new online shopping forms ( Andersson and Pitz, 2021 ). In 2021, an online survey conducted in Poland, Spain, France, and the United Kingdom reported that 7,261 respondents were interested in livestreaming on e-commerce website/app and 6,602 were interested in social media livestreaming ( Forrester, 2021 ). Hence, investigation would provide insight into how e-tailers promote featured products on live commerce platforms in China, and how consumers perceive this marketing.

E-commerce livestreaming shopping re-establishes the relationship between merchants, commodities, and consumers ( Liu, 2021 ). In a livestreaming shopping room, the anchor creates an immersive experience for consumers ( Luo et al., 2020 ) and stimulates impulsive purchase through a series of strategies ( Xu et al., 2020 ). In e-commerce livestreaming shopping, it takes only a moment for consumers to be attracted by live product promotion introduced by anchors regardless of whether consumers are hedonistic or utilitarian in outlook ( Xu et al., 2020 ). Triggered consumption behavior is mostly impulsive purchase ( Li, 2020 ). According to the iMedia Research report “User Research and Analysis of China’s Live Streaming E-commerce in the First Half of 2020,” 65.2% of livestreaming viewers purchased goods in the livestreaming shopping room, and 49.5% admitted that their purchases were impulsive ( IMedia Research, 2020 ). A recent study on online purchase intention in China asserts that online shopping in the social commerce setting is driven more by hedonistic than utilitarian motivation ( Akram et al., 2021 ), and that impulsive buying contains hedonic features ( Akram et al., 2018b ). IMedia Research (2020) considered that the most direct support for the incremental performance of e-commerce is to trigger more consumers to purchase impulsively for unplanned needs when watching livestreaming. Awakening unplanned consumption is a long-term and deep driving force for e-tailers using livestreaming. Thus, this study investigated the practical significance of impulse purchase behavior in e-commerce live broadcasts to inform marketing strategy aimed at impulsive buying.

Good sales performance is inseparable from effective marketing strategy ( Akram et al., 2018a ; Varadarajan, 2020 ). The continuing growth of livestreaming shopping makes it important for e-commerce investors and managers to understand influencing factors for impulse buying in livestreaming shopping. Livestreaming e-commerce aims to sell products and services to consumers ( Hu and Chaudhry, 2020 ; Wongkitrungrueng et al., 2020 ). This business model contains the basic elements of the “People-Product-Place” theory essential in retailing ( Guo and Xu, 2021 ), but in different forms. E-commerce livestreaming shopping reconstructs a retail scenario comprising “People-Product-Place” to realize the real-time, situationalization and visualization of communication in the entire process of e-commerce livestreaming shopping, and to bring remarkable features of strong interactivity and authenticity ( Duan, 2020 ). Researchers have suggested that anchor promotion, product promotion, and livestreaming atmosphere are likely to trigger strong emotions in consumers, leading to impulsive purchases ( Xu et al., 2019 , 2020 ; Lee and Chen, 2021 ; Ming et al., 2021 ). Empirical studies find that online consumers are easily triggered by marketing stimuli to make impulsive purchases, and rich marketing methods help consumers avoid monotony and frustration, thus enhancing the shopping experience ( Sundström et al., 2019 ).

Purchase behavior in live-broadcast shopping rooms has become a popular research topic. Researchers have investigated external stimuli such as atmosphere clues ( Gong et al., 2019 ), IT affordances ( Dong and Wang, 2018 ), discounted prices and scarcity ( Wang S. Q., 2021 ; Yan, 2021 ); and inherent characteristics of livestreaming marketing, including the attributes of anchors ( Han and Xu, 2020 ), their communication styles ( Wu N. et al., 2020 ), their identity and information source characteristics ( Liu F. J. et al., 2020 ), relationship ties and customer commitment ( Peng et al., 2021 ); opinion leaders ( Yin, 2020 ; Lakhan, 2021 ); interaction ( Wang S. Q., 2021 ; Yan, 2021 ); perceived enjoyment ( Lakhan, 2021 ; Lee and Chen, 2021 ); and perceived product usefulness ( Lee and Chen, 2021 ). There are few studies on the impact of livestreaming marketing strategies on impulsive purchase. Livestreaming is jointly constructed by various stakeholders, including those from the three retail marketing elements “People-Product-Place.” Clearly, effective multiparty relationships are central to any effective livestreaming marketing strategy.

In this context, based on stimulus-organism-response (SOR) theory, this study introduced the “People-Product-Place” marketing strategies for livestreaming from the perspective of consumer perception, to study how livestreaming influences impulsive purchasing. This study aimed to more comprehensively explain the perspective for research on the impact of consumer impulsive purchase behavior. As well as to enhance the understanding of the “People-Product-Place” marketing model of e-commerce livestreaming, guide marketers to improve the profitability of livestreaming e-commerce, and provide reference for the healthy and sustainable development of e-commerce livestreaming industry. Then, hopefully to provide reference of economic recovery under the impact of the normalization of the COVID-19 epidemic for many other countries.

The rest of the article is structured as follows: the theoretical framework and hypothesis development are discussed in the following section. Research design and methodology, and data analysis and hypotheses testing are described in the subsequent sections. The findings and their implications, study limitations, and further research are discussed in the final section.

Theoretical Framework and Hypothesis Development

Theoretical framework.

SOR theory ( Mehrabian and Russell, 1974 ) underpins the study. Stimulus refers to external stimuli; Organism represents the internal state of an individual when that individual perceives a stimulus; and Response is the behavior of the individual in response to stimuli. The SOR model is a mediation model in which a stimulus provokes a response through the mediating effects of the organism. The SOR model has been applied to studies of online purchasing behavior ( Hashmi et al., 2019 ; Huang and Suo, 2021 ; Karim et al., 2021 ; Lee and Chen, 2021 ; Ming et al., 2021 ). Impulse purchases are unplanned and occur when consumers are stimulated internally and/or externally ( Rook, 1987 ; Lim et al., 2017 ). Piron (1991) believed that stimuli can come from products, the shopping environment, or the people who accompany you for shopping. This is consistent with the elements “People-Product-Place.” Stimuli in livestreaming have some similar and some different characteristics with traditional online and offline shopping ( Gong et al., 2019 ). This study used “People-Product-Place” as the stimulus factor (S), involvement as internal state of an individual (O), and explored the effect on impulsive purchase behavior (R).

Hypothesis Development

Impulsive purchase behavior.

Impulse buying is a popular subject in the domain of consumer decision-making. Researchers claim that impulse buying accounts for 40–80% of all purchases ( Rodrigues et al., 2021 ). Lee and Chen (2021) stress that instant reactivity and convenience trigger impulsive purchase in mobile commerce. Studies have identified products that are bought impulsively, including groceries ( Inman et al., 2009 ; Bellini et al., 2017 ), financial products ( Lučić et al., 2021 ), milk tea ( Guo et al., 2017 ; Wu et al., 2021 ), necessities during COVID-19 pandemic ( Islam et al., 2021 ), “unhealthy” foods ( Verplanken et al., 2005 ), and brand-related user-generated content products ( Kim and Johnson, 2016 ). Some studies are keen to discover how different purchase channels influence impulsive buying, for instance, online markets ( Kim and Johnson, 2016 ; Guo et al., 2017 ; Aragoncillo and Orus, 2018 ; Pal, 2021 ; Rejikumar and Asokan-Ajitha, 2021 ; Wu et al., 2021 ), mobile commerce ( San-Martin and López-Catalán, 2013 ; Chen et al., 2021 ), and offline/in-store shopping ( Rook and Fisher, 1995 ; Inman et al., 2009 ; Tendai and Crispen, 2009 ; Sharma et al., 2010 ; Aragoncillo and Orus, 2018 ). However, few studies focus on impulsive purchase through the livestream shopping channel ( Lee and Chen, 2021 ; Wang S. Q., 2021 ).

There are various definitions of impulse buying. Rook (1987) describes impulse buying as spontaneous and hedonic purchase driven by an urgent, forceful, and persistent craving, regardless of possible consequences. Lučić et al. (2021) assert that impulsive purchase is actuated by irrational emotions. Researchers also claim that impulsive purchase is the result of an irresistible reaction that is triggered by often deliberately designed stimuli ( Stern, 1962 ; Rook, 1987 ; Liu et al., 2013 ; Kim and Johnson, 2016 ; Aragoncillo and Orus, 2018 ). Aragoncillo and Orus (2018) summarized and categorized features that induce impulsive purchase in offline and online channels, from previous studies. They argue that “greater product assortment and variety, sophisticated marketing techniques, credit cards, anonymity, lack of human contact, and easy access and convenience are the encouraging factors to online impulsive purchase” ( Aragoncillo and Orus, 2018 , p. 47). Akram et al. (2017) believe that “impulsive purchasing is an immediate, unplanned, compelling, and sudden purchase behavior while shopping” (p. 76) ( Akram et al., 2017 ). When comparing characteristics of online store to livestreaming shopping, it is not difficult to notice that livestreaming shopping contains all the encouraging factors mentioned and offers higher levels of stimuli. Specifically, payment is made easier by biometric fingerprint scanning on smart mobile devices, bypassing typed-in credit card passwords. Livestreaming provides for more comprehensive product display by e-commerce anchors than predesigned images and text on webpages. In livestreaming shopping rooms, there is instant interaction and sharing of product experience between anchors and customers, and between customers. Free shipping and unconditional returns or refunds encourage impulse buying. Big data analytics facilitate tailor-designed promotions and accurate/precise targeting of individual consumers. Therefore, based on discussion above, this study assumes that marketers use effective strategies to stimulate impulse buying on livestreaming shopping platforms. Specific constructs of interest include perceived e-commerce anchor attributes, perceived scarcity, immersion, and involvement.

Marketing Strategies Applied to Livestreaming E-commerce

The success of livestreaming retailing lies in its high interactivity, entertainment value, authenticity, and visibility ( Bründl et al., 2017 ). As shown in Figure 1 , livestreaming ( Figure 1C ) is different from shopping in a physical store ( Figure 1A ) and online shopping ( Figure 1B ) because consumers can watch, interact, comment, and purchase using a mobile device anywhere and anytime ( Liu L. et al., 2020 ). Traditional online shopping is search-based, requiring searching, comparing, and choosing before purchasing. Traditional online retailing thus relies on consumer initiative, and retailing success relies to a significant extent on consumers looking for products, with clear objectives in mind ( Virdi et al., 2020 ). In livestreaming retailing, consumers are guided by anchors who actively promote products to them ( Bründl et al., 2017 ; Ang et al., 2018 ). Figure 1 illustrates the three types of shopping experience:

Figure 1. Three types of shopping experience.

Marketing strategy is the most direct embodiment of merchants facing consumers. Effective marketing strategy in retailing is to provide effective stimuli for buying ( Zhu et al., 2019 ), and specific stimuli often lead to impulse buying ( Floh and Madlberger, 2013 ). In traditional e-commerce, users browse the goods on a shopping platform and typically spend considerable amounts of time considering purchase: this is “decision-making consumption” ( Rezaei, 2015 ). In livestreaming, consumers are provided with various designed entertainments. Whether they are hedonic or utilitarian consumers are readily attracted by personal charm, product introduction, promotion information, and livestreaming scenes deliberately designed to lead to consumption behavior ( Liu F. J. et al., 2020 ; Xu et al., 2020 ). Impulsive purchase is awakened by marketing strategies planned by anchors.

The success of livestreaming retailing lies in the good coordination of the elements “People-Product-Place” ( Luo et al., 2020 ), which is a perspective that should not be ignored when studying marketing strategy ( Wang K., 2021 ). “People” represents the anchor, who is the key factor to attract “followers” to watch. Anchor attributes is an important influencing factor in purchase decisions in livestreaming shopping Liu F. J. et al., 2020 ; Meng et al., 2020 ; Xu et al., 2020 ). Anchor attributes marketing becomes one of the major streams in livestreaming e-commerce by introducing products to their audience ( Liu, 2021 ). “Product” represents promoted goods recommended by the anchor. The main marketing method for “goods” is hunger marketing through the creation of availability stimuli, “limited time,” and “limited quantity.” The anchor creates the phenomenon of “short supply” that increases consumer perceived time pressure and product scarcity, stimulating impulsive purchase ( Gupta and Gentry, 2019 ; Cheng et al., 2020 ). “Place” is the final presentation of the e-commerce livestreaming scene. With the support of “people” and “product,” the internet platform built a communication scene integrating shopping, livestreaming, communication, and other functions. In the broadcast room, the extraordinary sense of temporal presence allows participants to sink into the immersive experience. Immersive marketing develops consumption into an “entertainment game” of shopping ( Luo et al., 2020 ). Therefore, we studied the influence of three elements in marketing strategy on impulsive purchase: anchor attributions, hunger marketing, and immersive marketing.

Perceived E-commerce Anchor Attributes and Impulsive Purchase Behavior

E-commerce anchors are the core of marketing strategy in livestreaming retailing. An e-commerce anchor is one who introduces and displays products comprehensively to customers ( Zhu et al., 2021 ). Unlike traditional television broadcasters, e-commerce anchors provide guidance to customers by sharing experiences based on their own consumption of the promoted products, answer viewers’ questions in real time, and interact with viewers based on their requests, and display products in ways that static images and texts cannot ( Sun et al., 2019 ; Han and Xu, 2020 ). Research has shown that the attributes, features, or characteristics of e-commerce anchors significantly influence purchase decisions or impulsive purchase on livestream shopping platform ( Li, 2021 ; Zhao and Feng, 2021 ; Zhu et al., 2021 ). Zhu et al. (2021) classify anchors’ characteristics into physical attractiveness, professional ability, and social attractiveness. Zhao and Feng (2021) assert that interactivity, professionalism, and charisma are important characteristics of e-commerce anchors who are opinion leaders. Their findings indicate that anchor characteristics positively influence consumer purchase intention. In their qualitative study, Han and Xu (2020) interviewed 68 livestreaming shoppers and summarized the attributes of e-commerce anchors. These authors argue that charisma, recommendation attributes, and display and interaction attributes are essential attributes of an e-commerce anchor. In summary, the literature discusses important attributes of e-commerce anchors, but studies on how these attributes influence impulsive purchase behavior are still limited. In addition, because of recent tax evasion by several famous livestream anchors in China, the essential requirements for becoming e-commerce anchors have been raised. As discussed, consumers’ perception of e-commerce anchor attributes is important. In this study, these attributes are defined in terms of how consumers/viewers perceive the presented image of an anchor. These attributes consist of whether the anchor observes discipline and law, his/her communication and professional skills, and whether consumers find the anchor reliable and have professional knowledge on the products being promoted. Thus, this study proposes the following hypothesis:

H1a: Perceived e-commerce anchor attributes have a positive effect on impulsive purchase behavior.

Perceived Scarcity and Impulsive Purchase Behavior

Studies show that perceived scarcity significantly affects panic buying ( Islam et al., 2021 ; Li et al., 2021 ) and influences decision-making in impulse buying ( Wu et al., 2021 ). By deliberately manipulating the supply of products, anchors create an ambiance of the shortage of goods in livestreaming shopping. In this study, perceived scarcity is intentional creation of limited time and quantity scarcity by anchors in livestreaming shopping ( Aggarwal et al., 2011 ; Gupta and Gentry, 2019 ; Islam et al., 2021 ). Following introduction of product functions, quality, and any other information consumers need to know, anchors specifically emphasize the limited availability of the products for on-the-spot purchase, especially when viewers significantly outnumber product units. Anchors also magnify the countdown process, which signals urgency to buy as soon as the countdown ends. Such a situation creates perceived product scarcity and competitive purchase pressure ( Guo et al., 2017 ).

Perceived scarcity has been studied as an independent variable ( Guo et al., 2017 ; Akram et al., 2018b ; Gupta and Gentry, 2019 ; Islam et al., 2021 ; Wu et al., 2021 ), a mediating factor ( Li et al., 2021 ), and a moderating factor ( Cheng et al., 2020 ) purchase decision, panic buying, urgency to buy, or impulse buying. These studies indicate that perceived scarcity positively influences panic buying ( Li et al., 2021 ) and indirectly influences panic or impulse buying through perceived arousal ( Guo et al., 2017 ; Islam et al., 2021 ; Wu et al., 2021 ). Perceived scarcity is shown to strongly predict online impulsive buying in Chinese social commerce ( Akram et al., 2018b ). Interestingly, studies find in in-store consumers, perceived scarcity does not directly affect urgency to buy, but perceived scarcity triggers in-store hording behavior (e.g., holding the clothes in hand while shopping) and in-store hiding behavior (e.g., hiding clothes somewhere else other than the place they should be) ( Gupta and Gentry, 2019 ). These authors assert that the relationship between perceived scarcity and urgency to buy is mediated by anticipated regret ( Gupta and Gentry, 2019 ). A moderating effect of perceived scarcity was not found in Cheng et al.’s (2020) study. Because the literature reports various results and inconsistent findings, this study aimed to contribute more evidence on perceived scarcity as both a direct and indirect influencing factor on impulsive purchase. Thus, this study postulates the following hypothesis:

H1b: Perceived scarcity has a positive effect on impulsive purchase behavior.

Immersion and Impulsive Purchase Behavior

In a study about viewers’ complete absorption of co-viewing experience on video websites, immersion is described as a joyful feeling that one is deeply absorbed in a mediated world, meanwhile forgetting or failing to pay attention to people or environment around him/her ( Fang et al., 2018 ). In a virtual reality environment, the use of augmented reality is expected to give users a higher level of immersion. In this context, immersion is described as a complete engrossing feeling of neglecting the actual environment ( Yim et al., 2017 ). Hudson et al. (2019) argue that individuals’ perceived levels of immersion differ, hence, their study focused on subjectively experienced immersion. Previous research has focused mainly on the mediating role of immersion in various activities. For instance, Yang et al. (2021) examined the mediating effect of immersion between social presence and customer loyalty intentions toward recommendation vlogs. Their findings confirm the proposed hypothesis and indicate that increased consumer immersion positively influences customer loyalty. In a study of try-on experience of wrist watches with augmented reality, Song et al. (2020) found that immersion mediates the relationship between environmental embedding and feelings of ownership. Immersion and perceived benefit have been found to mediate between social presence and customer loyalty on co-viewing experience in video websites ( Fang et al., 2018 ). In a study of fashion product purchase intention, immersion was studied as a mediator between five characteristics of fashion influencers and their followers’ purchase intentions ( Kim et al., 2021 ). Evidence that immersion in augmented reality positively affects online tourists’ willingness to pay was found in a study about AR tourism destination experience, without highlighting the mediating role of immersion ( Huang, 2021 ).

The literature indicates that the immediate relationship between immersion and impulsive purchase is rarely studied. A study of interactive marketing ( Kabadayi and Gupta, 2005 ) showed that deeply immersed customers tend to indulge in longer hours of digital media. A study in Taiwan provides evidence that high level of the absorbed-in state greatly influences unplanned buying online, and consumers are willing to pay more in such situations ( Niu and Chang, 2014 ). Thus, this study defines immersion as a joyful state of being absorbed and engrossed, losing awareness of time and forgetting about one’s surroundings when watching livestream promotions. The following hypothesis was proposed:

H1c: Immersion has a positive effect on impulsive purchase behavior.

Involvement

Involvement is an important variable affecting consumers’ purchase decision-making. Zaichkowsky (1985 , p. 32) defines involvement as “a person’s perceived relevance of the object based on inherent needs, values, and interests” and suggests that this definition could be applied to research on purchase decisions. Much research has evaluated “product” involvement in purchase decisions. For instance, Cheng et al. (2020) adapted Zaichkowsky’s (1985) 10 measurement dimensions to evaluate product involvement in livestreaming shopping. A study on online ordering behavior for food delivery measured product involvement based on nine external cues for action. These external cues are mainly concerned with nine different aspects of safety and customer rating, including food safety, advertisement safety, and safety of food retailers ( Mehrolia et al., 2020 ). A study on purchase decisions of halal products measured product involvement in two aspects: consumer perceptions of the degree of importance of targeted products and the number of attributes of a halal product that consumers regard as imperative ( Rachmawati et al., 2020 ).

To measure the involvement construct more comprehensively, some researchers extend the definition of involvement by including more aspects of involvement besides product attributes. In a study of customer satisfaction in mobile commerce, “buying-selling environment” was included as a measurement item for involvement ( San-Martin and López-Catalán, 2013 ). Mou et al. (2019) studied the influence of product involvement, characterized as cognitive and affective involvement, and platform involvement, characterized as enduring and situational involvement, on consumer purchase intention in cross-border e-commerce. Based on the foregoing discussion, this study defines involvement as a variable that includes aspects of consumer’s interests and valuation of promoted products and services, and perceived relevance and importance of the shopping environment (livestreaming shopping).

Marketing Strategies and Involvement

To create strong bonding between consumers and shopping platform, e-commerce managers have worked on measures to make consumers feel connected or intrigued. E-commerce managers design marketing to attract, retain, and connect viewers in livestreaming shopping, with the aim of increasing their involvement, and inducing impulse buying. In the literature, antecedents of involvement include product description ( Mou et al., 2019 ) and innovativeness in new technology ( San-Martín et al., 2017 ). In the domain of livestreaming shopping, there are likely to be more antecedents of involvement to be discovered. Thus, this study postulates the following hypotheses:

H2a: Perceived e-commerce anchor attributes positively affect involvement.

H2b: Immersion positively affects involvement.

H2c: Perceived product scarcity positively affects involvement.

Involvement and Impulsive Purchase Behavior

Studies have also examined the direct impact of involvement on behaviors such as consumer satisfaction, purchase intention, or decision-making ( San-Martin and López-Catalán, 2013 ; Mehrolia et al., 2020 ; Rachmawati et al., 2020 ). Several studies provide evidence that high involvement induces purchase intentions/decisions ( San-Martin and López-Catalán, 2013 ; Siala, 2013 ; Mehrolia et al., 2020 ; Rachmawati et al., 2020 ). Thus, this study postulates the following hypothesis:

H3: Involvement positively affects impulsive purchase behavior.

The Mediating Role of Involvement

Involvement has been studied as a mediating factor between product description and purchase intention in cross-border e-commerce. Three out of four dimensions of involvement are found to mediate between product description and purchase intention in that study ( Mou et al., 2019 ). To contribute empirical evidence for the mediating effect of involvement between relationships various variables and impulsive purchase, this study postulates the following hypotheses:

H4: Involvement mediates the relationship between marketing strategies applied in livestreaming shopping room and impulsive purchase behavior.

H4a: Involvement mediates the relationship between perceived e-commerce anchor attributes and impulsive purchase behavior.

H4b: Involvement mediates the relationship between immersion and impulsive purchase behavior.

H4c: Involvement mediates the relationship between perceived scarcity and impulsive purchase behavior.

Research Design and Methodology

This study adopts a positivist paradigm. Data were collected online using self-reporting questionnaires, and structured equations were used to evaluate the relationship between variables and to test study hypotheses. This topic focuses on the research on the influence mechanism of consumers’ impulsive purchase behavior in the context of livestreaming e-commerce. To ensure the validity of the research results, people who have had an online livestreaming shopping experience are selected as the target group, which is conducive to objectively assessing consumers’ impulsive purchase behavior on livestreaming e-commerce.

Research Design

The study focuses on the marketing-generated stimuli that influence impulse purchase in livestreaming retailing: anchor attributes, hunger marketing, and immersive marketing. Perceived anchor attributes , perceived scarcity , and immersion are independent variables; impulsive purchase behavior is the dependent variable; and involvement is a mediator. Figure 2 shows the framework that links these variables.

Figure 2. Research model.

Instrument Development

The study constructed a SOR model and attempted to explain consumer impulsive purchase from a psychological and behavioral perspective. Data were collected through an online self-administered questionnaire. To ensure validity reliability of measurement of the variables, the study defined these variables, and identified and adapted/modified measurement items from the literature to fit the context and object of the study from the literature. All the measurement items were rated on a 5-point Likert scale anchored on 1 (strongly disagree) and 5 (strongly agree). Table 1 shows the measurement items.

Table 1. Measurement items.

The online questionnaire comprised three parts: an introduction describing the purpose of the questionnaire, to dispel respondents’ concerns, and to enable respondents to recall live e-commerce shopping by describing live shopping. The second part comprised the questionnaire that measured perceived anchor attributes, perceived scarcity, immersion, and impulsive purchase behavior. The third part elicited respondent demographics, i.e., gender, age, education level, income, and occupation.

Data Collection

We conducted online questionnaire survey by using Questionnaire Star 2 , which is a platform with 2.6 million database members, covering more than 90% of universities and research institutions in China. Convenience sampling was used, and we elicited voluntary responses from individuals with experience of shopping in live e-commerce. We asked respondents to respond to the items based on their live e-commerce shopping experience. Screening of the 456 response sets received yielded 437 sets for analysis, giving an effective response rate of 94.96%.

Table 2 shows the demographic characteristics of respondents.

Table 2. Demographic data of respondents.

Women accounted for 79.6% of respondents, a relatively large percentage. In four age groups, participants are mainly between 18 and 30 years, reaching 52.2 and 97.5% of participants are younger than 50 according to the statistics. This shows that most of the people participating in live shopping are younger groups, and they are more likely to embrace new shopping channels. From the perspective of education level, 80.8% have a bachelor’s degree or above, indicating that the participants’ education level is relatively high. The highest monthly income is less than or equal to 2,000, accounting for 41.4%, and the proportion of students is 41.9%, which is consistent with the proportion of monthly income less than or equal to 2,000. This suggests that the young student group is the backbone of livestreaming shopping.

Data Analysis

SPSS and AMOS statistical software were used to analyze the data. Confirmatory factor analysis (CFA) was used to test the reliability validity of the measurement model. We then verified the model using structural equation modeling (SEM) in AMOS software, using regression analysis to analyze the relationship between variables, and bootstrapping to test the hypothesis of the mediating effect.

Results and Findings

Construct validity and reliability.

CFA is usually used to test data reliability and validity of data to evaluate questionnaire quality ( Hou et al., 2004 ). In this study, AMOS software was used to establish a measurement model with five latent variables, including three independent variables, one dependent variable, and one mediator, and CFA was conducted. The test results of the measurement model are shown in Tables 3 – 5 .

Table 3. Goodness-of-fit statistics of the measurement model.

Table 4. Reliability and validity results of measurement mode.

Table 5. Analysis of discriminant validity.

The indices of fit ( Table 3 ) for the measurement model were chi-square fit statistics/degree of freedom (CMIN/DF) = 1.79, comparative fit index (CFI) = 0.971, goodness of fit index (GFI) = 0.939, TFI = 0.966, normed fit index (NFI) = 0.9011, and root mean square error of approximation (RMSEA) = 0.043. According to the criteria in Table 3 , the model fit is good. As shown in Table 4 , individual item loadings are required to be above 0.50 for adequate and Cronbach’s alpha and CR value are higher than the recommended value of 0.700. The average variance extracted (AVE) scores were higher than the recommended value of 0.500, indicating the internal consistency and component reliability of each variable are good. Table 3 also shows that inter-construct correlations are all smaller than the square root of AVE, indicating the data have good discriminant validity. The reliability and validity of the data were therefore good and suitable for further analysis.

Hypothesis Testing

Before hypothesis testing, degree-of-fit testing is carried out to test the relationship between variables in the structural model. The model fit statistics were CMIN/DF = 2.573, CFI = 0.942, GFI = 0.914, TFI = 0.932, NFI = 0.942, and RMSEA = 0.06. According to the judgment criteria shown in Table 3 , the model fit is good.

Path Coefficient Testing

AMOS software was used to test path coefficients to verify the hypothesis of direct relationship. Table 6 shows the standardized path coefficients.

Table 6. Results of direct effects.

In the relationship between consumer-perceived marketing strategy and impulsive purchase behavior, perceived anchor attributes (β = 0.122, p = 0.01), immersion (β = 0.522, p = 0.000), and perceived scarcity (β = 0.105, p = 0.02) positively influence impulsive purchase behavior, supporting H1a, H1b, and H1c. In the relationship between consumer-perceived marketing strategy and impulsive purchase behavior, perceived anchor attributes (β = 0.322, p = 0.000), immersion (β = 0.469, p = 0.000), and perceived scarcity (β = 0.236, p = 0.000) positively influence impulsive purchase behavior, supporting H2a, H2b, and H2c. In the relationship between involvement and impulsive purchase behavior, involvement (β = 0.273, p = 0.000) positively influenced impulsive purchase behavior, supporting H3.

Testing for Mediating Effect

In the bootstrap method to detect a mediating effect, the bootstrap iteration was set to 2,000 times. Whether the mediating effect is significant is judged by whether the 95% confidence interval contains 0. The three mediation paths are perceived anchor attributes → involvement → impulsive purchase behavior , immersion → involvement → impulsive purchase behavior , and perceived scarcity → involvement → impulsive purchase behavior; the analysis results are shown in Table 7 , as follows:

Table 7. Results of mediating effects.

In the path perceived anchor attributes → involvement → impulsive purchase behavior , the 95% confidence intervals [0.041, 0.16] did not include 0, p = 0.000. The mediating effect was thus significant, supporting H3a. The 95% confidence intervals for total and direct effects were respectively, [0.091, 0.329] and [0.005, 0.245]; both sets of intervals did not contain 0, so involvement partially mediates the relationship between perceived anchor attributes and impulsive purchase behavior.

In the path perceived scarcity → involvement → impulsive purchase behavior , the 95% confidence intervals [0.025, 0.117] do not contain 0, p = 0.000, indicating that the mediation effect is significant and H3b is supported. The 95% confidence intervals of total effect [0.055, 0.279] do not contain 0, but the 95% confidence interval of direct effect [-0.015, 0.213] contains 0, so involvement fully mediates the relationship between perceived scarcity and impulsive purchase behavior.

In the path immersion → involvement → impulsive purchase behavior , the 95% confidence intervals of total effect [0.063, 0.204] do not contain 0, p = 0.001, indicating it is significant. The 95% confidence intervals of total and direct effects [0.551, 0.732] and [0.404, 0.636], respectively, both do not contain 0. Therefore, involvement partially mediates the relationship between immersion and impulsive purchase behavior.

Discussion and Implications

Based on SOR theory, this study used a framework of “marketing stimulus—involvement—impulsive purchase behavior” to investigate consumer impulsive purchase behavior in livestreaming shopping. By introducing the “People-Product-Place,” three elements of marketing stimulus as a marketing strategy of the livestreaming shopping platform, this study investigates the influencing mechanism and examines the influencing effects of perceived e-commerce anchor attributes, perceived scarcity, and immersion on consumers’ impulsive purchase behavior based on user perception perspective. Moreover, this study also examines the mediating effect of involvement between the three stimulus elements and consumers’ impulsive purchase behavior. Therefore, to explain the influencing mechanism of different marketing strategies in the livestreaming room on consumers’ impulsive purchase behavior. Findings of this research are discussed as follows.

Perceived e-commerce anchor attributes, perceived scarcity, and immersion positively influence impulsive purchase behavior. These findings are similar to previous studies, which report that e-commerce anchor attributes ( Li, 2021 ) and scarcity influence impulsive purchase behavior ( Akram et al., 2018b ). This study indicates that building e-commerce anchor attributes, creating pressure to purchase, and developing immersion are important and effective measures to trigger impulse buying. This finding is similar to previous studies that claim that sales promotion significantly affects online impulsive purchasing ( Akram et al., 2018a ).

Involvement mediates between perceived e-commerce anchor attributes, perceived scarcity, and immersion and impulsive purchase behavior. Partially mediating effects of involvement are found between perceived e-commerce anchor attributes and consumer impulsive purchase behavior; and between immersion and impulsive purchase behavior. Involvement fully mediates between perceived scarcity and impulsive purchase behavior. The findings indicate that perceived e-commerce anchor attributes and immersion can directly or indirectly affect impulsive purchase behavior through involvement. However, perceived scarcity only influences impulsive purchase behavior through involvement.

Implications for Theory

Technological innovations in the Internet and information systems seem to have driven changes in concepts and habits of consumption. A major development of online shopping is livestreaming shopping that is centered on consumer experience, and this study provides an understanding of consumer purchase behavior in livestreaming shopping. Consumer decision-making is greatly influenced by stimuli carefully designed by marketers. The study extends behavioral theory in two aspects.

First, this study innovatively combined the SOR theory of environmental psychology with the “People-Product-Place” marketing model and established a research framework of “marketing method stimulus-involvement-impulsive purchase behavior” corresponding to the three elements of “People-Product-Place.” The study investigated the influencing mechanisms of perceived e-commerce anchor attributes, perceived scarcity, and immersion on impulsive purchase behavior from the consumers’ perspective. The study enriches the theoretical understanding of impulsive purchase behavior in livestreaming shopping and provides a theoretical basis for further research.

Second, previous studies have mostly considered immersion as a mediating factor. This study considered consumers’ perceptions of immersion in livestreaming shopping rooms as an independent variable, expanding the research scope of immersion.

Implications for E-commerce and Its Regulation

Path analysis indicates that immersion is the strongest predictor of involvement and impulsive purchase behavior. Involvement is a significant predictor of impulsive purchase behavior. Thus, anchors, merchants, and platforms should actively expand shopping scenarios, enrich consumers’ experience of watching livestreaming, and fulfill their diverse consumption needs. The consumer experience can be enhanced or optimized using virtual reality, artificial intelligence and big data analytics, enabling consumers to experience immersive shopping, enhancing their sense of authenticity and their trust in online shopping. Marketers can focus on creating a joyful atmosphere in livestreaming shopping rooms so as to infect consumers with the ambience when they watch and experience immersive shopping. Anchors should avoid applying the same marketing strategy to all products but should use different strategies to keep consumers feeling fresh, integrate themselves into the shopping atmosphere, and increase their involvement, and eventually purchase. Perceived scarcity is also a significant predictor of involvement and impulsive purchase. In practice, creating a limited-time-quantity promotion atmosphere is key. Consumers are led by the idea of “what is scarce is valuable.” Perceived scarcity increases perceived value, increases the desire to buy, and enables the goal of product promotion to be achieved.

In China, rapid development and tax evasion by several e-commerce livestreaming shopping anchors have recently brought stricter regulatory standards to the industry, and thus higher implications for its managers. It seems essential for government to strengthen regulation of the five main participants in the livestreaming shopping industry: merchants, anchors, platform operators, anchor service agencies, and consumers. Regulation needs to clarify the rights and responsibilities of these parties and standardize online livestreaming marketing. Relevant regulatory legislation for the livestreaming shopping industry to improve supervision is expected in the future. For platforms, it is necessary to strictly review the qualifications of merchants and anchors, increase the legal awareness and integrity of anchors, and improve their professionalism. The accounts of anchors who deceive and mislead consumers should be banned or closed in a timely manner. The platform should also strictly monitor for livestreaming data fraud, establish an anti-false data supervision mechanism, and purify the e-commerce livestreaming ecosystem. Anchors must promote products according to objective facts; not fabricate or exaggerate effects or facts. As influential public figures, anchors must abide by the law and regulations, be socially responsible, and accept public oversight. Anchors should strive to improve their professionalism by improving product selection standards and product quality, so as to guarantee consumer satisfaction. It is necessary for e-commerce anchors to strengthen their livestreaming image. The greater the charm of the anchor, the more psychological pleasure it can bring to consumers. Anchors should pay attention to the creation of personal image by strengthening their characteristics, creating more interaction with consumers and increasing their stickiness, establishing an emotional connection with fans, and creating a warm and charming anchor image.

China’s livestreaming shopping industry has made remarkable achievements, especially in assisting economic recovery in the current postepidemic period. The success of this industry stems from the reshaping of the established “People-Product-Place” retailing strategy. Hopefully this experience may provide valuable lessons for the livestreaming industry in other countries.

Study Limitations and Further Research

There are three methodological limitations. One is that respondents are from China. This limitation does not restrict application of the study findings to livestreaming shopping platforms in China. Consumers are stimulated by the “People-Product-Place” marketing strategy. Impulse buying in livestreaming shopping rooms is significantly affected by perceived e-commerce anchor attributes, perceived scarcity, immersion, and involvement. The second methodological limitation is the use of convenience sampling, and more systematic sampling should be used in future studies. Despite these limitations, this study contributes to the literature on impulsive purchase behavior in livestreaming shopping platforms. Thirdly, this study used self-reported data that might not reflect actual decision-making. For instance, consumers may not willingly admit to being impulsive. Further studies could use quasi-experimental methods or a data analytics approach using data provided by shopping platforms.

Further research should also consider different types of e-commerce anchor, including key opinion leaders, celebrities, famous e-commerce anchors, and merchant-employed anchors. These types of anchors might employ different marketing strategies and factors that influence impulsive purchase might differ. Thus, it is recommended to investigate how different factors influence consumer purchase behavior from different types of e-commerce anchors and the specific strategies they apply. Finally, this study selected perceived anchor attributes, perceived scarcity, immersion, and involvement as predictors of impulsive purchase behavior. Further exploration is needed to identify and characterize other influencing factors for impulsive buying and extends the theoretical framework.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

BC and JW: conceptualization, methodology, software, formal analysis, resources, data curation, writing—original draft preparation, visualization, and project administration. LW and HR: validation, writing—review and editing, and supervision. LW: investigation. All authors have read and agreed to the published version of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

• ^ Stockholm-based live video services providers.
• ^ https://www.wjx.cn/

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Keywords : e-commerce live streaming shopping, impulsive purchase behavior, People-Product-Place, marketing strategy, COVID-19

Citation: Chen B, Wang L, Rasool H and Wang J (2022) Research on the Impact of Marketing Strategy on Consumers’ Impulsive Purchase Behavior in Livestreaming E-commerce. Front. Psychol. 13:905531. doi: 10.3389/fpsyg.2022.905531

Received: 27 March 2022; Accepted: 09 May 2022; Published: 16 June 2022.

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*Correspondence: Jun Wang, [email protected]

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Influencer advertising has emerged as an integral part of social media marketing. Within this realm, consumer engagement is a critical indicator for gauging the impact of influencer advertisements, as it encompasses the proactive involvement of consumers in spreading advertisements and creating value. Therefore, investigating the mechanisms behind consumer engagement holds significant relevance for formulating effective influencer advertising strategies. The current study, grounded in self-determination theory and employing a stimulus-organism-response framework, constructs a general model to assess the impact of influencer factors, advertisement information, and social factors on consumer engagement. Analyzing data from 522 samples using structural equation modeling, the findings reveal: (1) Social media influencers are effective at generating initial online traffic but have limited influence on deeper levels of consumer engagement, cautioning advertisers against overestimating their impact; (2) The essence of higher-level engagement lies in the ad information factor, affirming that in the new media era, content remains ‘king’; (3) Interpersonal factors should also be given importance, as influencing the surrounding social groups of consumers is one of the effective ways to enhance the impact of advertising. Theoretically, current research broadens the scope of both social media and advertising effectiveness studies, forming a bridge between influencer marketing and consumer engagement. Practically, the findings offer macro-level strategic insights for influencer marketing.

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Introduction.

Recent studies have highlighted an escalating aversion among audiences towards traditional online ads, leading to a diminishing effectiveness of traditional online advertising methods (Lou et al., 2019 ). In an effort to overcome these challenges, an increasing number of brands are turning to influencers as their spokespersons for advertising. Utilizing influencers not only capitalizes on their significant influence over their fan base but also allows for the dissemination of advertising messages in a more native and organic manner. Consequently, influencer-endorsed advertising has become a pivotal component and a growing trend in social media advertising (Gräve & Bartsch, 2022 ). Although the topic of influencer-endorsed advertising has garnered increasing attention from scholars, the field is still in its infancy, offering ample opportunities for in-depth research and exploration (Barta et al., 2023 ).

Presently, social media influencers—individuals with substantial follower bases—have emerged as the new vanguard in advertising (Hudders & Lou, 2023 ). Their tweets and videos possess the remarkable potential to sway the purchasing decisions of thousands if not millions. This influence largely hinges on consumer engagement behaviors, implying that the impact of advertising can proliferate throughout a consumer’s entire social network (Abbasi et al., 2023 ). Consequently, exploring ways to enhance consumer engagement is of paramount theoretical and practical significance for advertising effectiveness research (Xiao et al., 2023 ). This necessitates researchers to delve deeper into the exploration of the stimulating factors and psychological mechanisms influencing consumer engagement behaviors (Vander Schee et al., 2020 ), which is the gap this study seeks to address.

The Stimulus-Organism-Response (S-O-R) framework has been extensively applied in the study of consumer engagement behaviors (Tak & Gupta, 2021 ) and has been shown to integrate effectively with self-determination theory (Yang et al., 2019 ). Therefore, employing the S-O-R framework to investigate consumer engagement behaviors in the context of influencer advertising is considered a rational approach. The current study embarks on an in-depth analysis of the transformation process from three distinct dimensions. In the Stimulus (S) phase, we focus on how influencer factors, advertising message factors, and social influence factors act as external stimuli. This phase scrutinizes the external environment’s role in triggering consumer reactions. During the Organism (O) phase, the research explores the intrinsic psychological motivations affecting individual behavior as posited in self-determination theory. This includes the willingness for self-disclosure, the desire for innovation, and trust in advertising messages. The investigation in this phase aims to understand how these internal motivations shape consumer attitudes and perceptions in the context of influencer marketing. Finally, in the Response (R) phase, the study examines how these psychological factors influence consumer engagement behavior. This part of the research seeks to understand the transition from internal psychological states to actual consumer behavior, particularly how these states drive the consumers’ deep integration and interaction with the influencer content.

Despite the inherent limitations of cross-sectional analysis in capturing the full temporal dynamics of consumer engagement, this study seeks to unveil the dynamic interplay between consumers’ psychological needs—autonomy, competence, and relatedness—and their varying engagement levels in social media influencer marketing, grounded in self-determination theory. Through this lens, by analyzing factors related to influencers, content, and social context, we aim to infer potential dynamic shifts in engagement behaviors as psychological needs evolve. This approach allows us to offer a snapshot of the complex, multi-dimensional nature of consumer engagement dynamics, providing valuable insights for both theoretical exploration and practical application in the constantly evolving domain of social media marketing. Moreover, the current study underscores the significance of adapting to the dynamic digital environment and highlights the evolving nature of consumer engagement in the realm of digital marketing.

Literature review

Stimulus-organism-response (s-o-r) model.

The Stimulus-Response (S-R) model, originating from behaviorist psychology and introduced by psychologist Watson ( 1917 ), posits that individual behaviors are directly induced by external environmental stimuli. However, this model overlooks internal personal factors, complicating the explanation of psychological states. Mehrabian and Russell ( 1974 ) expanded this by incorporating the individual’s cognitive component (organism) into the model, creating the Stimulus-Organism-Response (S-O-R) framework. This model has become a crucial theoretical framework in consumer psychology as it interprets internal psychological cognitions as mediators between stimuli and responses. Integrating with psychological theories, the S-O-R model effectively analyzes and explains the significant impact of internal psychological factors on behavior (Koay et al., 2020 ; Zhang et al., 2021 ), and is extensively applied in investigating user behavior on social media platforms (Hewei & Youngsook, 2022 ). This study combines the S-O-R framework with self-determination theory to examine consumer engagement behaviors in the context of social media influencer advertising, a logic also supported by some studies (Yang et al., 2021 ).

Self-determination theory

Self-determination theory, proposed by Richard and Edward (2000), is a theoretical framework exploring human behavioral motivation and personality. The theory emphasizes motivational processes, positing that individual behaviors are developed based on factors satisfying their psychological needs. It suggests that individual behavioral tendencies are influenced by the needs for competence, relatedness, and autonomy. Furthermore, self-determination theory, along with organic integration theory, indicates that individual behavioral tendencies are also affected by internal psychological motivations and external situational factors.

Self-determination theory has been validated by scholars in the study of online user behaviors. For example, Sweet applied the theory to the investigation of community building in online networks, analyzing knowledge-sharing behaviors among online community members (Sweet et al., 2020 ). Further literature review reveals the applicability of self-determination theory to consumer engagement behaviors, particularly in the context of influencer marketing advertisements. Firstly, self-determination theory is widely applied in studying the psychological motivations behind online behaviors, suggesting that the internal and external motivations outlined within the theory might also apply to exploring consumer behaviors in influencer marketing scenarios (Itani et al., 2022 ). Secondly, although research on consumer engagement in the social media influencer advertising context is still in its early stages, some studies have utilized SDT to explore behaviors such as information sharing and electronic word-of-mouth dissemination (Astuti & Hariyawan, 2021 ). These behaviors, which are part of the content contribution and creation dimensions of consumer engagement, may share similarities in the underlying psychological motivational mechanisms. Thus, this study will build upon these foundations to construct the Organism (O) component of the S-O-R model, integrating insights from SDT to further understand consumer engagement in influencer marketing.

Consumer engagement

Although scholars generally agree at a macro level to define consumer engagement as the creation of additional value by consumers or customers beyond purchasing products, the specific categorization of consumer engagement varies in different studies. For instance, Simon and Tossan interpret consumer engagement as a psychological willingness to interact with influencers (Simon & Tossan, 2018 ). However, such a broad definition lacks precision in describing various levels of engagement. Other scholars directly use tangible metrics on social media platforms, such as likes, saves, comments, and shares, to represent consumer engagement (Lee et al., 2018 ). While this quantitative approach is not flawed and can be highly effective in practical applications, it overlooks the content aspect of engagement, contradicting the “content is king” principle of advertising and marketing. We advocate for combining consumer engagement with the content aspect, as content engagement not only generates more traces of consumer online behavior (Oestreicher-Singer & Zalmanson, 2013 ) but, more importantly, content contribution and creation are central to social media advertising and marketing, going beyond mere content consumption (Qiu & Kumar, 2017 ). Meanwhile, we also need to emphasize that engagement is not a fixed state but a fluctuating process influenced by ongoing interactions between consumers and influencers, mediated by the evolving nature of social media platforms and the shifting sands of consumer preferences (Pradhan et al., 2023 ). Consumer engagement in digital environments undergoes continuous change, reflecting a journey rather than a destination (Viswanathan et al., 2017 ).

The current study adopts a widely accepted definition of consumer engagement from existing research, offering operational feasibility and aligning well with the research objectives of this paper. Consumer engagement behaviors in the context of this study encompass three dimensions: content consumption, content contribution, and content creation (Muntinga et al., 2011 ). These dimensions reflect a spectrum of digital engagement behaviors ranging from low to high levels (Schivinski et al., 2016 ). Specifically, content consumption on social media platforms represents a lower level of engagement, where consumers merely click and read the information but do not actively contribute or create user-generated content. Some studies consider this level of engagement as less significant for in-depth exploration because content consumption, compared to other forms, generates fewer visible traces of consumer behavior (Brodie et al., 2013 ). Even in a study by Qiu and Kumar, it was noted that the conversion rate of content consumption is low, contributing minimally to the success of social media marketing (Qiu & Kumar, 2017 ).

On the other hand, content contribution, especially content creation, is central to social media marketing. When consumers comment on influencer content or share information with their network nodes, it is termed content contribution, representing a medium level of online consumer engagement (Piehler et al., 2019 ). Furthermore, when consumers actively upload and post brand-related content on social media, this higher level of behavior is referred to as content creation. Content creation represents the highest level of consumer engagement (Cheung et al., 2021 ). Although medium and high levels of consumer engagement are more valuable for social media advertising and marketing, this exploratory study still retains the content consumption dimension of consumer engagement behaviors.

Theoretical framework

Internal organism factors: self-disclosure willingness, innovativeness, and information trust.

In existing research based on self-determination theory that focuses on online behavior, competence, relatedness, and autonomy are commonly considered as internal factors influencing users’ online behaviors. However, this approach sometimes strays from the context of online consumption. Therefore, in studies related to online consumption, scholars often use self-disclosure willingness as an overt representation of autonomy, innovativeness as a representation of competence, and trust as a representation of relatedness (Mahmood et al., 2019 ).

The use of these overt variables can be logically explained as follows: According to self-determination theory, individuals with a higher level of self-determination are more likely to adopt compensatory mechanisms to facilitate behavior compared to those with lower self-determination (Wehmeyer, 1999 ). Self-disclosure, a voluntary act of sharing personal information with others, is considered a key behavior in the development of interpersonal relationships. In social environments, self-disclosure can effectively alleviate stress and build social connections, while also seeking societal validation of personal ideas (Altman & Taylor, 1973 ). Social networks, as para-social entities, possess the interactive attributes of real societies and are likely to exhibit similar mechanisms. In consumer contexts, personal disclosures can include voluntary sharing of product interests, consumption experiences, and future purchase intentions (Robertshaw & Marr, 2006 ). While material incentives can prompt personal information disclosure, many consumers disclose personal information online voluntarily, which can be traced back to an intrinsic need for autonomy (Stutzman et al., 2011 ). Thus, in this study, we consider the self-disclosure willingness as a representation of high autonomy.

Innovativeness refers to an individual’s internal level of seeking novelty and represents their personality and tendency for novelty (Okazaki, 2009 ). Often used in consumer research, innovative consumers are inclined to try new technologies and possess an intrinsic motivation to use new products. Previous studies have shown that consumers with high innovativeness are more likely to search for information on new products and share their experiences and expertise with others, reflecting a recognition of their own competence (Kaushik & Rahman, 2014 ). Therefore, in consumer contexts, innovativeness is often regarded as the competence dimension within the intrinsic factors of self-determination (Wang et al., 2016 ), with external motivations like information novelty enhancing this intrinsic motivation (Lee et al., 2015 ).

Trust refers to an individual’s willingness to rely on the opinions of others they believe in. From a social psychological perspective, trust indicates the willingness to assume the risk of being harmed by another party (McAllister, 1995 ). Widely applied in social media contexts for relational marketing, information trust has been proven to positively influence the exchange and dissemination of consumer information, representing a close and advanced relationship between consumers and businesses, brands, or advertising endorsers (Steinhoff et al., 2019 ). Consumers who trust brands or social media influencers are more willing to share information without fear of exploitation (Pop et al., 2022 ), making trust a commonly used representation of the relatedness dimension in self-determination within consumer contexts.

Construction of the path from organism to response: self-determination internal factors and consumer engagement behavior

Following the logic outlined above, the current study represents the internal factors of self-determination theory through three variables: self-disclosure willingness, innovativeness, and information trust. Next, the study explores the association between these self-determination internal factors and consumer engagement behavior, thereby constructing the link between Organism (O) and Response (R).

Self-disclosure willingness and consumer engagement behavior

In the realm of social sciences, the concept of self-disclosure willingness has been thoroughly examined from diverse disciplinary perspectives, encompassing communication studies, sociology, and psychology. Viewing from the lens of social interaction dynamics, self-disclosure is acknowledged as a fundamental precondition for the initiation and development of online social relationships and interactive engagements (Luo & Hancock, 2020 ). It constitutes an indispensable component within the spectrum of interactive behaviors and the evolution of interpersonal connections. Voluntary self-disclosure is characterized by individuals divulging information about themselves, which typically remains unknown to others and is inaccessible through alternative sources. This concept aligns with the tenets of uncertainty reduction theory, which argues that during interpersonal engagements, individuals seek information about their counterparts as a means to mitigate uncertainties inherent in social interactions (Lee et al., 2008 ). Self-disclosure allows others to gain more personal information, thereby helping to reduce the uncertainty in interpersonal relationships. Such disclosure is voluntary rather than coerced, and this sharing of information can facilitate the development of relationships between individuals (Towner et al., 2022 ). Furthermore, individuals who actively engage in social media interactions (such as liking, sharing, and commenting on others’ content) often exhibit higher levels of self-disclosure (Chu et al., 2023 ); additional research indicates a positive correlation between self-disclosure and online engagement behaviors (Lee et al., 2023 ). Taking the context of the current study, the autonomous self-disclosure willingness can incline social media users to read advertising content more attentively and share information with others, and even create evaluative content. Therefore, this paper proposes the following research hypothesis:

H1a: The self-disclosure willingness is positively correlated with content consumption in consumer engagement behavior.

H1b: The self-disclosure willingness is positively correlated with content contribution in consumer engagement behavior.

H1c: The self-disclosure willingness is positively correlated with content creation in consumer engagement behavior.

Innovativeness and consumer engagement behavior

Innovativeness represents an individual’s propensity to favor new technologies and the motivation to use new products, associated with the cognitive perception of one’s self-competence. Individuals with a need for self-competence recognition often exhibit higher innovativeness (Kelley & Alden, 2016 ). Existing research indicates that users with higher levels of innovativeness are more inclined to accept new product information and share their experiences and discoveries with others in their social networks (Yusuf & Busalim, 2018 ). Similarly, in the context of this study, individuals, as followers of influencers, signify an endorsement of the influencer. Driven by innovativeness, they may be more eager to actively receive information from influencers. If they find the information valuable, they are likely to share it and even engage in active content re-creation to meet their expectations of self-image. Therefore, this paper proposes the following research hypotheses:

H2a: The innovativeness of social media users is positively correlated with content consumption in consumer engagement behavior.

H2b: The innovativeness of social media users is positively correlated with content contribution in consumer engagement behavior.

H2c: The innovativeness of social media users is positively correlated with content creation in consumer engagement behavior.

Information trust and consumer engagement

Trust refers to an individual’s willingness to rely on the statements and opinions of a target object (Moorman et al., 1993 ). Extensive research indicates that trust positively impacts information dissemination and content sharing in interpersonal communication environments (Majerczak & Strzelecki, 2022 ); when trust is established, individuals are more willing to share their resources and less suspicious of being exploited. Trust has also been shown to influence consumers’ participation in community building and content sharing on social media, demonstrating cross-cultural universality (Anaya-Sánchez et al., 2020 ).

Trust in influencer advertising information is also a key predictor of consumers’ information exchange online. With many social media users now operating under real-name policies, there is an increased inclination to trust information shared on social media over that posted by corporate accounts or anonymously. Additionally, as users’ social networks partially overlap with their real-life interpersonal networks, extensive research shows that more consumers increasingly rely on information posted and shared on social networks when making purchase decisions (Wang et al., 2016 ). This aligns with the effectiveness goals of influencer marketing advertisements and the characteristics of consumer engagement. Trust in the content posted by influencers is considered a manifestation of a strong relationship between fans and influencers, central to relationship marketing (Kim & Kim, 2021 ). Based on trust in the influencer, which then extends to trust in their content, people are more inclined to browse information posted by influencers, share this information with others, and even create their own content without fear of exploitation or negative consequences. Therefore, this paper proposes the following research hypotheses:

H3a: Information trust is positively correlated with content consumption in consumer engagement behavior.

H3b: Information trust is positively correlated with content contribution in consumer engagement behavior.

H3c: Information trust is positively correlated with content creation in consumer engagement behavior.

Construction of the path from stimulus to organism: influencer factors, advertising information factors, social factors, and self-determination internal factors

Having established the logical connection from Organism (O) to Response (R), we further construct the influence path from Stimulus (S) to Organism (O). Revisiting the definition of influencer advertising in social media, companies, and brands leverage influencers on social media platforms to disseminate advertising content, utilizing the influencers’ relationships and influence over consumers for marketing purposes. In addition to consumer’s internal factors, elements such as companies, brands, influencers, and the advertisements themselves also impact consumer behavior. Although factors like the brand image perception of companies may influence consumer behavior, considering that in influencer marketing, companies and brands do not directly interact with consumers, this study prioritizes the dimensions of influencers and advertisements. Furthermore, the impact of social factors on individual cognition and behavior is significant, thus, the current study integrates influencers, advertisements, and social dimensions as the Stimulus (S) component.

Influencer factors: parasocial identification

Self-determination theory posits that relationships are one of the key motivators influencing individual behavior. In the context of social media research, users anticipate establishing a parasocial relationship with influencers, resembling real-life relationships. Hence, we consider the parasocial identification arising from users’ parasocial interactions with influencers as the relational motivator. Parasocial interaction refers to the one-sided personal relationship that individuals develop with media characters (Donald & Richard, 1956 ). During this process, individuals believe that the media character is directly communicating with them, creating a sense of positive intimacy (Giles, 2002 ). Over time, through repeated unilateral interactions with media characters, individuals develop a parasocial relationship, leading to parasocial identification. However, parasocial identification should not be directly equated with the concept of social identification in social identity theory. Social identification occurs when individuals psychologically de-individualize themselves, perceiving the characteristics of their social group as their own, upon identifying themselves as part of that group. In contrast, parasocial identification refers to the one-sided interactional identification with media characters (such as celebrities or influencers) over time (Chen et al., 2021 ). Particularly when individuals’ needs for interpersonal interaction are not met in their daily lives, they turn to parasocial interactions to fulfill these needs (Shan et al., 2020 ). Especially on social media, which is characterized by its high visibility and interactivity, users can easily develop a strong parasocial identification with the influencers they follow (Wei et al., 2022 ).

Parasocial identification and self-disclosure willingness

Theories like uncertainty reduction, personal construct, and social exchange are often applied to explain the emergence of parasocial identification. Social media, with its convenient and interactive modes of information dissemination, enables consumers to easily follow influencers on media platforms. They can perceive the personality of influencers through their online content, viewing them as familiar individuals or even friends. Once parasocial identification develops, this pleasurable experience can significantly influence consumers’ cognitions and thus their behavioral responses. Research has explored the impact of parasocial identification on consumer behavior. For instance, Bond et al. found that on Twitter, the intensity of users’ parasocial identification with influencers positively correlates with their continuous monitoring of these influencers’ activities (Bond, 2016 ). Analogous to real life, where we tend to pay more attention to our friends in our social networks, a similar phenomenon occurs in the relationship between consumers and brands. This type of parasocial identification not only makes consumers willing to follow brand pages but also more inclined to voluntarily provide personal information (Chen et al., 2021 ). Based on this logic, we speculate that a similar relationship may exist between social media influencers and their fans. Fans develop parasocial identification with influencers through social media interactions, making them more willing to disclose their information, opinions, and views in the comment sections of the influencers they follow, engaging in more frequent social interactions (Chung & Cho, 2017 ), even if the content at times may be brand or company-embedded marketing advertisements. In other words, in the presence of influencers with whom they have established parasocial relationships, they are more inclined to disclose personal information, thereby promoting consumer engagement behavior. Therefore, we propose the following research hypotheses:

H4: Parasocial identification is positively correlated with consumer self-disclosure willingness.

H4a: Self-disclosure willingness mediates the impact of parasocial identification on content consumption in consumer engagement behavior.

H4b: Self-disclosure willingness mediates the impact of parasocial identification on content contribution in consumer engagement behavior.

H4c: Self-disclosure willingness mediates the impact of parasocial identification on content creation in consumer engagement behavior.

Parasocial identification and information trust

Information Trust refers to consumers’ willingness to trust the information contained in advertisements and to place themselves at risk. These risks include purchasing products inconsistent with the advertised information and the negative social consequences of erroneously spreading this information to others, leading to unpleasant consumption experiences (Minton, 2015 ). In advertising marketing, gaining consumers’ trust in advertising information is crucial. In the context of influencer marketing on social media, companies, and brands leverage the social connection between influencers and their fans. According to cognitive empathy theory, consumers project their trust in influencers onto the products endorsed, explaining the phenomenon of ‘loving the house for the crow on its roof.’ Research indicates that parasocial identification with influencers is a necessary condition for trust development. Consumers engage in parasocial interactions with influencers on social media, leading to parasocial identification (Jin et al., 2021 ). Consumers tend to reduce their cognitive load and simplify their decision-making processes, thus naturally adopting a positive attitude and trust towards advertising information disseminated by influencers with whom they have established parasocial identification. This forms the core logic behind the success of influencer marketing advertisements (Breves et al., 2021 ); furthermore, as mentioned earlier, because consumers trust these advertisements, they are also willing to share this information with friends and family and even engage in content re-creation. Therefore, we propose the following research hypotheses:

H5: Parasocial identification is positively correlated with information trust.

H5a: Information trust mediates the impact of parasocial identification on content consumption in consumer engagement behavior.

H5b: Information trust mediates the impact of parasocial identification on content contribution in consumer engagement behavior.

H5c: Information trust mediates the impact of parasocial identification on content creation in consumer engagement behavior.

Influencer factors: source credibility

Source credibility refers to the degree of trust consumers place in the influencer as a source, based on the influencer’s reliability and expertise. Numerous studies have validated the effectiveness of the endorsement effect in advertising (Schouten et al., 2021 ). The Source Credibility Model, proposed by the renowned American communication scholar Hovland and the “Yale School,” posits that in the process of information dissemination, the credibility of the source can influence the audience’s decision to accept the information. The credibility of the information is determined by two aspects of the source: reliability and expertise. Reliability refers to the audience’s trust in the “communicator’s objective and honest approach to providing information,” while expertise refers to the audience’s trust in the “communicator being perceived as an effective source of information” (Hovland et al., 1953 ). Hovland’s definitions reveal that the interpretation of source credibility is not about the inherent traits of the source itself but rather the audience’s perception of the source (Jang et al., 2021 ). This differs from trust and serves as a precursor to the development of trust. Specifically, reliability and expertise are based on the audience’s perception; thus, this aligns closely with the audience’s perception of influencers (Kim & Kim, 2021 ). This credibility is a cognitive statement about the source of information.

Source credibility and self-disclosure willingness

Some studies have confirmed the positive impact of an influencer’s self-disclosure on their credibility as a source (Leite & Baptista, 2022 ). However, few have explored the impact of an influencer’s credibility, as a source, on consumers’ self-disclosure willingness. Undoubtedly, an impact exists; self-disclosure is considered a method to attempt to increase intimacy with others (Leite et al., 2022 ). According to social exchange theory, people promote relationships through the exchange of information in interpersonal communication to gain benefits (Cropanzano & Mitchell, 2005 ). Credibility, deriving from an influencer’s expertise and reliability, means that a highly credible influencer may provide more valuable information to consumers. Therefore, based on the social exchange theory’s logic of reciprocal benefits, consumers might be more willing to disclose their information to trustworthy influencers, potentially even expanding social interactions through further consumer engagement behaviors. Thus, we propose the following research hypotheses:

H6: Source credibility is positively correlated with self-disclosure willingness.

H6a: Self-disclosure willingness mediates the impact of Source credibility on content consumption in consumer engagement behavior.

H6b: Self-disclosure willingness mediates the impact of Source credibility on content contribution in consumer engagement behavior.

H6c: Self-disclosure willingness mediates the impact of Source credibility on content creation in consumer engagement behavior.

Source credibility and information trust

Based on the Source Credibility Model, the credibility of an endorser as an information source can significantly influence consumers’ acceptance of the information (Shan et al., 2020 ). Existing research has demonstrated the positive impact of source credibility on consumers. Djafarova, in a study based on Instagram, noted through in-depth interviews with 18 users that an influencer’s credibility significantly affects respondents’ trust in the information they post. This credibility is composed of expertise and relevance to consumers, and influencers on social media are considered more trustworthy than traditional celebrities (Djafarova & Rushworth, 2017 ). Subsequently, Bao and colleagues validated in the Chinese consumer context, based on the ELM model and commitment-trust theory, that the credibility of brand pages on Weibo effectively fosters consumer trust in the brand, encouraging participation in marketing activities (Bao & Wang, 2021 ). Moreover, Hsieh et al. found that in e-commerce contexts, the credibility of the source is a significant factor influencing consumers’ trust in advertising information (Hsieh & Li, 2020 ). In summary, existing research has proven that the credibility of the source can promote consumer trust. Influencer credibility is a significant antecedent affecting consumers’ trust in the advertised content they publish. In brand communities, trust can foster consumer engagement behaviors (Habibi et al., 2014 ). Specifically, consumers are more likely to trust the advertising content published by influencers with higher credibility (more expertise and reliability), and as previously mentioned, consumer engagement behavior is more likely to occur. Based on this, the study proposes the following research hypotheses:

H7: Source credibility is positively correlated with information trust.

H7a: Information trust mediates the impact of source credibility on content consumption in consumer engagement behavior.

H7b: Information trust mediates the impact of source credibility on content contribution in consumer engagement behavior.

H7c: Information trust mediates the impact of source credibility on content creation in consumer engagement behavior.

Advertising information factors: informative value

Advertising value refers to “the relative utility value of advertising information to consumers and is a subjective evaluation by consumers.” In his research, Ducoffe pointed out that in the context of online advertising, the informative value of advertising is a significant component of advertising value (Ducoffe, 1995 ). Subsequent studies have proven that consumers’ perception of advertising value can effectively promote their behavioral response to advertisements (Van-Tien Dao et al., 2014 ). Informative value of advertising refers to “the information about products needed by consumers provided by the advertisement and its ability to enhance consumer purchase satisfaction.” From the perspective of information dissemination, valuable advertising information should help consumers make better purchasing decisions and reduce the effort spent searching for product information. The informational aspect of advertising has been proven to effectively influence consumers’ cognition and, in turn, their behavior (Haida & Rahim, 2015 ).

Informative value and innovativeness

As previously discussed, consumers’ innovativeness refers to their psychological trait of favoring new things. Studies have shown that consumers with high innovativeness prefer novel and valuable product information, as it satisfies their need for newness and information about new products, making it an important factor in social media advertising engagement (Shi, 2018 ). This paper also hypothesizes that advertisements with high informative value can activate consumers’ innovativeness, as the novelty of information is one of the measures of informative value (León et al., 2009 ). Acquiring valuable information can make individuals feel good about themselves and fulfill their perception of a “novel image.” According to social exchange theory, consumers can gain social capital in interpersonal interactions (such as social recognition) by sharing information about these new products they perceive as valuable. Therefore, the current study proposes the following research hypothesis:

H8: Informative value is positively correlated with innovativeness.

H8a: Innovativeness mediates the impact of informative value on content consumption in consumer engagement behavior.

H8b: Innovativeness mediates the impact of informative value on content contribution in consumer engagement behavior.

H8c: Innovativeness mediates the impact of informative value on content creation in consumer engagement behavior.

Informative value and information trust

Trust is a multi-layered concept explored across various disciplines, including communication, marketing, sociology, and psychology. For the purposes of this paper, a deep analysis of different levels of trust is not undertaken. Here, trust specifically refers to the trust in influencer advertising information within the context of social media marketing, denoting consumers’ belief in and reliance on the advertising information endorsed by influencers. Racherla et al. investigated the factors influencing consumers’ trust in online reviews, suggesting that information quality and value contribute to increasing trust (Racherla et al., 2012 ). Similarly, Luo and Yuan, in a study based on social media marketing, also confirmed that the value of advertising information posted on brand pages can foster consumer trust in the content (Lou & Yuan, 2019 ). Therefore, by analogy, this paper posits that the informative value of influencer-endorsed advertising can also promote consumer trust in that advertising information. The relationship between trust in advertising information and consumer engagement behavior has been discussed earlier. Thus, the current study proposes the following research hypotheses:

H9: Informative value is positively correlated with information trust.

H9a: Information trust mediates the impact of informative value on content consumption in consumer engagement behavior.

H9b: Information trust mediates the impact of informative value on content contribution in consumer engagement behavior.

H9c: Information trust mediates the impact of informative value on content creation in consumer engagement behavior.

Advertising information factors: ad targeting accuracy

Ad targeting accuracy refers to the degree of match between the substantive information contained in advertising content and consumer needs. Advertisements containing precise information often yield good advertising outcomes. In marketing practice, advertisers frequently use information technology to analyze the characteristics of different consumer groups in the target market and then target their advertisements accordingly to achieve precise dissemination and, consequently, effective advertising results. The utility of ad targeting accuracy has been confirmed by many studies. For instance, in the research by Qiu and Chen, using a modified UTAUT model, it was demonstrated that the accuracy of advertising effectively promotes consumer acceptance of advertisements in WeChat Moments (Qiu & Chen, 2018 ). Although some studies on targeted advertising also indicate that overly precise ads may raise concerns about personal privacy (Zhang et al., 2019 ), overall, the accuracy of advertising information is effective in enhancing advertising outcomes and is a key element in the success of targeted advertising.

Ad targeting accuracy and information trust

In influencer marketing advertisements, due to the special relationship recognition between consumers and influencers, the privacy concerns associated with ad targeting accuracy are alleviated (Vrontis et al., 2021 ). Meanwhile, the informative value brought by targeting accuracy is highlighted. More precise advertising content implies higher informative value and also signifies that the advertising content is more worthy of consumer trust (Della Vigna, Gentzkow, 2010 ). As previously discussed, people are more inclined to read and engage with advertising content they trust and recognize. Therefore, the current study proposes the following research hypotheses:

H10: Ad targeting accuracy is positively correlated with information trust.

H10a: Information trust mediates the impact of ad targeting accuracy on content consumption in consumer engagement behavior.

H10b: Information trust mediates the impact of ad targeting accuracy on content contribution in consumer engagement behavior.

H10c: Information trust mediates the impact of ad targeting accuracy on content creation in consumer engagement behavior.

Social factors: subjective norm

The Theory of Planned Behavior, proposed by Ajzen ( 1991 ), suggests that individuals’ actions are preceded by conscious choices and are underlain by plans. TPB has been widely used by scholars in studying personal online behaviors, these studies collectively validate the applicability of TPB in the context of social media for researching online behaviors (Huang, 2023 ). Additionally, the self-determination theory, which underpins this chapter’s research, also supports the notion that individuals’ behavioral decisions are based on internal cognitions, aligning with TPB’s assertions. Therefore, this paper intends to select subjective norms from TPB as a factor of social influence. Subjective norm refers to an individual’s perception of the expectations of significant others in their social relationships regarding their behavior. Empirical research in the consumption field has demonstrated the significant impact of subjective norms on individual psychological cognition (Yang & Jolly, 2009 ). A meta-analysis by Hagger, Chatzisarantis ( 2009 ) even highlighted the statistically significant association between subjective norms and self-determination factors. Consequently, this study further explores its application in the context of influencer marketing advertisements on social media.

Subjective norm and self-disclosure willingness

In numerous studies on social media privacy, subjective norms significantly influence an individual’s self-disclosure willingness. Wirth et al. ( 2019 ) based on the privacy calculus theory, surveyed 1,466 participants and found that personal self-disclosure on social media is influenced by the behavioral expectations of other significant reference groups around them. Their research confirmed that subjective norms positively influence self-disclosure of information and highlighted that individuals’ cognitions and behaviors cannot ignore social and environmental factors. Heirman et al. ( 2013 ) in an experiment with Instagram users, also noted that subjective norms could promote positive consumer behavioral responses. Specifically, when important family members and friends highly regard social media influencers as trustworthy, we may also be more inclined to disclose our information to influencers and share this information with our surrounding family and friends without fear of disapproval. In our subjective norms, this is considered a positive and valuable interactive behavior, leading us to exhibit engagement behaviors. Based on this logic, we propose the following research hypotheses:

H11: Subjective norms are positively correlated with self-disclosure willingness.

H11a: Self-disclosure willingness mediates the impact of subjective norms on content consumption in consumer engagement behavior.

H11b: Self-disclosure willingness mediates the impact of subjective norms on content contribution in consumer engagement behavior.

H11c: Self-disclosure willingness mediates the impact of subjective norms on content creation in consumer engagement behavior.

Subjective norm and information trust

Numerous studies have indicated that subjective norms significantly influence trust (Roh et al., 2022 ). This can be explained by reference group theory, suggesting people tend to minimize the effort expended in decision-making processes, often looking to the behaviors or attitudes of others as a point of reference; for instance, subjective norms can foster acceptance of technology by enhancing trust (Gupta et al., 2021 ). Analogously, if a consumer’s social network generally holds positive attitudes toward influencer advertising, they are also more likely to trust the endorsed advertisement information, as it conserves the extensive effort required in gathering product information (Chetioui et al., 2020 ). Therefore, this paper proposes the following research hypotheses:

H12: Subjective norms are positively correlated with information trust.

H12a: Information trust mediates the impact of subjective norms on content consumption in consumer engagement behavior.

H12b: Information trust mediates the impact of subjective norms on content contribution in consumer engagement behavior.

H12c: Information trust mediates the impact of subjective norms on content creation in consumer engagement behavior.

Conceptual model

In summary, based on the Stimulus (S)-Organism (O)-Response (R) framework, this study constructs the external stimulus factors (S) from three dimensions: influencer factors (parasocial identification, source credibility), advertising information factors (informative value, Ad targeting accuracy), and social influence factors (subjective norms). This is grounded in social capital theory and the theory of planned behavior. drawing on self-determination theory, the current study constructs the individual psychological factors (O) using self-disclosure willingness, innovativeness, and information trust. Finally, the behavioral response (R) is constructed using consumer engagement, which includes content consumption, content contribution, and content creation, as illustrated in Fig. 1 .

Consumer engagement behavior impact model based on SOR framework.

Materials and methods

Participants and procedures.

The current study conducted a survey through the Wenjuanxing platform to collect data. Participants were recruited through social media platforms such as WeChat, Douyin, Weibo et al., as samples drawn from social media users better align with the research purpose of our research and ensure the validity of the sample. Before the survey commenced, all participants were explicitly informed about the purpose of this study, and it was made clear that volunteers could withdraw from the survey at any time. Initially, 600 questionnaires were collected, with 78 invalid responses excluded. The criteria for valid questionnaires were as follows: (1) Respondents must have answered “Yes” to the question, “Do you follow any influencers (internet celebrities) on social media platforms?” as samples not using social media or not following influencers do not meet the study’s objective, making this question a prerequisite for continuing the survey; (2) Respondents had to correctly answer two hidden screening questions within the questionnaire to ensure that they did not randomly select scores; (3) The total time taken to complete the questionnaire had to exceed one minute, ensuring that respondents had sufficient time to understand and thoughtfully answer each question; (4) Respondents were not allowed to choose the same score for eight consecutive questions. Ultimately, 522 valid questionnaires were obtained, with an effective rate of 87.00%, meeting the basic sample size requirements for research models (Gefen et al., 2011 ). Detailed demographic information of the study participants is presented in Table 1 .

Measurements

To ensure the validity and reliability of the data analysis results in this study, the measurement tools and scales used in this chapter were designed with reference to existing established research. The main variables in the survey questionnaire include parasocial identification, source credibility, informative value, ad targeting accuracy, subjective norms, self-disclosure willingness, innovativeness, information trust, content consumption, content contribution, and content creation. The measurement scale for parasocial identification was adapted from the research of Schramm and Hartmann, comprising 6 items (Schramm & Hartmann, 2008 ). The source credibility scale was combined from the studies of Cheung et al. and Luo & Yuan’s research in the context of social media influencer marketing, including 4 items (Cheung et al., 2009 ; Lou & Yuan, 2019 ). The scale for informative value was modified based on Voss et al.‘s research, consisting of 4 items (Voss et al., 2003 ). The ad targeting accuracy scale was derived from the research by Qiu Aimei et al., 2018 ) including 3 items. The subjective norm scale was adapted from Ajzen’s original scale, comprising 3 items (Ajzen, 2002 ). The self-disclosure willingness scale was developed based on Chu and Kim’s research, including 3 items (Chu & Kim, 2011 ). The innovativeness scale was formulated following the study by Sun et al., comprising 4 items (Sun et al., 2006 ). The information trust scale was created in reference to Chu and Choi’s research, including 3 items (Chu & Choi, 2011 ). The scales for the three components of social media consumer engagement—content consumption, content contribution, and content creation—were sourced from the research by Buzeta et al., encompassing 8 items in total (Buzeta et al., 2020 ).

All scales were appropriately revised for the context of social media influencer marketing. To avoid issues with scoring neutral attitudes, a uniform Likert seven-point scale was used for each measurement item (ranging from 1 to 7, representing a spectrum from ‘strongly disagree’ to ‘strongly agree’). After the overall design of the questionnaire was completed, a pre-test was conducted with 30 social media users to ensure that potential respondents could clearly understand the meaning of each question and that there were no obstacles to answering. This pre-test aimed to prevent any difficulties or misunderstandings in the questionnaire items. The final version of the questionnaire is presented in Table 2 .

Data analysis

Since the model framework of the current study is derived from theoretical deductions of existing research and, while logically constructed, does not originate from an existing research model, this study still falls under the category of exploratory research. According to the analysis suggestions of Hair and other scholars, in cases of exploratory research model frameworks, it is more appropriate to choose Smart PLS for Partial Least Squares Path Analysis (PLS) to conduct data analysis and testing of the research model (Hair et al., 2012 ).

Measurement of model

In this study, careful data collection and management resulted in no missing values in the dataset. This ensured the integrity and reliability of the subsequent data analysis. As shown in Table 3 , after deleting measurement items with factor loadings below 0.5, the final factor loadings of the measurement items in this study range from 0.730 to 0.964. This indicates that all measurement items meet the retention criteria. Additionally, the Cronbach’s α values of the latent variables range from 0.805 to 0.924, and all latent variables have Composite Reliability (CR) values greater than the acceptable value of 0.7, demonstrating that the scales of this study have passed the reliability test requirements (Hair et al., 2019 ). All latent variables in this study have Average Variance Extracted (AVE) values greater than the standard acceptance value of 0.5, indicating that the convergent validity of the variables also meets the standard (Fornell & Larcker, 1981 ). Furthermore, the results show that the Variance Inflation Factor (VIF) values for each factor are below 10, indicating that there are no multicollinearity issues with the scales in this study (Hair, 2009 ).

The current study then further verified the discriminant validity of the variables, with specific results shown in Table 4 . The square roots of the average variance extracted (AVE) values for all variables (bolded on the diagonal) are greater than the Pearson correlation coefficients between the variables, indicating that the discriminant validity of the scales in this study meets the required standards (Fornell & Larcker, 1981 ). Additionally, a single-factor test method was employed to examine common method bias in the data. The first unrotated factor accounted for 29.71% of the variance, which is less than the critical threshold of 40%. Therefore, the study passed the test and did not exhibit serious common method bias (Podsakoff et al., 2003 ).

To ensure the robustness and appropriateness of our structural equation model, we also conducted a thorough evaluation of the model fit. Initially, through PLS Algorithm calculations, the R 2 values of each variable were greater than the standard acceptance value of 0.1, indicating good predictive accuracy of the model. Subsequently, Blindfolding calculations were performed, and the results showed that the Stone-Geisser Q 2 values of each variable were greater than 0, demonstrating that the model of this study effectively predicts the relationships between variables (Dijkstra & Henseler, 2015 ). In addition, through CFA, we also obtained some indicator values, specifically, χ 2 /df = 2.528 < 0.3, RMSEA = 0.059 < 0.06, SRMR = 0.055 < 0.08. Given its sensitivity to sample size, we primarily focused on the CFI, TLI, and NFI values, CFI = 0.953 > 0.9, TLI = 0.942 > 0.9, and NFI = 0.923 > 0.9 indicating a good fit. Additionally, RMSEA values below 0.06 and SRMR values below 0.08 were considered indicative of a good model fit. These indices collectively suggested that our model demonstrates a satisfactory fit with the data, thereby reinforcing the validity of our findings.

Research hypothesis testing

The current study employed a Bootstrapping test with a sample size of 5000 on the collected raw data to explore the coefficients and significance of the paths in the research model. The final test data results of this study’s model are presented in Table 5 .

The current study employs S-O-R model as the framework, grounded in theories such as self-determination theory and theory of planned behavior, to construct an influence model of consumer engagement behavior in the context of social media influencer marketing. It examines how influencer factors, advertisement information factors, and social influence factors affect consumer engagement behavior by impacting consumers’ psychological cognitions. Using structural equation modeling to analyze collected data ( N  = 522), it was found that self-disclosure willingness, innovativeness, and information trust positively influence consumer engagement behavior, with innovativeness having the largest impact on higher levels of engagement. Influencer factors, advertisement information factors, and social factors serve as effective external stimuli, influencing psychological motivators and, consequently, consumer engagement behavior. The specific research results are illustrated in Fig. 2 .

Tested structural model of consumer engagement behavior.

The impact of psychological motivators on different levels of consumer engagement: self-disclosure willingness, innovativeness, and information trust

The research analysis indicates that self-disclosure willingness and information trust are key drivers for content consumption (H1a, H2a validated). This aligns with previous findings that individuals with a higher willingness to disclose themselves show greater levels of engagement behavior (Chu et al., 2023 ); likewise, individuals who trust advertisement information are more inclined to engage with advertisement content (Kim, Kim, 2021 ). Moreover, our study finds that information trust has a stronger impact on content consumption, underscoring the importance of trust in the dissemination of advertisement information. However, no significant association was found between individual innovativeness and content consumption (H3a not validated).

Regarding the dimension of content contribution in consumer engagement, self-disclosure willingness, information trust, and innovativeness all positively impact it (H1b, H2b, and H3b all validated). This is consistent with earlier research findings that individuals with higher self-disclosure willingness are more likely to like, comment on, or share content posted by influencers on social media platforms (Towner et al., 2022 ); the conclusions of this paper also support that innovativeness is an important psychological driver for active participation in social media interactions (Kamboj & Sharma, 2023 ). However, at the level of consumer engagement in content contribution, while information trust also exerts a positive effect, its impact is the weakest, although information trust has the strongest impact on content consumption.

In social media advertising, the ideal outcome is the highest level of consumer engagement, i.e., content creation, meaning consumers actively join in brand content creation, seeing themselves as co-creators with the brand (Nadeem et al., 2021 ). Our findings reveal that self-disclosure willingness, innovativeness, and information trust all positively influence content creation (H1c, H2c, and H3c all validated). The analysis found that similar to the impact on content contribution, innovativeness has the most significant effect on encouraging individual content creation, followed by self-disclosure willingness, with information trust having the least impact.

In summary, while some previous studies have shown that self-disclosure willingness, innovativeness, and information trust are important factors in promoting consumer engagement (Chu et al., 2023 ; Nadeem et al., 2021 ; Geng et al., 2021 ), this study goes further by integrating and comparing all three within the same research framework. It was found that to trigger higher levels of consumer engagement behavior, trust is not the most crucial psychological motivator; rather, the most effective method is to stimulate consumers’ innovativeness, thus complementing previous research. Subsequently, this study further explores the impact of different stimulus factors on various psychological motivators.

The influence of external stimulus factors on psychological motivators: influencer factors, advertisement information factors, and social factors

The current findings indicate that influencer factors, such as parasocial identification and source credibility, effectively enhance consumer engagement by influencing self-disclosure willingness and information trust. This aligns with prior research highlighting the significance of parasocial identification (Shan et al., 2020 ). Studies suggest parasocial identification positively impacts consumer engagement by boosting self-disclosure willingness and information trust (validated H4a, H4b, H4c, and H5a), but not content contribution or creation through information trust (H5b, H5c not validated). Source credibility’s influence on self-disclosure willingness was not significant (H6 not validated), thus negating the mediating effect of self-disclosure willingness (H6a, H6b, H6c not validated). Influencer credibility mainly affects engagement through information trust (H7a, H7b, H7c validated), supporting previous findings (Shan et al., 2020 ).

Advertisement factors (informative value and ad targeting accuracy) promote engagement through innovativeness and information trust. Informative value significantly impacts higher-level content contribution and creation through innovativeness (H8b, H8c validated), while ad targeting accuracy influences consumer engagement at all levels mainly through information trust (H10a, H10b, H10c validated).

Social factors (subjective norms) enhance self-disclosure willingness and information trust, consistent with previous research (Wirth et al., 2019 ; Gupta et al., 2021 ), and further promote consumer engagement across all levels (H11a, H11b, H11c, H12a, H12b, and H12c all validated).

In summary, influencer, advertisement, and social factors impact consumer engagement behavior by influencing psychological motivators, with influencer factors having the greatest effect on content consumption, advertisement content factors significantly raising higher-level consumer engagement through innovativeness, and social factors also influencing engagement through self-disclosure willingness and information trust.

Implication

From a theoretical perspective, current research presents a comprehensive model of consumer engagement within the context of influencer advertising on social media. This model not only expands the research horizon in the fields of social media influencer advertising and consumer engagement but also serves as a bridge between two crucial themes in new media advertising studies. Influencer advertising has become an integral part of social media advertising, and the construction of a macro model aids researchers in understanding consumer psychological processes and behavioral patterns. It also assists advertisers in formulating more effective strategies. Consumer engagement, focusing on the active role of consumers in disseminating information and the long-term impact on advertising effectiveness, aligns more closely with the advertising effectiveness measures in the new media context than traditional advertising metrics. However, the intersection of these two vital themes lacks comprehensive research and a universal model. This study constructs a model that elucidates the effects of various stimuli on consumer psychology and engagement behaviors, exploring the connections and mechanisms through different mediating pathways. By differentiating levels of engagement, the study offers more nuanced conclusions for diverse advertising objectives. Furthermore, this research validates the applicability of self-determination theory in the context of influencer advertising effectiveness. While this psychological theory has been utilized in communication behavior research, its effectiveness in the field of advertising requires further exploration. The current study introduces self-determination theory into the realm of influencer advertising and consumer engagement, thereby expanding its application in the field of advertising communication. It also responds to the call from the advertising and marketing academic community to incorporate more psychological theories to explain the ‘black box’ of consumer psychology. The inclusion of this theory re-emphasizes the people-centric approach of this research and highlights the primary role of individuals in advertising communication studies.

From a practical perspective, this study provides significant insights for adapting marketing strategies to the evolving media landscape and the empowered role of audiences. Firstly, in the face of changes in the communication environment and the empowerment of audience communication capabilities, traditional marketing approaches are becoming inadequate for new media advertising needs. Traditional advertising focuses on direct, point-to-point effects, whereas social media advertising aims for broader, point-to-mass communication, leveraging audience proactivity to facilitate the viral spread of content across online social networks. Secondly, for brands, the general influence model proposed in this study offers guidance for influencer advertising strategy. If the goal is to maximize reach and brand recognition with a substantial advertising budget, partnering with top influencers who have a large following can be an effective strategy. However, if the objective is to maximize cost-effectiveness with a limited budget by leveraging consumer initiative for secondary spread, the focus should be on designing advertising content that stimulates consumer creativity and willingness to innovate. Thirdly, influencers are advised to remain true to their followers. In influencer marketing, influencers attract advertisers through their influence over followers, converting this influence into commercial gain. This influence stems from the trust followers place in the influencer, thus influencers should maintain professional integrity and prioritize the quality of information they share, even when presented with advertising opportunities. Lastly, influencers should assert more control over their relationships with advertisers. In traditional advertising, companies and brands often exert significant control over the content. However, in the social media era, influencers should negotiate more creative freedom in their advertising partnerships, asserting a more equal relationship with advertisers. This approach ensures that content quality remains high, maintaining the trust influencers have built with their followers.

Limitations and future directions

while this study offers valuable insights into the dynamics of influencer marketing and consumer engagement on social media, several limitations should be acknowledged: Firstly, constrained by the research objectives and scope, this study’s proposed general impact model covers three dimensions: influencers, advertisement information, and social factors. However, these dimensions are not limited to the five variables discussed in this paper. Therefore, we call for future research to supplement and explore more crucial factors. Secondly, in the actual communication environment, there may be differences in the impact of communication effectiveness across various social media platforms. Thus, future research could also involve comparative studies and explorations between different social media platforms. Thirdly, the current study primarily examines the direct effects of various factors on consumer engagement. However, the potential interaction effects between these variables (e.g., how influencers’ credibility might interact with advertisement information quality) are not extensively explored. Future research could investigate these complex interrelationships for a more holistic understanding. Lastly, our study, being cross-sectional, offers preliminary insights into the complex and dynamic nature of engagement between social media influencers and consumers, yet it does not incorporate the temporal dimension. The diverse impacts of psychological needs on engagement behaviors hint at an underlying dynamism that merits further investigation. Future research should consider employing longitudinal designs to directly observe how these dynamics evolve over time.

The findings of the current study not only theoretically validate the applicability of self-determination theory in the field of social media influencer marketing advertising research but also broaden the scope of advertising effectiveness research from the perspective of consumer engagement. Moreover, the research framework offers strategic guidance and reference for influencer marketing strategies. The main conclusions of this study can be summarized as follows.

Innovativeness is the key factor in high-level consumer engagement behavior. Content contribution represents a higher level of consumer engagement compared to content consumption, as it not only requires consumers to dedicate attention to viewing advertising content but also to share this information across adjacent nodes within their social networks. This dissemination of information is a pivotal factor in the success of influencer marketing advertisements. Hence, companies and brands prioritize consumers’ content contribution over mere viewing of advertising content (Qiu & Kumar, 2017 ). Compared to content consumption and contribution, content creation is considered the highest level of consumer engagement, where consumers actively create and upload brand-related content, and it represents the most advanced outcome sought by enterprises and brands in advertising campaigns (Cheung et al., 2021 ). The current study posits that to pursue better outcomes in social media influencer advertising marketing, enhancing consumers’ willingness for self-disclosure, innovativeness, and trust in advertising information are effective strategies. However, the crux lies in leveraging the consumer’s subjective initiative, particularly in boosting their innovativeness. If the goal is simply to achieve content consumption rather than higher levels of consumer engagement, the focus should be on fostering trust in advertising information. There is no hierarchy in the efficacy of different strategies; they should align with varying marketing contexts and advertising objectives.

The greatest role of social media influencers lies in attracting online traffic. information trust is the core element driving content consumption, and influencer factors mainly affect consumer engagement behaviors through information trust. Therefore, this study suggests that the primary role of influencers in social media advertising is to attract online traffic, i.e., increase consumer behavior regarding ad content consumption (reducing avoidance of ad content), and help brands achieve the initial goal of making consumers “see and complete ads.” However, their impact on further high-level consumer engagement behaviors is limited. This mechanism serves as a reminder to advertisers not to overestimate the effects of influencers in marketing. Currently, top influencers command a significant portion of the ad budget, which could squeeze the budget for other aspects of advertising, potentially affecting the overall effectiveness of the campaign. Businesses and brands should consider deeper strategic implications when planning their advertising campaigns.

Valuing Advertising Information Factors, Content Remains King. Our study posits that in the social media influencer marketing context, the key to enhancing consumer contribution and creation of advertising content lies primarily in the advertising information factors. In other words, while content consumption is important, advertisers should objectively assess the role influencers play in advertising. In the era of social media, content remains ‘king’ in advertising. This view indirectly echoes the points made in the previous paragraph: influencers effectively perform initial ‘online traffic generation’ tasks in social media, but this role should not be overly romanticized or exaggerated. Whether it’s companies, brands, or influencers, providing consumers with advertisements rich in informational value is crucial to achieving better advertising outcomes and potentially converting consumers into stakeholders.

Subjective norm is an unignorable social influence factor. Social media is characterized by its network structure of information dissemination, where a node’s information is visible to adjacent nodes. For instance, if user A likes a piece of content C from influencer I, A’s follower B, who may not follow influencer I, can still see content C via user A’s page. The aim of marketing in the social media era is to influence a node and then spread the information to adjacent nodes, either secondarily or multiple times (Kumar & Panda, 2020 ). According to the Theory of Planned Behavior, an individual’s actions are influenced by significant others in their lives, such as family and friends. Previous studies have proven the effectiveness of the Theory of Planned Behavior in influencing attitudes toward social media advertising (Ranjbarian et al., 2012 ). Current research further confirms that subjective norms also influence consumer engagement behaviors in influencer marketing on social media. Therefore, in advertising practice, brands should not only focus on individual consumers but also invest efforts in groups that can influence consumer decisions. Changing consumer behavior in the era of social media marketing doesn’t solely rely on the company’s efforts.

As communication technology advances, media platforms will further empower individual communicative capabilities, moving beyond the era of the “magic bullet” theory. The distinction between being a recipient and a transmitter of information is increasingly blurred. In an era where everyone is both an audience and an influencer, research confined to the role of the ‘recipient’ falls short of addressing the dynamics of ‘transmission’. Future research in marketing and advertising should thus focus more on the power of individual transmission. Furthermore, as Marshall McLuhan famously said, “the medium is the extension of man.” The evolution of media technology remains human-centric. Accordingly, future marketing research, while paying heed to media transformations, should emphasize the centrality of the ‘human’ element.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to privacy issues. Making the full data set publicly available could potentially breach the privacy that was promised to participants when they agreed to take part, and may breach the ethics approval for the study. The data are available from the corresponding author on reasonable request.

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The authors thank all the participants of this study. The participants were all informed about the purpose and content of the study and voluntarily agreed to participate. The participants were able to stop participating at any time without penalty. Funding for this study was provided by Minjiang University Research Start-up Funds (No. 324-32404314).

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Gu, C., Duan, Q. Exploring the dynamics of consumer engagement in social media influencer marketing: from the self-determination theory perspective. Humanit Soc Sci Commun 11 , 587 (2024). https://doi.org/10.1057/s41599-024-03127-w

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Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda

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Although social media use is gaining increasing importance as a component of firms’ portfolio of strategies, scant research has systematically consolidated and extended knowledge on social media marketing strategies (SMMSs). To fill this research gap, we first define SMMS, using social media and marketing strategy dimensions. This is followed by a conceptualization of the developmental process of SMMSs, which comprises four major components, namely drivers, inputs, throughputs, and outputs. Next, we propose a taxonomy that classifies SMMSs into four types according to their strategic maturity level: social commerce strategy, social content strategy, social monitoring strategy, and social CRM strategy. We subsequently validate this taxonomy of SMMSs using information derived from prior empirical studies, as well with data collected from in-depth interviews and a quantitive survey among social media marketing managers. Finally, we suggest fruitful directions for future research based on input received from scholars specializing in the field.

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Introduction

The past decade has witnessed the development of complex, multifarious, and intensified interactions between firms and their customers through social media usage. On the one hand, firms are taking advantage of social media platforms to expand geographic reach to buyers (Gao et al. 2018 ), bolster brand evaluations (Naylor et al. 2012 ), and build closer connections with customers (Rapp et al. 2013 ). On the other hand, customers are increasingly empowered by social media and taking control of the marketing communication process, and they are becoming creators, collaborators, and commentators of messages (Hamilton et al. 2016 ). As the role of social media has gradually evolved from a single marketing tool to that of a marketing intelligence source (in which firms can observe, analyze, and predict customer behaviors), it has become increasingly imperative for marketers to strategically use and leverage social media to achieve competitive advantage and superior performance (Lamberton and Stephen 2016 ).

Despite widespread understanding among marketers of the need to engage customers on social media platforms, relatively few firms have properly strategized their social media appearance and involvement (Choi and Thoeni 2016 ; Griffiths and Mclean 2015 ). Rather, for most companies, the ongoing challenge is not to initiate social media campaigns, but to combine social media with their marketing strategy to engage customers in order to build valuable and long-term relationships with them (Lamberton and Stephen 2016 ; Schultz and Peltier 2013 ). However, despite the vast opportunities social media offer to companies, there is no clear definition or comprehensive framework to guide the integration of social media with marketing strategies, to gain a rigorous understanding of the nature and role of social media marketing strategies (SMMSs) (Effing and Spil 2016 ).

Although some reviews focusing on the social media phenomenon are available (e.g., Lamberton and Stephen 2016 ; Salo 2017 ), to date, an integrative evaluation effort focusing on the strategic marketing perspective of social media is missing. This is partly because the social media literature largely derives elements from widely disparate fields, such as marketing, management, consumer psychology, and computer science (Aral et al. 2013 ). Moreover, research on SMMSs mainly covers very specific, isolated, and scattered aspects, which creates confusion and limits understanding of the subject (Lamberton and Stephen 2016 ). Furthermore, research deals only tangentially with a conceptualization, operationalization, and categorization of SMMSs, which limits theory advancement and practice development (Tafesse and Wien 2018 ).

To address these problems, and also to respond to repeated pleas from scholars in the field (e.g., Aral et al. 2013 ; Guesalaga 2016 ; Moorman and Day 2016 ; Schultz and Peltier 2013 to identify appropriate strategies to leverage social media in today’s changing marketing landscape, we aim to systematically consolidate and extend the knowledge accumulated from previous research on SMMSs. Specifically, our objectives are fivefold: (1) to clearly define SMMS by blending issues derived from the social media and marketing strategy literature streams; (2) to conceptualize the process of developing SMMSs and provide a theoretical understanding of its constituent parts; (3) to provide a taxonomy of SMMSs according to their level of strategic maturity; (4) to validate the practical value of this taxonomy using information derived from previous empirical studies, as well as from primary data collection among social media marketing managers; and (5) to develop an agenda for promising areas of future research on the subject.

Our study makes three major contributions to the social media marketing literature. First, it offers a definition and a conceptualization of SMMS that help alleviate definitional deficiency and increase conceptual clarity on the subject. By focusing on the role of social connectedness and interactions in resource integration, we stress the importance of transforming social media interactions and networks into marketing resources to help achieve specific strategic goals for the firm. In this regard, we provide theoretical justification of social media from a strategic marketing perspective. Second, using customer engagement as an overarching theory, we develop a model conceptualizing the SMMS developmental process. Through an analysis of each component of this process, we emphasize the role of insights from both firms and customers to better understand the dynamics of SMMS formulation. We also suggest certain theories to specifically explain the particular role played by each of these components in developing sound SMMSs. Third, we propose a taxonomy of SMMSs based on their level of strategic maturity that can serve as the basis for developing specific marketing strategy concepts and measurement scales within a social media context. We also expect this taxonomy to provide social media marketing practitioners with fruitful insights on why to select and how to use a particular SMMS in order to achieve superior marketing results.

Defining SMMS

Although researchers have often used the term “social media marketing strategy” in their studies (e.g., Choi and Thoeni 2016 ; Kumar et al. 2013 ; Zhang et al. 2017 ), they have yet to propose a clear definition. Despite the introduction of several close terms in the past, including “social media strategy” (Aral et al. 2013 ; Effing and Spil 2016 ), “online marketing strategy” (Micu et al. 2017 ), and “strategic social media marketing” (Felix et al. 2017 ), these either fail to take into consideration the different functions/features of social media or neglect key marketing strategy issues. What is therefore required is an all-encompassing definition of SMMS that will capture two fundamental elements—namely, social media and marketing strategy. Table 1 draws a comparison between social media and marketing strategy on five dimensions (i.e., core, orientation, resource, purpose, and premise) and presents the resulting profile of SMMS.

• Social media

In a marketing context, social media are considered platforms on which people build networks and share information and/or sentiments (Kaplan and Haenlein 2010 ). With their distinctive nature of being “dynamic, interconnected, egalitarian, and interactive organisms” (Peters et al. 2013 , p. 281), social media have generated three fundamental shifts in the marketplace. First, social media enable firms and customers to connect in ways that were not possible in the past. Such connectedness is empowered by various platforms, such as social networking sites (e.g., Facebook), microblogging sites (e.g., Twitter), and content communities (e.g., YouTube), that allow social networks to build from shared interests and values (Kaplan and Haenlein 2010 ). In this regard, “social connectedness” has also been termed as “social ties” (e.g., Muller and Peres 2019 ; Quinton and Wilson 2016 ), and the strength and span of these ties determine whether they are strong or weak (Granovetter 1973 ). Prior studies have shown that tie strength is an important determinant of customer referral behaviors (e.g., Verlegh et al. 2013 ).

Second, social media have transformed the way firms and customers interact and influence each other. Social interaction involves “actions,” whether through communications or passive observations, that influence others’ choices and consumption behaviors (Chen et al. 2011 ). Nair et al. ( 2010 ) labeled such social interactions as “word-of-mouth (WOM) effect” or “contagion effects.” Muller and Peres ( 2019 ) argue that social interactions rely strongly on the social network structure and provide firms with measurable value (also referred to as “social equity”). In social media studies, researchers have long recognized the importance of social influence in affecting consumer decisions, and recent studies have shown that people’s connection patterns and the strength of social ties can signify the intensity of social interactions (e.g., Aral and Walker 2014 ; Katona et al. 2011 ).

Third, the proliferation of social media data has made it increasingly possible for companies to better manage customer relationships and enhance decision making in business (Libai et al. 2010 ). Social media data, together with other digital data, are widely characterized by the 3Vs (i.e., volume, variety, and velocity), which refer to the vast quantity of data, various sources of data, and expansive real-time data (Alharthi et al. 2017 ). A huge amount of social media data derived from different venues (e.g., social networks, blogs, forums) and in various formats (e.g., text, video, image) can now be easily extracted and usefully exploited with the aid of modern information technologies (Moe and Schweidel 2017 ). Thus, social media data can serve as an important source of customer analysis, market research, and crowdsourcing of new ideas, while capturing and creating value through social media data represents the development of a new strategic resource that can improve marketing outcomes (Gnizy 2019 ).

• Marketing strategy

According to Varadarajan ( 2010 ), a marketing strategy consists of an integrated set of decisions that helps the firm make critical choices regarding marketing activities in selected markets and segments, with the aim to create, communicate, and deliver value to customers in exchange for accomplishing its specific financial, market, and other objectives. According to the resource-based view of the firm (Barney 1991 ), organizational resources (e.g., financial, human, physical, informational, relational) help firms enhance their marketing strategies, achieve sustainable competitive advantage, and gain better performance. These resources can be either tangible or intangible and can be transformed into higher-order resources (i.e., competencies and capabilities), enabling the delivery of superior value to targeted buyers (Hunt and Morgan 1995 ; Teece and Pisano 1994 ).

Different marketing strategies can be arranged on a continuum, on which transaction marketing strategy and relationship marketing strategy represent its two ends, while in between are various mixed marketing strategies (Grönroos 1991 ). Webster ( 1992 ) notes that long-standing customer relationships should be at the core of marketing strategy, because customer interaction and engagement can be developed into valuable relational resources (Hunt et al. 2006 ). Morgan and Hunt ( 1999 ) also claim that firms capitalizing on long-term and trustworthy customer relationships can help design value-enhancing marketing strategies that will subsequently generate competitive advantages and lead to superior performance.

From a strategic marketing perspective, social media interaction entails a process that allows not only firms, but also customers to exchange resources. For example, Hollebeek et al. ( 2019 ) assert that customers can devote operant (e.g., knowledge) and operand (e.g., equipment) resources while interacting with firms. Importantly, Gummesson and Mele ( 2010 ) argue that interactions occur not simply in dyads, but also between multiple actors within a network, underscoring the critical role of network interaction in resource integration. Notably, customer-to-customer interactions are also essential, especially for the higher level of engagement behaviors (Fehrer et al. 2018 ).

Thus, social media interconnectedness and interactions (i.e., between firm–customer and between customer–customer) can be considered strategic resources, which can be further converted into marketing capabilities (Morgan and Hunt 1999 ). A case in point is social customer relationship management (CRM) capabilities, in which the firm cultivates the competency to use information generated from social media interactions to identify and develop loyal customers (Trainor et al. 2014 ). With the expanding role of social media from a single communication tool to one of gaining customer and market knowledge, marketers can strategically develop distinct resources from social media based on extant organizational resources and capabilities.

Drawing on the previous argumentation, we define SMMS as an organization’s integrated pattern of activities that, based on a careful assessment of customers’ motivations for brand-related social media use and the undertaking of deliberate engagement initiatives, transform social media connectedness (networks) and interactions (influences) into valuable strategic means to achieve desirable marketing outcomes. This definition is parsimonious because it captures the uniqueness of the social media phenomenon, takes into consideration the fundamental premises of marketing strategy, and clearly defines the scope of activities pertaining to SMMS.

Although the underlying roots of traditional marketing strategy and SMMS are similar, the two strategies have three distinctive differences: (1) as opposed to the traditional approach, which pays peripheral attention to the heterogeneity of motivations driving customer engagement, SMMS emphasizes that social media users must be motivated on intellectual, social, cultural, or other grounds to engage with firms (and perhaps more importantly with other customers) (Peters et al. 2013 ; Venkatesan 2017 ); (2) the consequences of SMMS are jointly decided by the firm and its customers (rather than by individual actors’ behaviors), and it is only when the firm and its customers interact and build relationships that social media technological platforms become real resource integrators (Singaraju et al. 2016 ; Stewart and Pavlou 2002 ); and (3) while customer value in traditional marketing strategies is narrowly defined to solely capture purchase behavior through customer lifetime value, in the case of SMMS, this value is expressed through customer engagement, comprising both direct (e.g., customer purchases) and indirect (e.g., product referrals to other customers) contributions to the value of the firm (Kumar and Pansari 2016 ; Venkatesan 2017 ).

Conceptualizing the process of developing SMMSs

The conceptualization of the process of developing SMMSs is anchored on customer engagement theory, which posits that firms need to take deliberate initiatives to motivate and empower customers to maximize their engagement value and yield superior marketing results (Harmeling et al. 2017 ). Kumar et al. ( 2010 ) distinguish between four different dimensions of customer engagement value, namely customer lifetime value, customer referral value, customer influence value, and customer knowledge value. This metric has provided a new approach for customer valuation, which can help marketers to make more effective and efficient strategic decisions that enable long-term value contributions to customers. In a social media context, this customer engagement value enables firms to capitalize on crucial customer resources (i.e., network assets, persuasion capital, knowledge stores, and creativity), of which the leverage can provide firms with a sustainable competitive advantage (Harmeling et al. 2017 ).

Customer engagement theory highlights the importance of understanding customer motivations as a prerequisite for the firm to develop effective SMMSs, because heterogeneous customer motivations resulting from different attitudes and attachments can influence their social media behaviors and inevitably SMMS outcomes (Venkatesan 2017 ). It also stresses the role of inputs from both firm (i.e., social media engagement initiatives) and customers (i.e., social media behaviors), as well as the importance of different degrees of interactivity and interconnectedness in yielding sound marketing outcomes (Harmeling et al. 2017 ). Pansari and Kumar et al. ( 2017 ) argue that firms can benefit from such customer engagement in both tangible (e.g., higher revenues, market share, profits) and intangible (e.g., feedbacks or new ideas that help to product/service development) ways.

Based on consumer engagement theory, we therefore conceive the process of developing an SMMS as consisting of four interlocking parts: (1) drivers , that is, the firm’s social media marketing objectives and the customers’ social media use motivations; (2) inputs , that is, the firm’s social media engagement initiatives and the customers’ social media behaviors; (3) throughputs , that is, the way the firm connects and interacts with customers to exchange resources and satisfy needs; and (4) outputs , that is, the resulting customer engagement outcome. Figure 1 shows this developmental process of SMMS, while Table 2 indicates the specific theoretical underpinnings of each part comprising this process.

A conceptualization of the process of developing social media marketing strategies

Firms’ social media marketing objectives

Though operating in a similar context, SMMSs may differ depending on the firm’s strategic objectives (Varadarajan 2010 ). According to resource dependence theory (Pfeffer and Salancik 1978 ), the firm’s social media marketing objectives can be justified by the need to acquire external resources (which do not exist internally) that will help it accommodate the challenges of environmental contingencies. In a social media context, customers can serve as providers of resources, which can take several forms (Harmeling et al. 2017 ). Felix et al. ( 2017 ) distinguish between proactive and reactive social media marketing objectives, which can differ by the type of market targeted (e.g., B2B vs. B2C) and firm size. While for proactive objectives, firms use social media to increase brand awareness, generate online traffic, and stimulate sales, in the case of reactive objectives, the emphasis is on monitoring and analyzing customer activities.

Customers’ social media use motivations

Social media use motivations refer to various incentives that drive people’s selection and use of specific social media (Muntinga et al. 2011 ). The existence of these motivations is theoretically grounded on uses and gratifications theory (Katz et al. 1973 ), which maintains that consumers are actively and selectively involved in media usage to gratify their psychological and social needs. In a social media context, motivations can range from utilitarian and hedonic purposes (e.g., incentives, entertainment) to relational reasons (e.g., identification, brand connection) (Rohm et al. 2013 ). Muntinga et al. ( 2011 ) also categorize consumer–brand social media interactions as motivated primarily by entertainment, information, remuneration, personal identity, social interaction, and empowerment.

Firms’ social media engagement initiatives

Firms take initiatives to motivate and engage customers so that they can make voluntary contributions in return (Harmeling et al. 2017 ; Pansari and Kumar 2017 ). These firm actions can also be theoretically explained by resource dependence theory (Pfeffer and Salancik 1978 ), which argues that firms need to take initiatives to encourage customers to interact with them, to generate useful autonomous contributions that will alleviate resource shortages. Harmeling et al. ( 2017 ) identify two primary forms of a firm’s marketing initiatives to engage customers using social media: task-based and experiential. While task-based engagement initiatives encourage customer engagement behaviors with structured tasks (e.g., writing a review) and usually take place in the early stages of the firm’s social media marketing efforts, experiential engagement initiatives employ experiential events (e.g., multisensory events) to intrinsically motivate customer engagement and foster emotional attachment. Thus, firm engagement initiatives can be viewed as a continuum, where at one end, the firm uses monetary rewards to engage customers and, at the other end, the firm proactively works to deliver effective experiential incentives to motivate customer engagement.

Customers’ social media behaviors

The use of social media by customers yields different behavioral manifestations, ranging from passive (e.g., observing) to active (e.g., co-creation) (Maslowska et al. 2016 ). These customer social media behaviors can be either positive (e.g., sharing) or negative (e.g., create negative content), depending on customers’ attitudes and information processes during interactions (Dolan et al. 2016 ). Harmeling et al. ( 2017 ) characterize customers with positive behaviors as “pseudo marketers” because they contribute to firms’ marketing functions using their own resources, while those with negative behaviors may turn firm-created “hashtags” into “bashtags.” Drawing on uses and gratifications theory, Muntinga et al. ( 2011 ) also categorize customers’ brand-related behaviors in social media into three groups: consuming (e.g., reading a brand’s posts), contributing (e.g., rating products), and creating (e.g., publishing brand-related content).

Throughputs

Within the context of social media, both social connectedness and social interaction can be explained by social exchange theory, which proposes that social interactions are exchanges through which two parties acquire benefits (Blau 1964 ). Based on this theory, such a social exchange involves a sequence of interactions between firms and customers that are usually interdependent and contingent on others’ actions, with the goal to generate sound relationships (Cropanzano and Mitchell 2005 ). Thus, successful exchanges can advance interpersonal connections (referred to as social exchange relationships) with beneficial effects for the interacting parties (Cropanzano and Mitchell 2005 ).

Social connectedness

Social connectedness indicates the number of ties an individual has on social networks (Goldenberg et al. 2009 ), while Kumar et al. ( 2010 ) define connectedness with additional dimensions, including the number of connections, the strength of the connections, and the location in the network. Social media research suggests that connectedness has a significant impact on social influence. For example, Hinz et al. ( 2011 ) show that the use of “hubs” (highly connected people) in viral marketing campaigns can be eight times more successful than strategies using less connected people. Verlegh et al. ( 2013 ) also examine the impact of tie strength on making referrals in social media and confirm that people tend to interpret ambiguous information received from strong ties positively, but negatively when this information comes from weak ties.

Social interaction

Social interaction within a social media context is quite complex, as it represents multidirectional and interconnected information flows, rather than a pure firm monologue (Hennig-Thurau et al. 2013 ). This is because, on the one hand, social media have empowered customers to be equal actors in firm–customer interactions through sharing, gaming, expressing, and networking, while, on the other hand, customer–customer interactions have emerged as a growing market force, as customers can influence each other with regard to their attitudinal or behavioral changes (Peters et al. 2013 ). Chen et al. ( 2011 ) identify two types of social interactions—namely, opinion- or preference-based interactions (e.g., WOM) and action- or behavior-based interactions (e.g., observational learning)—with each requiring different strategic actions to be taken. Chahine and Malhotra ( 2018 ) also show that two-way (multiway) interaction strategies that allow reciprocity result in higher market reactions and more positive relationships.

• Customer engagement

The outputs are expressed in terms of customer engagement, which reflects the outcome of firm–customer (as well as customer–customer) connectedness and interaction in social media (Harmeling et al. 2017 ). Footnote 1 It is essentially a reflection of “the intensity of an individual’s participation in and connection with an organization’s offerings and/or organizational activities, which either the customer or the firm initiates” (Vivek et al. 2012 , p. 127). The more customers connect and interact with the firm’s activities, the higher is the level of customer engagement created (Kumar and Pansari 2016 ; Malthouse et al. 2013 ) and the higher the customer’s value addition to the firm (Pansari and Kumar 2017 ). Although the theoretical explanation of the notion of customer engagement has attracted a great deal of debate among scholars in the field, research (e.g., Brodie et al. 2011 ; Hollebeek et al. 2019 ; Kumar et al. 2019 ) has also begun adopting the service-dominant (S-D) logic (Vargo and Lusch 2004 ) because of its emphasis on customers’ interactive and value co-creation experiences in market relationships. Following the service-dominant (S-D) logic, Hollebeek et al. ( 2019 ) stress the role of customer resource integration, customer knowledge sharing, and learning as foundational in the customer engagement process, which can subsequently lead to customer individual/interpersonal operant resource development and co-creation.

Despite its pivotal role in social media marketing, extant literature has not yet attained agreement on the specific measurement of customer engagement. For example, Muntinga et al. ( 2011 ) conceptualize customer engagement in social media as comprising three stages: consuming (e.g., following, viewing content), contributing (e.g., rating, commenting), and creating (e.g., user-generated content). Maslowska et al. ( 2016 ) propose three levels of customer engagement behaviors: observing (e.g., reading content), participating (e.g., commenting on a post), and co-creating (e.g., partaking in product development). Moreover, Kumar et al. ( 2010 ) distinguish between transactional (i.e., buying the product) and non-transactional (i.e., sharing, commenting, referring, influencing) behaviors of customer engagement derived from social media connectedness and interactions.

Taxonomy of SMMSs

The distinctive differences among firms engaged in social media marketing with regard to their strategic objectives, organizational resources and capabilities, and focal industries and market structures, imply that there must also be differences in the SMMSs pursued. In this section, we first explain the criteria classifying SMMSs into different groups and then provide an analysis of their content.

Classification criteria of SMMSs

Drawing from the extant literature, we propose three important criteria that can be used to distinguish SMMSs: the nature of the firm’s strategic social media objectives with regard to using social media, the direction of interactions taking place between the firm and the customers, and the level of customer engagement achieved.

Strategic social media objectives refer to the specific organizational goals to be achieved by implementing SMMSs (Choi and Thoeni 2016 ; Felix et al. 2017 ). These can range from transactional to relational-oriented, depending on the strategist’s mental models of business–customer interactions (Rydén et al. 2015 ). Different mental models have a distinctive impact on managers’ social media sense-making, which is responsible for framing the specific role defined by social media in their marketing activities (Rydén et al. 2015 ). Rydén et al. ( 2015 ) identify four types of social media marketing objectives with four different mental models that can guide SMMSs —namely, to promote and sell (i.e., business-to-customers), to connect and collaborate (i.e., business-with-customers), to listen and learn (i.e., business-from-customers), and to empower and engage (i.e., business-for-customers).

The direction of the social media interactions can take three different forms. These include (1) one-way interaction , that is, traditional one-way communication in which the firm disseminates content (e.g., advertising) on social media and customers passively observe and react (Hoffman and Thomas 1996 ); (2) two-way interaction , that is, reciprocal and interactive communication with exchanges on social media, which can be further distinguished into firm-initiated interaction (in which the firm takes the initiative to begin the conversation) and customer participation (by liking, sharing, or commenting on the content) and customer-initiated interaction (in which the customer is the initiator of conversations by inquiring, giving feedback, or even posting negative comments about the firm, while the firm listens and responds to customer voice) (Van Noort and Willemsen 2012 ); and (3) collaborative interaction, that is, the highest level of interaction that builds on frequent and reciprocal activities in which both the firm and the customer have the power to influence each other (Joshi 2009 ).

With regard to the level of customer engagement, as noted previously, this heavily depends on the strength of connections and the intensity of interactions between the firm and the customers in social media, comprising both transactional and non-transactional elements (Kumar et al. 2010 ). Because customer engagement is the result of a dynamic and iterative process, which makes specifying the exact stage from participating to producing rather difficult (Brodie et al. 2011 ), we adopt the approach proposed by various scholars in the field (e.g., Dolan et al. 2016 ; Malthouse et al. 2013 ) to view this as a continuum, ranging from very low levels of engagement (e.g., “liking” a page) to very high levels of engagement (e.g., co-creation).

Types of SMMSs

With these three classificatory criteria, we can identify four distinct SMMSs, representing increasing levels of strategic maturity: social commerce strategy, social content strategy, social monitoring strategy, and social CRM strategy. Footnote 2 Fig.  2 illustrates this taxonomy for SMMSs, Table 3 shows the differences between these four strategies, while Appendix Table 6 provides real company examples using these strategies. In the following, we analyze each of these SMMSs by explaining their nature and characteristics, the particular role played by social media, and the specific organizational capabilities required for their adoption.

Taxonomy of social media marketing strategies

Social commerce strategy

Social commerce strategy refers to the “exchange-related activities that occur in, or are influenced by, an individual’s social network in computer-mediated social environments, whereby the activities correspond to the need recognition, pre-purchase, purchase, and post-purchase stages of a focal exchange” (Yadav et al. 2013 , p. 312). Rydén et al. ( 2015 , p. 6) claim that this way of using social media is not to create conversation and/or engagement; rather, the reasons for “the initial contact and the end purpose are to sell.” Similarly, Malthouse et al. ( 2013 ) argue that social media promotional activities do not actively engage customers because they do not make full use of the interactive role of social media. Thus, social commerce strategy can be considered as the least mature SMMS because it has a mainly transactional nature and is preoccupied with short-term goal-oriented activities (Grönroos 1994 ). It is essentially a one-way communication strategy intended to attract customers in the short run.

In this strategy, social media are claimed to be the new selling tool that has changed the way buyers and sellers interact (Marshall et al. 2012 ). They offer a new opportunity for sellers to obtain customer information and make the initial interaction with the customer more efficient (Rodriguez et al. 2012 ). Meanwhile, firms are also increasingly using social media as promising outlets for promotional/advertising purposes given their global reach (e.g., Dao et al. 2014 ; Zhang and Mao 2016 ), especially to the millennial generation (Confos and Davis 2016 ). However, as firms’ social media activities in this strategy are more transactional-oriented, customers tend to be passive and reactive. Customers contribute transactional value through purchases, but without a higher level of engagement. Therefore, we conclude that, within the context of this strategy, customers exchange their monetary resources (e.g., purchases) with the firm’s promotional offerings.

To better develop this strategy, Guesalaga ( 2016 ) highlights the need to understand the drivers of using social media in the selling process. He further stresses that personal commitment plays a crucial role in using social media as selling tools. Similarly, Järvinen and Taiminen ( 2016 ) urged for an integration of marketing with the sales department in order to gain better insights from social media marketing efforts. The importance of synergistic effects between social media and traditional media (e.g., press mentions, television, in-store promotions) has also been stressed in supporting social commerce activities (e.g., Jayson et al. 2018 ; Kumar et al. 2016 ; Stephen and Galak 2012 ). Thus, selling capabilities are crucial in this strategy, requiring the possession of adequate selling skills and the use of multiple selling channels to synergize social media effects.

Social content strategy

Social content strategy refers to “the creation and distribution of educational and/or compelling content in multiple formats to attract and/or retain customers” (Pulizzi and Barrett 2009 , p. 8). Thus, this type of SMMS aims to create and deliver timely and valuable content based on customer needs, rather than promoting products (Järvinen and Taiminen 2016 ). By attracting audiences with valuable content, the increase in customer engagement may ultimately boost product/service sales (Malthouse et al. 2013 ). Holliman and Rowley ( 2014 , p. 269) also claim that content marketing is a customer-centric strategy and describe the value of content as “being useful, relevant, compelling, and timely.” Therefore, this strategy provides a two-way communication in which firms take the initiative to deliver useful content and customers react positively to this content. The basic premises of this strategy are to create brand awareness and popularity through content virality, stimulate customer interactions, and spread positive WOM (De Vries et al. 2012 ; Swani et al. 2017 ).

Social media in this strategy have been widely used as communication tools for branding and WOM purposes (Holliman and Rowley 2014 ; Libai et al. 2013 ). On the one hand, firms generate content by their own efforts on social media (termed as ‘firm-generated’ or ‘marker-generated’ content) to actively engage consumers. On the other hand, firms encourage customers to generate the content (termed as ‘user-generated’ content) through the power of customer-to-customer interactions, as in the case of exchanging comments and sharing the brand-related content. In this way, firms provide valuable content in exchange for customer-owned resources, such as network assets and persuasion capital, to generate positive WOM and achieve a sustainable trusted brand status.

To pursue a social content strategy, firms build on capabilities focusing on how content is designed and presented (expressed in the form of a social message strategy) and how content is disseminated (expressed in the form of a seeding strategy). Thus, understanding customer engagement motivations and social media interactive characteristics is central to designing valuable content and facilitating customer interactions that would help to stimulate content sharing among customers (Malthouse et al. 2013 ). Designing compelling and valuable content in order to transform passive social media observers into active participants and collaborators is also key capability required by firms adopting this strategy (Holliman and Rowley 2014 ). Empowering customers and letting them speak for the brand is another way to engage customers with brands. Therefore, in this strategy, marketing communication capabilities are important for effective marketing content development and dissemination.

Social monitoring strategy

Social monitoring strategy refers to “a listening and response process through which marketers themselves become engaged” (Barger et al. 2016 , p. 278). In contrast with social content strategy, which is more of a “push” communication approach with content delivered, social monitoring strategy requires the firm’s active involvement in the whole communication process (from content delivery to customer response) (Barger et al. 2016 ). More specifically, social monitoring strategy is not only to observe and analyze the behaviors of customers in social media (Lamberton and Stephen 2016 ), but also to actively search for and respond to customer online needs and complaints (Van Noort and Willemsen 2012 ). A social monitoring strategy is thus characterized by a two-way communication process, in which the initiation comes from customers who comment and behave on social media, while the company takes advantage of customer behavior data to listen, learn, and react to its customers. Thus, the key objective of this strategy is to enhance customer satisfaction and cultivate stronger relationships with customers through ongoing social media listening and responding.

With today’s abundance of attitudinal and behavioral data, firms adopting this strategy use social media platforms as “tools” or “windows” to listen to customer voices and gain important market insights to support their marketing decisions (Moe and Schweidel 2017 ). Moreover, Carlson et al. ( 2018 ) argue that firms can take advantage of social media data to identify innovation opportunities and facilitate the innovation process. Hence, social media monitoring enables firms to assess consumers’ reactions, evaluate the prosperity of social media marketing initiatives, and allocate resources to different types of conversations and customer groups (Homburg et al. 2015 ). In other words, customers in this strategy are expected to be active in social media interactions, providing instantaneous and real-time feedback. This has in a way helped product development and experience improvements with resource inputs from customers’ knowledge stores.

Social monitoring strategy emphasizes the importance of carefully listening and responding to social media activities to have a better understanding of customer needs, gain critical market insights, and build stronger customer relationships (e.g., Timoshenko and Hauser 2019 ). It therefore requires firms to be actively involved in the whole communication process with customers, as customer engagement is not dependent on rewards, but is developed through the ongoing reciprocity between the firm and its customers (Barger et al. 2016 ). Thus, organizational capabilities, such as marketing sensing through effective information acquisition, interpretation and responding, are essential for the successful implementation of this strategy. More specifically, monitoring and text analysis techniques are needed to gather and capture social media data rapidly (Schweidel and Moe 2014 ). Noting the damage caused by electronic negative word of mouth (e-NWOM) on social media, firms adopting this strategy also require special capabilities to appropriately respond to customer online complaints and requests (Kim et al. 2016 ).

Social CRM strategy

Among the four SMMSs identified, social CRM strategy is characterized by the highest degree of strategic maturity, because it reflects “a philosophy and a business strategy supported by a technology platform, business rules, processes, and social characteristics, designed to engage the customer in a collaborative conversation in order to provide mutually beneficial value in a trusted and transparent business environment” (Greenberg 2009 , p. 34). The concept of social CRM is designed to combine the benefits derived from both the social media dimension (e.g., customer engagement) and the CRM dimension (e.g., customer retention) (Malthouse et al. 2013 ). In contrast with the traditional CRM approach, which assumes that customers are passive and only contribute to customer life value, social CRM strategy emphasizes the active role of customers who are empowered by social media and can make a contribution to multiple forms of value (Kumar et al. 2010 ). In brief, a social CRM strategy is a form of collaborative interaction, including firm–customer, inter-organizational, and inter-customer interactions, that are intended to engage and empower customers, so as to build mutually beneficial relationships with the firm and lead to superior performance.

Social media have become powerful enablers of CRM (Choudhury and Harrigan 2014 ). For example, Charoensukmongkol and Sasatanun ( 2017 ) argue that the integration of social media and CRM provides a possibility for firms to segment their customers based on similar characteristics, and can customize marketing offerings to the specific preferences of individual customers. With social CRM strategy, firms can enhance the likelihood of customer engagement through one-to-one social media interactions. Customers at this stage are collaborative and interactive in value creation, such as voluntarily providing innovative ideas and collaborating with brands (Jaakkola and Alexander 2014 ). Hence, besides resource like network assets, persuasion capital, and knowledge stores, engaged customers also contribute their creativity resource for value co-creation.

Social CRM capability is “a firm-level capability and refers to a firm’s competency in generating, integrating, and responding to information obtained from customer interactions that are facilitated by social media technologies” (Trainor et al. 2014 , p. 271). Therefore, firms should be extremely creative to combine social media data with its CRM system, as well as to link the massive social media data on customer activities to other data sources (e.g., customer service records) to generate better customer-learning and innovation opportunities (Choudhury and Harrigan 2014 ; Moe and Schweidel 2017 ). Social CRM strategy also emphasizes the significance of reciprocal information sharing and collaborations that are supported by the firm’s culture and commitment, operational resources, and cross-functional cooperation (Malthouse et al. 2013 ; Schultz and Peltier 2013 ). To sum up, social CRM capabilities, organizational learning capabilities connected with relationship management and innovation are essential prerequisites to building an effective social CRM strategy.

Validation of proposed SMMSs

Using the previously developed classification of SMMSs (i.e., social commerce strategy, social content strategy, social monitoring strategy, and social CRM strategy) as a basis, we reviewed the pertinent literature to collate useful knowledge supporting the content of each of these strategies. Table 4 provides a summary of the key empirical insights derived from the extant studies reviewed, together with resulting managerial lessons.

To validate the practical usefulness of our proposed classificatory framework of SMMSs, we first conducted a series of in-depth interviews with 15 social media marketing practitioners, who had their own firm/brand accounts on social media platforms, at least one year of social media marketing experience, and at least three years’ experience in their current organization (see Web Appendix 1 ). Interviewees represented companies located in China (8 companies), Finland (5 companies), and Sweden (2 companies) and involved in a variety of industries (e.g., digital tech, tourism, food, sport). All interviews were based on a specially designed guide (which was sent to participants in advance to prepare them for the interview) and were audiotaped and subsequently transcribed verbatim (see Web Appendix 2 ).

The main findings of this qualitative study are the following: (1) social media are mainly used as a key marketing channel to achieve business objectives, which, however, differentiates in terms of product-market type, organization size, and managerial mindset; (2) distinct differences exist across organizations in terms of their social media initiatives to deliver content, generate reactions, and develop social CRM; (3) there are marked variations in customer engagement levels across participant firms, resulting from the adoption of different SMMSs; (4) the firm’s propensity to use a specific SMMSs is enhanced by infrastructures, systems, and technologies that help to actively search, access, and integrate data from different sources, as well as facilitate the sharing and coordination of activities with customers; and (5) the adoption of a specific SMMS does not follow a sequential pattern in terms of strategic maturity development, but rather, depends on the firm’s strategic objectives, its willingness to commit the required resources, and the deployment of appropriate organizational capabilities.

To further confirm the existence of differences in profile characteristics among the four types of SMMSs, we conducted an electronic survey among a sample of 52 U.S. social media marketing managers who were randomly selected. For this purpose, we designed a structured questionnaire incorporating the key parameters related to SMMSs, namely firms’ strategic objectives, firms’ engagement initiatives, customers’ social media behaviors, social media resources and capabilities required, direction of interactions, and customer engagement levels (see Web Appendix 3 ).

Specifically, we found that: (1) each of the four SMMSs emphasize different types of strategic objectives, ranging from promoting and selling, in the case of social commerce strategy, to empowering and engaging in social CRM strategy; (2) experiential engagement initiatives geared to customer engagement were more evident at the advanced level, as opposed to the lower level strategies; (3) passive customer social media behaviors were more characteristic of the social commerce strategy, while more active customer behaviors were observed in the case of social CRM strategy; (4) the more advanced the maturity of the SMMS employed, the higher the level customer engagement, as well as the higher requirements in terms of organizational resources and specialized capabilities; and (5) one-way interaction was associated more with social commerce strategy, two-way interaction was more evident in the social content strategy and the social monitoring strategy, and collaborative interaction was a dominant feature in the social CRM strategy (see Web Appendix 4 ).

Future research directions

While the extant research offers insightful information and increased knowledge on SMMSs, there is still plenty of room to expand this field of research with other issues, especially given the rapidly changing developments in social media marketing practice. To gain a more accurate picture about the future of research on the subject, we sought the opinions of academic experts in the field through an electronically conducted survey among authors of academic journal articles written on the subject. We specifically asked them: (1) to suggest the three most important areas that research on SMMSs should focus on in the future; (2) within each of the areas suggested, to indicate three specific topics that need to be addressed more; and (3) within each topic, to illustrate analytical issues that warrant particular attention (see Web Appendix 5 ). Altogether, we received input from 43 social media marketing scholars who suggested 6 broad areas, 13 specific topics, and 82 focal issues for future research, which are presented in Table 5 .

Among the research issues proposed, finding appropriate metrics to measure performance in SMMSs seems to be an area to which top priority should be given. This is because performance is the ultimate outcome of these strategies, for which there is still little understanding due to the idiosyncratic nature of social media as a marketing tool (e.g., Beckers et al. 2017 ; Trainor et al. 2014 ). In particular, it is important to shed light on both short-term and long-term performance, as well as its effectiveness, efficiency, and adaptiveness aspects (e.g., Barger et al. 2016 ). Another key priority area stressed by experts in the field involves integrating to a greater extent various strategic issues regarding each of the marketing-mix elements in a social media context. This would help achieve better coordination between traditional and online marketing tools (e.g., Kolsarici and Vakratsas 2018 ; Kumar et al. 2017 ).

Respondents in our academic survey also stressed the evolutionary nature of knowledge with regard to each of the four SMMSs and proposed multiple issues for each of them. Particular attention should be paid to how inputs from customers and firms are interrelated in each of these strategies, taking into consideration the central role played by customer engagement behaviors and firm initiatives (e.g., Sheng 2019 ). Respondents also pinpointed the need for more emphasis on social CRM strategy (which is relatively under-researched), while there should also be a closer assessment of new developments in both marketing (e.g., concepts and tools) and social media (e.g., technologies and platforms) that can lead to the emergence of new types of SMMSs (e.g., Ahani et al. 2017 ; Choudhury and Harrigan 2014 ).

Respondents also noted that up to now the preparatory phase for designing SMMSs has been overlooked, and that therefore there is a need to shed more light on this because of its decisive role in achieving positive results. For example, issues relating to market/competitor analysis, macro-environmental scanning, and target marketing should be carefully studied in conjunction with formulating sound SMMSs, to better exploit opportunities and neutralize threats in a social media context (e.g., De Vries et al. 2017 ). By contrast, our survey among scholars in the field stressed the crucial nature of issues relating to SMMS implementation and control, which are of equal, or even greater, importance than those of strategy formulation (e.g., Järvinen and Taiminen 2016 ). The academics also indicated that, by their very nature, social media transcend national boundaries, thus leaving plenty of room to investigate the international ramifications of SMMSs, using cross-cultural research (e.g., Johnston et al. 2018 ).

Implications and conclusions

Theoretical implications.

Given the limited research on SMMSs, this study has several important theoretical implications. First, we are taking a step in this new theoretical direction by providing a workable definition and conceptualization of SMMS that combines both social media and marketing strategy dimensions. The study complements and extends previous research (e.g., Harmeling et al. 2017 ; Singaraju et al. 2016 ) that emphasized the value of social media as resource integrator in exchanging customer-owned resources, which can provide researchers with new angles to address the issue of integrating social media with marketing strategy. Such integrative efforts can have a meaningful long-term impact on building a new theory (or theories) of social media marketing. They also point to a deeper theoretical understanding of the roles played by resource identification, utilization, and reconfiguration in a SMMS context.

We have also extended the idea of “social interaction” and “social connectedness” in a social media context, which is critical because the power of a customer enabled by social media connections and interactions is of paramount importance in explaining the significance of SMMSs (Hennig-Thurau et al. 2013 ). More importantly, our study suggests that firms should take the initiative to motivate and engage customers, which will lead to wider and more extensive interactions. In particular, we show that a firm can leverage its social media usage through the use of different engagement initiatives to enforce customer interactivity and interconnectedness. Such enquiries can provide useful theoretical insights into the strategic marketing role played by social media in today’s highly digitalized and globalized world.

We are also furthering the customer engagement literature by proposing an SMMS developmental process. As firm–customer relationships evolve in a social media era, it is critical to identify those factors that have an impact on customer engagement. Although prior studies (e.g., Harmeling et al. 2017 ; Pansari and Kumar 2017 ) have demonstrated the engagement value contributed by customers and the need for engagement initiatives taken by firms, we are extending this idea to provide a more holistic view by highlighting the role of insights from both firms and customers to better understand the dynamics of SMMS formulation. We also suggest certain theories to specifically explain the role played by each of the components of the process in developing sound SMMSs. We capture the unique characteristics of social media by suggesting that these networks and interactions are tightly interrelated with the outcome of SMMS, which is customer engagement. Our proposed SMMS developmental process may therefore provide critical input for new studies focusing on customer engagement research.

 Finally, we build on various criteria to distinguish among four SMMSs, each representing a different level of strategic maturity. We show that a SMMS is not homogeneous, but needs to be understood in a wider, more nuanced way, as having different strategies relying on different goals and deriving insights from firms and their customers, ultimately leading to different customer engagement levels. In this regard, the identification of the key SMMSs stemming from our analysis can serve as the basis for developing specific marketing strategy constructs and scales within a social media context. We also indicate that different SMMSs can be implemented and yield superior competitive advantage only when the firm is in a position to devote to it the right amount and type of resources and capabilities (e.g., Gao et al. 2018 ; Kumar and Pansari 2016 ).

Managerial implications

Our study also has serious implications for managers. First, our analysis revealed that the ever-changing digital landscape on a global scale calls for a reassessment of the ways to strategically manage brands and customers in a social media context. This requires companies to understand the different goals for using social media and to develop their strategies accordingly. As a starting point, firms could explore customer motivations for using social media and effectively deploy the necessary resources to accommodate these motivations. They should also think carefully about how to engage customers when implementing their marketing strategies, because social media become resource integrators only when customers interact with and provide information on them (Singaraju et al. 2016 ).

Managers need to set objectives at the outset to guide the effective development, implementation, and control of SMMSs. Our study suggests four key SMMSs achieving different business goals. For example, the goal of social commerce strategy is to attract customers with transactional interests, that of social content strategy and social monitoring strategy is to deliver valuable content and service to customers, and that of social CRM strategy is to build mutually beneficial customer relationships by integrating social media data with current organizational processes. Unfortunately, many companies, especially smaller ones, tend to create their social media presence for a single purpose only: to disseminate massive commercial information on their social media web pages in the hope of attracting customers, even though these customers may find commercially intensive content annoying.

This study also suggests that social media investments should focus on the integration of social media platforms with internal company systems to build special social media capabilities (i.e., creating, combining, and reacting to information obtained from customer interactions on social media). Such capabilities are vital in developing a sustainable competitive advantage, superior market and financial performance. However, to achieve this, firms must have the right organizational structural and cultural transformation, as well as substantial management commitment and continuous investment.

Lastly, social media have become powerful tools for CRM, helping to transform it from traditional one-way interaction to collaborative interaction. This implies that customer engagement means not only encouraging customer engagement on social media, but also proactively learning from and collaborating with customers. As Pansari and Kumar et al. ( 2017 ) indicate, customer engagement can contribute both directly (e.g., purchase) and indirectly (e.g., customer knowledge value) to the firm. Therefore, interacting with customers via social media provides tremendous opportunities for firms to learn more about their customers and opens up new possibilities for product/service co-creation.

Conclusions

The exploding use of social media in the past decade has underscored the need for guidance on how to build SMMSs that foster relationships with customers, advance customer engagement, and increase marketing performance. However, a comprehensive definition, conceptualization, and framework to guide the analysis and development of SMMSs are lacking. This can be attributed to the recent introduction of social media as a strategic marketing tool, while both academics and practitioners still lack the necessary knowledge on how to convert social media data into actionable strategic marketing tools (Moe and Schweidel 2017 ). This insufficiency also stems from the fact that the adoption of more advanced SMMSs requires the possession of specific organizational capabilities that can be used to leverage social media, with the support of a culture that encourages breaking free from obsolete mindsets, emphasizing employee skills with intelligence in data and customer analytical insights, and operational excellence in organizational structure and business processes (Malthouse et al. 2013 ).

Our study takes the first step toward addressing this issue and provides useful guidelines for leveraging social media use in strategic marketing. In particular, we provide a systematic consolidation and extension of the extant pertinent SMMS literature to offer a robust definition, conceptualization, taxonomy, and validation of SMMSs. Specifically, we have amply demonstrated that the mere use of social media alone does not generate customer value, which instead is attained through the generation of connections and interactions between the firm and its customers, as well as among customers themselves. These generated social networks and influences can subsequently be used strategically for resource transformation and exchanges between the interacting parties. Our conceptualization of the SMMS developmental process also suggests that firms first need to recognize customers’ motivations to engage in brand-related social media activities and encourage their voluntary contributions.

Although the four SMMSs identified in our study (i.e., social commerce strategy, social content strategy, social monitoring strategy, and social CRM strategy) denote progressing levels of strategic maturity, their adoption does not follow a sequential pattern. As our validation procedures revealed, this will be determined by the firm’s strategic objectives, resources, and capabilities. Moreover, the success of the various SMMSs will depend on the firm’s ability to identify and leverage customer-owned resources, as in the case of transforming customers from passive receivers of the firm’s social media offerings to active value contributors. It will also depend on the firm’s willingness to allocate resources in order to foster collaborative conversations, develop appropriate responses, and enhance customer relationships. These will all ultimately help to build a sustainable competitive advantage and enhance business performance.

Although in our conceptualization of the process of developing SMMSs we treat customer engagement as the output of this process, we fully acknowledge that firms’ ultimate objective to engage in social media marketing activities is to improve their market (e.g., customer equity) and financial (e.g., revenues) performance. In fact, extant social media marketing research (e.g., Kumar et al. 2010 ; Kumar and Pansari 2016 ; Harmeling et al. 2017 ) repeatedly stresses the conducive role of customer engagement in ensuring high performance results.

SMMSs are difficult to operationalize by focusing solely on the elements of the marketing mix (i.e., product, price, distribution, and promotion), mainly because many other important parameters are involved in their conceptualization, such as relationship management, market development, and business innovation issues. However, each SMMS seems to have a different marketing mix focus, with social commerce strategy emphasizing advertising and sales, social content strategy emphasizing branding and communication, social monitoring strategy emphasizing service and product development, and social CRM strategy emphasizing customer management and innovation.

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Li, F., Larimo, J. & Leonidou, L.C. Social media marketing strategy: definition, conceptualization, taxonomy, validation, and future agenda. J. of the Acad. Mark. Sci. 49 , 51–70 (2021). https://doi.org/10.1007/s11747-020-00733-3

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Artificial intelligence in strategy

Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform .

Joanna Pachner: What does artificial intelligence mean in the context of strategy?

Yuval Atsmon: When people talk about artificial intelligence, they include everything to do with analytics, automation, and data analysis. Marvin Minsky, the pioneer of artificial intelligence research in the 1960s, talked about AI as a “suitcase word”—a term into which you can stuff whatever you want—and that still seems to be the case. We are comfortable with that because we think companies should use all the capabilities of more traditional analysis while increasing automation in strategy that can free up management or analyst time and, gradually, introducing tools that can augment human thinking.

Joanna Pachner: AI has been embraced by many business functions, but strategy seems to be largely immune to its charms. Why do you think that is?

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Yuval Atsmon: You’re right about the limited adoption. Only 7 percent of respondents to our survey about the use of AI say they use it in strategy or even financial planning, whereas in areas like marketing, supply chain, and service operations, it’s 25 or 30 percent. One reason adoption is lagging is that strategy is one of the most integrative conceptual practices. When executives think about strategy automation, many are looking too far ahead—at AI capabilities that would decide, in place of the business leader, what the right strategy is. They are missing opportunities to use AI in the building blocks of strategy that could significantly improve outcomes.

I like to use the analogy to virtual assistants. Many of us use Alexa or Siri but very few people use these tools to do more than dictate a text message or shut off the lights. We don’t feel comfortable with the technology’s ability to understand the context in more sophisticated applications. AI in strategy is similar: it’s hard for AI to know everything an executive knows, but it can help executives with certain tasks.

When executives think about strategy automation, many are looking too far ahead—at AI deciding the right strategy. They are missing opportunities to use AI in the building blocks of strategy.

Joanna Pachner: What kind of tasks can AI help strategists execute today?

Yuval Atsmon: We talk about six stages of AI development. The earliest is simple analytics, which we refer to as descriptive intelligence. Companies use dashboards for competitive analysis or to study performance in different parts of the business that are automatically updated. Some have interactive capabilities for refinement and testing.

The second level is diagnostic intelligence, which is the ability to look backward at the business and understand root causes and drivers of performance. The level after that is predictive intelligence: being able to anticipate certain scenarios or options and the value of things in the future based on momentum from the past as well as signals picked in the market. Both diagnostics and prediction are areas that AI can greatly improve today. The tools can augment executives’ analysis and become areas where you develop capabilities. For example, on diagnostic intelligence, you can organize your portfolio into segments to understand granularly where performance is coming from and do it in a much more continuous way than analysts could. You can try 20 different ways in an hour versus deploying one hundred analysts to tackle the problem.

Predictive AI is both more difficult and more risky. Executives shouldn’t fully rely on predictive AI, but it provides another systematic viewpoint in the room. Because strategic decisions have significant consequences, a key consideration is to use AI transparently in the sense of understanding why it is making a certain prediction and what extrapolations it is making from which information. You can then assess if you trust the prediction or not. You can even use AI to track the evolution of the assumptions for that prediction.

Those are the levels available today. The next three levels will take time to develop. There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis. From there, you go to delegating certain decision authority to AI, with constraints and supervision. Eventually, there is the point where fully autonomous AI analyzes and decides with no human interaction.

Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.

Joanna Pachner: What kind of businesses or industries could gain the greatest benefits from embracing AI at its current level of sophistication?

Yuval Atsmon: Every business probably has some opportunity to use AI more than it does today. The first thing to look at is the availability of data. Do you have performance data that can be organized in a systematic way? Companies that have deep data on their portfolios down to business line, SKU, inventory, and raw ingredients have the biggest opportunities to use machines to gain granular insights that humans could not.

Companies whose strategies rely on a few big decisions with limited data would get less from AI. Likewise, those facing a lot of volatility and vulnerability to external events would benefit less than companies with controlled and systematic portfolios, although they could deploy AI to better predict those external events and identify what they can and cannot control.

Third, the velocity of decisions matters. Most companies develop strategies every three to five years, which then become annual budgets. If you think about strategy in that way, the role of AI is relatively limited other than potentially accelerating analyses that are inputs into the strategy. However, some companies regularly revisit big decisions they made based on assumptions about the world that may have since changed, affecting the projected ROI of initiatives. Such shifts would affect how you deploy talent and executive time, how you spend money and focus sales efforts, and AI can be valuable in guiding that. The value of AI is even bigger when you can make decisions close to the time of deploying resources, because AI can signal that your previous assumptions have changed from when you made your plan.

Joanna Pachner: Can you provide any examples of companies employing AI to address specific strategic challenges?

Yuval Atsmon: Some of the most innovative users of AI, not coincidentally, are AI- and digital-native companies. Some of these companies have seen massive benefits from AI and have increased its usage in other areas of the business. One mobility player adjusts its financial planning based on pricing patterns it observes in the market. Its business has relatively high flexibility to demand but less so to supply, so the company uses AI to continuously signal back when pricing dynamics are trending in a way that would affect profitability or where demand is rising. This allows the company to quickly react to create more capacity because its profitability is highly sensitive to keeping demand and supply in equilibrium.

Joanna Pachner: Given how quickly things change today, doesn’t AI seem to be more a tactical than a strategic tool, providing time-sensitive input on isolated elements of strategy?

Yuval Atsmon: It’s interesting that you make the distinction between strategic and tactical. Of course, every decision can be broken down into smaller ones, and where AI can be affordably used in strategy today is for building blocks of the strategy. It might feel tactical, but it can make a massive difference. One of the world’s leading investment firms, for example, has started to use AI to scan for certain patterns rather than scanning individual companies directly. AI looks for consumer mobile usage that suggests a company’s technology is catching on quickly, giving the firm an opportunity to invest in that company before others do. That created a significant strategic edge for them, even though the tool itself may be relatively tactical.

Joanna Pachner: McKinsey has written a lot about cognitive biases  and social dynamics that can skew decision making. Can AI help with these challenges?

Yuval Atsmon: When we talk to executives about using AI in strategy development, the first reaction we get is, “Those are really big decisions; what if AI gets them wrong?” The first answer is that humans also get them wrong—a lot. [Amos] Tversky, [Daniel] Kahneman, and others have proven that some of those errors are systemic, observable, and predictable. The first thing AI can do is spot situations likely to give rise to biases. For example, imagine that AI is listening in on a strategy session where the CEO proposes something and everyone says “Aye” without debate and discussion. AI could inform the room, “We might have a sunflower bias here,” which could trigger more conversation and remind the CEO that it’s in their own interest to encourage some devil’s advocacy.

We also often see confirmation bias, where people focus their analysis on proving the wisdom of what they already want to do, as opposed to looking for a fact-based reality. Just having AI perform a default analysis that doesn’t aim to satisfy the boss is useful, and the team can then try to understand why that is different than the management hypothesis, triggering a much richer debate.

In terms of social dynamics, agency problems can create conflicts of interest. Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business. AI provides a neutral way based on systematic data to manage those debates. It’s also useful for executives with decision authority, since we all know that short-term pressures and the need to make the quarterly and annual numbers lead people to make different decisions on the 31st of December than they do on January 1st or October 1st. Like the story of Ulysses and the sirens, you can use AI to remind you that you wanted something different three months earlier. The CEO still decides; AI can just provide that extra nudge.

Joanna Pachner: It’s like you have Spock next to you, who is dispassionate and purely analytical.

Yuval Atsmon: That is not a bad analogy—for Star Trek fans anyway.

Joanna Pachner: Do you have a favorite application of AI in strategy?

Yuval Atsmon: I have worked a lot on resource allocation, and one of the challenges, which we call the hockey stick phenomenon, is that executives are always overly optimistic about what will happen. They know that resource allocation will inevitably be defined by what you believe about the future, not necessarily by past performance. AI can provide an objective prediction of performance starting from a default momentum case: based on everything that happened in the past and some indicators about the future, what is the forecast of performance if we do nothing? This is before we say, “But I will hire these people and develop this new product and improve my marketing”— things that every executive thinks will help them overdeliver relative to the past. The neutral momentum case, which AI can calculate in a cold, Spock-like manner, can change the dynamics of the resource allocation discussion. It’s a form of predictive intelligence accessible today and while it’s not meant to be definitive, it provides a basis for better decisions.

Joanna Pachner: Do you see access to technology talent as one of the obstacles to the adoption of AI in strategy, especially at large companies?

Yuval Atsmon: I would make a distinction. If you mean machine-learning and data science talent or software engineers who build the digital tools, they are definitely not easy to get. However, companies can increasingly use platforms that provide access to AI tools and require less from individual companies. Also, this domain of strategy is exciting—it’s cutting-edge, so it’s probably easier to get technology talent for that than it might be for manufacturing work.

The bigger challenge, ironically, is finding strategists or people with business expertise to contribute to the effort. You will not solve strategy problems with AI without the involvement of people who understand the customer experience and what you are trying to achieve. Those who know best, like senior executives, don’t have time to be product managers for the AI team. An even bigger constraint is that, in some cases, you are asking people to get involved in an initiative that may make their jobs less important. There could be plenty of opportunities for incorpo­rating AI into existing jobs, but it’s something companies need to reflect on. The best approach may be to create a digital factory where a different team tests and builds AI applications, with oversight from senior stakeholders.

The big challenge is finding strategists to contribute to the AI effort. You are asking people to get involved in an initiative that may make their jobs less important.

Joanna Pachner: Do you think this worry about job security and the potential that AI will automate strategy is realistic?

Yuval Atsmon: The question of whether AI will replace human judgment and put humanity out of its job is a big one that I would leave for other experts.

The pertinent question is shorter-term automation. Because of its complexity, strategy would be one of the later domains to be affected by automation, but we are seeing it in many other domains. However, the trend for more than two hundred years has been that automation creates new jobs, although ones requiring different skills. That doesn’t take away the fear some people have of a machine exposing their mistakes or doing their job better than they do it.

Joanna Pachner: We recently published an article about strategic courage in an age of volatility  that talked about three types of edge business leaders need to develop. One of them is an edge in insights. Do you think AI has a role to play in furnishing a proprietary insight edge?

Yuval Atsmon: One of the challenges most strategists face is the overwhelming complexity of the world we operate in—the number of unknowns, the information overload. At one level, it may seem that AI will provide another layer of complexity. In reality, it can be a sharp knife that cuts through some of the clutter. The question to ask is, Can AI simplify my life by giving me sharper, more timely insights more easily?

Joanna Pachner: You have been working in strategy for a long time. What sparked your interest in exploring this intersection of strategy and new technology?

Yuval Atsmon: I have always been intrigued by things at the boundaries of what seems possible. Science fiction writer Arthur C. Clarke’s second law is that to discover the limits of the possible, you have to venture a little past them into the impossible, and I find that particularly alluring in this arena.

AI in strategy is in very nascent stages but could be very consequential for companies and for the profession. For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today. It’s conceivable that competitive advantage will increasingly rest in having executives who know how to apply AI well. In some domains, like investment, that is already happening, and the difference in returns can be staggering. I find helping companies be part of that evolution very exciting.

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