literature review on impact of e commerce

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• Published: 04 May 2024

E-commerce and foreign direct investment: pioneering a new era of trade strategies

• Yugang He   ORCID: orcid.org/0000-0001-5758-069X 1  

Humanities and Social Sciences Communications volume  11 , Article number:  566 ( 2024 ) Cite this article

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• Business and management

This study explores the dynamic interplay between foreign direct investment, e-commerce, and China’s export growth from 2005 to 2022 against the backdrop of the rapidly evolving global economy. Utilizing advanced analytical models that combine province- and year-fixed effects with fully modified ordinary least squares and dynamic ordinary least-squares methodologies, we delve into how foreign direct investment and e-commerce collectively boost China’s export capabilities. Our findings highlight a significant alignment between China’s export expansion and the global sustainable development agenda. We observe that China’s export growth transcends mere international investment and digital market engagement, incorporating sustainable practices such as effective utilization of local labor resources and an emphasis on technological advancements. This study also uncovers how knowledge capital and educational attainment positively impact export figures. A notable regional disparity is observed, with the eastern regions of China being more responsive to foreign direct investment and e-commerce influences on export trade compared to their western counterparts. This disparity underscores the need for region-specific policy approaches and sustainable strategies to evenly distribute the benefits of foreign direct investment and e-commerce. The study concludes that while foreign direct investment and e-commerce are crucial for China’s export growth, the underlying theme is sustainable development, with technological innovation and human capital being key to ongoing export success. The findings advocate for policies that balance economic drivers with sustainable development goals, ensuring both economic prosperity and environmental sustainability.

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Introduction.

In its ascent towards global economic preeminence, China has undergone transformative alterations in its provincial export trade architecture, metamorphosed by the intricate orchestration of economic vectors and technological advents within the globally interconnected milieu. Central to this paradigm shift is the synthesis of foreign direct investment, the burgeoning trajectory of e-commerce, the proper deployment of indigenous labor resources, and tactically channeled technological capital. An adept comprehension of these intricate dynamics becomes essential for informed forecasts pertaining to China’s export evolution and its symbiotic relationship with sustainable developmental objectives. The exponential proliferation of China’s export vertical can be attributed to its accurate incorporation of foreign direct investment, pivotal in catalyzing technological assimilations, fortifying workforce competencies, and forging novel market corridors. In tandem, the surge in e-commerce has revolutionized market penetration modalities, enabling Chinese offerings to seamlessly infiltrate global commerce arenas. Moreover, China’s abundant labor capital, juxtaposed with deliberate technological ventures, has accentuated its competitive foothold in global trade arenas. Yet, the velocity of this expansive trajectory beckons a meticulous assessment through a prism of sustainability, addressing facets of resource optimization, laboral integrity, and ecological prudence.

In the current academic landscape, a significant emphasis has been placed on dissecting the myriad ramifications of foreign direct investment on export enhancement, with studies underscoring its cardinal role in technological integration and amplifying operational efficacy. The integration of e-commerce facets, as delineated by Hao et al. ( 2023 ), offered a refined perspective, spotlighting the instrumental role of digital conduits in transcending conventional trade barriers. The interrelation of labor capital, as articulated by Zhang et al. ( 2016 ), in concert with technological advancements, as expounded by Autor et al. ( 2015 ), underscored the salience of indigenous assets and frontier innovations in the export dialog. However, despite the expansive literature, a comprehensive appraisal amalgamating these aspects, especially within the framework of China’s regional disparities, is palpably lacking. From a methodological standpoint, diverse econometric paradigms have been employed in antecedent research, yet province- and year-fixed effect models are increasingly lauded for their analytical precision. The eastern corridors, advantaged by their littoral proximity, have conventionally steered the export zeitgeist. Academic contributions, such as those by Duan et al. ( 2020 ), underscored this region’s proficiency in harnessing foreign direct investment and e-commerce potentialities. In juxtaposition, the central and western sectors, albeit resource-rich and labor-abundant, evince a marked lag in technological embrace and foreign direct investment influx. This regional polarization, as theorized by Zhong et al. ( 2022 ), accentuates the necessity for tailored policy interventions to promulgate balanced and sustainable growth vectors. In this context, our scholarly pursuit seeks to redress the prevailing knowledge chasm. The intricate interplay of foreign direct investment, e-commerce, labor dynamics, and technological innovation in molding China’s export tapestry is indubitable. Yet, exhaustive scrutiny, particularly one sensitive to regional grades, stands as an academic imperative. Grounded in methodological robustness and echoing sustainability principles, this study aims to demystify this intricate interconnection, catalyze informed policy deliberations, and buttress China’s odyssey towards a sustainable export paradigm.

Drawing upon the aforementioned analytical discourse, this research delves into the complex relationship between foreign direct investment, e-commerce, and the growth of exports in China from 2005 to 2022, situated within the context of a rapidly changing global economic landscape. Using advanced statistical methods, such as province- and year-fixed effects analysis along with fully modified ordinary least squares and dynamic ordinary least-squares methods, the study gives a more complete picture of how foreign direct investment and e-commerce work together to make China’s exports stronger. A key aspect of this study is its alignment with the global sustainable development agenda, examining how China’s export growth extends beyond basic international investment and digital commerce. It integrates sustainable practices, such as the effective use of local labor and a focus on technological advancement, offering insights into the role of knowledge capital and educational attainment in boosting export figures. Our analysis reveals a pronounced regional variation in the impact of foreign direct investment and e-commerce on export trade, with Eastern China showing greater responsiveness compared to the Western regions. This finding highlights the necessity for region-specific policies and sustainable strategies to ensure a balanced distribution of foreign direct investment and e-commerce benefits across the country. The study’s methodology stands out in the existing literature for its comprehensive approach, combining advanced econometric techniques to dissect the multifaceted influences on China’s export sector. It addresses a gap in previous research by providing a clearer picture of the interplay between foreign direct investment, e-commerce, and export growth within the unique context of China’s evolving economy. The research emphasizes the need for a nuanced understanding of China’s position in the global economy, exploring the relationship between foreign direct investment and e-commerce in a way that prior empirical studies have not fully captured. By doing so, the study offers valuable insights for policymakers and stakeholders, advocating for strategies that not only foster economic growth but also align with sustainable development objectives, ensuring the long-term prosperity and environmental sustainability of China’s economy.

This study presents several significant contributions to the current academic understanding of China’s export sector, particularly focusing on sustainable development. First, our analysis synthesizes the roles of foreign direct investment and e-commerce, offering fresh insights into their collective influence on China’s exports. This aspect builds upon the work of Fidrmuc and Korhonen ( 2010 ), who underscored the impacts of global capital and digital advancements on emerging economies. Our study extends this perspective by explicitly linking these factors to export growth in the Chinese context. Second, we introduce a nuanced approach by examining regional variations in export performance, moving beyond the limitations of previous studies that often treated China’s economy as a uniform entity. Grübler et al. ( 2007 ), who emphasized the value of regional analysis in producing more thorough economic insights than national overviews, served as an inspiration for this strategy. Third, our research highlights the role of local labor resources as a key component of sustainable export strategies. This aligns with Sun’s ( 2022 ) assertion of human capital as a critical driver of economic growth, positioning it as a sustainable asset in China’s export framework. Fourth, the study delves into the impact of technological investment on sustainable export growth, expanding upon Qian et al. ( 2021 ) thesis that technology is fundamental to achieving green growth. We explore how technological advancements contribute specifically to the sustainability of China’s export sector. Lastly, the research advocates for a balanced approach to economic growth and environmental sustainability, echoing Wright’s ( 2019 ) argument for the necessity of balancing economic development with ecological preservation. Our study furthers this dialog by illustrating how such a balance can be achieved within the context of China’s export dynamics. Together, these facets of our research offer new perspectives on the complex relationship between economic activities, technological innovation, and sustainable development in the context of China’s growing role in the global market. These insights are particularly relevant for policymakers and business leaders looking to navigate the challenges and opportunities presented by China’s evolving export landscape.

The subsequent sections of this article are structured as follows: section “Literature review” delves into a comprehensive review of extant literature, shedding light on prior research in this domain. In the section “Variables and model”, we elucidate the methodological approach, detailing the variables employed and the underlying model. Section “Empirical results” offers a synthesis of the empirical results, coupled with a discussion of the implications. Lastly, the section “Conclusions” culminates with conclusions, policy recommendations, and avenues for future research in this field.

Literature review

In today’s global trade environment, the interplay between foreign direct investment and e-commerce has become a critical factor influencing export trends. Current research highlights foreign direct investment’s pivotal role in driving technology transfer and expanding markets. Concurrently, e-commerce platforms have revolutionized trading patterns, facilitating instantaneous market connections and broadening international reach. Additionally, elements like labor resource allocation and technological progress intertwine with these primary factors, creating a multifaceted framework that reveals the complexities of modern export strategies.

In contemporary academic discourse, the impact of foreign direct investment on China’s export trade has received significant attention. Yet, the complex relationship with e-commerce remains insufficiently explored. The prevailing literature, as seen in the works of Li et al. ( 2019 ), Wang et al. ( 2020 ), and Jin and Huang ( 2023 ), mainly focuses on the direct effects of foreign direct investment on export efficiency through capital infusion and technological transfers. These studies, however, tend to overlook the burgeoning dimension of digital commerce. Addressing this gap, Fu et al. ( 2016 ), Chen et al. ( 2023 ), and Lei and Xie ( 2023 ) provide a more nuanced perspective by acknowledging the role of digital transformation in global trade. They underscore e-commerce’s potential to complement foreign direct investment, particularly in enhancing market access for Chinese exports. Expanding on this viewpoint, Qi et al. ( 2020 ), Klimenko and Qu ( 2023 ), and Yan et al. ( 2023 ) examine how e-commerce platforms democratize export opportunities, even for smaller entities, thus amplifying foreign direct investment’s impact. The insights of Zhang and Yang ( 2022 ), Mahalik et al. ( 2023 ), and Cordes and Marinova ( 2023 ) served as inspiration for this research’s more integrative approach. It goes beyond the traditional analysis of foreign direct investment’s influence on exports to include the transformative role of e-commerce. This methodological advancement builds upon and extends the analyses of Götz ( 2020 ), Auboin et al. ( 2021 ), and Ha ( 2022 ), who, despite their thoroughness, did not fully address the synergistic relationship between foreign direct investment and digital trade channels. Aligned with the analytical frameworks of Agarwal and Wu ( 2015 ), He et al. ( 2021 ), and Shanmugalingam et al. ( 2023 ), this study emphasizes a thorough understanding of trade dynamics in the digital era. By incorporating e-commerce as a key variable alongside foreign direct investment, it fills a critical gap in the literature. This approach resonates with the findings of Zhang and Zeng ( 2023 ), Xiao and Abula ( 2023 ), and Sun et al. ( 2024 ) on the growing influence of digital platforms on trade and extends their work by empirically quantifying this impact within the context of China’s export landscape. In conclusion, this research contributes significantly to the existing body of literature by integrating the crucial role of e-commerce. It provides a more comprehensive view of the dynamics shaping China’s export trade, thereby addressing a vital need in the ongoing academic conversation.

The existing literature recognizes the impact of e-commerce on China’s export trade but lacks a thorough exploration of its synergistic effects with foreign direct investment and traditional trade mechanisms. Previous studies, such as those by Giuffrida et al. ( 2017 ) and Li et al. ( 2019 ), have primarily focused on the direct impact of e-commerce on market expansion and customer engagement, emphasizing its role in broadening the global reach of Chinese products. However, these studies often treat e-commerce as an isolated factor, not integrating it with broader economic elements like foreign direct investment. A more nuanced perspective is emerging from research such as Blanchard, Jean-Marc ( 2019 ), Villegas-Mateos ( 2022 ), and Singh and Singh ( 2022 ), which begin to address the interaction between e-commerce and foreign direct investment but do not provide a comprehensive analysis. These studies show how e-commerce platforms can enhance export efficiency in conjunction with foreign direct investment, yet they stop short of examining how e-commerce is transforming traditional export models. This research addresses this gap by adopting an integrative methodology, drawing on the approaches of Wang et al. ( 2021 ) and Yin and Choi ( 2023 ). This methodology extends beyond evaluating the direct effects of e-commerce on exports to also consider its interplay with foreign direct investment. Such an approach expands upon the frameworks used in studies by Zhang ( 2019 ) and Phang et al. ( 2019 ), which, while insightful, did not fully capture e-commerce’s complex dynamics within China’s integrated market economy. Additionally, this study aligns with the emerging literature, such as the works of Gao ( 2018 ) and Li et al. ( 2020 ), advocating for a holistic view of digital trade’s role in economic growth. By incorporating a comprehensive array of variables, including technological advancement and digital infrastructure quality, this research provides a more robust analysis than previous studies like those by Katz and Callorda ( 2018 ), Sinha et al. ( 2020 ), and Wei and Ullah ( 2022 ). In conclusion, this study overcomes previous shortcomings in academic research by offering a detailed empirical examination of how e-commerce, in conjunction with foreign direct investment and traditional trade mechanisms, shapes China’s export landscape. It contributes significantly to academic discourse by presenting a more complete understanding of e-commerce’s role in the modern economy, thus fulfilling a critical need in the ongoing narrative on global trade and digital economics.

In analyzing China’s export sector, the influence of labor resource allocation, technological advancements, knowledge capital, and educational attainment, particularly in relation to e-commerce, warrants a deeper exploration. Initial research efforts, exemplified by Bhaumik et al. ( 2016 ), Song and Wang ( 2018 ), and Liu and Xie ( 2020 ), have individually evaluated the impacts of labor and technology on export performance, underscoring their roles in bolstering China’s position in international markets. Yet, these studies typically overlooked the integration of e-commerce into their analytical models. Recent scholarly works, including those by Kwak et al. ( 2019 ), Elia et al. ( 2021 ), and Tang and Li ( 2023 ), have started to recognize the combined effect of technological prowess and labor skills within the framework of e-commerce. However, these investigations fall short of comprehensively examining how knowledge capital and education intersect with e-commerce to affect export trends. The methodologies of Lin et al. ( 2020 ), Hanelt et al. ( 2021 ), and Abdul-Rahim et al. ( 2022 ) served as the foundation for this study’s holistic approach to closing this research gap. Our approach is comprehensive, assessing not just the direct impacts of labor, technology, education, and knowledge on exports but also situating these impacts in the context of the growing e-commerce domain. This method expands upon the analytical scope of previous studies like Wei et al. ( 2020 ) and Li et al. ( 2023 ), which, despite their thoroughness, did not fully delve into the complex relationship between e-commerce and China’s export dynamics. Furthermore, our study aligns with the evolving scholarly narrative, as seen in the works of Banalieva and Dhanaraj ( 2019 ) and Huang et al. ( 2023 ), advocating for an integrated view of digital commerce’s interaction with traditional economic variables. By including an extensive analysis of factors such as digital infrastructure and market development in e-commerce, this research offers a more detailed examination than earlier studies by Gorla et al. ( 2017 ) and Wang et al. ( 2024 ). In summary, this research fills existing gaps in the literature by thoroughly investigating how labor resources, technological investments, knowledge capital, and education, in conjunction with e-commerce, shape the export sector in China. It provides a comprehensive perspective on the synergy between traditional economic elements and digital trade, addressing a critical need in the ongoing discussion of global trade and economic progression.

Variables and model

Numerous studies have explored the significant impact of foreign direct investment on a nation’s export trends, highlighting foreign direct investment’s critical role in reshaping export strategies. Researchers like Choong ( 2012 ) and Otchere et al. ( 2016 ) have pointed out that foreign direct investment not only provides essential capital but also facilitates technological transfer, thereby boosting efficiency and productivity in host countries. Moreover, the aspect of sustainability is increasingly becoming interlinked with foreign direct investment, often bringing eco-friendly technologies and sustainable methodologies to the forefront, enhancing a nation’s prospects for long-term export stability, as noted by Perrini and Tencati ( 2006 ). Simultaneously, the influence of the digital revolution, particularly the rise of e-commerce, has significantly transformed the nature of exports. Studies by Wang ( 2010 ) and Teng et al. ( 2022 ) highlight that in China’s expanding digital landscape, e-commerce platforms have leveled the playing field, allowing even smaller businesses to access the global market. According to Rita and Ramos ( 2022 ), Amornkitvikai et al. ( 2022 ), and He et al. ( 2021 ), e-commerce is also in line with the global trend towards sustainable trading due to its traceable and transparent nature. Considering these complex interactions, export trade volume becomes an appropriate variable to study, representing the combined and sustainable effects of foreign direct investment and e-commerce. This research, therefore, focuses on the export trade volumes of China’s provinces, incorporating foreign direct investment inflows and e-commerce transaction data as independent variables. This approach aims to shed light on their hypothesized influence on provincial export patterns.

To fully grasp the complex factors affecting export trade, it’s crucial to look beyond conventional indicators like foreign direct investment and e-commerce. A deeper exploration into academic literature and fundamental economic theories uncovers the critical role of labor resource allocation and technological advancements in shaping export patterns. The foundational Heckscher-Ohlin theorem, supported by research from Castilho et al. ( 2012 ) and Antràs et al. ( 2017 ), underscores the vital impact of labor resources on global trade trends. Darku ( 2021 ) extends this perspective, emphasizing the sustainability aspects and suggesting that effectively managed labor resources can contribute to more equitable and environmentally responsible trading practices. Additionally, examining the role of technology provides insights into the nuances of export competitiveness. Rooted in Romer’s theory of endogenous growth and backed by findings from Jones ( 2019 ) and Anzoategui et al. ( 2019 ), there is a consensus that deliberate technology investments boost productivity and support sustainable growth through cleaner, more efficient production methods. Zhou et al. ( 2021 ) further clarify this idea, showing how sustainable technologies and competitive exports are interlinked. Recognizing the importance of these two factors, this study incorporates labor resource allocation and technological inputs as key control variables. To empirically anchor these theoretical concepts, we use urban employment data from various provinces as indicators of labor resource allocation and local government spending on technology as a reflection of technological investments. Building on the work of Mansion and Bausch ( 2020 ), Lyu et al. ( 2022 ), and Mohammad Shafiee et al. ( 2023 ), this paper also introduces knowledge capital quantified by the number of patent licenses. Following Atkin ( 2016 ), Ahmed et al. ( 2020 ), and Blanchard and Olney ( 2017 ), the paper incorporates education level, measured by the average number of schooling years.

Due to data availability, this paper selects balanced provincial-level data from 2005 to 2022. Since 2005, e-commerce across various Chinese provinces has seen rapid development, making this period particularly relevant to the study’s context. The unavailability of data from Tibet necessitates the inclusion of 30 other provinces and municipalities in China, namely Beijing, Tianjin, Shanxi, Inner Mongolia, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang. The data used in this study is sourced from three official databases, each providing specific insights into our variables of interest. The Bureau of Statistics of China supplies data on export trade volume, knowledge capital, education level, labor resource endowment, and technological investment. Information on e-commerce is obtained from the China E-commerce Report, while data on foreign direct investment is sourced from the Statistical Bulletin of China’s Outward Foreign Direct Investment.

In examining the interplay between foreign direct investment and e-commerce on export trade using China’s province data, it is imperative to adopt a robust econometric technique that effectively captures both time-invariant and entity-specific heterogeneities. The two-way fixed effects regression model, as elaborated upon by Wooldridge ( 2010 ) and advocated by Baltagi ( 2021 ), is particularly adept at mitigating potential omitted variable biases in panel data, making it especially suitable for our study’s empirical context. By incorporating both entity and time-fixed effects, this approach controls for unobserved province-specific factors that may influence export trade (such as local policies or geographical advantages) and time-specific shocks (like global economic trends or national regulatory shifts) that uniformly affect all provinces. By accounting for these dual dimensions of variability, the model ensures that the estimated effects of foreign direct investment and e-commerce are purged of confounding influences, thus bolstering the credibility of causal inferences drawn from the results. Given the dynamism of China’s economic landscape, combined with the evolving trajectories of foreign direct investment and e-commerce, leveraging the two-way fixed effects regression offers a rigorous and robust approach to discerning their impact on export trade. The model is shown as follows:

In Eq. ( 1 ), the subscript ‘ i ’ represents individual provinces, while t delineates the temporal dimension, capturing the yearly variations. Within this model, ex symbolizes the export trade volume, serving as our dependent variable. On the explanatory side, ec corresponds to e-commerce metrics, ‘fdi’ quantifies foreign direct investment inflows, ‘lab’ encapsulates labor resource endowment, and ‘tec’ signifies the magnitude of technological investment. ‘kn’ indicates knowledge capital. ‘ed’ stands for education level. The term a 0 denotes the intercept, providing a baseline measure for our regression. The vector [ a 1 , a 6 ] comprises the coefficients estimated for each explanatory variable, reflecting their respective strengths and directions of influence on export trade. To control for potential unobserved heterogeneities, η embodies province-specific fixed effects, while δ accounts for year-specific fixed effects, ensuring that time-invariant provincial attributes and common temporal shocks are appropriately adjusted for. The error term, ϵ , is presumed to follow a white noise process, indicating randomness and the absence of serial correlation. The empirical focal points of this study are the coefficients a 1 and a 2 . In our analytical framework, the ‘+’ symbol is strategically used to represent the expected positive effect of various independent variables—including e-commerce, foreign direct investment, labor resource endowment, technological investment, knowledge capital, and education level—on the export volumes of Chinese provinces. This symbolism is central to our hypothesis, positing that these variables play a beneficial role in shaping export trends across different provinces. The underlying premise of this hypothesis is that variables like foreign direct investment, enhanced e-commerce capabilities, and other pertinent factors positively stimulate export activities. In essence, the '+' sign indicates a probable correlation where increases or improvements in these independent variables are likely to correspond with a rise in export volumes from the provinces. Such a correlation is instrumental in dissecting how various economic elements and technological progressions, specific to China’s varied regional landscapes, can bolster the nation’s export capacity. This exploration is particularly salient for understanding China’s export mechanics. It provides a nuanced view of how strategic investments in technology, human capital development, and leveraging local resources can collectively uplift the export sector, reinforcing China’s position in the global economy. The ‘+‘ sign, therefore, not only signifies a positive correlation but also serves as a gateway to understanding the multifaceted drivers that enhance export efficiency in the context of China’s evolving economic landscape.

Robustness test

Considering the relatively modest scale of our sample in this study, there exists a plausible risk of heteroskedasticity and autocorrelation in the outcomes estimated through the use of annual and provincial fixed effects models. These statistical issues could potentially introduce biases into our analytical results, thereby affecting the reliability of our conclusions. To mitigate this challenge, our research strategically implements two advanced econometric methodologies: fully modified ordinary least squares and dynamic ordinary least squares. The fully modified ordinary least-squares technique, a refined version of the ordinary least-squares methodology, is particularly adept at addressing complexities arising from heteroskedasticity and autocorrelation. The efficacy of this approach in handling such statistical nuances is well documented in the works of scholars such as Pedroni ( 2001 ), Christou and Pittis ( 2002 ), Trapani ( 2015 ), Li et al. ( 2020 ), Kripfganz and Sarafidis ( 2021 ), Norkutė et al. ( 2021 ), and Kheifets and Phillips ( 2023 ). These studies validate the use of fully modified ordinary least squares as a robust tool for enhancing the accuracy of econometric estimations, especially in scenarios similar to those in our study. Similarly, the dynamic ordinary least squares method offers a comprehensive solution for addressing the challenges of endogeneity and serial correlation, which are common in time-series data. Research by Chudik and Pesaran ( 2015 ), Moon and Weidner ( 2017 ), Liu et al. ( 2020 ), Ahn and Thomas ( 2023 ), Hartono et al. ( 2023 ), and Fingleton ( 2023 ) underscore the effectiveness of dynamic ordinary least squares in ensuring more precise and reliable results in econometric analysis. This technique, by adjusting for both the lead and lag dynamics of the variables, enhances the accuracy of regression coefficients, thereby providing a more nuanced understanding of the underlying data patterns. Both fully modified ordinary least squares and dynamic ordinary least squares are sophisticated enhancements of the traditional least-squares approach. These methods have been specifically adapted to address the intricate statistical issues inherent in panel data analysis, like the one employed in our study. By incorporating these advanced techniques, we aim to mitigate potential biases arising from heteroskedasticity, autocorrelation, and endogeneity, thereby enhancing the credibility and robustness of our findings. Equation ( 2 ) in our study meticulously outlines the application of these methods, demonstrating their integration into our analytical framework to yield more reliable and insightful results.

This outcome is directly derived from the differential regression, as shown in Eq. ( 3 ).

Let’s consider \(\tilde{\Theta }\) and \(\tilde{\Psi }\) to represent the long-term covariance matrix calculated using the residuals denoted by \([\tilde{{\uptau }_{{\rm{t}}}}=(\tilde{{\uptau }_{1{\rm{t}}}},\tilde{{\uptau }_{2{\rm{t}}{^\prime} }}){^\prime} ]\) . Based on this assumption, we are able to represent the modified data as depicted in Eq. ( 4 ). This representation considers the complex interdependencies reflected in the covariance matrix, laying the groundwork for subsequent examination and understanding of the data within the framework of our chosen model.

In our study, the term for bias correction, crucial for refining our model, is detailed in Eq. ( 5 ). This component is essential for enhancing the accuracy and dependability of our results, as it compensates for possible biases encountered during the estimation phase.

Therefore, the formulation of the fully modified ordinary least-squares estimator, pivotal to our analysis, is encapsulated in Eq. ( 6 ). This estimator is integral to refining our estimations, as it addresses potential issues of serial correlation and endogeneity within our regression models. By employing the fully modified ordinary least-squares method, we gain a more accurate and insightful comprehension of the relationships present in our dataset.

In Eq. ( 6 ), \({\rm{Z}}_{\rm{t}}=({\rm{y}}_{\rm{t}}^{{\prime} }{\rm{D}}_{\rm{t}}^{{\prime} })\) . Developing estimators for the long-term covariance matrix, a critical component in the implementation of fully modified ordinary least squares, is highlighted in studies by Atil et al. ( 2023 ), Wagner ( 2023 ), Phillips and Kheifets ( 2024 ), and Pelagatti and Sbrana ( 2024 ). This process is essential for the precision and efficacy of the fully modified ordinary least-squares approach. It entails refining the OLS regression by including both preceding and subsequent factors, ensuring that the error component in Eq. ( 1 ) remains uncorrelated with the entire historical sequence of random regressor variations. This method, as detailed in the research by Mark and Sul ( 2003 ), Panopoulou and Pittis ( 2004 ), Bruns et al. ( 2021 ), and Wang et al. ( 2024 ), is efficiently captured in Eq. ( 7 ).

By incorporating q lags and r leads of the differenced regressors, the persistent correlation between variables τ 1 t and τ 2 t is effectively neutralized. This adjustment allows the estimation of φ  = ( β ′, γ ′)’ through the least-squares estimator to align with the asymptotic distribution achieved via the fully modified ordinary least-squares method. These methods are notably effective, as emphasized by Bai et al. ( 2021 ), Chebrolu et al. ( 2021 ), De Menezes et al. ( 2021 ), Zhao et al. ( 2022 ), and Bollen et al. ( 2022 ), in overcoming challenges like endogeneity, serial correlation, and biases that are typically prevalent in studies with smaller sample sizes.

Empirical results

Descriptive statistical analysis.

For the purpose of this study, data extraction was conducted, harnessing information from 31 distinct provincial datasets covering the temporal bracket of 2005–2022. This compilation was sourced directly from the authoritative National Bureau of Statistics of China, ensuring data authenticity and integrity. An initial stage of rigorous analytical procedures was executed, encompassing both qualitative descriptive statistical evaluations and quantitative correlation analyses. This served to provide a holistic view of the data landscape, enabling the identification of patterns and inter-variable relationships. The culminating findings from this analytical phase are methodically tabulated in Table 1 . For clarity and comprehensive representation, the results are segmented into two distinct panels: Panel A elucidates the statistical analysis of variable description, while Panel B delineates the correlation matrices.

Within Panel A of Table 2 , an examination of the data yields insights into provincial economic dynamics. The export trade registers an average value of 2.226, complemented by a notably narrow standard deviation of 0.085. This suggests a trend of ascent in export trade across the majority of provinces. Conversely, the foreign direct investment landscape, with a mean of 0.241 and a slightly more dispersed standard deviation of 0.117, indicates a predominant trajectory of foreign direct investment enhancement among provinces, albeit with some variability. E-commerce, represented by a mean of 2.276, portrays a positive trend; however, its relatively expansive standard deviation of 0.572 implies a diverse range of advancements and perhaps volatility within this sector. This is emblematic of the rapidly evolving and heterogeneous landscape of e-commerce in China, a reflection that aligns with empirical observations on the nation’s digital commerce forefront. The labor resource endowment is quantified with a mean of 2.791 and a standard deviation of 0.315, providing insights into a generally favorable labor capital across provinces. The metrics for technological investment, with an average of 0.997 and a standard deviation of 0.441, underline the ongoing endeavors in technological innovation but also hint at disparities in the extent and pace of such investments across the provinces. Finally, the metric for knowledge capital is calculated with an average value of 4.012 and a standard deviation of 1.506. Meanwhile, the education level is measured, showing an average value of 0.907 and a standard deviation of 0.217.

In the wake of conducting a correlation analysis, the subsequent findings are articulated in Panel B of Table 1 . An inaugural examination of the data reveals a discernible positive relationship between foreign direct investment and e-commerce relative to the scope of China’s provincial export trade. Parallel to this, a deeper analytical traverse into the data underscores a tangible connection between labor resource endowment and technological forays as pivotal determinants of export trajectories. This interrelationship accentuates the premise that provinces emphasizing sustainable labor methodologies and avant-garde technological endeavors are not solely shaping a resilient economic structure but are concurrently enhancing their export trade capacities. This synergy between sustainability-oriented strategies and burgeoning trade volumes fortifies the argument that sustainability stands as a potent stimulant, accentuating both foreign direct investment and e-commerce outcomes. Furthermore, the analysis essentially establishes a positive correlation between knowledge capital, education level, and the export trade of China’s provinces.

In this investigation, a quintet of econometric techniques is deployed to discern the nuanced impacts of foreign direct investment and e-commerce on export trade. These methodologies encompass pooled ordinary least squares (Model 1), panel ordinary least squares (Model 2), province-specific fixed effects (Model 3), year-fixed effects (Model 4), and a provincial and year-fixed-effects approach integrating both provincial and annual dimensions (Model 5). The outcomes of these estimations are documented in Table 2 . Upon evaluating the data through the prism of the Chow test, we discerned a clear rejection of the null hypothesis, indicating the inadequacy of pooled ordinary least squares for this dataset. Subsequent to this, the Hausman test was executed, which further rejected the null hypothesis, rendering the province-fixed effect model suboptimal. The decision to employ Model 5—integrating both province- and year-fixed effects—is grounded in several advanced econometric postulations. Kropko and Kubinec ( 2020 ), Hill et al. ( 2020 ), and Fernández-Val and Weidner ( 2018 ) posited that in the presence of unobserved heterogeneity—factors that remained constant over time but vary across entities or vice versa—implementing province- and year-fixed effects can yield unbiased and consistent estimators. This became particularly salient when considering phenomena such as global economic oscillations or overarching regulatory changes, which exerted a consistent impact across all provinces. By accounting for these twin axes of variability, Model 5 ensures the extrication of extraneous influences from the core relationship between foreign direct investment, e-commerce, and export trade. This approach enhances the robustness of the analysis, fortifying the validity of causal extrapolations drawn from the empirical results.

In Table 2 , our primary focus is on the insights garnered from Model 5. However, it is crucial to recognize the crucial role that the outcomes of the additional four models played. These models act as a robustness check, lending further credibility to our main findings. Model 5’s empirical data highlights a robust and statistically significant link between the surge in foreign direct investment and the increase in export trade within Chinese provinces. Specifically, a 1% increase in foreign direct investment inflows is associated with a 0.209% rise in provincial export trade volume. Shifting our analysis to the impact of e-commerce on the export landscape of Chinese provinces, we observe a compelling dynamic. E-commerce is identified as a significant driver of export growth. Quantitatively speaking, a 1% growth in e-commerce activities results in a 0.405% increase in provincial export volumes. Moreover, our research identifies critical factors influencing export patterns in Chinese provinces, notably labor resources and technological investments. The study reveals that a 1% elevation in labor resource availability correlates with a 0.715% increment in export volumes at the provincial level. In the same vein, a 1% rise in technological investments is linked to a 0.304% boost in exports. Additionally, the study brings to light the constructive effects of knowledge capital and education levels on provincial export trade. An increase of 1% in these variables is found to enhance export volumes by 0.083% and 0.101%, respectively.

The positive correlation between foreign direct investment inflows and increased export trade can be understood through various theoretical frameworks and empirical studies. Drawing on the research of Adikari et al. ( 2021 ), Rehman et al. ( 2023 ), and Zhang and Chen ( 2020 ), the eclectic paradigm suggests that foreign direct investment promotes export trade by transferring advanced technologies, managerial expertise, and marketing skills to the host country. These spillover effects enhance the competitiveness of domestic firms, boosting their export potential. Additionally, foreign direct investment helps to establish export-oriented industries within host economies, as seen in China’s Special Economic Zones, which act as production and export hubs (Chiang and De Micheaux, 2022 ; Ngoc et al., 2022 ; Huang et al., 2023 ; Vukmirović et al., 2021 ). This influx of capital, technology, and knowledge through foreign direct investment acts as a catalyst, creating a trade-friendly environment and aligning provinces with a more globally integrated economic path. Several factors support e-commerce’s positive impact on provincial export volumes. Firstly, e-commerce reduces informational disparities, fostering a transparent market conducive to robust exports. Additionally, as e-commerce platforms grow, their value proposition to users strengthens, encouraging an environment ripe for increasing transactions, including exports. Thirdly, e-commerce inherently reduces transactional friction, enabling businesses to engage more effectively in international trade. The theories and results of researchers like Onjewu et al. ( 2022 ), who contend that e-commerce lowers traditional trade barriers and enables even small businesses to participate in global markets, support this viewpoint. Lipton et al. ( 2018 ) and Fritz et al. ( 2004 ) show that online platforms allow businesses to overcome geographic limitations, thus expanding their export reach. Tolstoy et al. ( 2021 ) and Zhong et al. ( 2022 ) discuss how e-commerce’s digital footprint lessens the constraints of geographical distance, creating a more fluid international trade environment. Khan and Khan ( 2021 ) and Watson et al. ( 2018 ) illustrate how digital trade avenues boost export growth by adapting to market changes and consumer preferences. Additionally, Xi et al. ( 2023 ) and Deng et al. ( 2023 ) highlight the relationship between digital infrastructures and export portfolio diversification, with e-commerce spurring product innovation and differentiation. In conclusion, the integration of these theoretical insights and empirical evidence underlines the significant role of e-commerce as a key driver in enhancing the scale of export trade in Chinese provinces.

Labor resources and technological investments have been identified as key factors positively influencing the scale of export trade in Chinese provinces. This result is consistent with the Heckscher–Ohlin theorem, which states that regions typically export goods that effectively use their most abundant resources, according to research from Kunroo and Ahmad ( 2023 ) and Akther et al. ( 2022 ). Given China’s substantial labor force, provinces endowed with richer labor resources are naturally capable of higher production, thereby supporting larger export volumes. Conversely, the relationship between technological investments and the strength of exports is anchored in contemporary economic growth theories, particularly those emphasizing the role of technology in economic development. Aghion et al. ( 1998 ) reinforce this notion, demonstrating that technological investment in regions not only enhances productivity but also provides a competitive advantage in international markets, thus boosting export capacity. Moreover, the study finds that both knowledge capital and education levels positively impact the scale of export trade in Chinese provinces. This underscores the importance of intellectual resources and educational attainment as drivers of export dynamics in a rapidly evolving economy like China’s. The correlation with knowledge capital reflects China’s strategic emphasis on innovation and intellectual property. Liu et al. ( 2017 ) emphasize that investments in research and development, especially in technology and sciences, have significantly enhanced China’s export capabilities, leading to an increase in patents and technological breakthroughs. Due to these advancements, Chinese products now have a competitive advantage in the global market with higher value and higher quality. Similarly, the significance of education in boosting export trade is notable. Yang ( 2012 ) points out that China’s focus on higher education and vocational training has equipped its workforce with the necessary skills for export-oriented industries, facilitating the production of more sophisticated, high-value products. Chen et al. ( 2022 ) further discuss how the synergy between technological advancement and educational development contributes to a more dynamic and diversified export sector. This interplay is vital for China’s ability to adapt to global economic changes and more effectively participate in international trade. In conclusion, the increase in exports due to heightened knowledge capital and education levels signifies China’s strategic transition towards a knowledge-based economy. This shift is reshaping the structure of its domestic industries and redefining China’s role and competitiveness in the global market.

In this study, meticulous measures were taken to guarantee both the accuracy and reliability of the results, especially those obtained from the analysis using the province and year-fixed effect models. To ensure the dependability of our findings, an extensive robustness check was conducted on the outcomes of the province and year-fixed effect model. This involved the use of two econometric techniques: fully modified ordinary least squares and dynamic ordinary least squares. The implementation of fully modified ordinary least squares and dynamic ordinary least squares was critical in substantiating the integrity of the inferences drawn from the province and year-fixed effect model. The employment of these methods not only bolsters the solidity of our results but also reflects a commitment to the best standards of empirical rigor and methodological thoroughness. This approach to data verification underlies the credibility and trustworthiness of our conclusions. The specifics of these findings are systematically outlined in Table 3 .

Table 3 presents a detailed evaluation of the estimated parameters, focusing on both their magnitude and statistical significance. Remarkably, the findings obtained through the application of fully modified ordinary least squares and dynamic ordinary least squares align closely with those from the initial province and year-fixed effect model. This alignment between fully modified ordinary least squares and dynamic ordinary least squares, in comparison to the province and year-fixed effect model, robustly confirms the accuracy of the original model. The consistency observed across these varied econometric methods not only strengthens the trustworthiness of the province and year-fixed effect model but also substantiates the reliability of the study’s overall findings. The convergence of results across these methodologies indicates that the initial province and year-fixed effect model was meticulously crafted and successfully captured the essential dynamics of the variables under examination. The adoption of this comprehensive cross-validation process, which incorporates multiple analytical techniques, reinforces the solidity and validity of the research’s conclusions. This multi-faceted approach to analysis assures a high level of confidence in the integrity and reliability of the study’s results.

Regional heterogeneity analysis

Spanning a considerable geographical expanse, China is officially categorized into three distinct regional demarcations: eastern, central, and western. The eastern precinct is widely acknowledged as the epitome of China’s developmental zenith, encapsulating its most economically advanced locales. Conversely, the central sector is recognized for its intermediary developmental status, while the western swathes are often delineated by developmental lacunae. These territories, though unified under a single nationhood, manifest disparate attributes ranging from their economic growth trajectories, state-directed policy nuances, and infrastructural development gradients to their inherent geographical peculiarities. To delve into the multifaceted influence of foreign direct investment and e-commerce on regional export dynamics, our empirical approach disaggregated the core dataset, structuring it into three region-specific sub-samples. This strategic bifurcation aimed at discerning the variable intensities of foreign direct investment and e-commerce influences across these heterogeneous regions. The analytical outcomes derived from this region-centric examination are detailed in Table 4 .

Reflected in Table 4 , the repercussions of foreign direct investment on export trade reveal intricate regional gradations within China’s geographical tapestry. Concretely, a marginal ascent of 1% in foreign direct investment is associated with a 0.278% enhancement in the export dynamism of the eastern provinces. This increment tapers to 0.179% for central provinces and further diminishes to 0.161% for their western counterparts. The scholarly discourses of Contractor et al. ( 2020 ), Dang and Zhao ( 2020 ), and Batschauer da Cruz et al. ( 2022 ) elucidated that the synergy between foreign direct investment and export growth hinged upon a triad of factors: intrinsic firm capabilities, locational attributes, and the operational modus operandi. The eastern provinces, historically recognized as China’s economic epicenter, are imbued with a robust infrastructural matrix, streamlined trade corridors, and a business milieu that gravitates towards global market integration. These intrinsic locational advantages, complemented by the spatial competition theory proposed by Proost and Thisse ( 2019 ), Redding and Rossi-Hansberg ( 2017 ), and Goerzen et al. ( 2013 ), amplify the efficacy of foreign direct investment in spurring export trade. On the contrary, the central belt, despite its ascending economic trajectory, is intermittently stymied by transitional economic impediments, occasionally attenuating the foreign direct investment-export nexus. The western provinces, albeit burgeoning, still navigate developmental constraints, resonating with Wang and Zhao ( 2015 ) and Jiang et al. ( 2016 )‘s backwash effects, wherein peripheral regions grapple to harness the complete spectrum of foreign direct investment benefits. From a sustainability lens, echoing the tenets of Milne and Gray’s ( 2013 ) Triple Bottom Line framework, the magnitude and mode of foreign direct investment’s assimilation should be judiciously balanced to ensure economic, social, and environmental equanimity. The immediate economic impetus observed, particularly in the eastern provinces, warrants an integrated approach wherein foreign direct investment infusion aligns with sustainable practices, ensuring that regional development dovetails with ecological stewardship and socio-cultural inclusivity. Such a harmonized trajectory ensures that the fruits of foreign direct investment are not ephemeral but perennial, fostering a resilient and sustainable export landscape across all regions.

Referring to the results presented in Table 4 , an augmentation of 1% in e-commerce transaction volume is observed to lead to a differentiated impact on the export trade across China’s tripartite regional structure: specifically, a surge of 0.397% in the eastern provinces, an enhancement of 0.365% in the central provinces, and a growth of 0.325% in the western provinces. This regional heterogeneity in the influence of e-commerce on export trade can be supported by a confluence of academic perspectives and established theoretical underpinnings. Drawing insights from Porter, Michael’s ( 2011 ) Competitive advantage theory, the eastern provinces, having established themselves as economic powerhouses, have already harnessed advanced infrastructural frameworks and digital ecosystems. This enables them to efficiently leverage the capabilities of e-commerce, thereby reflecting a more pronounced augmentation in their export trade. The central provinces, as highlighted by North and Douglass’s ( 1989 ) theory of institutional change, are navigating through evolving institutional landscapes, mediating between traditional trade mechanisms and burgeoning digital frontiers. While they have made significant strides, the transformational gaps that exist temper the full realization of e-commerce benefits in the domain of exports. The western provinces, on the other hand, are still grappling with foundational challenges. Drawing from Sachs and Warner’s ( 2001 ) resource curse hypothesis, these provinces, abundant in natural resources, might have historically focused more on primary sectors, leading to a lag in the adoption and integration of e-commerce into their economic tapestry. This could partially elucidate the relatively muted growth in export trade from e-commerce advancements. Incorporating the sustainability ethos, as expounded in the triple bottom line approach by Elkington ( 1998 ), the expansion of e-commerce should not merely serve economic objectives. It should be orchestrated in a manner that respects ecological boundaries and promotes social inclusivity. Especially in regions like the western provinces, where development is paramount, it is critical to ensure that the surge in e-commerce-driven exports is not at the expense of environmental degradation or social disparities, thereby upholding a balanced, sustainable developmental trajectory.

Conclusions

Amidst the fast-paced evolution of the global economy, key factors such as foreign direct investment and e-commerce have become instrumental in reshaping China’s export sector between 2005 and 2022. Our analytical models, which utilize a combination of province- and year-fixed effects analysis along with fully modified ordinary least squares and dynamic ordinary least-squares methodologies, shed light on how foreign direct investment and e-commerce synergistically enhance China’s export capabilities. Significantly, this expansion in China’s exports aligns with the global agenda for sustainable development. It’s encouraging to see that China’s growth in exports extends beyond the realms of international investment and digital marketplaces, intertwining sustainable practices like optimizing local labor resources and prioritizing technological advancements. These approaches contribute to a more sustainable export environment. Our findings further reveal that variables such as knowledge capital and educational levels positively influence China’s export figures. Additionally, our analysis of regional disparities provides a deeper understanding. The eastern regions of China show greater responsiveness to foreign direct investment and e-commerce in driving export trade, whereas the western regions respond more modestly. This variation highlights the need for tailored policies and sustainability strategies to ensure a fair distribution of the benefits from foreign direct investment and e-commerce across all regions. In conclusion, while foreign direct investment and e-commerce are key drivers of China’s export growth, the broader story is one of sustainable development. Technological innovation and human capital development are pivotal to China’s continued success in exports. Moving forward, it is essential for policymakers to maintain a careful equilibrium between these economic drivers and sustainable development goals, fostering a balance between economic growth and environmental sustainability.

Drawing upon the insights derived from this study, we elucidate several policy recommendations along with practical solutions. First, for policymakers and business leaders, investing in technology and education is identified as a crucial strategy. The significant impact of technological innovation and a well-educated workforce on export growth underscores the necessity for ongoing investment in these domains. For academia, this opens avenues for further research into specific types of educational programs and technological innovations that most effectively enhance export capabilities. Businesses, especially in the export sector, should prioritize employee training and the adoption of cutting-edge technologies to maintain competitiveness. Second, considering the varied responsiveness to foreign direct investment and e-commerce between China’s eastern and western regions, it’s imperative for regional authorities and business managers to customize their policies and strategies to suit the unique needs and strengths of their regions. This could involve targeted investments in infrastructure and digital capabilities in the eastern regions while simultaneously focusing on cultivating other competitive advantages in the western regions. Academia can play a role by conducting region-specific research to identify the most effective strategies for each area. Third, the intersection of export growth with sustainable development goals necessitates a comprehensive approach to policymaking. Managers in the export sector are encouraged to integrate sustainable practices into their business models, such as the utilization of environmentally friendly technologies and adherence to fair labor practices. This area also presents an opportunity for academic research into the effective implementation of sustainable practices in the export sector, aiming to balance profitability and competitiveness. Finally, our findings suggest that although foreign direct investment and e-commerce are significant drivers of export growth, their benefits are not uniformly experienced across all regions. This indicates the need for balanced development strategies that ensure equitable benefits from foreign direct investment and e-commerce across various regions. Strategies might include enhancing e-commerce infrastructure in less-developed areas or offering incentives for foreign investment in regions currently less engaged with these investments. For academics, this highlights the necessity of researching ways to optimize the impact of foreign direct investment and e-commerce across diverse regions, promoting equitable economic growth. These policy implications offer a strategic roadmap for leveraging key drivers of export growth in China, highlighting the importance of regional customization, sustainable development, and balanced economic strategies.

In the course of this research, certain limitations emerged that warrant acknowledgment. Firstly, the study’s timeframe, spanning from 2005 to 2022, may not fully capture the evolving dynamics of foreign direct investment and e-commerce in the context of China’s longer-term economic history. A more expansive temporal analysis could provide deeper historical insights. Secondly, while the fully modified ordinary least squares and dynamic ordinary least-squares methodologies and fixed effect models offer robustness, they may not encompass all the nuanced intricacies of the interactions between the chosen variables. Future research could employ mixed-method approaches, blending quantitative and qualitative inquiries to attain a richer understanding. Thirdly, our focus on regional heterogeneity, while pivotal, may overlook intra-regional variances that can significantly influence export trends. Subsequent studies might delve deeper into micro-level analyses, probing district- or city-level data. Fourthly, the emphasis on sustainability, though aligned with global imperatives, is predominantly viewed through the lenses of labor and technology. Incorporating other sustainability metrics, such as environmental or social indicators, could render a holistic view. Lastly, the external validity of our findings, primarily centered on China, might be limited in their generalizability to other nations. Comparative studies juxtaposing China’s experiences with those of other global players could bridge this gap. Addressing these limitations would not only refine the existing body of knowledge but also ensure a more comprehensive alignment of economic strategies with sustainable development goals.

Data availability

All data generated or analyzed during this study are included in this published article.

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He, Y. E-commerce and foreign direct investment: pioneering a new era of trade strategies. Humanit Soc Sci Commun 11 , 566 (2024). https://doi.org/10.1057/s41599-024-03062-w

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How E-Commerce Fits into Retail’s Post-Pandemic Future

• Kathy Gramling,
• Jeff Orschell,
• Joshua Chernoff

New data from Ernst & Young suggests it will be an important part of the consumer experience — but not everything.

The pandemic has changed consumer behavior in big and small ways — and retailers are responding in kind. Since the early days of the pandemic Ernst & Young has been tracking these shifting trends using the EY Future Consumer Index and EY embryonic platform, which show a significant and widespread industry shift toward e-commerce. In this article, the authors suggest that while e-commerce will continue to be an essential element of retail strategy, the future success of retailers will ultimately depend on creating a cohesive customer experience, both online and in stores.

If we have learned one thing from the past year, it’s that things can change in an instant — changes we thought we had years to prepare for, behaviors we assumed we’d stick to forever, expectations we have of ourselves and our organizations. This is true of the way we live, the way we work, and the way we shop and buy as consumers.

• KG Kathy Gramling is EY Americas Consumer Industry Markets Leader
• JO Jeff Orschell is EY Americas Consumer Retail Leader
• JC Joshua Chernoff is EY-Parthenon Americas Managing Director

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Artificial intelligence in E-Commerce: a bibliometric study and literature review

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• Published: 18 March 2022
• Volume 32 , pages 297–338, ( 2022 )

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• Ransome Epie Bawack 1 ,
• Samuel Fosso Wamba 2 ,
• Kevin Daniel André Carillo 2 &
• Shahriar Akter 3  

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This paper synthesises research on artificial intelligence (AI) in e-commerce and proposes guidelines on how information systems (IS) research could contribute to this research stream. To this end, the innovative approach of combining bibliometric analysis with an extensive literature review was used. Bibliometric data from 4335 documents were analysed, and 229 articles published in leading IS journals were reviewed. The bibliometric analysis revealed that research on AI in e-commerce focuses primarily on recommender systems. Sentiment analysis, trust, personalisation, and optimisation were identified as the core research themes. It also places China-based institutions as leaders in this researcher area. Also, most research papers on AI in e-commerce were published in computer science, AI, business, and management outlets. The literature review reveals the main research topics, styles and themes that have been of interest to IS scholars. Proposals for future research are made based on these findings. This paper presents the first study that attempts to synthesise research on AI in e-commerce. For researchers, it contributes ideas to the way forward in this research area. To practitioners, it provides an organised source of information on how AI can support their e-commerce endeavours.

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Introduction

Electronic commerce (e-commerce) can be defined as activities or services related to buying and selling products or services over the internet (Holsapple & Singh, 2000 ; Kalakota & Whinston, 1997 ). Firms increasingly indulge in e-commerce because of customers' rising demand for online services and its ability to create a competitive advantage (Gielens & Steenkamp, 2019 ; Hamad et al., 2018 ; Tan et al., 2019 ). However, firms struggle with this e-business practice due to its integration with rapidly evolving, easily adopted, and highly affordable information technology (IT). This forces firms to constantly adapt their business models to changing customer needs (Gielens & Steenkamp, 2019 ; Klaus & Changchit, 2019 ; Tan et al., 2007 ). Artificial intelligence (AI) is the latest of such technologies. It is transforming e-commerce through its ability to “correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” (Kaplan & Haenlein, 2019 . p. 15). Depending on the context, AI could be a system, a tool, a technique, or an algorithm (Akter et al., 2021 ; Bawack et al., 2021 ; Benbya et al., 2021 ). It creates opportunities for firms to gain a competitive advantage by using big data to uniquely meet their customers' needs through personalised services (Deng et al., 2019 ; Kumar, Rajan, et al., 2019 ; Kumar, Venugopal, et al., 2019 ).

AI in e-commerce can be defined as using AI techniques, systems, tools, or algorithms to support activities related to buying and selling products or services over the internet. Research on AI in e-commerce has been going on for the past three decades. About 4000 academic research articles have been published on the topic across multiple disciplines, both at the consumer (de Bellis & Venkataramani Johar, 2020 ; Sohn & Kwon, 2020 ) and organisational levels (Campbell et al., 2020 ; Kietzmann et al., 2018 ; Vanneschi et al., 2018 ). However, knowledge on the topic has not been synthesised despite its rapid growth and dispersion. This lack of synthesis makes it difficult for researchers to determine how much the extant literature covers concepts of interest or addresses relevant research gaps. Synthesising research on AI in e-commerce is an essential condition for advancing knowledge by providing the background needed to describe, understand, or explain phenomena, to develop/test new theories, and to develop teaching orientations in this research area (Cram et al., 2020 ; Paré et al., 2015 ). Thus, this study aims to synthesise research on AI in e-commerce and propose directions for future research in the IS discipline. The innovative approach of combining bibliometric analysis with an extensive literature review is used to answer two specific research questions: (i) what is the current state of research on AI in e-commerce? (ii) what research should be done next on AI in e-commence in general, and within information systems (IS) research in particular?

This study's findings show that AI in e-commerce primarily focuses on recommender systems and the main research themes are sentiment analysis, optimisation, trust, and personalisation. This study makes timely contributions to ongoing debates on the connections between business strategy and the use of AI technologies (Borges et al., 2020 ; Dwivedi et al., 2019 , 2020 ). It also contributes to research on how firms can address challenges regarding the use of AI-related benefits and opportunities for new product or service developments and productivity improvements (Makridakis, 2017 ). Furthermore, no study currently synthesises AI in e-commerce research despite its rapid evolution in the last decade triggered by big data, advanced machine learning (ML) algorithms, and cloud computing. Using well-established e-commerce classification frameworks (Ngai & Wat, 2002 ; Wareham et al., 2005 ), this study classifies information systems (IS) literature on AI in e-commerce. These classifications make it easier for researchers and managers to identify relevant literature based on the topic area, research style, and research theme. A future research agenda is proposed based on the gaps revealed during the classification to guide researchers on making meaningful contributions to AI knowledge in e-commerce.

Research method

Bibliometric analysis.

Bibliometric analysis has been increasingly used in academic research in general and in IS research to evaluate the quality, impact, and influence of authors, journals, and institutions in a specific research area (Hassan & Loebbecke, 2017 ; Lowry et al., 2004 , 2013 ). It has also been used extensively to understand AI research on specific fields or topics (Hinojo-Lucena et al., 2019 ; Tran et al., 2019 ; Zhao, Dai, et al., 2020 ; Zhao, Lou, et al., 2020 ). In this study, a bibliometric analysis was conducted to understand research on AI in e-commerce using the approach Aria and Cuccurullo ( 2017 ) proposed. This methodology involves three main phases: data collection, data analysis, and data visualisation & reporting. The data collection phase involves querying, selecting, and exporting data from selected databases. This study's data sample was obtained by querying the Web of Science (WoS) core databases for publications from 1975 to 2020. This database was chosen over others like Google Scholar or Scopus because WoS provides better quality bibliometric information due to its lower rate of duplicate records (Aria et al., 2020 ) and greater coverage of high-impact journals (Aghaei Chadegani et al., 2013 ). The following search string was used to query the title, keywords, and abstracts of all documents in the WoS collection:

(‘‘Electronic Commerce’’ OR ‘‘Electronic business’’ OR ‘‘Internet Commerce’’ OR “e-business” OR “ebusiness” OR "e-commerce” OR “ecommerce” OR “online shopping” OR “online purchase” OR “internet shopping” OR “e-purchase” OR “online store” OR “electronic shopping”). AND (“Artificial intelligence” OR “Artificial neural network” OR “case-based reasoning” OR “cognitive computing” OR “cognitive science” OR “computer vision” OR “data mining” OR “data science” OR “deep learning” OR “expert system” OR “fuzzy linguistic modelling” OR “fuzzy logic” OR “genetic algorithm” OR “image recognition” OR “k-means” OR “knowledge-based system” OR “logic programming” OR “machine learning” OR “machine vision” OR “natural language processing” OR “neural network” OR “pattern recognition” OR “recommendation system” OR “recommender system” OR “semantic network” OR “speech recognition” OR “support vector machine” OR “SVM” OR “text mining”).

This search string led to 4414 documents that made up the initial dataset of this study. For quality reasons, only document types tagged as articles, reviews, and proceeding papers were selected for this study because they are most likely to have undergone a rigorous peer-review process before publication (Milian et al., 2019 ). Thus, editorial material, letters, news items, meeting abstracts, and retracted publications were removed from the dataset, leaving 4335 documents that made up the final dataset used for bibliometric analysis. Figure  1 summarises the data collection phase.

Summary of the data collection phase

Table 1 summarises the main information about the dataset regarding the timespan, document sources, document types, document contents, authors, and author collaborations. The dataset consists of documents from 2599 sources, published by 8663 authors and 84,474 references.

Bibliometrix Footnote 1 is the R package used to conduct bibliometric analysis (Aria & Cuccurullo, 2017 ). This package has been extensively used to conduct bibliometric studies published in top-tier journals. It incorporates the most renowned bibliometric tools for citation analysis (Esfahani et al., 2019 ; Fosso Wamba, 2020 ; Pourkhani et al., 2019 ). It was specifically used to analyse the sources, documents, conceptual, and intellectual structure of AI in e-commerce research. Publication sources and their source impacts were analysed based on their h-index quality factors (Hirsch, 2010 ). The most significant, impactful, prestigious, influential, and quality publication sources, affiliations, and countries regarding research on AI in e-commerce were identified. This contributed to the identification of the most relevant disciplines in this area of research. Documents were analysed using total citations to identify the most cited documents in the dataset. Through content analysis, the most relevant topics/concepts, AI technologies/techniques, research methods, and application domains were identified.

Furthermore, citation analysis and reference publication year spectroscopy (RPYS) were used to identify research contributions that form the foundations of research on AI in e-commerce (Marx et al., 2014 ; Rhaiem & Bornmann, 2018 ). These techniques were also used to identify the most significant changes in the research area. Co-word network analysis on author-provided keywords using the Louvain clustering algorithm was used to understand the research area's conceptual structure. This algorithm is a greedy optimisation method used to identify communities in large networks by comparing the density of links inside communities with links between communities (Blondel et al., 2008 ). This study used it to identify key research themes by analysing author-provided keywords. Co-citation network analysis using the Louvain clustering algorithm was also used to analyse publication sources through which journals communities were identified. It further contributed to identifying the most relevant disciplines in this research area by revealing journal clusters.

The bibliometric analysis results were reported from functionalist, normative, and interpretive perspectives (Hassan & Loebbecke, 2017 ). The functionalist perspective presents the results of the key concepts and topics investigated in this research area. The normative perspective focuses on the foundations and norms of the research area. The interpretive perspective emphasises the main themes that drive AI in e-commerce research.

• Literature review

An extensive review and classification of IS literature on AI in e-commerce complemented the bibliometric analysis. It provides more details on how research in this area is conducted in the IS discipline. The review was delimited to the most impactful and influential management information systems (MIS) journals identified during the bibliometric analysis and completed by other well-established MIS journals known for their contributions to e-commerce research (Ngai & Wat, 2002 ; Wareham et al., 2005 ). Thus, 20 journals were selected for this review: Decision sciences, Decision support systems, Electronic commerce research and applications, Electronic markets, E-service journal, European journal of information systems, Information and management, Information sciences, Information systems research, International journal of electronic commerce, International journal of information management, Journal of information systems, Journal of information technology, Journal of management information systems, Journal of organisational computing and electronic commerce, Journal of strategic information systems, Journal of the association for information systems, Knowledge-based systems, Management science, MIS Quarterly .

The literature review was conducted in three stages (Templier & Paré, 2015 ; Webster & Watson, 2002 ): (i) identify and analyse all relevant articles from the targeted journals found in the bibliometric dataset (ii) use the keyword string to search for other relevant articles found on the official publication platforms of the targeted journals, and (iii) identify relevant articles from the references of the articles identified in stages one and two found within the target journals. All articles with content that did not focus on AI in e-commerce were eliminated. This process led to a final dataset of 229 research articles on AI in e-commerce. The articles were classified into three main categories: by topic area (Ngai & Wat, 2002 ), by research style (Wareham et al., 2005 ), and by research themes (from bibliometric analysis).

Classification by topic area involved classifying relevant literature into four broad categories: (i) applications, (ii) technological issues, (iii) support and implementation, and (iv) others. Applications refer to the specific domain in which the research was conducted (marketing, advertising, retailing…). Technological issues contain e-commerce research by AI technologies, systems, algorithms, or methodologies that support or enhance e-commerce applications. Support and Implementation include articles that discuss how AI supports public policy and corporate strategy. Others contain all other studies that do not fall into any of the above categories. It includes articles on foundational concepts, adoption, and usage. Classification by research style involved organizing the relevant literature by type of AI studied, the research approach, and the research method used in the studies. The research themes identified in the bibliometric analysis stage were used to classify the relevant IS literature by research theme.

Results of the bibliometric analysis

Scientific publications on AI in e-commerce began in 1991 with an annual publication growth rate of 10.45%. Figure  2 presents the number of publications per year. Observe the steady increase in the number of publications since 2013.

Number of publications on AI in e-commerce per year

Institutions in Asia, especially China, are leading this research area. The leading institution is Beijing University of Posts and Telecommunications, with 88 articles, followed by Hong Kong Polytechnic University with 84 articles. Table 2 presents the top 20 institutions publishing on AI in e-commerce.

As expected, China-based affiliations appear most frequently in publications (4261 times). They have over 2.5 times as many appearances as US-based affiliations (1481 times). Interestingly, publications with US-based affiliations attract more citations than those in China. Table 3 presents the number of times authors from a given country feature in publications and the corresponding total number of citations.

Functional perspective

Analysing the most globally cited documents Footnote 2 in the dataset (those with 100 citations) reveals that recommender systems are the main topic of interest in this research area (Appendix Table 10 ). Recommender systems are software agents that make recommendations for consumers by implicitly or explicitly evoking their interests or preferences (Bo et al., 2007 ). The topic has been investigated in many flavours, including hybrid recommender systems (Burke, 2002 ), personalised recommender systems (Cho et al., 2002 ), collaborative recommender systems (Lin et al., 2002 ) and social recommender systems (Li et al., 2013 ). The central concept of interest is personalisation, specifically leveraging recommender systems to offer more personalised product/service recommendations to customers using e-commerce platforms. Thus, designing recommender systems that surpass existing ones is the leading orientation of AI in e-commerce research. Researchers have mostly adopted experimental rather than theory-driven research designs to meet this overarching research objective. Research efforts focus more on improving the performance of recommendations using advanced AI algorithms than on understanding and modelling the interests and preferences of individual consumers. Nevertheless, the advanced AI algorithms developed are trained primarily using customer product reviews.

Interpretive perspective

Four themes characterise research on AI in e-commerce: sentiment analysis, trust & personalisation, optimisation, AI concepts, and related technologies. The keyword clusters that led to the identification of these themes are presented in Table 4 . The sentiment analysis theme represents the stream of research focused on interpreting and classifying emotions and opinions within text data in e-commerce using AI techniques like ML and natural language processing (NLP). The trust and personalisation theme represents research that focuses on establishing trust and making personalised recommendations for consumers in e-commerce using AI techniques like collaborative filtering, case-based reasoning, and clustering algorithms. The optimisation theme represents research that focuses on using AI algorithms like genetic algorithms to solve optimisation problems in e-commerce. Finally, the AI concepts and related technologies theme represent research that focuses on using different techniques and concepts used in the research area.

Normative perspective

Research on AI in e-commerce is published in two main journal subject areas: computer science & AI and business & management. This result confirms the multidisciplinary nature of this research area, which has both business and technical orientations. Table 5 presents the most active publication outlets in each subject area. The outlets listed in the table could help researchers from different disciplines to select the proper outlet for their research results. It could also help researchers identify the outlets wherein they are most likely to find relevant information for their research on AI in e-commerce.

However, some disciplines set the foundations and standards of research on AI in e-commerce through the impact of their contributions to its body of knowledge. Analysing document references shows that the most cited contributions come from journals in the IS, computer science, AI, management science, and operations research disciplines (Table 6 ). It shows the importance of these disciplines to AI's foundations and standards in e-commerce research and their major publication outlets.

The IS discipline is a significant contributor to AI in e-commerce research, given that 24 out of the 40 top publications in the area can be assimilated to IS sources. Table 7 also shows that 7 out of the top 10 most impactful publication sources are assimilated to the IS discipline. The leading paper from the IS field reviews approaches to automatic schema matching (Rahm & Bernstein, 2001 ) and it is the second most globally cited paper in the research area. Meanwhile, the leading paper from the MIS subfield reviews recommender system application developments (Lu et al., 2015 ).

Collaborative filtering, recommender systems, social information filtering, latent Dirichlet allocation, and matrix factoring techniques are the foundational topics in research on AI in e-commerce (Table 8 ). They were identified by analysing the most cited references in the dataset. These references were mostly literature reviews and documents that discussed the basic ideas and concepts behind specific technologies or techniques used in recommender systems.

Furthermore, the specific documents that set the foundations of research on AI in e-commerce and present the most significant historical contributions and turning points in the field were identified using RPYS (Appendix Table 11 ). 2001, 2005, 2007, 2011, and 2015 are the years with the highest number of documents referenced by the documents in the sample. The most cited studies published in 2001 focused on recommendation algorithms, especially item-based collaborative filtering, random forest, gradient boosting machine, and data mining. The main concept of interest was how to personalise product recommendations. In 2005, the most referenced documents focused on enhancing recommendation systems using hybrid collaborative filtering, advanced machine learning tools and techniques, and topic diversification. That year also contributed a solid foundation for research on trust in recommender systems. In 2007, significant contributions continued on enhanced collaborative filtering techniques for recommender systems. Meanwhile, Bo & Benbasat ( 2007 ) set the basis for research on recommender systems' characteristics, use, and impact, shifting from traditional studies focused on underlying algorithms towards a more consumer-centric approach. In 2011, major contributions were made to enhance recommender systems, like developing a new library for support vector machines (Chang & Lin, 2011 ) and the Scikit-learn package for machine learning in Python (Pedregosa et al., 2011 ). In 2015, the most critical contributions primarily focused on deep learning algorithms, especially with an essential contribution to using them in recommender systems (Wang et al., 2015 ).

Results of the literature review study

Classification by topic area.

Most articles on AI in e-commerce focus on technological issues (107 articles, 47%), followed by applications (87 articles, 38%), support and implementation (20 articles, 9%), then others (15 articles, 6%). Specifically, most articles focus on AI algorithms, models, and methodologies that support or improve e-commerce applications (76 articles, 33.2%) or emphasise the applications of AI in marketing, advertising, and sales-related issues (38 articles, 16.6%). Figure  3 presents the distribution of articles, while Appendix Table 12 presents the articles in each topic area.

Classification of MIS literature on AI in e-commerce by topic area

Classification by research style

Most authors discuss AI algorithms, models, computational approaches, or methodologies (168 articles, 73%). Specifically, current research focuses on how AI algorithms like ML, deep learning (DL), NLP, and related techniques could be used to model and understand phenomena in the e-commerce environment. It also focuses on studies that involve designing intelligent agent algorithms that support learning processes in e-commerce systems. Many studies also focus on AI as systems (31 articles, 14%), especially on recommender systems and expert systems that leverage AI algorithms in the back end. The “others” category harboured all articles that did not clearly refer to AI as either an algorithm or as a system (30 articles, 13%) (see Fig.  4 and Appendix Table 13 ).

Classification of MIS literature on AI in e-commerce by type of AI

Furthermore, most publications use the design science research approach (198 articles, 86%). Researchers prefer this approach because it allows them to develop their algorithms and models or improve existing ones, thereby creating a new IS artefact (see Fig.  5 and Appendix Table 14 ).

Classification of MIS literature on AI in e-commerce by research approach

Also, authors adopt experimental methods in their papers (157 articles, 69%), especially those who adopted a design science research approach. They mostly use experiments to test their algorithms or prove their concepts (see Fig.  6 and Appendix Table 15 ).

Classification of MIS literature on AI in e-commerce by research method

Classification by research theme

Based on the main research themes on AI in e-commerce identified during the bibliometric analysis, most authors published on optimisation (63 articles, 27%). They mostly focused on optimising recommender accuracy (25 articles), prediction accuracy (29 articles), and other optimisation aspects (9 articles) like storage optimisation. This trend was followed by publications on trust & personalisation (31 articles, 14%), wherein more articles were published on personalisation (17 articles) than on trust (14 articles). Twenty-nine articles focused on sentiment analysis (13%). The rest of the papers focus on AI design, tools and techniques (46 articles), decision support (30 articles), customer behaviour (13 articles), AI concepts (9 articles), and intelligent agents (8 articles) (see Fig.  7 and Appendix Table 16 ).

Classification of MIS literature on AI in e-commerce by current research themes

This study's overall objective was to synthesise research on AI in e-commerce and propose avenues for future research. Thus, it sought to answer two research questions: (i) what is the current state of research on AI in e-commerce? (ii) what research should be done next on AI in e-commerce in general and within IS research in particular? This section summarises the findings of the bibliometric analysis and literature review. It highlights some key insights from the results, starting with the leading role of China and the USA in this research area. This highlight is followed by discussions on the focus of current research on recommender systems, the extensive use of design science and experiments in this research area, and a limited focus on modelling consumer behaviour. This section also discusses the little research found on some research themes and the limited number of publications from some research areas. Implications for research and practice are discussed at the end of this section.

Need for more research from other countries

Research on AI in e-commerce has been rising steadily since 2013. Overall, these results indicate a growing interest in the applications of AI in e-commerce. China-based institutions lead this research area, although US-based affiliations attract more citations. Tables 2 and 3 indicate that China is in the leading position regarding research on AI in e-commerce. Observe that Amazon Inc. (USA), JD.com (China), Alibaba Group Holding Ltd. (China), Suning.com (China), Meituan (China), Wayfair (USA), eBay (USA), and Groupon (USA) are referenced among the largest e-commerce companies in the world (in terms of market capitalisation, revenue, and the number of employees). Footnote 3 These companies are primarily from China and the USA. These findings correlate with Table 3 , which could indicate that China and the USA are investing more in the research and development of AI applications in e-commerce (especially China, based on Table 2 ) because of the positions they occupy in the industry. This logic would imply that companies seeking to penetrate the e-commerce industry and remain competitive should also consider investing more in the research and development of AI applications in the area. The list of universities provided could become partner universities for countries with institutions that have less experience in the research area. Especially with the COVID-19 pandemic, e-commerce has become a global practice. Thus, other countries need to contribute more research on the realities of e-commerce in their respective contexts to develop more globally acceptable AI solutions in e-commerce practices. It is essential because different countries approach e-commerce differently. For example, although Amazon’s marketplace is well-developed in continents like Europe, Asia, and North America, it has difficulty penetrating Africa because the context is very different (culturally and infrastructurally). While mobile wallet payment systems are fully developed on the African continent, Amazon’s marketplace does not accommodate this payment method. Therefore, it would be impossible for many Africans to use Amazon’s Alexa to purchase products online. What does this mean for research on digital inclusion? Are there any other cross-cultural differences between countries that affect the adoption and use of AI in e-commerce? Are there any legal boundaries that affect the implementation and internationalisation of AI in e-commerce? Such questions highlight the need for more country-specific research on AI in e-commerce to ensure more inclusion.

Focus on recommender systems

AI in e-commerce research is essentially focused on recommender systems in the past years. The results indicate that in the last 20 years, AI in e-commerce research has primarily focused on using AI algorithms to enhance recommender systems. This trend is understandable because recommender systems have become an integral part of almost every e-commerce platform nowadays (Dokyun Lee & Hosanagar, 2021 ; Stöckli & Khobzi, 2021 ). As years go by, observe how novel AI algorithms have been proposed, the most recent being deep learning (Chaudhuri et al., 2021 ; Liu et al., 2020 ; Xiong et al., 2021 ; Zhang et al., 2021 ). Thus, researchers are increasingly interested in how advanced AI algorithms can enable recommender systems in e-commerce platforms to correctly interpret external data, learn from such data, and use those learnings to improve the quality of user recommendations through flexible adaptation. With the advent of AI-powered chatbots and voice assistants, firms increasingly include these technologies in their e-commerce platforms (Ngai et al., 2021 ). Thus, researchers are increasingly interested in conversational recommender systems (De Carolis et al., 2017 ; Jannach et al., 2021 ; Viswanathan et al., 2020 ). These systems can play the role of recommender systems and interact with the user through natural language (Iovine et al., 2020 ). Thus, conversational recommender systems is an up-and-coming research area for AI-powered recommender systems, especially given the ubiquitous presence of voice assistants in society today. Therefore, researchers may want to investigate how conversational recommender systems can be designed effectively and the factors that influence their adoption.

Limited research themes

The main research themes in AI in e-commerce are sentiment analysis, trust, personalisation, and optimisation. Researchers have focused on these themes to provide more personalised recommendations to recommendation system users. Personalising recommendations based on users’ sentiment and trust circle has been significantly researched. Extensive research has also been conducted on how to optimise the algorithmic performance of recommender systems. ML, DL, NLP are the leading AI algorithms and techniques currently researched in this area. The foundational topics for applying these algorithms include collaborative filtering, latent Dirichlet allocation, matrix factoring techniques, and social information filtering.

Current research shows how using AI for personalisation would enable firms to deliver high-quality customer experiences through precise personalisation based on real-time information (Huang & Rust, 2018 , 2020 ). It is highly effective in data-rich environments and can help firms to significantly improve customer satisfaction, acquisition, and retention rates, thereby ideal for service personalisation (Huang & Rust, 2018 ). AI could enable firms to personalise products based on preferences, personalise prices based on willingness to pay, personalise frontline interactions, and personalise promotional content in real-time (Huang & Rust, 2021 ).

Research also shows how AI could help firms optimise product prices by channel and customer (Huang & Rust, 2021 ; Huang & Rust, 2020 ) and develop accurate and personalised recommendations for customers. It is beneficial when the firm lacks initial data on customers that it can use to make recommendations (cold start problem) (Guan et al., 2019 ; Wang, Feng, et al., 2018 ; Wang, Jhou, et al., 2018 ; Wang, Li, et al., 2018 ; Wang, Lu, et al., 2018 ). It also gives firms the ability to automatically estimate optimal prices for their products/services and define dynamic pricing strategies that increase profits and revenue (Bauer & Jannach, 2018 ; Greenstein-Messica & Rokach, 2018 ). It also gives firms the ability to predict consumer behaviours like customer churn (Bose & Chen, 2009 ), preferences based on their personalities (Buettner, 2017 ), engagement (Ayvaz et al., 2021 ; Sung et al., 2021 ; Yim et al., 2021 ), and customer payment default (Vanneschi et al., 2018 ). AI also gives firms the ability to predict product, service, or feature demand and sales (Cardoso & Gomide, 2007 ; Castillo et al., 2017 ; Ryoba et al., 2021 ), thereby giving firms the ability to anticipate and dynamically adjust their advertising and sales strategies (Chen et al., 2014 ; Greenstein-Messica & Rokach, 2020 ). Even further, it gives firms the ability to predict the success or failure of these strategies (Chen & Chung, 2015 ).

Researchers have shown that using AI to build trust-based recommender systems can help e-commerce firms increase user acceptance of the recommendations made by e-commerce platforms (Bedi & Vashisth, 2014 ). This trust is created by accurately measuring the level of trust customers have in the recommendations made by the firm’s e-commerce platforms (Fang et al., 2018 ) or by making recommendations based on the recommendations of people the customers’ trust in their social sphere (Guo et al., 2014 ; Zhang et al., 2017 ).

Sentiment analytics using AI could give e-commerce firms the ability to provide accurate and personalised recommendations to customers by assessing their opinions expressed online such as through customer reviews (Al-Natour & Turetken, 2020 ; Qiu et al., 2018 ). It has also proven effective in helping brands better understand their customers over time and predict their behaviours (Das & Chen, 2007 ; Ghiassi et al., 2016 ; Pengnate & Riggins, 2020 ). For example, it helps firms better understand customer requirements for product improvements (Ou et al., 2018 ; Qi et al., 2016 ) and predict product sales based on customer sentiments (Li, Wang, et al., 2019 ; Li, Wu, et al., 2019 ; Li, Zhang, et al., 2019 ). Thus, firms can accurately guide their customers towards discovering desirable products (Liang & Wang, 2019 ) and predict the prices they would be willing to pay for products based on their sentiments (Tseng et al., 2018 ). Thus, firms that use AI-powered sentiment analytics would have the ability to constantly adapt their product development, sales, and pricing strategies while improving the quality of their e-commerce services and personalised recommendations for their customers.

While the current research themes are exciting and remain relevant in today’s context, it highlights the need for researchers to explore other research themes. For example, privacy, explainable, and ethical AI are trendy research themes in AI research nowadays. These themes are relevant to research on AI in e-commerce as well. Thus, developing these research themes would make significant contributions to research on AI in e-commerce. In the IS discipline, marketing & advertising is where AI applications in e-commerce have been researched the most. This finding complements Davenport et al. ( 2020 )’s argument, suggesting that marketing functions have the most to gain from AI. Most publications focus on technological issues like algorithms, support systems, and security. Very few studies investigated privacy, and none was found on topics like ethical, explainable, or sustainable AI. Therefore, future research should pay more attention to other relevant application domains like education & training, auctions, electronic payment systems, inter-organisational e-commerce, travel, hospitality, and leisure (Blöcher & Alt, 2021 ; Manthiou et al., 2021 ; Neuhofer et al., 2021 ). To this end, questions that may interest researchers include, what are the privacy challenges caused by using AI in e-commerce? How can AI improve e-commerce services in education and training? How can AI improve e-commerce services in healthcare? How can AI bring about sustainable e-commerce practices?

Furthermore, research on AI in e-commerce is published in two main journal categories: computer science & AI and business & management. Most citations come from the information systems, computer science, artificial intelligence, management science, and operations research disciplines. Thus, researchers interested in research on AI in e-commerce are most likely to find relevant information in such journals (see Tables 5 and 6 ). Researchers seeking to publish their research on AI in e-commerce can also target such journals. However, researchers are encouraged to publish their work in other equally important journal categories. For example, law and government-oriented journals would greatly benefit from research on AI in e-commerce. International laws and government policies could affect how AI is used in e-commerce. For example, due to the General Data Protection Regulation (GDPR), how firms use AI algorithms and applications to analyse user data in Europe may differ from how they would in the US. Such factors may have profound performance implications given that AI systems are as good as the volume and quality of data they can access for analysis. Thus, future research in categories other than those currently researched would benefit the research community.

More experiment than theory-driven research

Most of the research done on AI in e-commerce have adopted experimental approaches. Very few adopted theory-driven designs. This trend is also observed in IS research, where 69% of the studies used experimental research methods and 86% adopted a design science research approach instead of the positivist research approach often adopted in general e-commerce research (Wareham et al., 2005 ). However, this study's findings complement a recent review that shows that laboratory experiments and secondary data analysis were becoming increasingly popular in e-commerce research. Given that recommender systems support customer decision-making, this study also complements recent studies that show the rising use of design science research methods in decision support systems research (Arnott & Pervan, 2014 ) and in IS research in general (Jeyaraj & Zadeh, 2020 ). This finding could be explained by the fact that researchers primarily focused on enhancing the performance of AI algorithms used in recommender systems. Therefore, to test the performance of their algorithms in the real world, the researchers have to build a prototype and test it in real-life contexts. Using performance accuracy scores, the researchers would then tell the extent to which their proposed algorithm is performant. However, ML has been highlighted as a powerful tool that can help advance theory in behavioural IS research (Abdel-Karim et al., 2021 ). Therefore, key research questions on AI in e-commerce could be approached using ML as a tool for theory testing in behavioural studies. Researchers could consider going beyond using AI algorithms for optimising recommender systems to understand its users' behaviour. In Fig.  4 , observe that 73% of IS researcher papers reviewed approached AI as an algorithm or methodology to solve problems in e-commerce. Only 14% approached AI as a system. Researchers can adopt both approaches in the same study in the sense that they can leverage ML algorithms to understand human interactions with AI systems, not just for optimisation. This approach could provide users with insights by answering questions regarding the adoption and use of AI systems.

Furthermore, only 6% of the studies focus on consumer behaviour. Thus, most researchers on AI in e-commerce this far have focused more on algorithm performance than on modelling the behaviour of consumers who use AI systems. It is also clear that behavioural aspects of using recommender systems are often overlooked (Adomavicius et al., 2013 ). There is relatively limited research on the adoption, use, characteristics, and impact of AI algorithms or systems on its users. This issue was raised as a fundamental problem in this research area (Bo et al., 2007 ) and seems to remain the case today. However, understanding consumer behaviour could help improve the accuracy of AI algorithms. Thus, behavioural science researchers need to conduct more research on modelling consumer behaviours regarding consumers' acceptance, adoption, use, and post-adoption behaviours targeted by AI applications in e-commerce. As AI algorithms, systems, and use cases multiply in e-commerce, studies explaining their unique characteristics, adoption, use, and impact at different levels (individual, organisational, and societal) should also increase. It implies adopting a more theory-driven approach to research on AI in e-commerce. Therefore, behavioural science researchers should be looking into questions on the behavioural factors that affect the adoption of AI in e-commerce.

Implications for research

This study contributes to research by innovatively synthesising the literature on AI in e-commerce. Despite the recent evolution of AI and the steady rise of research on how it could affect e-commerce environments, no review has been conducted to understand this research area's state and evolution. Yet, a recent study shows that e-commerce and AI are currently key research topics and themes in the IS discipline (Jeyaraj & Zadeh, 2020 ). This paper has attempted to fill this research gap by providing researchers with a global view of AI research in e-commerce. It offers a multidimensional view of the knowledge structure and citation behaviour in this research area by presenting the study's findings from functional, normative, and interpretive perspectives. Specifically, it reveals the most relevant topics, concepts, and themes on AI in e-commerce from a multidisciplinary perspective.

This contribution could help researchers evaluate the value and contributions of their research topics in the research area with respect to other disciplines and choose the best publication outlets for their research projects. This study also reveals the importance of AI in designing recommender systems and shows the foundational literature on which this research area is built. Thus, researchers could use this study to design the content of AI or e-commerce courses in universities and higher education institutions. Its content provides future researchers and practitioners with the foundational knowledge required to build quality recommender systems. Researchers could also use this study to inform their fields on the relevance of their research topics and the specific gaps to fill therein. For example, this study reveals the extent to which the IS discipline has appropriated research on AI in e-commerce. It also shows contributions of the IS discipline to the current research themes, making it easier for IS researchers to identify research gaps as well as gaps between IS theory and practice.

Implications for practice

This study shows that AI in e-commerce primarily focuses on recommender systems. It highlights sentiment analysis, optimisation, trust, and personalisation as the core themes in the research area. Thus, managers could tap into these resources to improve the quality of their recommender systems. Specifically, it could help them understand how to develop optimised, personalised, trust-based and sentiment-based analytics supported by uniquely designed AI algorithms. This knowledge would make imitating or replicating the quality of recommendations rendered through e-commerce platforms practically impossible for competitors. Firms willing to use AI in e-commerce would need unique access and ownership of customer data, AI algorithms, and expertise in analytics (De Smedt et al., 2021 ; Kandula et al., 2021 ; Shi et al., 2020 ). The competition cannot imitate these resources because they are unique to the firm, especially if patented (Pantano & Pizzi, 2020 ). Also, this research paper classifies IS literature on AI in e-commerce by topic area, research style, and research theme. Thus, IS practitioners interested in implementing AI in e-commerce platforms would easily find the research papers that best meet their needs. It saves them the time to search for articles themselves, which may not always be relevant and reliable.

Limitations

This study has some limitations. It was challenging to select a category for each article in the sample dataset. Most of those articles could be rightfully placed in several categories of the classification frameworks. However, assigning articles to a single category in each framework simplifies the research area's conceptualisation and understanding (Wareham et al., 2005 ). Thus, categories were assigned to each article based on the most apparent orientation from the papers' titles, keywords, and abstracts. Another challenge was whether or not to include a research paper in the review. For example, although some studies on recommender systems featured in the keyword search results, the authors did not specify if the system's underlying algorithms were AI algorithms. Consequently, such articles were not classified to ensure that those included in this review certainly had an AI orientation. Despite our efforts, we humbly acknowledge that this study may have missed some publications, and others may have been published since this paper started the review process. Thus, in no way does this study claim to be exhaustive but rather extensive. Nonetheless, the findings from our rigorous literature review process strongly match the bibliometric analysis findings and those from similar studies we referenced. Therefore, we believe our contributions to IS research on AI in e-commerce remain relevant.

Future research

In addition to recommendations for future research discussed in the previous sections, the findings of this study are critically analysed through the lens of recent AI research published in leading IS journals. The aim is to identify other potential gaps for future research on AI in e-commerce that could interest the IS community.

One of the fundamental issues with AI research in IS today is understanding the AI concept (Ågerfalk, 2020 ). Our findings show that researchers have mostly considered algorithms and techniques like ML, DL, and NLP AI in their e-commerce research. Are these algorithms and techniques AI? Does the fact that an algorithm helps to analyse data and make predictions about e-commerce activities mean that the algorithm is AI? It is crucial for researchers to clearly explain what they mean by AI and differentiate between different types of AI used in their studies to avoid ambiguity. This explanation would help prevent confusion between AI and business intelligence & analytics in e-commerce. It would also help distinguish between AI as a social actor and AI as a technology with the computational capability to perform cognitive functions.

A second fundamental issue with AI research in IS is context (Ågerfalk, 2020 ). Using the same data, an AI system would/should be able to interpret the message communicated or sought by the user based on context. Context gives meaning to the data, making the AI system’s output relevant in the real world. Research on AI in e-commerce did not show much importance to context. Many authors used existing datasets to test their algorithms without connecting them to a social context. Thus, it is difficult to assess whether the performance of the proposed algorithms is relevant in every social context. Future research should consider using AI algorithms to analyse behavioural data alongside ‘hard’ data (facts) to identify patterns and draw conclusions in specific contexts. It implies answering the crucial question, what type of AI best suits which e-commerce context? Thus, researchers would need to collaborate with practitioners to better understand and delineate contexts (Ågerfalk, 2020 ) of investigation rather than make general claims on fraud detection or product prices, for example.

The IS community is also interested in understanding ethical choices and challenges organisations face when adopting AI systems and algorithms. What ethical decisions do e-commerce firms need to make when implementing AI solutions? What are the ethical challenges e-commerce firms face when implementing AI solutions? Following a sociotechnical approach, firms seeking to implement AI systems need to make ethical choices. These include transparent vs black-boxed algorithms, slow & careful vs expedited & timely designs, passive vs active implementation approach, obscure vs open system implementation, compliance vs risk-taking, and contextualised vs standardised use of AI systems (Marabelli et al., 2021 ). Thus, future research on AI in e-commerce should investigate how e-commerce firms address these ethical choices when implementing their AI solutions and the challenges they face in the process.

AI and the future of work is another primary source of controversy in the IS community (Huysman, 2020 ; Willcocks, 2020a , b ). Several researchers are investigating how AI is transforming the work configurations of organisations. Workplace technology platforms are increasingly observed to integrate office applications, social media features and AI-driven self-learning capabilities (Baptista et al., 2020 ; Grønsund & Aanestad, 2020 ; Lyytinen et al., 2020 ). Is this emergent digital/human work configuration also happening in e-commerce firms? How is this changing the future of work in the e-commerce industry?

IS researchers have increasingly called for research on how AI transforms decision making. For example, they are interested in understanding how AI could help augment mental processing, change managerial mindsets and actions, and affect the rationality of economic agents (Brynjolfsson et al., 2021 ). A recent study also makes several research propositions for IS researchers regarding conceptual and theoretical development, AI-human interaction, and AI implementation in the context of decision making (Duan et al., 2019 ). This study shows that decision-making is not a fundamental research theme as it accounts for only 13% of the research papers reviewed. Thus, future research on AI in e-commerce should contribute to developing this AI research theme in the e-commerce context. It involves proposing answers to questions like how AI affects managerial mindsets and actions in e-commerce? How is AI affecting the rationality of consumers who use e-commerce platforms?

This study shows that relatively few research papers on AI in e-commerce are theory-driven. Most adopted experimental research methods and design science research approaches wherein they use AI algorithms to explain phenomena. The IS community is increasingly interested in developing theories using AI algorithms (Abdel-Karim et al., 2021 ). Contrary to traditional theory development approaches, such theories developed based on AI algorithms like ML are called to be focused, context-specific, and as transparent as possible (Chiarini Tremblay et al., 2021 ). Thus, rather than altogether abandoning the algorithm-oriented approach used for AI in e-commerce research, researchers who master it should develop skills to use it as a basis for theorising.

Last but not least, more research is needed on the role of AI-powered voice-based AI in e-commerce. It is becoming common for consumers to use intelligent personal assistants like Google’s Google Assistant, Amazon’s Alexa, and Apple’s Siri for shopping activities since many retail organisations are making them an integral part of their e-commerce platforms (de Barcelos Silva et al., 2020 ). Given the rising adoption of smart speakers by consumers, research on voice commerce should become a priority for researchers on AI in e-commerce. Yet, this study shows that researchers are still mostly focused on web-based, social networking (social commerce), and mobile (m-commerce) platforms. Therefore, research on the factors that affect the adoption and use of voice assistants in e-commerce and the impact on consumers and e-commerce firms would make valuable contributions to e-commerce research. Table 9 summarises the main research directions recommended in this paper.

Conclusions

AI has emerged as a technology that can differentiate between two competing firms in e-commerce environments. This study presents the state of research of AI in e-commerce based on bibliometric analysis and a literature review of IS research. The bibliometric analysis highlights China and the USA as leaders in this research area. Recommender systems are the most investigated technology. The main research themes in this area of research are optimisation, trust & personalisation, sentiment analysis, and AI concepts & related technologies. Most research papers on AI in e-commerce are published in computer science, AI, business, and management outlets. Researchers in the IS discipline has focused on AI applications and technology-related issues like algorithm performance. Their focus has been more on AI algorithms and methodologies than AI systems. Also, most studies have adopted a design science research approach and experiment-style research methods. In addition to the core research themes of the area, IS researchers have also focused their research on AI design, tools and techniques, decision support, consumer behaviour, AI concepts, and intelligent agents. The paper discusses opportunities for future research revealed directly by analysing the results of this study. It also discusses future research directions based on current debates on AI research in the IS community. Thus, we hope that this paper will help inform future research on AI in e-commerce.

Download the bibliometrix R package and read more here: https://www.bibliometrix.org/index.html

Global citation refers to the total number of times the document has been cited in other documents in general and local citations refer to the total number of times a document has been cited by other documents in our dataset.

https://axiomq.com/blog/8-largest-e-commerce-companies-in-the-world/

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Bawack, R.E., Wamba, S.F., Carillo, K.D.A. et al. Artificial intelligence in E-Commerce: a bibliometric study and literature review. Electron Markets 32 , 297–338 (2022). https://doi.org/10.1007/s12525-022-00537-z

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Received : 11 September 2021

Accepted : 22 February 2022

Published : 18 March 2022

Issue Date : March 2022

DOI : https://doi.org/10.1007/s12525-022-00537-z

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