literature review customer care

Focusing the customer through smart services: a literature review

• Research Paper
• Open access
• Published: 09 February 2019
• Volume 29 , pages 55–78, ( 2019 )

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• Sonja Dreyer   ORCID: orcid.org/0000-0001-8933-3714 1 ,
• Daniel Olivotti 1 ,
• Benedikt Lebek 2 &
• Michael H. Breitner 1  

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Smart services serve customers and their individual, continuously changing needs; information and communications technology enables such services. The interactions between customers and service providers form the basis for co-created value. A growing interest in smart services has been reported in the literature in recent years. However, a categorization of the literature and relevant research fields is still missing. This article presents a structured literature search in which 109 relevant publications were identified. The publications are clustered in 13 topics and across five phases of the lifecycle of a smart service. The status quo is analyzed, and a heat map is created that graphically shows the research intensity in various dimensions. The results show that there is diverse knowledge related to the various topics associated with smart services. One finding suggests that economic aspects such as new business models or pricing strategies are rarely considered in the literature. Additionally, the customer plays a minor role in IS publications. Machine learning and knowledge management are identified as promising fields that should be the focus of further research and practical applications. Concrete ideas for future research are presented and discussed and will contribute to academic knowledge. Addressing the identified research gaps can help practitioners successfully provide smart services.

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Introduction

Increasing digitalization and the emergence of the Internet of Things have fostered growing interest in smart services in recent years (Georgakopoulos and Jayaraman 2016 ). Smart services are characterized by the fact that the service provider and the customer interact to create value. This process is called value co-creation (Gavrilova and Kokoulina 2015 ) and enables service providers to continuously adjust to a customer’s individual and constantly changing needs (Massink et al. 2010 ). Customers are supported, and new business models are realized via smart services.

The number of publications that have focused on smart services has greatly increased in recent years. Although these publications have answered many relevant research questions, none have yet articulated a systematic and comprehensive research agenda for smart services. Systematic insights in different topics help to provide a broader view on the subject (Kamp et al. 2016 ). Therefore, the objective of the article is to present a holistic overview of past research and opportunities for further research in the field of smart services.

The presented literature review clusters existing publications related to smart services based on topics and lifecycle phases. Both in theory and practice, the lifecycle concept is adopted to describe a product or service from the design to the continual improvement (e.g., Fischbach et al. 2013 ; Wiesner et al. 2015 ). It helps to organize the complex structure of a product or a service and makes it more transparent. In this article, this approach is also applied to smart services, since the lifecycle indicates that they are dynamic and constantly refined. How to involve customers to meet the requirements and to provide successful smart services varies in the different phases. Only passing the lifecycle together with the customers enables value co-creation. By investigating existing research and future research opportunities in the context of a smart service lifecycle, a new viewpoint is taken that is not yet considered in literature. It enables to consciously view a specific step from the strategy development to the continual improvement. Through the lifecycle, not only a topic-centered focus is considered in the present review but also an organizational perspective. It enables to identify phases that are unexplored.

The research intensity in different fields is discussed. It aims at getting an overview of smart service research. Past results are presented briefly, and research gaps are identified. For example, the customer is generally accepted as an essential component of successful smart services but has rarely been the focus of research. Additionally, few viewpoints on operating smart services exist.

Investigating concrete starting points for further research will advance the understanding of smart services in the IS literature. For example, some possible future contributions are identifying appropriate technology and necessary data streams. In practice, knowledge gained through theoretical research can be used to introduce new perspectives on smart services. For practitioners, designing, realizing and maintaining them is a key need (Kamp et al. 2016 ). Because such services are a relatively new development, best practice approaches have not been well defined. Theoretical investigations can help practitioners to provide smart services successfully.

To present a comprehensive literature review including suggestions for further research opportunities, the following research questions are investigated:

RQ 1: How does academic literature conceptually approach smart services along the smart service lifecycle?

RQ 2: Which research gaps and related further research opportunities can be derived from prior research on smart services?

Based on the approach presented by Webster and Watson ( 2002 ), a literature review is conducted by searching through academic databases using a set of predefined search terms. This review includes a forward and backward search, as well as a search using the Tool for Semantic Indexing and Similarity Queries (TSISQ), a literature tool presented by Koukal et al. ( 2014 ). In total, 109 publications were found to be relevant and are included in the literature review. After assigning each publication to at least one of the smart service lifecycle phases, an analysis of several topics is performed. Five lifecycle phases, in addition to the investigated topics, are used to develop a heat map. The heat map shows the research intensity for each combination of topics and lifecycle phases. Selected areas of the heat map are analyzed and discussed. Based on the findings, directions for further research are presented.

The remainder of this article is structured as follows: a definition of smart services is derived, and the smart service lifecycle is presented. Next, the research design is explained, and the results of the literature review are presented. Finally, results are discussed and directions for further research are developed. The article concludes with limitations and conclusions.

Smart services and a smart service lifecycle

Although there is little controversy regarding how to define smart services, various authors emphasize different aspects and characteristics of the fundamental topics. Smart services co-create value by the customers and providers via connected systems and machine intelligence (Gavrilova and Kokoulina 2015 ). Interaction between customer and provider is necessary, in addition to the service offered by the technology itself (Baldoni et al. 2010 ; Wünderlich et al. 2012 ; Demirkan et al. 2015 ). Through collaboration the service provider knows the current needs and thus can adapt the smart service constantly. Lee et al. ( 2012 ) suggested that value co-creation does not require direct input from a customer because functionalities should be provided in a convenient way. Nevertheless, the present article indicates that the customer and the environment are involved and form an important part in all phases from a strategic development to the improvement of operational smart services. This interaction can be direct, e.g. in form of feedback, or indirect, e.g. by providing information. By considering individual needs it is possible to improve and simplify the customers’ tasks and processes, both in the business-to-business (B2B) and the business-to-consumer (B2C) sector (Massink et al. 2010 ). Smart services are quality-based services, thus services in which the quality of processes plays a decisive role for economic success (Gerke and Tamm 2009 ).

Smart services are strictly based on field intelligence (Allmendinger and Lombreglia 2005 ). Field intelligence refers to the concept that connected systems and devices pave the way to intelligence that is higher than the intelligence of the individual parts. It is enabled by context information and high dynamics (Oh et al. 2010 ; Byun and Park 2011 ). Support from technology such as information and communications technology, as well as the ability to react to an individual’s context and its changes make up “smart” service (Calza et al. 2015 ). Intelligent sensors (i.e. sensors that not only collect data, but also prepare and preprocess them) are often used to determine the current contexts (Byun and Park 2011 ; Delfanti et al. 2015 ), combined with continuous communication and feedback (Wünderlich et al. 2015 ). Information from several sources, including technology, the environment and social contexts (Alahmadi and Qureshi 2015 ; Lee et al. 2012 ) is collected and then presented, or suggestions are made via data analysis (Kynsilehto and Olsson 2012 ).

Based on the aspects named in the literature, the following definition of smart services is derived, forming the basis for this article:

Smart services are individual, highly dynamic and quality-based service solutions that are convenient for the customer, realized with field intelligence and analyses of technology, environment and social context data (partially in real-time), resulting in co-creating value between the customer and the provider in all phases from the strategic development to the improvement of a smart service.

The definition contains the necessary attributes of a smart service from the article’s point of view. Individual customer needs are not mentioned as precondition because they must be considered to be able to offer individual smart services. Additionally, customer needs often are the result of data analyses what forms part of the definition. Information and communications technology is not named explicitly because it is necessary for field intelligence and analyses, what forms part of the definition. The reaction to the individual’s context is represented by naming that smart services are highly dynamic as well as individual. Intelligent sensors are only one possibility to receive data which is why it is not mentioned in the general definition. The same applies to the feedback of the customer. For what the collected data and information from different sources are used depends on the specific smart service and cannot be answered generally. As business and private customers are the only two possible target groups, it is assumed to be not a necessary part of the definition.

Predictive maintenance for production machines is an example for a smart service. Depending on the machines, production processes, current production planning and further factors, maintenance activities are planned and continually adapted. For example, mathematical models and artificial intelligence in connection with data in real-time contribute to an individual and dynamic solution. Constant exchange of knowledge and information enables the development as well as continual adaption and optimization of the applied predictive maintenance service. Additionally, feedback of the customer enables to adapt the scope of service. Thinking of predictive maintenance, after a while the customer might want that the provider does not only carry out predictive maintenance activities, but also the spare parts supply. The service provider orders spare parts when they are required, thereby fixed capital is reduced. Considering this aspect further, another form of collaboration between customer and service provider is imaginable; the customer can become a co-producer. When small spare parts, e.g. gearwheels, are needed, they could be printed in 3D by the customer itself. Thereby, machine downtimes resulting from delivery times are reduced. Detailed data and information that are required for the 3D print are supplied by the service provider.

For physical products, it is common to think in terms of their lifecycle, from the planning and development to the improvement stages. Services have a similar lifecycle (Fischbach et al. 2013 ). Several publications describe services in a lifecycle using a model that has phases progressing from idea to improvement (e.g., Niemann et al. 2009 ; Wiesner et al. 2015 ). Smart services are also subject to a lifecycle that ranges from strategic development to service improvement. In this article, it is referred to the basic service lifecycle defined in the Information Technology Infrastructure Library (ITIL) framework because it contains and clearly defines the phases of a service lifecycle. The sub-processes described in the ITIL lifecycle are not considered. This framework is widely accepted and contains best practice approaches, from both the public and private sectors (Cater-Steel et al. 2011 ). It is suitable for quality-based services that use information technology (Gerke and Tamm 2009 ). These characteristics apply to smart services, which is why the framework is considered adequate.

The service lifecycle of the current version from 2011 consists of five phases. In the first phase, the process objective is defined. Based on customer requirements, a service strategy is developed, and the necessary capabilities are defined. A more theoretical view of smart services discusses business alignment due to this kind of services. The second phase, namely service design , uses a predefined strategy to design services. The phase considers all articles that propose new smart services as a whole, i.e. infrastructures, service platforms, or related necessary aids. Deployment of the designed services is covered in the service transition phase, describing the way in which a new or changed smart service is implemented. Descriptions of a concrete implementation within a use case are included. The fourth phase is the service operation phase; this phase contains failure management, maintenance, and the execution of tasks and processes. Publications describing challenges and requirements during the use phase of a service are included. The final phase of the service lifecycle is continual service improvement . Learning from past successes and failures is a key component of this phase. This phase also describes how to continually adapt a service, use its pertinent data and information, and involve the customers. Figure 1 illustrates the described service lifecycle transferred to smart services.

Smart service lifecycle following the ITIL framework

Research design

The research design for this study includes two components: identifying relevant literature and analyzing it. The first part describes how the comprehensive and broad literature search is conducted. The second part presents how the identified literature is analyzed. The research design enables to review existing contributions to obtain a comprehensive overview of the status quo.

Identifying relevant literature

To find relevant literature that focuses on smart services, a systematic search was performed. To ensure a structured and broad overview, the approach by Webster and Watson ( 2002 ) was chosen as the underlying methodology. According to Vom Brocke et al. ( 2009 ), validity and reliability are essential components of a rigorous literature search. In general, validity is defined as the degree of accuracy, and, for a literature review, the validity is regarded as the degree to which all publications relevant to a topic are discovered (Vom Brocke et al. 2009 ). For this study, the validity of the literature search was considered by examining the selected databases, the predefined search terms, the performance of forward and backward searches, and the use of the TSISQ (Koukal et al. 2014 ). The TSISQ uses the concept of latent semantic indexing and is an extension of conventional term-matching methods. Reliability is generally understood to be the formal precision of a scientific study. In the case of a literature search, reliability is the replicability of the search process; thus, it is necessary to comprehensively document the search process (Vom Brocke et al. 2009 ).

Searches were carried out in the following eight databases: ACM, AISeL, Emerald Insight, IEEEXplore, InformsOnline, JSTOR, Science Direct, and SpringerLink. These databases provide articles from the most important outlets in the ISR field and yield different rankings, such as the VHB-JOURQUAL3 ranking. A search was not only carried out for the term “smart service” but also for digital and electronic services; The latter two terms have sometimes been used as synonyms for smart services, especially in earlier publications. To summarize, the three predefined search terms were as follows:

“smart service” OR “smart services”

“digital service” OR “digital services”

“electronic service” OR “electronic services” OR “e-service” OR “e-services”

A search was performed in the listed databases to determine whether a publication contained at least one of the search terms in the title or abstract. For SpringerLink and InformsOnline, it was not possible to specify the criteria in the search field; therefore, a full-text search was conducted on these databases. There were 25,056 hits in total from all the databases.

Inclusion and exclusion criteria were defined to identify the most relevant articles. Publications that were non-academic articles or not peer-reviewed were filtered out. However, to be sure of achieving a broad literature review, the search was not limited to high-ranking journals and conferences. According to Webster and Watson ( 2002 ), a topic-centric view of the literature is much more valuable than a view limited to a few top journals. Articles that were not written in English were excluded, which is why only English search terms were used to identify relevant literature. It was assumed that potentially relevant articles in the field of smart services would be in English because most researchers write in English, aiming to address a broad target group. To avoid regional overrepresentation of research in the formal analysis, articles in other languages were excluded. This choice also helped avoid regional bias based on differences in research topics. After implementing the named inclusion and exclusion criteria, 10,012 potentially relevant hits remained. In this literature review smart services are viewed from an ISR perspective. Articles in different disciplines such as history or art were excluded. This criterion was applied by using the filters whenever possible while searching the different databases and disciplines. A publication by Bianchi ( 2015 ), which includes a discussion of the roles of risks and trust in art exchanges, is an example of an article that was not from the ISR field and thus excluded. Additionally, articles that only used the terms smart/digital/electronic service, without subsequently focusing on these topics, were not considered. One example is a technical analysis and presentation of strategies for network scenarios (Sohn and Gwak 2016 ).

Most of the articles were found using second and third search terms; that is, they contained the terms “digital” or “electronic”, but not “smart”. The definition of smart services presented earlier was used to determine whether an article was using the terms “digital service” or “electronic service” as a synonym for smart services. Implementing this criterion led to a large reduction in potentially relevant articles, because most of the articles that used the second and third search terms did not consider “smart” services in accordance with the definition presented in this article. Appendix Table 4 shows the number of hits and their reduction for each search term in the different databases. If it was not possible to decide whether the terms used in an article complied with the definition of smart services considered in this article, the full text was examined. An article by Mecella and Pernici ( 2001 ) is an example of a hit using the search term “electronic service” that was eventually excluded. They define electronic services as open, developed for interaction in an organization and between organizations and as easily composable. Using this definition, electronic services are not necessarily based on context information or data analytics.

Following the search described above, both a backward and a forward search were conducted (Webster and Watson 2002 ). For the backward search step, the citations of the articles were screened manually for additional relevant literature. Google Scholar was used for the forward search to find articles that cited the identified literature, resulting in seven additional articles. Finally, the literature tool TSISQ (Koukal et al. 2014 ) was used to enhance the keyword-based search via latent semantic indexing. The tool compares unstructured texts and identifies semantically similar texts in a database. The database contains IS literature from the “AIS basket of eight” and other IS conferences and led to the identification of two further articles. In total, 109 articles were considered in the literature review. Figure 2 illustrates the literature search process.

Literature search process

Analyzing the identified literature

In the second phase of the literature review an analysis of the identified articles was conducted, involving the following steps: identifying relevant aspects and issues, categorizing them and discussing the highlights and results. First, a formal exploration of the 109 articles was conducted. The years of publication were examined to identify a possible trend. The identified industries used as context were also determined. Next, the articles were analyzed thematically. The smart service lifecycle explained in section two was used to identify the phases covered by each article. During analysis, it was found that considering the service lifecycle is helpful for organizing the relevant publications. Associating research with a specific lifecycle phase enabled to draw more concrete conclusions and to better understand the opportunities and challenges. For each article in the literature review, it was determined which phases of the smart service lifecycle were covered. The service lifecycle is also relevant in practice. Niemann et al. ( 2009 ) indicated that a given topic must be examined at multiple points in the smart service process. All existing publications focus on specific lifecycle phases but do not consider the entire lifecycle. The considered topics were also analyzed. In the different phases it is focused on different topics. As not all articles identified can form part of the findings section, publications are selected that, in total, represent the diversity of research results.

Based on the categorizations, a heat map was created to show the number of articles published for each topic and lifecycle phase. The heat map formed the basis for a discussion of important research fields. As a result, research gaps were identified that form promising areas for further research.

Based on the 109 identified publications, both a formal and a content analysis were conducted. The formal analysis provides a first insight into the publications covering smart services. The publications were then categorized based on the phases of the smart service lifecycle that they covered and the topics they considered. This forms the basis for the subsequently developed heat map and the discussion of research gaps.

Categorization of the literature

The issue of smart services was first mentioned in the literature in 2003 (Fig.  3 a). Initially, the term “electronic service” emerged for individual and interactive services. One year later, the term “smart service” appeared for the first time in the literature and finally became widely established in 2010. For three years, the interest was constant or growing. Although research in this field declined temporarily, it reached a new peak during the past years.

a Years of publication. b Research contexts

There is no outlet that concentrates publications dealing with smart services. Considering the outlets that researchers chose more than once (Appendix Table 5 ), conferences are the most common. The research context is also broad. Smart services are most often discussed in literature in the fields of manufacturing and healthcare. The topics of smart cities and smart homes also occur frequently. General approaches that do not specialize in a specific industry are also common (Fig. 3 b). An overview of the industries considered by each publication is presented in Appendix Table 6 . Details on the origins of the first authors and the target groups can be found in Appendix Fig.  5 . The identified publications were categorized according to the phase of the smart service lifecycle that they represent (Table 1 ). The design and the transition phases were the most frequently considered parts of the service lifecycle. To date, little research has been done in the field of smart service operation.

After assigning the publications to the relevant phases of the service lifecycle, the publications were categorized according to their focus. These topics were not predefined but were derived continuously throughout the literature review. In total, 13 topics were identified (Table 2 ), and each article was reviewed to determine whether it covered each topic. Each article focuses on at least one of the key topics.

Smart service strategy

In total, 25 articles deal with the strategic level of smart services; this number is relatively low compared to the later phases of the smart service lifecycle. Discussions about the most suitable technologies and how to include them in the strategy occurred in eleven articles (e.g., Ferretti and D'Angelo 2016 ; Perera et al. 2014 ). Six different risk types in the context of technology-based service innovations, namely privacy, functional, financial, psychological, temporal and social risks revealed challenges (Paluch and Wünderlich 2016 ). Interviews with both providers and customers enabled to identify the most important risks. From a provider’s perspective, it is privacy risks while functional risks are named most frequently by customers.

The eight articles (e.g., Smith et al. 2016 ; Tien 2012 ) that considered data during the strategy phase suggested that data represent a key factor in providing smart services. Using remote maintenance services as an example, real-time data are necessary to make such services possible. Looking at technologies, they should support data collection (Holgado and Macchi 2014 ).

The context in which a smart service is provided is a key ingredient for satisfying customer demands (Lee et al. 2012 ). The customer should be involved to find a strategy for satisfying individual needs (Spottke et al. 2016 ; Wang et al. 2012 ). A certain level of service quality contributes to customer satisfaction. The perception of service quality strongly impacts the probability that a service will be used again (Zo 2003 ). This is especially true when multiple services are interconnected (Wang et al. 2012 ).

Smart service design

In total, 59 articles examined the smart service design phase, covering a set of diverse topics. Service design should always be context-aware (Weijie et al. 2012 ). A design approach showed how to use industrial equipment for smart services (Priller et al. 2014 ). Considering existing production lines, the approach enables to make equipment smart to integrate it into the smart service world. A similar approach resulted in a platform as basis for smart mobile health services (Alti et al. 2015 ).

In contrast to the strategic phase, standardization, security and privacy concerns were frequently investigated. The dominant conclusion regarding standardization was that standards are necessary to combine and extend smart services and apply them to individual customer requirements. Open standards are necessary when designing new services (Kryvinska et al. 2008 ). Such standards enable a rapid and cost-effective development (Mihaylov et al. 2015 ). A model-driven approach that refers to several standards and contains different combined methods enabled the design of service systems (De Oliveira and Silva 2015 ). A case study showed applicability in practice.

A total of 14 articles (e.g., Gretzel et al. 2015 ; Roduner and Langheinrich 2010 ) highlighted the importance of security and privacy issues. Privacy is a fundamental challenge with respect to the concept of “smartness” (Cellary 2013 ). In the field of smart governance, the possibility of identifying any person at any time causes privacy problems. Therefore, rules and regulations regarding security and privacy are required for a successful smart service. Broadly, security is the primary concern regarding the Internet of Things technology (Keskin and Kennedy 2015 ). Therefore, it must be considered during the service design phase. The implementation of a maintained and frequently updated knowledge management helps to quantify security and privacy risks, e.g., through providing lists of risks for different situations or technologies (Moawad et al. 2015 ).

Eleven articles (e.g., Buchanan and McMenemy 2012 ; Yong and Hui-ying 2013 ) investigated how to involve the customer during the smart service design process. The importance of customer involvement is justified by the fact that value should be co-created (Wünderlich et al. 2012 ). A framework for user involvement helps to optimize the service design process (Gillig and Sailer 2012 ). A study enabled to develop the framework and shows that the customers must be analyzed from the beginning, e.g. regarding their role, activities and environment. For example, machine learning techniques are helpful to analyze the customers and to identify preferences (Abdellatif et al. 2013 ).

Although business models are changing through the Internet of Things (Keskin and Kennedy 2015 ), it is rarely a subject of discussion when investigating smart services. Pricing is only named one time as a fundamental design dimension of a comprehensive business model (Williams et al. 2008 ).

Smart service transition

In total, 40 publications focused on smart service transition. As in other lifecycle phases, technologies and big data received the most attention in the transition phase. Technology-based services generally have great potential (Paluch and Wünderlich 2016 ). Especially context-aware tools enable smart services and help to adapt them to individual customer needs (Pistofidis and Emmanouilidis 2012 ). The implementation of a robotic system for smart home-care services illustrated that openness and flexibility is a key for successful smart service technology (Ma et al. 2010 ).

The use of large datasets, e.g. in form of real-time data, provides great potential regarding the identification of individual customer needs (Lê Tuán et al. 2012 ). But when introducing a service based on big data, several challenges and potential problems must be carefully investigated (Al Nuaimi et al. 2015 ). It must be ensured that the foundations are laid to be able to transform different types of data into storable datasets (Li et al. 2015 ). Dynamic environments require robustness when collecting, transforming and storing big data (Al Nuaimi et al. 2015 ).

The user interface plays a central role in the academic literature and was mentioned six times (e.g., Mukudu et al. 2016 ; Oh et al. 2010 ) in discussions of the smart service transition phase. All researchers that dealt with this topic agreed that an intuitively operable user interface is a key component for interaction with the customer. An interactive user interface improves efficiency and simplifies smart service transition (Pao et al. 2011 ). It is important to understand both usage behavior and a customer’s preferences and needs to implement a suitable user interface (Seeliger et al. 2015 ).

Knowledge management only occurred twice (Chu and Lin 2011 ; Li et al. 2015 ) in this phase as a topic of discussion. Knowledge is a valuable asset when offering smart services and improves the efficiency of communication and coordination (Chu and Lin 2011 ). Smart services are knowledge-intensive and require consistent knowledge management (Chu and Lin 2011 ; Li et al. 2015 ); this management includes collecting, packaging, distributing and reusing knowledge (Ferneley et al. 2002 ). That is why a knowledge management portal is a precondition to create and realize innovative services. The idea is to use knowledge management to implement a learning culture within an organization. A medical knowledge recommendation service for patients functions as starting point for designing an information framework to enable knowledge management in the health sector (Li et al. 2015 ). A prototype shows how to implement such a framework in practice. Connected to knowledge management, little attention has been paid to machine learning techniques. Kamp et al. ( 2016 ) are the only authors who recommended conducting data analyses using machine learning techniques. During the service transition phase, machine learning can help to ensure the quality of the processes.

Smart service operation

Only five articles dealt with the service operation phase, which received the least amount of attention in the literature. The three articles (Chatterjee and Armentano 2015 ; Lee et al. 2010 ; Yachir et al. 2009 ) concerned with technologies agreed that technology which operates smoothly is essential for individual and dynamic smart services. Taking the healthcare sector as an example, a system for electronic health services that bases on the Internet of Things contributes to smooth operation (Chatterjee and Armentano 2015 ). Technology that is used for monitoring especially is important to guarantee the functionality of a smart service (Lee et al. 2010 ). A real-time monitoring system enables to process sensor data during service operation (Lee et al. 2010 ). Looking at technologies for data analyses, effective fault diagnosis in wireless sensor networks is important for a successful service operation (Hamdan et al. 2012 ). Abstraction of functionalities for heterogeneous devices is required in order to support interoperability for concurrency management and failure detection in whole systems (Baldoni et al. 2010 ). Scalability of large data handling during an operation based on the number of connected devices is necessary (Lee et al. 2010 ). To compose different smart services, several parameters must be considered. One of the most important parameters is the quality of services (Yachir et al. 2009 ).

Continual smart service improvement

In total, 19 articles address the requirement to continually improve smart services. Seven of these articles (e.g., Kwak et al. 2014 ; Yu 2004 ) focused on service quality. In addition to discussing the importance of service quality, recommendations help to understand how it can be determined. For example, a mathematical model can be used to estimate the quality of a service. An algorithm was developed using previously defined preconditions and determines whether these are fulfilled (Yachir et al. 2009 ). Another approach is the provision of a list of indices. They help to measure service quality based on considerations regarding the service provider, customer, and platform (Hong et al. 2014 ).

Four articles (e.g., Böcker et al. 2010 ; Kuebel and Zarnekow 2015 ) concluded that the usage behavior should be considered when improving smart services. A system should be able to learn from usage behavior (Kynsilehto and Olsson 2011 ). Knowledge management systems that integrate domain knowledge and human expertise (Wang et al. 2011 ) enable to detect, analyze and respond to the customer’s local environment and the usage behavior (Kynsilehto and Olsson 2011 ).

Discussion of research gaps and further research topics

The discussion of opportunities for further research consists of two parts. First, the research intensity along the different topics and lifecycle phases is summarized to get an overview of the role of smart services in the literature. Second, five specific fields are discussed to provide promising starting points for further research.

Research intensity in the field of smart services

The results indicate a growing interest in the topic of smart services in the ISR field since 2010. A heat map was created using the 109 articles analyzed in the literature review (Fig.  4 ). Each article was assigned to at least one area of the heat map, and each area describes a combination of a topic and a lifecycle phase.

Summary of research intensity

a Origins of first authors. b Target groups of smart services

The colors in the heat map represent the number of articles in an area. A detailed overview of the publications and their respective areas can be found in Appendix Table 7 . As demonstrated by the heat map, research intensity in the different areas is highly variable. In many areas (the cold areas), there is hardly any research. It is necessary to examine whether these are interesting for research, because it is not sensible to look at a specific topic in each lifecycle phase; or whether they are good starting points for further research. In contrast, other areas (hot areas) include many articles. For these areas, the question is whether more research is needed; in other words, whether the area has already been comprehensively explored, or if it is a diverse area where research is still required.

In the following discussion, it is focused on five specific fields that seem to be interesting for discussion (Table 3 ). Each field integrates several areas that belong together because they address the same topic or lifecycle phase. Both topics and lifecycle phases are considered in the discussion for their potential to provide promising starting points for further research. The fields represent all areas because hot areas, cold areas and areas between these two extremes are considered.

Technologies and (big) data as key enablers in the service design and transition phases

Most research on smart services was carried out in the fields of technology and big data, particularly in the design and transition phases. In addition to existing research, a classification that can recommend technologies when a specific service is designed would be an important contribution. A discussion of the advantages and challenges of the different technologies would also be an interesting topic for further research. The design phase has a more theoretical perspective, which is why general predictions would support a broader overview of technologies in the field of smart services. Technologies that are necessary for such services can be implemented and are already well covered in the literature. However, previous studies generally discussed a specific application (e.g., Ferretti et al. 2016 ; Ma et al. 2010 ). Further analysis of technologies in the transition phase in various contexts would contribute to a broader understanding of smart service implementations.

In the service transition phase, data play an essential role, since smart services are mainly based on data. Real-time data such as sensor data, were emphasized as being interesting for satisfying individual customer needs (Lê Tuán et al. 2012 ). Specific applications were discussed that are suitable for a specific industry, such as the health sector (e.g., Alti et al. 2015 ). Prototypes were developed for a specific application and predefined types of customers, such as private end users (e.g., Mukudu et al. 2016 ). Apart from a specific industry, a structured analysis of the role of data in the design phase is missing. An investigation into the different data types and their importance for different smart services would help to structure the current knowledge of data and data analyses. In the transition phase, special attention is given to concrete examples of the implementation. Taking a more general approach on how to implement smart services on the customer side under consideration of data streams might result in a general framework.

Considering the customer

Although a characteristic of smart services is that value is co-created via interactions between the service provider and the customer, the role of the customer in the literature has not been as well explored as would be expected. Research has addressed the question of how to involve the customer in the innovation process (e.g., Gillig and Sailer 2012 ) but customer involvement in the operation and improvement phases is relatively unexamined. While exploratory case studies have already indicated the importance of the customer (e.g., Wang et al. 2012 ; Spottke et al. 2016 ), general conclusions across different applications and industries are still missing. A systematic overview of the customer’s role across all lifecycle phases of a smart service would help those engaged in the practice to improve their processes. A theoretical framework presenting the role of the customer from a more general perspective would contribute to academic knowledge.

Another aspect regarding the customer’s role would be to measure and predict their behavior. Investigating in detail how usage behavior influences smart services in all phases of the lifecycle would provide a better understanding of smart services. It would also be interesting to investigate how usage behavior can be influenced by designing smart services in a specific way. Knowledge about usage behavior would also help to improve customizable user interfaces as the user interface is a critical element for enabling interactions between customer and provider (Tien 2012 ). One possible approach would be to determine critical success factors related to user interfaces. Systemizing the current knowledge of user interfaces in connection with smart services would enable practitioners to satisfy customer needs.

Knowledge management and machine learning to gain, preserve and use information

Little attention has been paid to knowledge management and machine learning. Knowledge permits the improvement of the provision of smart services and ensures that past problems and challenges do not persist in the future. Therefore, a knowledge base should be filled, maintained and used (Moawad et al. 2015 ). A systematic investigation of the role of knowledge management for smart services would contribute to academic research. Studies that compare different approaches would close the gap between theory and practice. Another starting point for further research is the investigation of the extent to which reliable knowledge management can be a success factor for smart services.

Machine learning uses data to continually optimize the services provided. It generally contributes to satisfying individual customer requirements (Abdellatif et al. 2013 ). The topic of machine learning is diverse and methods suitable for smart services have not yet been established. Researching machine learning in the context of smart services has the potential to uncover new opportunities and may lead to new business concepts. A possible approach is to investigate as to which extend established machine learning methods are suitable for smart services.

Putting smart services into money

As economic decisions are mainly strategic decisions, business models and pricing strategies are important in the first two phases of the smart service lifecycle. Notably, business models for smart services have rarely been discussed. Business models are changing through smart service systems (Keskin and Kennedy 2015 ), which is why more attention should be devoted to this topic. One reason for the lack of research into business models for smart services could be the fact that they are still under development (Lee et al. 2016 ). It is difficult to develop a business model without knowing the concrete services that will be provided. An investigation could be carried out to determine whether there are new aspects of a smart service business model that are absent in other business models. This could result in a business model framework for smart services. Although these are relatively general approaches, they help place such services in the context of whole business models.

Pricing strategies have been emphasized and are fundamental for smart service design (Williams et al. 2008 ). Identifying the best pricing model for a specific smart service is a key for the success. Nevertheless, pricing strategies are rarely discussed in the literature. Depending on the industry concerned, pricing strategies differ. However, the industry is not the only factor that can influence the optimum pricing strategy. The type of customer (i.e. private or business customer) and the corporate strategy also play a role. A systematic investigation of possible additional influencing factors would help to introduce smart services. Based on these factors, it is possible to develop a decision support system that can identify the optimum pricing strategy for a particular service.

Using smart services in practice

Few academic studies have discussed the phase of smart service operation. Technology and especially sensors have been identified as important tools for smooth operation (Hamdan et al. 2012 ). Investigating how to run faultless and smooth technical systems for smart services may provide interesting possibilities to connect theory and practice. Researching critical success factors of technologies is interesting both for researchers and smart service providers. A model focusing on the interplay of technologies and further elements such as big data contributes to a comprehensive overview of smart services. Going further in the direction of big data and data collection in real-time, their handling in different application areas in practice may lead to a better understanding of the variety of data and how to process and store them in databases.

The attention that is paid in the smart service operation phase to topics apart from technologies and data is smaller, there are virtually no publications. Some of these cold fields are interesting starting points for further research. An example would be case studies of how security and privacy concerns are considered during the operation phase. The gained knowledge might form the basis for further investigations regarding the role of security and privacy in practice. It might be interesting to find out whether this topic forms a large part when operating a smart service. In turn, this influences the research of security and privacy in the operation phase. The same applies to knowledge management and machine learning. Conducting case studies would provide a first insight in their use in practice. Depending on the findings further research can be conducted, e.g., regarding relevance, challenges and possibilities of machine learning and knowledge management when operating smart services.

Standardization is one example that may be of less interest regarding operation. Generally, standards are determined in the design and transition phases. Although they may be used in the operation phase, this topic has lower potential resulting in new knowledge.

Many topics concerning the customer also contain interesting avenues for further research. Studying usage behavior during the smart service operation phase would be a good basis for subsequent service improvement. Studies on how to track usage behavior could lead to interesting results. Such investigations should not stop at the point of collecting information about the usage behavior, though; appropriately using this information is also an important aspect. Therefore, the operation phase should not be considered in isolation but in connection with the phase of continual improvement.

Limitations

As a starting point for the literature search, three search terms were predefined; further search terms were not included during the search process. A second search process could be conducted using additional search terms identified during the literature analysis. Similarly, the literature identification process was limited to eight different databases. Although these databases were identified to be the most important ones in the ISR field, searching additional databases could have generated additional results. Additionally, only peer-reviewed articles published in journals or conference proceedings were included in the literature review; whitepapers and book chapters were excluded. Considering these types of publications could have also yielded further results. Next, only one of the search terms contained the word “smart”. The definition for smart services was derived based on the literature identified using this search term. Consequently, literature that did not use the words “smart service” but rather “digital service” or “electronic service” was examined to determine whether the use of the terms was consistent with the definition of smart services presented in this article. The decision to include articles resulting from the second and third search terms was based on manual analysis. Considering another definition of smart services could have led to other results. Finally, the 13 identified topics were not predefined; they were determined during the literature analysis phase to ensure that all relevant topics were considered. Five fields were selected as starting points for discussion and identification of research fields. The remaining fields would likely also have provided interesting avenues for further research.

Conclusions

The literature review presented in this study provides a broad, structured overview of research in the field of smart services. A systematic search led to 109 publications dealing with smart services in the ISR field. The research context of the resulting literature is diverse and reflects the applicability of smart services in several industries (e.g., manufacturing, health, city).

To provide a better overview, the articles were categorized based on the phases of the smart service lifecycle that they covered. Next, the publications were classified according to their thematic focus. During the literature analysis phase, 13 topics were defined based on the focus of the articles (e.g., technologies, (big) data, standardization). Each publication covered at least one of these topics. A summary in the form of a heat map demonstrated the research intensity of topics regarding smart services. Possible research gaps were identified from the heat map (e.g., measuring and predicting customer’s behavior, role of knowledge management, using established machine learning methods for smart services).

The fact that several publications may have covered the same topic and lifecycle phase does not necessarily mean that no more research is required. Research is still needed in certain fields of technology and data in the context of smart services, even though these topics have been frequently discussed. A general approach considering e.g. the roles and different functions of technology and big data is still missing. In past publications topics related to technologies and big data are rather discussed for a specific field or application. Part from looking at specific forms of smart services enables to take an overall view in order to deepen the global understanding. Nevertheless, it must be ensured that the approaches are not too shallow without providing added value.

The heat map shows that only a few topics have been a major focus of research. Although the importance of the customer’s role is undisputed in the literature, this aspect has rarely been examined. On the one hand it is postulated that focusing on the customer is the key for successful smart services. On the other hand, this importance is not reflected in the number of publications dealing with the customer in the context of such services. Especially looking at the customer during the operation phase might provide interesting insights that are helpful for smart service research. Taking a broader view, overall findings of how to involve the customer and considering the usage behavior across all phases of the lifecycle are not yet investigated. This is important because it does not have to be neglected that the better the customer and the customer’s behavior is understood, the better and more precise appropriate smart services can be provided.

Economic aspects, such as the development of business models or pricing strategies, have rarely been discussed in the literature. For providers it is valid that offering smart services is only reasonable if it is profitable. But how to identify profitability and examining which information is necessary to make a feasibility study is yet to be explored. Investigating in what sense known business models are changing through smart services helps to understand how they work in practice.

The research of knowledge management and machine learning in the field of smart services stands mostly at the very beginning. This is interesting because especially for this type of service data interpretation is important. Smart services are frequently adapted to meet the customer’s requirements. Knowledge gained from past events, employees and further sources enables to continually improve them. Machine learning enables to turn data into information what also contributes to continual improvement. As smart services are a relatively new development, best practice approaches are not yet established. Research that investigates suggestions of how knowledge management and machine learning for smart services can be designed is interesting both for theory and practice. Related to the design it is also necessary to investigate how to embed it into the different contexts.

Not only the thematic focus is limited to only a few topics, but there is also a clear focus along the lifecycle. While both the design phase and the implementation phase have been frequently discussed, the operation of smart services has been neglected. This phase seems to be more practice-oriented than the other phases which provides great potential for research. Case studies are an appropriate opportunity to gain an insight into what the most important topics in practice are. The results help to improve theoretically developed results, such as models or frameworks. Therefore, detailed investigations are useful to both theory and practice.

To sum up, this article presented and discussed concrete ideas for further research that will help to draw a clearer and more comprehensive picture of smart services in the academic literature as well as for practical applications.

Abbate, T., de Luca, D., Gaeta, A., Lepore, M., Miranda, S., & Perano, M. (2015). Analysis of open innovation intermediaries platforms by considering the smart service system perspective. Procedia Manufacturing, 3 , 3575–3582.

Article   Google Scholar  

Abdellatif, A., Amor, N. B., & Mellouli, S. (2013). An intelligent framework for e-government personalized services. In Proceedings of the 14th Annual International Conference on Digital Government Research , Quebec City, Canada, June 17–20, 2013 (pp. 120–126).

Adeleke, I. A., & AbdulRahman, A. (2011). Co-creation of value: Applying the paradigm to government e-service. In Proceedings of the 2nd International Conference on Research and Innovation in Information Systems , Kuala Lumpur, Malaysia, November 23–24, 2011 (pp. 201–206).

Al Nuaimi, E., Al Neyadi, H., Mohamed, N., & Al-Jaroodi, J. (2015). Applications of big data to smart cities. Journal of Internet Services and Applications, 6 (25), 1–15.

Google Scholar  

Alahmadi, M., & Qureshi, M. R. J. (2015). Improved model to test applications using smart services. Science International, 27 (3), 2275–2280.

Allmendinger, G., & Lombreglia, R. (2005). Four strategies for the age of smart services. Harvard Business Review, 83 (10), 131–145.

Alti, A., Laborie, S., & Roose, P. (2015). Cloud semantic-based dynamic multimodal platform for building mHealth context-aware services. In Proceedings of the 11th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications , Abu Dhabi, United Arab Emirates, October 19-21, 2015 (pp. 357–364).

Anthopoulos, L., & Fitsilis, P. (2014). Exploring architectural and organizational features in smart cities. In Proceedings of the 16th International Conference on Advanced Communication Technology , Pyeong Chang, South Korea, February 16-19, 2014 (pp. 190–194).

Anthopoulos, L., Janssen, M., & Weerakkody, V. (2016). Smart service portfolios: Do the cities follow standards? In Proceedings of the 25th International Conference Companion on World Wide Web . Montréal, Canada, April, 11-15 , 2016 (pp. 357–362).

Anttiroiko, A.-V., Valkama, P., & Bailey, S. J. (2014). Smart cities in the new service economy: Building platforms for smart services. AI & SOCIETY, 29 (3), 323–334.

Baldoni, R., Mecella, M., & Querzoni, L. (2010). Smart services for home automation. Managing concurrency and failures: New wine in old bottles? In Proceedings of the 5th International Conference on Pervasive Computing and Applications , Maribor, Slovenia, December 1–3, 2010.

Barile, S., & Polese, F. (2010). Smart service systems and viable service systems: Applying systems theory to service science. Service Science, 2 (1–2), 21–40.

Batubara, F. R. (2015). Balancing functionality, risk, and cost in smart service networks. In Proceedings of the ICSOFT Doctoral Consortium , Colmar, France, July 20–23, 2015 (pp. 7–14).

Bedogni, L., Bononi, L., Di Felice, M., D'Elia, A., Mock, R., Montori, F., … Vergari, F. (2013). An interoperable architecture for Mobile smart services over the internet of energy. In Proceedings of the 14th IEEE International Symposium on ‘A World of Wireless, Mobile and Multimedia Networks’ , Madrid, Spain, June 4–7, 2013 (pp. 298–303).

Berna-Martinez, J., Macia-Perez, F., Ramos-Morillo, H., & Gilart-Iglesias, V. (2006). Distributed robotic architecture based on smart services. In Proceedings of the IEEE International Conference on Industrial Informatics , Singapore, Singapore, August 16–18, 2006 (pp. 480–485).

Bianchi, M. (2015). Willingness to believe and betrayal aversion: The special role of Trust in art Exchanges. Journal of Cultural Economics, 39 (2), 133–151.

Böcker, M., Rodriguez-Ascaso, A., Huttenrauch, H., Schneider, M., Pluke, M., & Zetterstrom, E. (2010). Identifying enablers for future e-services. In Proceedings of the 4th International ICST Conference on Pervasive Computing Technologies for Healthcare , Munich, Germany, March 22-25, 2010.

Brotman, R., Burleson, W., Forlizzi, J., Heywood, W., & Lee, J. (2015). Building change: Constructive design of smart domestic environments for goal achievement. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems , Seoul, Republic of Korea, April 18-23, 2015 (pp. 3083–3092).

Buchanan, S., & McMenemy, D. (2012). Digital service analysis and design: The role of process modelling. International Journal of Information Management, 32 (3), 251–256.

Buettner, R. (2016). Innovative personality-based digital services. In Proceedings of the Pacific Asia Conference on Information Systems , Chiayi, Taiwan, June 27 - July 1, 2016 (paper 278).

Byun, J., & Park, S. (2011). Development of a self-adapting intelligent system for building energy saving and context-aware smart services. IEEE Transactions on Consumer Electronics, 57 (1), 90–98.

Calza, F., Gaeta, M., Loia, V., Orciuoli, F., Piciocchi, P., Rarità, L., Spohrer, J., & Tommasetti, A. (2015). Fuzzy consensus model for governance in smart service systems. Procedia Manufacturing, 3 , 3567–3574.

Cardozo, N., & Clarke, S. (2015). Language design for developing smart adaptive services. In Proceedings of the 1st IEEE International Smart Cities Conference , Guadalajara, Mexico, October 25–28, 2015.

Cater-Steel, A., Zarnekow, R., & Wulf, J. (2011). IT service management in the academic curriculum: Comparing an Australian and German experience. In Proceedings of the 15th Pacific Asia Conference on Information Systems , Brisbane, Australia, July 7–11, 2011 (Paper 33).

Cellary, W. (2013). Smart governance for smart industries. In Proceedings of the 7th International Conference on Theory and Practice of Electronic Governance Seoul , Republic of Korea, October 22-25, 2013 (pp. 91–93).

Chatterjee, P., & Armentano, R. L. (2015). Internet of things for a smart and ubiquitous eHealth system. In Proceedings of the 7th International Conference on Computational Intelligence and Communication Networks , Jabalpur, India, December 12–14, 2015 (pp. 903–907).

Chen, J., Yuan, L., & Mingins, C. (2012). NeSD: Towards a new e-services development framework. In Proceedings of the 11th Wuhan International Conference on e-Business , Wuhan, China, May 26–27, 2012 (pp. 555–562).

Chu, Y.-Y., & Lin, S.-W. (2011). Network ontology and dynamics analysis for collaborative innovation in digital services. In Proceedings of the Portland International Center of Management of Engineering and Technology , Portland, OR, USA, July 31 - August 4, 2011.

Ciortea, A., Boissier, O., Zimmermann, A., & Florea, A. M. (2016). Responsive decentralized composition of service mashups for the internet of things. In Proceedings of the 6th International Conference on the Internet of Things , Stuttgart, Germany, November 07–09, 2016 (pp. 53–61).

Dawid, H., Decker, R., Hermann, T., Jahnke, H., Klat, W., König, R., & Stummer, C. (2016). Management science in the era of smart consumer products: Challenges and research perspectives. Central European Journal of Operations Research, 25 (1), 203–230.

De Oliveira, V. C., & Silva, J. R. (2015). A service-oriented framework to the design of information system service. Journal of Service Science Research, 7 (2), 55–96.

Delfanti, M., Esposito, G., Olivieri, V., & Zaninelli, D. (2015). SCUOLA project: The ‘hub of smart services’ for cities and communities. In Proceedings of the 4th International Conference on Renewable Energy Research and Applications , Palermo, Italy, November 22-25, 2015 (pp. 1502–1506).

Demirkan, H., Bess, C., Spohrer, J., Rayes, A., Allen, D., & Moghaddam, Y. (2015). Innovations with smart service systems: Analytics, big data, cognitive assistance, and the internet of everything. Communications of the Association for Information Systems, 37 (1), 733–752.

Domingues, J., Damaso, A., & Rosa, N. (2010). Smart: Service model for integrating wireless sensor networks and the internet. In Proceedings of the 16th IEEE International Conference on Parallel and Distributed Systems , Shanghai, China, December 8-10, 2010 (pp. 365–372).

Fan, Z., Chen, Q., Kalogridis, G., Tan, S., & Kaleshi, D. (2012). The power of data: Data analytics for M2M and smart grid. In Proceedings of the 3rd IEEE PES Innovative Smart Grid Technologies Europe , Berlin, Germany, October 14–17, 2012.

Fan, M., Sun, J., Zhou, B., & Chen, M. (2016). The smart health initiative in China: The case of Wuhan, Hubei Province. Journal of Medical Systems, 40 (3), 62.

Ferneley, E., Wetherill, M. & Rezgui, Y. (2002). Toward the construction knowledge economy: The E-Cognos project. In Proceedings of the 10th European Conference on Information Systems , Gdańsk, Poland, June 6–8, 2002 (pp. 1508–1516).

Ferretti, S., & D'Angelo, G. (2016). Smart shires: The revenge of Countrysides. In Proceedings of the 21st IEEE Symposium on Computers and Communication , Messina, Italy, June 27–30, 2016 (pp. 756–759).

Ferretti, S., D'Angelo, G., & Ghini, V. (2016). Smart multihoming in smart shires: Mobility and communication Management for Smart Services in Countrysides. In Proceedings of the 21st IEEE Symposium on Computers and Communication , Messina, Italy, June 27-30, 2016 (pp. 970–975).

Fischbach, M. M., Puschmann, T. & Alt, R. (2013). Enhancing Soa with service lifecycle management - towards a functional reference model. In Proceedings of the 21st European Conference on Information Systems , Utrecht, Netherlands, June 5-8, 2013 (Paper 170).

Gavrilova, T., & Kokoulina, L. (2015). Smart services classification framework. In Proceedings of the Federated Conference on Computer Science and Information Systems , Lodz, Poland, September 13-16, 2015 (pp. 203–207).

Georgakopoulos, D., & Jayaraman, P. P. (2016). Internet of things: From internet scale sensing to smart services. Computing, 98 (10), 1041–1058.

Gerke, K., & Tamm, G. (2009). Continuous quality improvement of IT processes based on reference models and process mining. In Proceedings of the Americas Conference on Information Systems , San Francisco, USA , August 6-9, 2009 (Paper 786).

Geum, Y., Jeon, H., & Lee, H. (2016). Developing new smart services using integrated morphological analysis: Integration of the market-pull and technology-push approach. Service Business, 10 (3), 531–555.

Gillig, H., & Sailer, K. (2012). User involvement in the innovation process: Development of a framework for e-services. In In Proceedings of the 18th International Conference on Engineering, Technology and Innovation, Munich, Germany, June 18–20 (p. 2010).

Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: Foundations and developments. Electronic Markets, 25 (3), 179–188.

Hamdan, D., Aktouf, O.-E.-K., Parissis, I., Hijazi, A., Sarkis, M., & El Hassan, B. (2012). SMART service for fault diagnosis in wireless sensor networks. In Proceedings of the 6th International Conference on Next Generation Mobile Applications, Services and Technologies , Paris, France, September 12-14, 2012 (pp. 211–216).

He, J., Zhang, Y., Huang, G., & Cao, J. (2012). A smart web service based on the context of things. ACM Transactions on Internet Technology, 11 (3), 1–23.

Holgado, M., & Macchi, M. (2014). Exploring the role of e-maintenance for value creation in service provision. In Proceedings of the International ICE Conference on Engineering, Technology and Innovation , Bergamo, Italy, June 23-25, 2014 (pp. 174–183).

Hong, L., She, Z., Ye, J., & Chen, X. (2014). An exploration research of establishing E-service dimensions model by building up service quality indexes based on process interaction. In Proceedings of the 11th International Conference on Service Systems and Service Management , Beijing, China, June 25–27, 2014.

Jerald, A. V., Rabara, S. A., & Bai, D. P. (2016). Secure IoT architecture for integrated smart services environment. In Proceedings of the 3rd International Conference on Computing for Sustainable Global Development , New Delhi, India, March 16-18, 2016 (pp. 800–805).

Kamp, B., Ochoa, A., & Diaz, J. (2016). Smart servitization within the context of industrial user-supplier relationships: Contingencies according to a machine tool manufacturer. International Journal on Interactive Design and Manufacturing 2016 .

Kennedy, D., & Keskin, T. (2016). A pricing model for the internet of things enabled smart service systems. In Proceedings of the 49th Hawaii International Conference on System Sciences , Kauai, HI, USA, January 5-8, 2016 (pp. 1782–1789).

Keskin, T., & Kennedy, D. (2015). Strategies in smart service systems enabled multi-sided markets: Business models for the internet of things. In Proceedings of the 48th Hawaii International Conference on System Sciences , Kauai, Hawaii, USA, January 5-8, 2015 (pp. 1443–1452).

Kim, M. J., Lee, J. H., Wang, X., & Kim, J. T. (2015). Health smart home services incorporating a MAR-based energy consumption awareness system. Journal of Intelligent and Robotic Systems, 79 (3), 523–535.

Knote, R., & Blohm, I. (2016). It's not about having ideas - It's about making ideas happen! Fostering exploratory innovation with Intrapreneur accelerator. In Proceedings of the 24th European Conference on Information Systems , Instanbul, Turkey, June 12-15, 2016 (Paper 59).

Kölmel, B., & Bulander, R. (2015). Sustainability and competitiveness through digital product-service-systems. In Proceedings of the eChallenges e-2015 Conference , Vilnius, Lithuania, November 25-26, 2015.

Korzun, D. G., Borodin, A. V., Timofeev, I. A., Paramonov, I. V., & Balandin, S. I. (2015). Digital assistance Services for Emergency Situations in personalized Mobile healthcare: Smart space based approach. In Proceedings of the International Conference on Biomedical Engineering and Computational Technologies , Novosibirsk, Russia, October 28 and 30, 2015 (pp. 62–67).

Koukal, A., Gleue, C., & Breitner, M. H. (2014). Enhancing literature review methods – Evaluation of a literature search approach based on latent semantic indexing. In Proceedings of the 35th International Conference on Information Systems , Auckland, New Zealand, December 14–17, 2014.

Kryvinska, N., Strauss, C., Auer, L., & Zinterhof, P. (2008). Conceptual framework for services creation/development environment in telecom domain. In Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services , Linz, Austria, November 24-26, 2008 (pp. 324–331).

Kuebel, H., & Zarnekow, R. (2015). Exploring platform adoption in the smart home case. In Proceedings of the 36th International Conference on Information Systems , Fort Worth, TX, USA, December 13–16, 2015.

Kwak, J.-Y., Kim, S.-T., Lee, K. H., & Yang, S. (2014). Service-oriented networking platform on smart devices. IET Communications, 9 (3), 429–439.

Kynsilehto, M., & Olsson, T. (2011). Intelligent ambient technology– friend or foe? In Proceedings of the 15th International Academic MindTrek Conference , Tampere, Finland, September 28-30, 2011 (pp. 99–106).

Kynsilehto, M., & Olsson, T. (2012). Checkpoints, hotspots and standalones – Placing smart services over time and place. In Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design , Copenhagen, Denmark, October 14-17, 2012 (pp. 209–218).

Lê Tuán, A., Mau Quoc, H. N., Serrano, M., Hauswirth, M., Soldatos, J., Papaioannou, T., & Aberer, K. (2012). Global sensor modeling and constrained application methods enabling cloud-based open space smart services. In Proceedings of the 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing , Fukuoka, Japan, September 4-7, 2012 (pp. 196–203).

Lebedev, N., Timofeev, I., & Zavialova, I. (2016). Design and implementation of the first aid assistance service based on smart-M3 platform. In Proceedings of the 18th Conference of Open Innovations Association and Seminar on Information Security and Protection of Information Technology , St. Petersburg, Russia, April 18-22, 2016 (pp. 174–180).

Lee, B.-H., Lim, S.-H., & Kim, J.-H. (2010). Scalable real-time monitoring system for ubiquitous smart space. Information Processing Letters, 110 (8–9), 294–299.

Lee, J. Y., Kim, M. K., La, H. J., & Kim, S. D. (2012). A software framework for enabling smart services. In Proceedings of the 5th IEEE International Conference on Service-Oriented Computing and Applications , Taipei, Taiwan, December 17-19, 2012 (pp. 55–62).

Lee, N., Lee, H., Lee, H., & Ryu, W. (2015). Implementation of smart home service over web of object architecture. In Proceedings of the 6th International Conference on Information and Communication Technology Convergence , Jeju Island, South Korea, October 28-30, 2015 (pp. 1215–1219).

Lee, H., Seol, H., Min, H., & Geum, Y. (2016). The identification of new service opportunities: A case-based morphological analysis. Service Business, 11 (1), 191–206.

Lesjak, C., Ruprechter, T., Bock, H., Haid, J., & Brenner, E. (2014). ESTADO — Enabling smart services for industrial equipment through a secured, transparent and ad-hoc data transmission online. In Proceedings of the 9th International Conference for Internet Technology and Secured Transactions , London, United Kingdom, December 8-10, 2014 (pp. 171–177).

Lesjak, C., Hein, D., Hofmann, M., Maritsch, M., Aldrian, A., Priller, P., … Pregartner, G. (2015). Securing smart maintenance services: Hardware-security and TLS for MQTT. In Proceedings of the 13th International Conference on Industrial Informatics , Cambridge, United Kingdom, July 22-24, 2015 (pp. 1243–1250).

Li, Y., Wan, Z., Huang, J., Chen, J., Huang, Z., & Zhong, N. (2015). A smart hospital information system for mental disorders. In Proceedings of the 2015 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, Singapore, Singapore, December 6-9, 2015 (pp. 321–324).

Ma, X., Qian, K., Dai, X., Fang, F., & Xing, Y. (2010). Framework design for distributed service robotic systems. In Proceedings of the 5th IEEE Conference on Industrial Electronics and Applications , Taichung, Taiwan, June 15-17, 2010 (pp. 234–239).

Maleshkova, M., Philipp, P., Sure-Vetter, Y., & Studer, R. (2016). Smart web services (SmartWS) – The future of services on the web. The IPSI BgD Transactions on Advanced Research, 12 (1), 15–26.

Martin, N., Gregor, S., & Hart, D. (2004). Using a common architecture in Australian e-government - the case of smart service Queensland. In Proceedings of the 6th International Conference on Electronic Commerce , Delft, Netherlands, October 25-27, 2004 (pp. 516–525).

Massink, M., Harrison, M., & Latella, D. (2010). Scalable analysis of collective behaviour in smart service systems. In Proceedings of the ACM Symposium on Applied Computing , Sierre, Switzerland, March 22-26, 2010 (pp. 1173–1180).

Mathes, M., Stoidner, C., Schwarzkopf, R., Heinzl, S., Dörnemann, T., Dohmann, H., & Freisleben, B. (2009). Time-constrained services: A framework for using real-time web Services in Industrial Automation. Service Oriented Computing and Applications, 3 (4), 239–262.

Mecella, M., & Pernici, B. (2001). Designing wrapper components for e-Services in Integrating Heterogeneous Systems. The VLDB Journal, 10 (1), 2–15.

Mihaylov, M., Mihovska, A., Kyriazakos, S., & Prasad, R. (2015). Interoperable eHealth platform for personalized smart services. In Proceedings of the IEEE International Conference on Communication Workshop , London, UK, June 8-12, 2015 (pp. 240–245).

Mihovska, A., Kyriazakos, S., Prasad, R., Pejanovic-Djurisic, M., & Poulkov, V. (2015). Integration of wireless and data technologies for personalized smart applications. In Proceedings of the 14th Wireless Telecommunications Symposium (WTS) , New York City, NY, USA, April 15-17, 2015 (pp. 304–311).

Mo, T., Li, W., Chu, W., & Wu, Z. (2010). CABS 3 : Context-awareness based smart service system. In Proceedings of the 6th International Conference on Wireless Communications Networking and Mobile Computing , Chengdu City, China, September 23-25, 2010 (pp. 1834–1837).

Moawad, A., Hartmann, T., Fouquet, F., Klein, J., & Le Traon, Y. (2015). Adaptive blurring of sensor data to balance privacy and utility for ubiquitous services. In Proceedings of the 30th Annual ACM Symposium on Applied Computing , Salamanca, Spain, April 13-17, 2015 (pp. 2271–2278).

Mukudu, N., Ventura, N., Mwangama, J., Elmangoush, A., Steinke, R., & Magedanz, T. (2016). Prototyping smart city applications over large scale M2M testbed. In Proceedings of the 2016 IST-Africa week conference, Durban, South Africa, May 11-13, 2016.

Nakajima, H., Shiga, T., & Hata, Y. (2012). Systems health care: Coevolutionary integration of smart devices and smart services. In Proceedings of the Annual SRII Global Conference , San Jose, CA, USA, July 24-27, 2012 (pp. 231–236).

Niemann, M., Appel, M., Repp, N., & Steinmetz, R. (2009). Towards a consistent service lifecycle model in service governance. In Proceedings of the Americas Conference on Information Systems, San Francisco, USA, August, 6-9 , 2009 (Paper 109).

Oh, J. S., Park, J. S., & Kwon, J. R. (2010). Ubiquitous infrastructure and smart service on City gas environments in Korea. In Proceedings of the 5th International Conference on Future Information Technology , Busan, South Korea, May 21–23, 2010.

Oslislo, L. J., Talevski, A., & Karduck, A. P. (2011). SMART CAMP: Benefits of media and smart service convergence. In Proceedings of the 25th IEEE Workshops of International Conference on Advanced Information Networking and Applications , Biopolis, Singapore, March 22-25, 2011 (pp. 763–768).

Paluch, S., & Wünderlich, N. V. (2016). Contrasting risk perceptions of technology-based service innovations in inter-organizational settings. Journal of Business Research, 69 (7), 2424–2431.

Pao, S.-Y., Reben, A. J., & Rayes, A. (2011). MoSS: Mobile smart services for ubiquitous network management. In Proceedings of the International Conference on Collaboration Technologies and Systems , Philadelphia, PA, USA, May 23-27, 2011 (pp. 48–52).

Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Sensing as a service model for smart cities supported by internet of things. Transactions on Emerging Telecommunications Technologies, 25 (1), 81–93.

Pistofidis, P., & Emmanouilidis, C. (2012). Developing advanced context aware tools for mobile maintenance. In Proceedings of the 2nd IFAC Workshop on Advanced Maintenance Engineering, Services and Technology , Sevilla, Spain, November 22-23, 2012 (pp. 133–138).

Priller, P., Aldrian, A., & Ebner, T. (2014). Case study: From legacy to connectivity: Migrating industrial devices into the world of smart services. In Proceedings of the 19th IEEE Emerging Technology and Factory Automation , Barcelona, Spain, September 16-19, 2014.

Ren, J., Ma, J., Huang, R., Jin, Q., & Chen, Z. (2014). A management system for cyber individuals and heterogeneous data. In Proceedings of the 11th IEEE International Conference on Ubiquitous Intelligence and Computing and 11th IEEE International Conference on Autonomic and Trusted Computing and 14th IEEE International Conference on Scalable Computing and Communications and Its Associated Workshops , Bali, Indonesia, December 9-12, 2014 (pp. 88–95).

Roduner, C., & Langheinrich, M. (2010). BIT — A framework and architecture for providing digital services for physical products. In Proceedings of the Internet of Things Conference , Tokyo, Japan, November 29 - December 1, 2010, (pp. 170–177).

Sakakibara, S., Saiki, S., Nakamura, M., & Matsumoto, S. (2016). Indoor environment sensing service in smart City using autonomous sensor box. In Proceedings of the 15th IEEE/ACIS International Conference on Computer and Information Science , Okayama, Japan, June 26–29, 2016.

Seeliger, R., Krauss, C., Wilson, A., Zwicklbauer, M., & Arbanowski, S. (2015). Towards personalized Smart City guide services in future internet environments. In Proceedings of the 24th International Conference on World Wide Web , Florence, Italy, May 18-22, 2015 (pp. 563–568).

Singhai, A. J., & Faizan, D. (2016). Transition of Indian ICT processes to smart E-services-way ahead. In Proceedings of the 5th International Conference on Advances in Computing, Communications and Informatics , Jaipur, India, September 21-24, 2016 (pp. 1479–1486).

Smirnov, A. V., Kashevnik, A. M., Ponomarev, A. V., & Savosin, S. V. (2015). Ontology-based organization of interactions between services in the smart space for hybrid system control. Scientific and Technical Information Processing, 42 (5), 367–374.

Smith, G., Ofe, H. A., & Sandberg, J. (2016). Digital service innovation from open data: Exploring the value proposition of an open data marketplace. In Proceedings of the 49th Hawaii International Conference on System Sciences , Kauai, HI, USA, January 5-8, 2016 (pp. 1277–1286).

Sohn, I., & Gwak, D. (2016). Single-RF MIMO-OFDM system with beam switching antenna. Journal on Wireless Communications and Networking , 2016(37).

Son, J.-Y., Park, J.-H., Moon, K.-D., & Lee, Y.-H. (2011). Resource-aware smart home management system by constructing resource relation graph. IEEE Transactions on Consumer Electronics, 57 (3), 1112–1119.

Spottke, B., Eck, A., & Wulf, J. (2016). A socio-technical approach to study consumer-centric information systems. In In Proceedings of the 37th International Conference on Information Systems, Dublin, Ireland, December 11–14 (p. 2016).

Strüker, J., & Kerschbaum, F. (2012). From a barrier to a bridge: Data-privacy in deregulated smart grids. In In Proceedings of the 33rd International Conference on Information Systems, Orlando, FL, USA, December 16–19 (p. 2012).

Tantatsanawong, P., Kawtrakul, A., & Lertwipatrakul, W. (2011). Enabling future education with smart services. In Proceedings of the Annual SRII Global Conference , San Jose, CA, USA, March 29 - April 2, 2011 (pp. 550–556).

Theocharis, S. A., & Tsihrintzis, G. A. (2013). Personalization as a means to improve e-services. In Proceedings of the 2nd International Conference on Computer, Information and Telecommunication Systems , Piraeus-Athens, Greece, May 7–8, 2013.

Tianyong, W., Xu Zhengliang, X., & Gel, G. (2006). Development and strategy of knowledge management for e-services. In Proceedings of the International Conference on Service Systems and Service Management , Troyes, France, October 25–27, 2006.

Tien, J. M. (2012). The next industrial revolution: Integrated services and goods. Journal of Systems Science and Systems Engineering, 21 (3), 257–296.

Vom Brocke, J., Simons, A., Niehaves, B., Reimer, K., Plattfaut, R., & Cleven, A. (2009). Reconstructing the Giant: On the importance of rigour in documenting the literature search process. In Proceedings of the 17th European Conference on Information Systems , Verona, Italy, June 8-10, 2009 (pp. 2206–2217).

Wang, C., Akella, R., Ramachandran, S., & Hinnant, D. (2011). Knowledge extraction and reuse within "smart" service centers. In Proceedings of the Annual SRII Global Conference , San Jose, CA, USA, March 29 - April 2, 2011 (pp. 163–176).

Wang, Y., Taher, Y., & van den Heuvel, W.-J. (2012). Towards smart service networks: An interdisciplinary service assessment metrics. In Proceedings of the 16th IEEE International Enterprise Distributed Object Computing Conference Workshops , Beijing, China, September 10-14, 2012 (pp. 94–103).

Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. Management Information Systems Quarterly, 26 (2), xiii–xxiii.

Weijie, C., Tong, M., Jie, C., Yuan, W., Jingmin, X., Weiping, L., & Huiping, L. (2012). A context-aware services development model. In Proceedings of the International Joint Conference on Service Sciences , Shanghai, China, May 24-26, 2012 (pp. 194–199).

Westwood, J. A., & Cazier, J. A. (2016). Work-life optimization: Using big data and analytics to facilitate work-life balance. In Proceedings of the 49th Hawaii International Conference on System Sciences , Kauai, HI, USA, January 5-8, 2016 (pp. 1701–1709).

Wiesner, S., Freitag, M., Westphal, I., & Thoben, K.-D. (2015). Interactions between service and product lifecycle management. In Proceedings of the 7th Industrial Product-Service Systems Conference , saint-Etienne, France, May 21-22, 2015 (pp. 36–41).

Williams, K., Chatterjee, S., & Rossi, M. (2008). Design of Emerging Digital Services: A taxonomy. European Journal of Information Systems, 17 (5), 505–517.

Wünderlich, N. V., von Wangenheim, F., & Bitner, M. J. (2012). High tech and high touch: A framework for understanding user attitudes and behaviors related to smart interactive services. Journal of Service Research, 16 (1), 3–20.

Wünderlich, N. V., Heinonen, K., Ostrom, A. L., Patricio, L., Sousa, R., Voss, C., & Lemmink, J. G. (2015). ‘Futurizing’ smart service: Implications for service researchers and managers. Journal of Services Marketing, 29 (6/7), 442–447.

Yachir, A., Tari, K., Amirat, Y., Chibani, A., & Badache, N. (2009). QoS based framework for ubiquitous robotic services composition. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems , St. Louis, MO, USA, October 10-15, 2009 (pp. 2019–2026).

Yang, Q. Z., Miao, C. Y., & Shen, Z. Q. (2015). Digital services innovation for aging-in-place. In Proceedings of the 22nd International Conference on Industrial Engineering and Engineering Management , Singapore, Singapore, December 6-9, 2015 (pp. 569–573).

Yong, M., & Hui-ying, C. (2013). Study on the value promotion and development strategy of smart tourism. In Proceedings of the 12th Wuhan International Conference on e-Business , Wuhan, China, May 25-26, 2013 (paper 45).

Yu, C.-C. (2004). A web-based consumer-oriented intelligent decision support system for personalized e-services. In Proceedings of the 6th International Conference on Electronic Commerce , Delft, Netherlands, October 25-27, 2004 (pp. 429–437).

Zhang, J., & Qi, B. (2011). Studies on interactive service platform for smart grid. In Proceedings of the IEEE PES Innovative Smart Grid Technologies Asia, Perth, Australia, November, 13-16 , 2011 (pp. 213–216).

Zo, H. (2003). Personalization vs. customization: Which is more effective in E-services? In Proceedings of the 9th Americas Conference on Information Systems , Tempa, FL, USA, August 4-6, 2003 (Paper 32).

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Customer-Focused Service Guarantees and Transparency Practices (2018)

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10 The Need for Customer-Focused Public Transit Ensuring customer satisfaction is essential for businesses because a customer’s satisfaction with a particular transaction often determines whether the customer will return and whether the customer will describe the interaction in a positive or negative way to others. Particularly true for private sector and for-profit organizations, customers have choices and may choose to purchase goods and services elsewhere if they are not satisfied. If customers choose competitors, companies will see declines in revenue and profit and may ultimately even have to close. In the public sector, the connection between customer satisfaction and organizational stability and financial standing may not be as direct—particularly in public services that are largely, if not wholly, provided by a single public entity. When there is only one service provider (or even a handful of them), “customers cannot express their dissatisfaction with the service . . . by switching to another operator” (Schiefelbusch 2009, p. 6). However, there are several services and regions in which both the public and private sectors essentially compete for customers (e.g., package delivery and transportation). In the services where competition exists, public agencies may have to continually win customers’ loyalty. The increasing ability for customers to make alternative choices is especially apparent in the public transit space, where shared mobility and similar for-hire transportation services have entered the marketplace. Advances in technology and social media also have provided customers of both public and private services increased capability to voice concerns, provide reviews, and rate service providers. Because those venues for comments and ratings may be completely open to the public and sharable with others, the damage of a negative review can have far-reaching implications. Alternatives to public transit and easy tools for sharing personal experiences can provide significant impetus for public transit agencies to become more customer focused. However, there is value simply in improving the satisfaction of citizens who interact with public services— high-quality public services may help improve citizens’ quality of life. Being customer focused helps produce a positive experience for pub- lic transit passengers—a stated or unstated goal of many public transit agencies. Customer-focused public transit can take different forms and be manifested in many different types of programs and initiatives. Examples include: • Implementing mystery shopper programs (Miller 1995), • Improving the quality of transit service based on customer feedback (Miller 1995, Foote 2004), • Measuring performance from a passenger’s perspective (Kesten and Ögüt 2014, Morton et al. 2016). C h a p t e r 2 Literature Review There is value in improving the satisfaction of citizens who interact with public services—high-quality public services may help improve citizens’ quality of life.

Literature review 11 • Providing a service guarantee—including money-back guarantees (Giard 2002, Lidén 2004), and • Increasing agency accountability to the customer and the public (Adams 2015). Although there are several examples of transit agencies that have worked to become more customer focused, there is little understanding of the actual impacts customer-focused public transit initiatives have on public transit customers and transit providers. TCRP Synthesis 45 found that “the public transportation industry uses relatively few spe- cific methods to achieve customer satisfaction” (Potts 2002, p. 26). Of the 33 transit agencies that responded to the TCRP Synthesis 45 survey, only nine used some form of service guarantee, and two had a passen- ger bill of rights. The synthesis made no mention of customer-focused transparency as a customer-focused practice. This report focuses on two particular customer-focused practices and their relationship with each other: service guarantees and customer-focused transparency. Although these two practices are not codependent, they go well together: service guarantees provide a way for a transit agency to address an individual’s specific negative experience, and transparency provides a mechanism for a transit agency to present its efforts to improve its aggregate performance to a broad spectrum of public transit stakeholders (customers and noncustomers). Service Guarantees In most cases, customers have an implicit expectation that they should have a positive experi- ence with a service provider or get some remuneration, if not all of their money back. This is true for restaurants, retailers, services, and even vending machines. Companies have often surpassed this implicit expectation and provided service guarantees—explicit promises to customers that delineate exactly what customers can expect in the event services do not meet expectations. Although research on service guarantees has been under way for several decades, there is still significant debate on the exact definition and purpose of a service guarantee. Generally speaking, a service guarantee refers to a promise made by a service provider that a customer will experience a certain level or quality of service. A service guarantee may include as a part of that promise some form of recompense or remuneration for customers whose experi- ences do not meet the minimum level of quality promised by the service guarantee. The Role of Service Guarantees Lidén (2004) presents a summary of perspectives on the roles that service guarantees can serve (see Figure 3). First, some researchers state that a service guarantee’s main feature is to “restore customer satisfaction after a service failure” (p. 10). Second, other researchers state that service guarantees help customers understand the quality of the service or product before a pur- chase or utilization. In this case, the service guarantee helps customers choose a service provider and becomes a marketing tool. Third, some researchers blend both the marketing and restorative features of service guarantees and argue that guarantees help increase the appeal of a product or service before purchase and help recover customer satisfaction after a negative experience. Research suggests that service guarantees help increase not only customer confidence when deciding whether to make a purchase or pay for a service (Hogreve and Gremler 2009, Neugebauer 2009) but also satisfaction of customers who invoke the guarantee (Giard 2002, Lidén 2004). Lidén (2004) found evidence that the satisfaction of public transit customers who TCRP Synthesis 45 found that “the public transportation industry uses relatively few specific methods to achieve customer satisfaction” (Potts 2002, p. 26).

12 Customer-Focused Service Guarantees and transparency practices invoked the service guarantee and had their claim resolved in their favor were more satisfied than they were before the service problem had occurred. Service guarantees not only work directly on customer satisfaction but also indirectly improve customer satisfaction by focusing transit agency employees on improving service quality. That is, service guar- antees can serve as a quality control tool (Neugebauer 2009) and an impetus behind quality improvements (Giard 2002, Lidén 2004). For example, Giard (2002) stated, “The arrival of the [service guarantee] has mobilized [Société de Transport de Laval]. . . . In less than two years, the number of late bus arrivals has been more than halved, while the time required for processing applications and claims has been slashed by 40%” (p. 6). (Société de Transport de Laval [STL] is a public transit operator in the city of Laval, Quebec, Canada.) The available evidence suggests that service guarantees—particularly those that provide for customer remuneration—can play an important role in public transit by: • Helping decrease the perceived risk of choosing to take transit, • Providing a tool for transit agencies to maintain and improve public transit customer satisfaction when there are service problems, and • Improving service quality through increased transit agency employee commitments to quality. Structure of Service Guarantees There is debate not only on the main purpose and role of service guarantees but also on the best specific contents of guarantees. As discussed by McDougall et al. (1998), service guarantees contain both a coverage statement (what services or qualities of service are guaranteed) and an Figure 3. Potential roles of service guarantees in both prepurchase and postpurchase decisions. Source: Created by author, based on Lidén (2004). Service guarantees not only work directly on customer satisfaction but also indirectly improve customer satisfaction by focusing transit agency employees on improving service quality.

Literature review 13 action statement (what the customer will receive in the event the covered services do not meet the set standard). (McDougall et al. use the term “payout” instead of “action,” but the author of the current synthesis uses “action” to refer to any action taken by a provider to fulfill its service guarantee, whether or not the action includes financial compensation.) The coverage and action statements in a service guarantee may be defined or undefined (see Table 1 for examples). McDougall et al. found that some customers preferred guarantees with well-defined coverage and action statements whereas others found undefined statements more appealing. In an oft-cited article, Hart (1988) argues that service guarantees should be unconditional— that is, the service provider should not place restrictions on the guarantee, and customers who are dissatisfied with their experiences should receive remuneration regardless of whether the cause of the service issue was within or without of the provider’s direct control. Hart also states that service guarantees should be: • Easy to understand and communicate, • Meaningful, • Easy to invoke, and • Easy and quick to collect on. Research continues to better define customer preferences for the content and impact of service guarantees across different private- and public-sector industries (e.g., see Lidén and Skålén 2003). In this synthesis on public transit, service guarantees are defined as any explicit commitment to a quality customer experience, regardless of whether the agency compensates or responds directly to individual customers in the event the commitment is not met. This definition of service guarantee is relatively loose and allowed transit agencies with or without statements labeled as a “guarantee” to be included in this synthesis. For example, some transit agencies have what Neugebauer (2009) considers merely a “quality or performance promise” (p. 36)—in other words, a statement that promises a certain level of service without any specific restitution pro- vided to the customer for promises not kept. In these cases, the transit agency may guarantee a specific attribute of a transit trip (e.g., reliability, cleanliness) but not define the specific param- eters by which quality would be defined. These guarantees, by their nature, also make it nearly impossible to offer an action to restore customer confidence because the transit agency is not explicit about what it deems acceptable performance. Other transit agencies have service guarantees that are part of an official passenger charter or similar stand-alone policy that lists the transit agency’s commitments to the customer. In many cases, these service guarantees define both the attribute of service and the expected level of quality (e.g., “trains will arrive in 15 minutes of the schedule”) but do not necessarily establish an action in the event service does not meet the standard. Some public transit service guarantees provide for customer remuneration (e.g., a refund or credit toward future transit fares) or other action in the event the service does not meet Coverage Defined Undefined Action Defined Delivery within 30 minutes or your money back. Satisfaction guaranteed or your money back. Undefined Delivery within 30 minutes. Period. Satisfaction guaranteed. Period. Source: Adapted from McDougall et al. (1998). Table 1. Example service guarantees with defined and undefined coverage and remuneration statements.

14 Customer-Focused Service Guarantees and transparency practices the level of quality promised in the guarantee (e.g., “Trains will arrive within 15 minutes of the schedule or your ride is free”). This form of guarantee was the rarest found during the literature review and the rarest found in the transit agency survey. Most service guarantees that do provide an action have several limitations or exclusions under which the guarantee does not apply (e.g., delays caused by extreme weather are not eligible for the guarantee). To demonstrate how these forms of service guarantees fit into the existing literature, several example service guarantees were developed for this report and categorized according to a modified version of McDougall et al.’s (1998) taxonomy for service guarantee structures (see Table 2). Because some transit agencies define what a customer should experience (e.g., reliability) but not the precise definition of that experience (e.g., trains will arrive within 15 minutes of the schedule), a type of coverage statement was added that allowed for defining the covered attribute of service but not defining the expected level of quality. A type of action statement was added called “no action” to reflect that transit agencies make service guarantees but rarely back them with actions to take when the guarantee is not met. Most transit agencies had service guarantees with defined attributes but undefined quality levels and no defined actions (see Chapter 3). The lack of standardization of service guarantees across transit agencies likely demonstrates the complex nature of the issue and the apparent dearth of resources available to transit agencies to make informed decisions about whether and how to construct their service guarantees. Customer-Focused Transparency Expectations and Variations of Transparency As with expectations for high-quality service, citizens are increasingly expecting transparency— particularly from governments and public service agencies. Transparency has been defined in many ways but is best summed up by Grimmelikhuijsen and Welch (2012) as “the disclosure of information by an organization that enables external actors to monitor and assess its internal workings and performance” (p. 563). Sharing information with the public may be done for many reasons: for example, to maintain political competitiveness (Bearfield and Bowman 2017), adhere to a formal rule or regulation, increase public participation (Welch 2012), or as part of a broad transparency strategy. Although the rationale may vary, transparency has been found (in some cases) to result in actual improve- ments in service performance and financial management (Cucciniello et al. 2017) and citizen satisfaction (Ma 2017). Coverage Defined Attribute and Quality Level Defined Attribute but No Quality Level Undefined Attribute Action Defined Trains will arrive within 15 minutes of the schedule or your ride is free. Trains will be reliable or your ride is free. You’ll be satisfied with your trip or your ride is free. Undefined Trains will arrive within 15 minutes of the schedule. Contact us if you experience a problem. Trains will be reliable. Contact us if you experience a problem. You’ll be satisfied with your trip, and contact us if you aren’t. No Action Trains will arrive within 15 minutes of the schedule. Trains will be reliable. You’ll be satisfied with your trip. Source: Adapted from McDougall et al. (1998) and informed by the author’s literature review. Table 2. Example transit service guarantees for all types of coverage and action statements.

Literature review 15 Organizations can choose the extent of their transparency initiatives by determining trans- parency type (data, policy, or both), topic (e.g., finance, human resources, maintenance), time period (days, weeks, months, or years), and level of detail (aggregated versus transactional). Not surprisingly, there is a great deal of variability around the extent to which local governments and other public organizations share information with the public (e.g., Cucciniello and Nasi 2014, Bearfield and Bowman 2017). Customer-Focused Transparency in Public Transit Transit agencies also exhibit this variability of transparency, so not all transit agency transparency is necessarily customer focused. Customer- focused transparency is not a well-defined term in the literature; how- ever, in this report, the term refers to any open and public reporting updated at least annually that includes customer-focused metrics. Customer-focused metrics are those that reflect some aspect of the customer experience, when either riding transit or interacting with the transit agency. Examples of customer-focused metrics are listed as metrics from the customer point of view in TCRP Report 88 (Nakanishi 2003). In addition to customer-focused transparency, a transit agency may be transparent in other ways: for example, by opening its schedule and real-time data to the public using standardized data sets such as the general transit feed specification (e.g., see Rojas 2012), or opening its financial data to the public [e.g., Capital Metro in Austin, Texas, reports its budget, audits, and even financial transactions to the public (Capital Metro n.d.)]. Although financial and service information can have value for transit customers, these data do not portray the quality of transit service and do not reflect the experience of customers. This synthesis project did not accept as customer-focused transparency any transparent reporting of finances, ridership, or other metrics that did not measure some aspect of the customer’s experience. Last, many agencies are required to report their performance data to a board or other oversight body, which often maintain openness to the public through meeting agendas and report contents. Transit agencies that report data only as a part of a board or oversight process and not as a stand- alone effort may not be included in this synthesis because reports to oversight bodies may not be easily found on a transit agency website. In addition, reporting only to oversight bodies may manifest a transit agency’s wishes to meet mandates without actually having a strategy for being intentionally transparent. Resources Available to Transit Agencies Although extensive research has been done on service guarantees and transparency, there is still little available in resources for transit agencies to understand best practices, implications, and implementation strategies. Previous relevant transit-specific guidance includes • TCRP Synthesis 45: Customer-Focused Transit: A Synthesis of Transit Practice (Potts 2002); • TCRP Report 88: A Guidebook for Developing a Transit Performance-Measurement System (Nakanishi 2003); • TCRP Report 141: A Methodology for Performance Measurement and Peer Comparison in the Public Transportation Industry (Ryus et al. 2010); • National Rural Transit Assistance Program’s Customer Driven Service: Learner’s Guide (National Rural Transit Assistance Program 2011); and • The Canadian Urban Transit Association’s (CUTA) Introducing a Passenger Charter: Your Guide for Success (CUTA 2013). Customer-focused metrics reflect some aspect of the customer experience, when either riding transit or interacting with the transit agency.

16 Customer-Focused Service Guarantees and transparency practices Although this handful of resources exist, they may not deal with specific topics (e.g., TCRP Synthesis 45 did not include transparency as a part of customer-focused transit), may be out- dated, or may be too general to be a ready and relevant resource to transit agencies specifically con sidering implementing a service guarantee or customer-focused transparency. The technol- ogy supporting transit agency services, passenger interactions, and transparent reporting has changed significantly in the last several years, and the operational context in which transit agencies operate and the mobility options available to potential transit customers look much different now than they did just a few years ago. As the literature review demonstrates, customer-focused service guarantees and transpar- ency may be beneficial, but they have many complexities, mixed theoretical underpinnings, and empirical evaluations. This synthesis builds on and extends previous transit-specific research to provide transit practitioners a snapshot of the current state of the industry concerning service guarantees and customer-focused transparency, including their prevalence, implementation strategies, benefits and challenges, and important lessons learned.

TRB's Transit Cooperative Research Program (TCRP) Synthesis 134: Customer-Focused Service Guarantees and Transparency Practices documents the nature and prevalence of customer-focused practices among transit providers in North America and supplements the discussion by including information from European transit providers.

A growing number of North American public transit agencies have adopted service guarantees or transparency practices as part of a customer-focused service strategy. Service guarantees describe the level of service customers can expect and the procedures they may follow if standards are not met. Transparency practices might include reporting performance metrics as online dashboards or report cards on the agency’s website. Currently, there is little existing research on these practices and experiences among U.S. transit providers.

Update June 29, 2018: Page i of the synthesis omits some of the authors. The correct author list is as follows:

Michael J. Walk

James P. Cardenas

Kristi Miller

Paige Ericson-Graber

Chris Simek

Texas A&M Transportation Institute

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A Brief Literature Review: Customer Relationship Management

Customer relationship management has been defined as “a business approach that integrates people, processes, and technology to maximise relationships with customers” Goldenberg (2008, p.3). Moreover, it has been stated that customer relationship management “characterises a management philosophy that is a complete orientation of the company toward existing and potential customer relationships” (Raab et al, 2008, p.6)

Mueller (2010) characterises customer relationship management aspect of the business as a highly dynamic, and convincingly argues that businesses have to adopt a proactive approach in devising relevant programs and initiatives in order to remain competitive in their industries.

Sinkovics and Ghauri (2009) relate the necessity for engaging in customer relationship management to high cost of direct sales, highly intensifying level of competition in the global level, and need for information about various aspects of the business in general, and consumer behaviour in particular, that can be used to increase the levels of sales.

According to Peppers and Rogers (2011), there is global tendency in customer relationship management that relates to the shift from transactional model towards the relationship model. In other words, Peppers and Rogers (2011) argue that satisfying customer needs as a result of on-time transaction is not sufficient today in order to ensure the long-term growth of the businesses.

Instead, businesses have to strive to maintain long-term relationships with their customers in order to maintain flexibility to adopt their increasing expectations and thus achieving their life-long loyalty. Peppers and Rogers (2011) further stress that, businesses that refuses to acknowledge this tendency in the global marketplace would be risking their market share and growth prospects in the future.

One of the most critical sources for the research is the book “Relationship Marketing and Customer Relationship Management” authored by Brink and Berndt (2009). The book offers an in-depth discussion of the concept of Customer Touch Map and discusses the role of information technology in facilitating customer relationship management.

The work of Mathur (2010) represents another significant contribution to the research area to be used in the study. Namely, the author provides a wide range of specific customer relationship management techniques and principles that are used by multinational businesses. The findings of Mathur (2010) can be compared to the primary data findings in the proposed research, thus enhancing the scope of the study.

Khurana (2010), on the other hand, discusses the concept of customer relationship management in a great detail, and also addresses advantages and disadvantages associated with a range of relevant software applications. The third edition of Pradan’s (2009) “Retailing Management” is another noteworthy source that is going to be used in the study. Specifically, Pradan (2009) identifies customer relationship management as an emerging aspect of marketing in retail and discusses its importance for ensuring long-term growth for retail businesses.

A global approach towards the issues of customer relationship management is adopted by Raab et al (2008) in “Customer relationship management: a global perspective”. The value of this specific work to the proposed research can be explained in a way that it will allow the comparison of customer relationship management principles to the similar principles exercised by other multinational retailers in a global marketplace.

Bhatia’s (2008) work, “Retail Management” is also going to be used in the proposed study due to the significance of the contribution of the work to the research area. Bhatia (2008) offers in-depth discussions related to the use of loyalty cards by retailers, and this represents a comprehensive analysis of the issue in the secondary data.

Moreover, Cox’s (2011) “Retail Analytics: The Secret Weapon” deserves also to be mentioned in here thanks to the most modern and fresh perspective the author adopts in order to approach the research issues. The most valuable part of this specific article is that it provides highly practical recommendations to retailers of various sizes in terms of increasing the levels of revenues through adopting a range of customer relationship management principles.

A range of academic models and writings relate to this research in direct and indirect ways and some of the most relevant models are going to be explored in the study. One of the most models to be used in the study is The Gap Model of Service Quality. “A model of service quality called the gap model identifies five gaps that can cause problems in service delivery and influence customer evaluations of service quality” (Lamb et al, 2011, p.189).

These five gaps are a) the gap between customer wants and the management perceptions about customer wants; b) the gap between the management perceptions about customer wants and the specifications of service developed; c) the gap between the service specifications and the actual service provided; d) the gap between the quality of service promised and the quality of service provided, and e) the gap between expected service and perceived service on behalf of customer.

Another relevant model to be tested during the study constitutes Relationship Model of customer relationship management proposed by Peppers and Rogers (2011). Specifically, the model advocates adopting a pro-active approach in sustaining customer relationships and proposes a set of specific principles that would assist to accomplish this task.

Bhatia, S.C. (2008) “Retail Management” John Wiley & Sons

Brink, A. & Berndt, A. (2009) “Relationship Marketing and Customer Relationship Management” Juta Publications

Goldenberg, B.J. (2008) “CRM in Real Time: Empowering Customer Relationships” Information Today, Inc.

Cox, E. (2011) “Retail Analytics: The Secret Weapon” John Wiley & Sons

Khurana, M. (2010) “Information Technology for Retailing” Tata McGraw-Hill Education

Lamb, C.W., Hair, J.F. & McDaniel, C. (2011) “Marketing: Student Edition” Cengage Learning

Mathur, U.C. (2010) “Retail Management: Text and Cases” I.K. International Pvt Ltd

Mueller, B. (2010) “Dynamics of International Advertising: Theoretical and Practical Perspectives” Peter Lang

Peppers, D. & Rogers, M. (2011) “Managing Customer Relationships: A Strategic Framework” John Miley & Sons

Pradan, S. (2009) “Retailing Management: Text & Cases”, 3 rd edition, Tata McGraw-Hill Education

Raab, G., Ajami, R.A., Gargeya V. & Goddard, G.J. (2008) “Customer relationship management: a global perspective” Gower Publishing

Sinkovics, R.R & Ghauri, P.N. (2009) “New Challenges to International Marketing” Emerald Group Publishing

Nursing: How to Write a Literature Review

• Traditional or Narrative Literature Review

Getting started

1. start with your research question, 2. search the literature, 3. read & evaluate, 4. finalize results, 5. write & revise, brainfuse online tutoring and writing review.

• RESEARCH HELP

The best way to approach your literature review is to break it down into steps.  Remember, research is an iterative process, not a linear one.  You will revisit steps and revise along the way.  Get started with the handout, information, and tips from various university Writing Centers below that provides an excellent overview.  Then move on to the specific steps recommended on this page.

• UNC- Chapel Hill Writing Center Literature Review Handout, from the University of North Carolina at Chapel Hill.
• University of Wisconsin-Madison Writing Center Learn how to write a review of literature, from the University of Wisconsin-Madison.
• University of Toronto-- Writing Advice The Literature Review: A few tips on conducting it, from the University of Toronto.
• Begin with a topic.
• Understand the topic. 
• Familiarize yourself with the terminology.  Note what words are being used and keep track of these for use as database search keywords. 
• See what research has been done on this topic before you commit to the topic.  Review articles can be helpful to understand what research has been done .
• Develop your research question.  (see handout below)
• How comprehensive should it be? 
• Is it for a course assignment or a dissertation? 
• How many years should it cover?
• Developing a good nursing research question Handout. Reviews PICO method and provides search tips.

Your next step is to construct a search strategy and then locate & retrieve articles.

•  There are often 2-4 key concepts in a research question.
• Search for primary sources (original research articles.)
• These are based on the key concepts in your research question.
• Remember to consider synonyms and related terms.
• Which databases to search?
• What limiters should be applied (peer-reviewed, publication date, geographic location, etc.)?

Review articles (secondary sources)

Use to identify literature on your topic, the way you would use a bibliography.  Then locate and retrieve the original studies discussed in the review article. Review articles are considered secondary sources.

• Once you have some relevant articles, review reference lists to see if there are any useful articles.
• Which articles were written later and have cited some of your useful articles?  Are these, in turn, articles that will be useful to you? 
• Keep track of what terms you used and what databases you searched. 
• Use database tools such as save search history in EBSCO to help.
• Keep track of the citations for the articles you will be using in your literature review. 
• Use RefWorks or another method of tracking this information. 
• Database Search Strategy Worksheet Handout. How to construct a search.
• TUTORIAL: How to do a search based on your research question This is a self-paced, interactive tutorial that reviews how to construct and perform a database search in CINAHL.

The next step is to read, review, and understand the articles.

• Start by reviewing abstracts. 
• Make sure you are selecting primary sources (original research articles).
• Note any keywords authors report using when searching for prior studies.
• You will need to evaluate and critique them and write a synthesis related to your research question.
• Consider using a matrix to organize and compare and contrast the articles . 
• Which authors are conducting research in this area?  Search by author.  
• Are there certain authors’ whose work is cited in many of your articles?  Did they write an early, seminal article that is often cited?
• Searching is a cyclical process where you will run searches, review results, modify searches, run again, review again, etc. 
• Critique articles.  Keep or exclude based on whether they are relevant to your research question.
• When you have done a thorough search using several databases plus Google Scholar, using appropriate keywords or subject terms, plus author’s names, and you begin to find the same articles over and over.
• Remember to consider the scope of your project and the length of your paper.  A dissertation will have a more exhaustive literature review than an 8 page paper, for example.
• What are common findings among each group or where do they disagree? 
• Identify common themes. Identify controversial or problematic areas in the research. 
• Use your matrix to organize this.
• Once you have read and re-read your articles and organized your findings, you are ready to begin the process of writing the literature review.

2. Synthesize.  (see handout below)

• Include a synthesis of the articles you have chosen for your literature review.
• A literature review is NOT a list or a summary of what has been written on a particular topic. 
• It analyzes the articles in terms of how they relate to your research question. 
• While reading, look for similarities and differences (compare and contrast) among the articles.  You will create your synthesis from this.
• Synthesis Examples Handout. Sample excerpts that illustrate synthesis.

Regis Online students have access to Brainfuse. Brainfuse is an online tutoring service available through a link in Moodle. Meet with a tutor in a live session or submit your paper for review.

• Brainfuse Tutoring and Writing Assistance for Regis Online Students by Tricia Reinhart Last Updated Oct 26, 2023 81 views this year
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Patient Satisfaction with Healthcare Services and the Techniques Used for its Assessment: A Systematic Literature Review and a Bibliometric Analysis

Diogo cunha ferreira.

1 Centre for Public Administration and Public Policies, Institute of Social and Political Sciences, Universidade de Lisboa, Rua Almerindo Lessa, 1300-663 Lisbon, Portugal

2 CERIS, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal

Inês Vieira

3 Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal

Maria Isabel Pedro

4 CEGIST, Instituto Superior Técnico, University of Lisbon, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal

Paulo Caldas

5 Business and Economic School, Instituto Superior de Gestão, Av. Mal. Craveiro Lopes 2A, 1700-284 Lisbon, Portugal

6 Centre for Local Government, UNE School of Business, University of New England, Armidale, NSW 2350, Australia

Miguel Varela

7 CEFAGE, Faculdade de Economia, Universidade do Algarve, Campus of Gambelas, 8005-139 Faro, Portugal

Associated Data

Not applicable.

Patient satisfaction with healthcare provision services and the factors influencing it are be-coming the main focus of many scientific studies. Assuring the quality of the provided services is essential for the fulfillment of patients’ expectations and needs. Thus, this systematic review seeks to find the determinants of patient satisfaction in a global setting. We perform an analysis to evaluate the collected literature and to fulfill the literature gap of bibliometric analysis within this theme. This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) approach. We conducted our database search in Scopus, Web of Science, and PubMed in June 2022. Studies from 2000–2021 that followed the inclusion and exclusion criteria and that were written in English were included in the sample. We ended up with 157 articles to review. A co-citation and bibliographic coupling analysis were employed to find the most relevant sources, authors, and documents. We divided the factors influencing patient satisfaction into criteria and explanatory variables. Medical care, communication with the patient, and patient’s age are among the most critical factors for researchers. The bibliometric analysis revealed the countries, institutions, documents, authors, and sources most productive and significant in patient satisfaction.

1. Introduction

Healthcare systems are continually changing and improving, and so it is necessary to find a way to assess outputs while evaluating the satisfaction of the service receiver, in this case, the patient. One can define patient satisfaction as a patient’s reaction to several aspects of their service experience. Assessing patient satisfaction may provide valuable and unique insights about daily hospital care and quality. One widely accepts it as an independent dimension of care quality that includes internal aspects of hospital care. Patient satisfaction is a concept that has long been neglected and cast aside, but is becoming gradually more important. Donabedian [ 1 ] includes it as an outcome of healthcare services; hence, it is of utmost importance to evaluate care quality. Several authors argue that satisfaction and the result in terms of the patient’s health status are related terms [ 2 , 3 , 4 , 5 ]. Thus, the present study sheds light on the factors that most influence patient satisfaction. With this information, managers can more efficiently allocate resources to improve patients’ experience and satisfaction [ 6 ].

Measuring healthcare quality and satisfaction constitutes an indispensable element for adequate resource management and allows for the focus on its users’ preferences, giving them a chance to construct a customized health service, better fitted to their needs and expectations [ 7 ]. When talking about public hospitals, there may not be a financial interest in performing these studies since they are not particularly interested in profit. However, with the increase in market competitiveness, private companies need to meet patients’ needs, satisfying them so that they then become loyal to the organization [ 8 ]. Patient satisfaction can be useful for structuring evaluations referring to patient judgments according to inpatient care. It is relevant from an organizational management perspective [ 9 ]. Patient satisfaction and quality of health services are, thus, crucial elements for the long-term success of health institutions [ 10 ].

Despite the high number of studies regarding this topic, the results are inconclusive and differ across each document [ 11 , 12 , 13 ]. Contradicting evidence exists across patient satisfaction studies due to its subjective nature [ 14 ]. Since each individual has his/her perceptions, satisfaction is nothing but a relative concept, influenced by individual expectations and evaluations of health services’ attributes [ 15 ].

Several systematic reviews have analyzed the determinants of patient satisfaction: Naidu [ 16 ], Almeida et al. [ 17 ], Farzianpour et al. [ 18 ], and Batbaatar et al. [ 14 ] are some noteworthy recent reviews. Similarly, reviews on the methods most utilized by researchers are scarce, and none of them delivers a comprehensive and profound analysis of literature through bibliometric tools. Thus, this analysis aims to assess the different aspects of patient satisfaction in a global healthcare context, along with the identification of the main countries, institutions, documents, authors, and journals of this research area, with co-citation and bibliographic coupling networks. This systematic review can contribute to the knowledge of patient satisfaction, whether the influential factors, or the most advised methodology, and be an essential input for researchers or scholars interested in the study of patient satisfaction. In addition, we conducted a meta-analysis by statistically analyzing the main factors underlying patient satisfaction and the main methodologies adopted for its study.

The incessant demand for improved results and quality of health services offered is of extreme importance in developing a more effective organizational policy adjusted to the patients’ needs. Health organizations recognize that service quality is especially pertinent regarding the healthcare market’s promotion and public image [ 19 ]. Hence, patient satisfaction surfaces as a variable for promoting health organizations’ quality, allowing an assessment and identification of patients’ most relevant dimensions and their satisfaction level. Patient satisfaction helps to measure the quality of healthcare, thus becoming an essential and frequently used indicator. It affects clinical outcomes, medical malpractice claims, and timely, efficient, and patient-centered healthcare delivery [ 20 ]. Patient satisfaction and quality of health services are a priority for the services industry due to increasing consumption and are critical elements for health institutions’ long-term success [ 10 , 21 ].

Even though satisfaction is an essential aspect of quality, the relationship between these two concepts is not linear. On the one hand, satisfaction studies’ results can be ambiguous and may not always be impartial. Given that patients evaluate physicians’ performance, most missing the necessary abilities, results can be based on affinity and not on the health professional’s technical skills. On the other hand, providers may have to face a trade-off between providing satisfaction to their patients or better treatment outcomes [ 22 ]. Since each person has his/her perceptions, satisfaction is nothing but a relative concept, influenced by individual expectations and evaluations of health services’ attributes [ 15 ]. Patient satisfaction is complex to assess, given its multidimensionality. It is composed of diverse aspects that may not be related to the patient’s service’s actual quality.

It is valuable to consider the highly cited Donabedian framework on how to examine health services’ quality in order to surpass the current lack of clarity on defining and measuring satisfaction, and to evaluate the quality of medical care using three components [ 1 , 23 , 24 , 25 ]—structure, process, and outcome (results):

• - Structure: Environment, provider’s skills, and administrative systems where healthcare occurs;
• - Process: The constituents of the received care (measures doctors and medical staff considered to deliver proper service); and
• - Outcome: The result of the care provided, such as recovery, avoidable readmission, and survival;

The conceptualization of patient satisfaction regarding expectations and perceptions is related to Donabedian’s triad. For instance, the patient will be satisfied with hospital attributes if his/her expectations are met [ 25 ]. However, one of the leading criticisms of patient satisfaction ratings is the incapacity to rationalize medical care expectations, which can be affected by previous healthcare experiences [ 26 ]. The same happens with the other two components. The patient will be satisfied with the process if symptoms are reduced. The outcome will be favorable if there is a recovery, demonstrating that received care perception meets prior expectations. Throughout his framework, Donabedian regarded “outcome” as the most crucial aspect, defining it as a change in a patient’s current and future health status that can be confidently attributed to antecedent care [ 22 ].

2. Materials and Methods: Data Collection and Extraction Method

We performed this research respecting the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A checklist of 27 parameters, including the title, abstract, methods, results, discussion, and funding was taken into consideration to ensure the complete reporting of systematic reviews. PRISMA assures that authors prepare a transparent and complete reporting of systematic reviews and meta-analyses [ 27 ]. The PRISMA statement starts with identifying possible studies to include in our further revision after searching in several databases. We searched papers in the Scopus, Web of Science, and PubMed databases during June 2022. After testing several keywords, the search strategy used the term “patient satisfaction” to extend the number of results. Reference lists from the collected articles were also searched for additional articles. Overall, one thousand six hundred fifty-three studies composed the list of our first search.

Once we concluded our search, we removed the duplicates (241), and the remaining documents (1412) were analyzed under the inclusion and exclusion criteria. Inclusion criteria included: articles from peer-review journals; written in English; published from January 2000 to December 2021; assessed which factors affect patient satisfaction (or a proxy of it); evaluated overall patient satisfaction with healthcare; quantitative studies; reviews; and international studies to provide a more comprehensive analysis. We also excluded reports, books or book chapters, conference proceedings, dissertations, theses, expert opinion, commentaries, editorials, and letters. We excluded 1197 studies from the list after removing duplicates because they failed all inclusion criteria or met at least one exclusion criterion.

We conducted a full-text analysis to assess the eligibility of the remaining 215 papers. Disease-centered studies that did not evaluate the general aspects of patient satisfaction were excluded. We also discarded papers with unclear data collection methods, papers with unclear results, and qualitative papers. We rejected a total of 58 papers in this step.

Figure 1 outlines the PRISMA diagram detailing the study selection process. Following such a statement, 157 studies met the inclusion criteria. Four of them were systematic reviews, and the remaining 153 apply quantitative methods for patient satisfaction analysis.

PRISMA statement.

3. Past Research on Patient Satisfaction: What Did the Systematic Reviews Tell Us about It?

The first systematic review [ 16 ] included a study of twenty-four articles published between 1978 and 2006. Through the analysis of these articles, health care output, access, caring, communication, and tangibles were the dimensions determining patient satisfaction. The patient’s socio-demographic characteristics that affected satisfaction were age, education, health status, race, marital status, and social class. SERVQUAL (service quality) was the most preferred instrument in satisfaction studies. In their review, the authors developed a conceptual model, claiming that healthcare quality and determinants influence patient satisfaction, influencing patient loyalty.

The second systematic review [ 17 ] included thirty-seven international articles from 2002 to 2013. Patient-professional interactions, the physical environment, and internal management processes were the most influential satisfaction constructs, except for specific services, such as home care, psychiatric or pediatric services. Almeida et al. disregarded patient’s socio-demographic characteristics from their review. In this type of service, phone contact and provided information were also considered powerful satisfaction constructs. The primary methodology used in the collected articles was factor analysis (exploratory factorial analysis (EFA) and confirmatory factor analysis (CFA)).

The third systematic review [ 14 ] comprised one hundred and nine international articles from 1980 to 2014. This study identified nine determinants of satisfaction: technical skills, interpersonal care, physical environment, accessibility, availability, finances, organizational characteristics, continuity of care, and care outcome. Technical skills constitute a cluster of medical care, nursing care, friendliness, concern, empathy, kindness, courtesy, and respect. The physical environment results from the atmosphere, room comfort, bedding, cleanliness, temperature, lighting, food, comfort, equipment, facilities, and parking. Accessibility is composed of location, waiting time, the admission process, the discharge process, and the effort to get an appointment. Availability represents the number of doctors, nurses, facilities, and equipment. Meanwhile, finances include the flexibility of payments, the status of insurance, and insurance coverage. Organization characteristics include reputation, image, administrative processes, doctors’ and nurses’ satisfaction level, and doctors’ socio-demographic characteristics. The authors found thirteen patient socio-demographic characteristics in their review: patient age, gender, education, socio-economic status, marital status, race, religion, geographic characteristics, visit regularity, length of stay, health status, personality, and expectations. The relationship between patient satisfaction and these socio-demographic characteristics was contradictory across the articles; thus, the authors achieved no conclusions. The most used methodology from the articles’ collection was not mentioned in the review.

Finally, the fourth and most recent systematic review [ 28 ] includes only thirty-eight international articles from 2000 to 2017. Provider’s attitudes, technical competence, accessibility, and efficacy were found to be the most influential attributes in this study. Provider’s attitudes comprise courtesy, friendliness, kindness, approachability, respect, responsiveness, attention, and concern. Accessibility also consists of a cluster of attributes, including the location, environment, equipment availability, appointment arrangement, and access equitability. Expectations, patient’s socio-demographic characteristics, and market competition were considered as antecedents to satisfaction while influencing it. There is no mention of the methods most used by the articles collected.

These four systematic reviews analyzed multiple articles to find the determinants of patient satisfaction. The gathered results and conclusions show coherence between the reviews. However, we can identify some limitations. The sample size of some reviews is somehow insufficient to achieve reliable conclusions. The methodology present in the articles collected should be studied more deeply to determine the different methodologies used in patient satisfaction studies and their advantages and disadvantages.

4. Results and Discussion

4.1. a summary of quantitative papers passing the prisma sieve.

After searching for quantitative papers within the patient satisfaction field, we constructed a table containing the most relevant data retrieved from them: author names, published year, country of study, sample size, satisfaction dimensions and drivers (if applicable), methodology, dependent variable, main factors affecting patient satisfaction, number of citations, and publisher. We considered a dependent variable field because, besides overall patient satisfaction, some articles studied proxies of satisfaction, such as willingness to recommend the hospital or willingness to return. Table 1 contains an excerpt of the articles with more than 100 citations (a total of 19 studies).

Review of collected articles.

Table 2 and Table 3 present statistical measures applied to the data collected. The sample size, the first object of analysis, shows a significant coefficient of variance due to the values’ dispersion, as shown through the minimum and maximum rows in both analyses. In Table 2 , the dispersion is more significant, with a higher coefficient of variation. The study with the most significant sample size in the first analysis is McFarland et al. [ 45 ], which analyzed 3907 private hospitals. For the second analysis, Aiken et al. [ 12 ] has the largest sample size.

Statistical measures applied to all collected data.

Statistical measures applied to the articles with more than 100 citations.

Regarding the methodology used, most studies applied only one method. However, some studies used two methods in a complementary way. The number of criteria used to assess patient satisfaction has a low variance. Researchers give more importance to criteria than to explanatory variables through the values present in these tables. The number of criteria has a higher mean, median, and mode, meaning that researchers tend to disregard the vital aspect of satisfaction drivers. The main difference between both analyses is the minimum and the maximum number of criteria applied in the studies. Table 2 shows a minimum of zero and a maximum of 26, meaning that studies only assessed the importance of explanatory variables. However, in Table 3 , the minimum is one, showing that studies with higher citations do not solely evaluate explanatory variables. The maximum number of criteria is also distinct in both tables, with a decrease of ten units in Table 3 . Explanatory variables have a 100% coefficient of variance, with equal mean and standard deviation on both analyses. The number of critical factors has low variance, and the minimum is equal to one because each study seeks to fix the determinants of patient satisfaction. On the one hand, in Table 2 , the number of citations presents a more dispersed pattern with a higher difference from the minimum to the maximum value. Over 20 years, it is reasonable to collect articles with an exact number of citations. On the other hand, Table 3 , showing only articles with more than 100 citations, presents a more cohesive dataset.

4.2. Statistical Analysis over the Utilization and Importance of Satisfaction Criteria and Explanatory Variables

Factors related to satisfaction can be either criteria or explanatory variables. The assessment of hospital service quality can be a complicated task that includes numerous criteria, qualitative and dubious factors that are difficult to assess [ 46 ].

From the collected papers, we analyzed each factor’s utilization related to patient satisfaction. We also verified the importance rate of each factor. The percentage of utilization is the ratio between the number of studies using it is and the total number of evaluated studies, while the importance rate of a factor measures the relative number of papers concluding that this factor is critical for patient satisfaction.

In general, studies about patient satisfaction try to unveil factors associated with his/her overall satisfaction with one or more services (96% of the collected studies) or willingness to recommend the hospital/clinic (9%) instead. A smaller percentage of studies (7%) included both dependent variables [ 12 , 40 , 47 , 48 , 49 , 50 , 51 , 52 ]. There is one dependent variable (typically the overall satisfaction) explained by a series of criteria and other external factors. However, one can also use other dependent dimensions as proxies for such overall satisfaction. Examples include the willingness to return [ 36 , 48 , 53 ], medical services satisfaction, accommodations services satisfaction, nursing services satisfaction [ 54 ], satisfaction with the quality of medical information [ 55 ], and healthcare quality [ 56 ]. Compared with the overall satisfaction and the willingness to recommend hospitals/clinics, other studies are scarce within the sample of papers passing the PRISMA sieve. Therefore, we conducted three different analyses because of the different dependent variables used in each article, i.e., global analysis regardless of the dependent variable used, overall patient satisfaction, and willingness to recommend a hospital/clinic.

To provide a more unambiguous graphic representation of the analysis, we grouped some patient satisfaction related factors related to each other into a single factor. These are some examples: (i) concern (from the doctor, the nurse, or other staff, either clinical or not); (ii) clinical staff social characteristics (assurance, attention, attitudes, kindness, skills, and specialty); (iii) hospital characteristics (image, location, quality, size, and type); and (iv) patient’s social characteristics (autonomy, dignity, emotional support, income, life expectancy, marital status, nationality, occupation, race, residence, satisfaction with life, and stress level).

4.2.1. Global Analysis about the Most Frequently Used Satisfaction Criteria and Explanatory Variables

We started by analyzing all factors related to patient satisfaction, clustering in terms of satisfaction criteria and explanatory variables, regardless of the dependent variable used by researchers. As their name indicates, explanatory variables are useful to find out potential drivers or determinants for satisfaction. They are nondiscretionary for hospital/clinic managers but may play a prominent role in infrastructure management. We divided the fifteen most utilized factors into criteria and explanatory variables; Figure 2 and Figure 3 represent them, respectively. These factors are the ones that most researchers use to study patient satisfaction and may not correspond to the most important and influential factors of patient satisfaction.

Analysis of the most utilized criteria in the literature (global analysis).

Analysis of the most utilized explanatory variables in the literature (global analysis).

Of the fifteen most used factors, eleven are criteria, and four are explanatory variables. The doctor’s characteristics, waiting time, medical care, and information provided have the highest utilization rates of utilization within the criteria. Patient’s social characteristics, patient’s age, patient’s education, and perceived health status also have the highest utilization rate, but are within the explanatory variables. However, this analysis is not directly related to the importance rate analysis; thus, the most utilized factors might not be the most important and influential.

Figure 4 ranks the criteria deemed as the most important to evaluate satisfaction. In contrast, Figure 5 presents the most critical explanatory nondiscretionary dimensions. This first analysis, the most complete one, resulted in fifty-six factors, divided into forty-seven criteria and nine explanatory variables. From Figure 4 , it is possible to conclude that the three criteria most important in collecting articles are medical care, waiting time, and communication with the patient. Despite not being in the top three, criteria related to the doctor’s social skills exhibit a high importance rate and should be noticed. It is interesting to note that researchers conclude that staff’s social skills, such as communication, are more critical than others, such as like food quality and comfort. Also, the criteria associated with the technical skills of staff appear to be less critical. This seems to be in line with some authors who claim that patients are usually unable to judge health professionals in those terms [ 2 , 3 , 4 , 5 ].

Analysis of criteria deemed as the most critical in the literature (global analysis).

Analysis of explanatory variables deemed as the most critical in the literature (global analysis).

Additionally, waiting time is one of the most critical criteria to study patient satisfaction. For instance, Ferreira et al. [ 25 ] classified this criterion as a critical must-have requirement. It means that patients take it for granted and neither get satisfied nor dissatisfied if the waiting time is null. However, their dissatisfaction increases intensely when waiting time becomes more substantial. The authors also verified that waiting time was the most crucial criterion for patients in medical appointment services.

Figure 5 shows that the patient’s age, the perceived health status, and the patient’s education are the variables that studies tend to consider as the most influential to patient satisfaction. Previous studies that say that age, education, and self-reported health status have an evident and significant influence on the satisfaction outcomes were confirmed [ 38 ]. Older patients or those with better self-perceived health status are typically more satisfied, while highly educated people are less satisfied with the healthcare services provided [ 34 , 42 ].

Differences arise after comparing the results from the utilization analysis and the importance rate analysis. Figure 2 and Figure 4 , both portraying criteria, reflect differences in the ranking positions. The doctor’s characteristics, the most utilized criterion, was placed fourth on the importance-related raking. Communication with the patient also occupies different positions in the analysis. Figure 2 shows this criterion in the seventh position, while in Figure 4 , it is the third criterion with the highest importance rate.

Regarding the explanatory variables, Figure 3 and Figure 5 also display disparities. The patient’s social characteristics are the most used cluster of explanatory variables, but it occupies the fourth position on the importance rate analysis. The patient’s age has the second-highest utilization rate and the highest importance rate. The patient’s education occupies the third position in both analyses. Lastly, perceived health status is ranked fourth in Figure 3 , but secondly in Figure 5 .

4.2.2. Overall Patient Satisfaction as the Dependent Variable

As in the previous case, we divided our analysis into criteria and explanatory variables. Identical to the global analysis, the fifteen factors deemed the most used in literature are eleven criteria and four explanatory variables. The results of this analysis are equal to the global analysis’ results, with a slight change in the percentage level. The doctor’s characteristics, waiting time, medical care, and information provided remain the most used criteria with a percentage of 43%, 38%, 28%, and 27% each. The four explanatory variables continue the same, with the patient’s characteristics, patient’s age, patient’s education, and perceived health status as the most utilized. The percentages of these variables suffered minor modifications.

Regarding the importance analysis, fifty-six factors are assessed, similar to the first analysis, with forty-seven criteria and nine explanatory variables. One can only observe a few differences between this and the previous analyses. It is mostly motivated by the fact that researchers often tend to look at the overall satisfaction instead of other related concepts like willingness to return or recommending the healthcare provider. The three most important criteria are medical care, waiting time, and communication with the patient. These are the same as the first analysis, but with a modification of each criterion’s percentages. As mentioned above, the doctor’s social skills also have a similar percentage as the top three global analysis criteria. However, in this second analysis, the criterion “accommodations” has a higher utilization percentage. The patient’s age, perceived health status, and patient education are the most important explanatory variables. The variables are the same as in Figure 5 but have different percentages.

4.2.3. The Willingness to Recommend the Healthcare Provider as the Dependent Variable

The third and last analysis regards the dependent variable “willingness to recommend.” For the utilization analysis, we also present the fifteen most used factors. However, since one sole factor is an explanatory variable (patient’s characteristics), both criteria and explanatory variables are presented on the same chart ( Figure 6 ).

Analysis of the most utilized factors in the literature (willingness to recommend analysis).

The results of this utilization analysis differ from the two utilization analyses presented above. Despite doctor’s and patient’s characteristics being the factors with the highest utilization rates (consistent with the previous analysis), the remaining factors and percentages suffered alterations. Waiting time and medical care, the second and third criteria with the highest utilization rates on the previous analyses, are now positioned in seventh and eighth place, respectively. Nurses’ characteristics and nursing care are the criteria with the most significant rate increase, at more than 30%. The change in the dependent variable is responsible for the alterations of these results. Studies that consider the dependent variable “willingness to recommend” tend to adopt different factors, focusing on nursing care and professionals, compared with the studies analyzing the “overall patient satisfaction.”

In the importance analysis, since there were only twenty factors assessed, including nineteen criteria, and only one explanatory variable, we show the results compiled in Figure 7 . Regarding the explanatory variables, only the patient’s age resulted from our search. It is a consistent result compared to the previous ones, given that the patient’s age is the most relevant explanatory variable in all three analyses. As one can see, the three most important criteria are waiting time, nursing care, and the doctor’s social skills. Medical care, the most frequent criteria in the other two analyses, is not as relevant in this analysis. Nursing care is more relevant in this analysis than in the previous ones. It is in line with the results of the utilization analysis for this dependent variable. Nursing related factors are more frequent and deemed crucial when the dependent variable is “willingness to recommend.”

Analysis of factors deemed as the most critical in the literature (willingness to recommend analysis).

4.3. Discussion on the Most Critical Patient Satisfaction Criteria and Explanatory Variables

When comparing the results from the three analyses carried out, it is possible to reach conclusions about each factor’s importance. According to the literature, the criteria of medical care, communication with the patient, and waiting time are the three most critical. The doctor’s social skills, accommodations, nursing care, and information provided can also be considered relevant criteria. The explanatory variables of patient’s age, perceived health status, and patient’s education are the remaining ones.

Some past systematic reviews have revealed that interpersonal or social skills (such as medical/nursing care and attitudes), technical skills, infrastructure and amenities, accommodations, environment, accessibility, continuity of care, and the outcome are the satisfaction criteria present in the majority of studies related to satisfaction in healthcare [ 14 , 16 , 17 , 18 ]. In terms of explanatory variables, these reviews also point out the frequent use of variables such as the patient’s gender, age, education, and marital status. These dimensions should affect customers’ satisfaction ratings, helping to understand them, but should not be confused with criteria.

Despite the similarity of results between the previous reviews and the current one, some other factors seem to assume a high relevance for researchers. They include waiting time and the information provided, which interestingly are not present in the previous reviews. On the one hand, waiting time is a determinant of healthcare dissatisfaction, regardless of the inpatient stage. Waiting time is clearly an obstacle to access. Meanwhile, efficient hospitals usually have short waiting times [ 15 ]. The longer the waiting time, the more dissatisfied the customer is [ 57 ]. However, the converse is not necessarily true. If the waiting time is very short or even null, the customer may take it for granted because she/he needs the medical/nursing procedure and be neither satisfied nor dissatisfied. It means that waiting time is usually pointed out as a must-have requirement [ 25 ].

On the other hand, the criterion information provided may refer to any care process, since the patient enters the system until he/she leaves it. Communication or the capacity of providing useful information includes the treatment guidelines, rights, duties, means of complaint and suggestions, current health status, and the post-discharge process at home. For instance, inadequate post-discharge care and lack of patients’ preparedness are two potential determinants of readmissions for further care [ 58 ]. Readmissions within a specific (short) period after discharge may reveal a lack of care appropriateness, either in terms of the clinical staff’s technical skills or the information provided about care at home. Therefore, it is an excellent practice to sufficiently prepare the discharged patient (or someone responsible for her/him) for proper care at home. Missing or confusing information provided by the clinical staff contributes to a lack of preparedness and, by consequence, to customer dissatisfaction. We should remark that communication should be a social skill of any healthcare worker. The fact that this criterion does not appear in previous reviews is perhaps the result of merging some criteria related to it. However, we point out the need for high discrimination of criteria during a satisfaction survey.

4.4. Methods Employed in the Literature

Figure 8 provides a chart comparing the different literature methods devoted to the patient’s satisfaction analysis. We identified four main methods: logistic regression analysis, factor analysis, structural equation modeling (SEM), and multiutility satisfaction analysis (MUSA). Nonetheless, other ancillary methods are suitable for satisfaction analysis when complementing those four.

Analysis of methods utilized in the literature.

We can observe that regression analysis is chosen by most researchers (64%). Factor analysis is in second place, with 24% utilization, followed by SEM (11%), and lastly MUSA (1%). From the 153 collected articles, 27 (18%) combined different methods in a complementary nature: factor analysis with regression analysis (16 of the 27 articles, or 59%), and factor analysis with SEM (11 of the 27 articles, or 41%), are the two combinations observed. The difference in the level of utilization of each method can be due to the difficulty of implementation. SEM and MUSA are more complex than the other two and thus harder to implement. Contrary to this, logistic regression and factor analysis are more straightforward to implement, becoming more attractive to the researcher. We provide a brief description of each method below to better understand the methodology used in the articles collected.

Factor analysis: A mathematical model explains the correlation between a broad set of variables in terms of a small number of underlying factors [ 59 ]. It uses procedures that summarize information included in a data matrix, replacing original variables with a small number of composite variables or factors [ 60 ].

Logistic regression analysis: This analysis is frequently employed to model the association between a response and potential explanatory variables. Every association is evaluated in terms of an odds ratio [ 61 ].

Structural Equation Modelling (SEM): This is a general modeling technique used to test the validity of theoretical models that define causal and hypothetical relations between variables [ 62 , 63 ]. Some researchers have combined SEM with other types of analyses for the study of satisfaction, although without an application to the case of healthcare, such as with Ciavolino et al. [ 64 ] and Sarnacchiaro et al. [ 65 ].

Multicriteria Satisfaction Analysis (MUSA): The basic principle of MUSA is the aggregation of individual judgments into a collective value function, assuming that customers’ global satisfaction depends on a set of criteria representing service characteristic dimensions [ 25 , 66 ]. MUSA has a generalization, called MUSA-INT [ 67 ], which accounts for positive and negative interactions among criteria.

4.5. Discussion on the Most Frequent Model/Method Used in Literature

As seen in Figure 8 , the most used method is logistic regression analysis due to its implementation simplicity and reliability of results. Logistic regression is a statistical method where one variable is explained or understood based on one or more variables. The variable being explained (typically the overall satisfaction or the willingness to return) is the dependent or response variable. The other variables used to explain or predict the response are the independent variables. Essential features of logistic regression include: (a) it provides a single regression coefficient estimate of covariates for each response category; (b) it follows stochastic ordering; (c) it is easy and straightforward to apply; (d) it needs a few parameters to estimate; and (e) the odds are proportional across the response variable are [ 71 , 72 ]. The outcome variable in an ordinal logistic regression model can have more than two levels. An estimation of the probability of being at or beneath an outcome level, depending on the explanatory variable, is executed in this analysis [ 73 ]. Limitations are, however, also a part of this model, since: (a) large samples are required since the coefficients are estimated by maximum likelihood estimate; (b) proportional odds assumption should be satisfied, meaning that the odds ratio is constant across the cut-off point for each of the covariates in the model. If this assumption is not truthful, the estimate of the parameters obtained is not valid [ 71 ].

Another alternative for categorical variables is MUSA [ 66 ]. This model can be applied to the satisfaction-related data [ 74 , 75 , 76 ] because it estimates (through optimization) the value functions associated with each criterion and value scales are no longer categorical. Unlikely logistic regressions, MUSA does not rely on the proportional odds assumption that rarely is satisfied in practice. In truth, MUSA is a model for satisfaction analysis, which is nothing but a robust ordinal regression. For that reason, we may argue that MUSA is the non-parametric version of logistic regression and, therefore, makes fewer assumptions than the latter. In other words, while some validity conditions must be met so that the result of parametric models is reliable, non-parametric models can be applied regardless of these conditions. However, the power of the parametric model is traditionally superior to the power of its non-parametric counterpart. Furthermore, MUSA is challenging to implement, limiting its use by more researchers. We should point out that it is good practice to apply several alternative models (namely MUSA and the logistic regression) and to test for the robustness of outcomes.

Other approaches have also been proposed in the literature to study satisfaction. One is the so-called Benefit of Doubt model [ 77 ], which is a particular case of the well-known Data Envelopment Analysis (DEA). Bulut [ 78 ] used the Benefit of Doubt to construct a composite index based on citizens’ emotions and senses. Such an alternative was not applied to the healthcare sector at that point in time. However, Löthgren and Tambour [ 79 ], Bayraktar et al. [ 80 ], Gok and Sezen [ 81 ], Mitropoulos et al. [ 82 ], and Mohanty and Kumar [ 83 ], to name a few, have included satisfaction data in their DEA exercise. The same critique made to the use of ordinal data in SEM and PCA applies to the case of the Benefit of Doubt (and DEA), as it is nothing but a linear programming model. The Benefit of Doubt is commonly used for benchmarking purposes. If one wishes to benchmark decision-making units based on satisfaction, one can couple MUSA and Doubt’s benefit. Indeed, recalling Grigoroudis and Siskos [ 66 ] and Ferreira et al. [ 25 ], one can establish a satisfaction index associated with each criterion and decision-making unit. Such an index is a function of the utilities per satisfaction level. Therefore, these indexes can replace the indicators traditionally used in the Benefit of Doubt model. Another alternative is the DEA-based maxmin model of Li et al. [ 84 ], Dong et al. [ 85 ], and Wu et al. [ 86 ] that allows the maximization of each decision-making unit’s satisfaction.

The principal component logistic regression is another alternative recently proposed. According to Lucadamo et al. [ 87 ], Labovitz [ 88 ] and O’Brien [ 89 ], “ proved that if the number of categories is sufficiently large (e.g., six or seven points), one can apply the product-moment correlations on ordinal variables with negligible bias .” However, such conclusions resulted from controlled simulation procedures, which might hardly apply to the real world. Although the logistic regression has its own merits regarding the analysis of satisfaction determinants, it also has limitations, as discussed above. Unless “ successive categories of the ordinal variables are equally spaced ” (an extreme assumption), then the merging of PCA and logistic regression is not likely to produce reliable results.

The multiobjective interval programming model proposed by Marcenaro-Gutierrez et al. [ 90 ] and Henriques et al. [ 91 , 92 ] which was applied to explore the trade-offs among different aspects of job satisfaction is an interesting one. In short, the model optimizes some coefficients related to those trade-offs by merging interval programming and econometric techniques. It should be explored in the future and applied to the healthcare sector.

4.6. Is There Any Association between the Adopted Method and the Criteria Deemed More Critical for Satisfaction Analysis?

To corroborate or invalidate the hypothesis of an association between adopted methods and critical factors, an analysis was performed where the critical factors mentioned per study were clustered in terms of the method adopted. As demonstrated above, the main critical factors are waiting time, medical care, communication with the patient, information provided, and patient’s age. Logically, these factors are the most prominent in each method, given their high importance rate.

From the logistic regression analysis, waiting time, patient’s age, communication with the patient, doctor’s characteristics, and medical care are the factors with the most notable presence. In factor analysis, doctor’s characteristics, medical care, waiting time, the information provided, and accommodations are the most relevant factors. Using SEM, accommodations and doctor’s characteristics are the more noticeable factors. Finally, with MUSA, accommodations, waiting time, doctor’s characteristics, and admission process are the most distinguishing factors. However, due to the reduced number of studies applying this latter method, it was not possible to assess any pattern of association between this method and the critical factors.

Assessing the analysis results, it is possible to conclude that most factors with a consistent presence within the different methods are critical factors. Thus, we found no association pattern between the most critical factors and the researcher’s method.

4.7. Is There Any Association between the Country and the Critical Factors?

To examine a possible relationship between the country of study and the critical factors, an analysis was performed on the five countries with the highest number of studies. This restriction was applied because of the reduced number of studies per country and the inability to reach conclusions with a reduced number of studies. The USA, the country with a higher number of studies, revealed that patients’ most critical factors are doctor’s characteristics, patient characteristics, waiting time, and patient’s age. In Germany, doctor’s characteristics, organization, outcome, and medical care are the most critical factors. Chinese patients consider medical expenditure and doctor’s characteristics to be the two most important factors. In Portugal, accommodations, waiting time, accessibility, and medical care are deemed the most critical factors. Finally, in Turkey, doctors’ and nurses’ characteristics and waiting time are the most critical factors for the patients.

It is possible that factors diverge from country to country, giving insight into patients’ preferences from different parts of the world [ 93 ]. The doctor’s social skills are the most important for most countries, followed by waiting time and medical care.

5. Bibliometric Analysis

Bibliometric analysis is a separate analysis that one can apply to evaluate research by analyzing bibliographic data and describing publication patterns within a determined field. Methods such as co-citation and bibliographic coupling, which are discussed below, can be considered relational techniques to explore research structure, indicating patterns of authors or affiliations and prominent topics or methods [ 94 , 95 ]. The number of articles included in this bibliometric analysis differs from the number included in the statistical analysis. For the collection of articles, the databases Scopus, Web of Science, and PubMed, as already mentioned, were all explored. However, when performing a bibliometric analysis, it was not possible to compile the citation files of different databases. Thus, to achieve and extract the maximum information possible, we choose the Scopus citation file as the one with the highest potential to perform the bibliometric analysis in the Bibliometrix R package software. The citation files have different sizes, the Scopus database being the one with the highest number of articles included, 140. Web of Science included 118, and PubMed only included eight articles. It is important to note that the same article can be present in more than one database.

Figure 9 presents the growth rate of the number of published articles and mean total citations (TC) in the collection. It can be observed that these two variables are not aligned with each other, meaning that when one grows, the other does not necessarily grow as well. In the years 2009, 2010, and 2011, the number of published articles peaked, with a percentage of 6%, 7%, and 9%, respectively. The number of publications then decreased and peaked in 2018, 2019, and 2020. The mean of TC reached peaks in 2000, 2011, 2002, and 2012, and thus was not directly related to the number of publications. Neither of these variables follows a linear path, since the inflation occurs in what seems to be random intervals of time.

Number of articles and mean of total citations (TC) per year.

In this collection of 140 articles, 100 journals served as sources. The ten journals displayed in Table 4 are the ones with the highest number of publications (in this collection) and alone represent 42% of the articles. Most of the journals are related to health, as would be expected, but patient satisfaction is multidisciplinary. Despite many articles related to health, business, social sciences, and biochemistry, there are examples of subject areas in which this topic is included. When comparing the number of TC for each journal with the articles in this collection, Social Science & Medicine is the journal where the highest cited articles are published.

Ten of the most utilized journals. The citations presented in this table are the sum of the total citations of the articles published by each journal.

In total, there is a reach of 57 countries in this review. However, only the ten countries with more publications are presented in Table 5 . It is noticeable that most of the articles collected are from the USA. Germany, China, and Portugal also have a particular emphasis, being the countries with the highest number of publications. It is important to note that many publications have affiliations with more than one country. Among the ten most impactful institutions, four are from the USA, and the remaining are also from countries mentioned in Table 5 .

Ten most studied countries from collected articles.

5.1. Co-Citation Analysis

A co-citation analysis measures the frequency of two publications cited together, indicating the affinity and proximity between them [ 96 ], and can also be applied to authors and sources. Two documents are co-cited when a third document cites them; having more documents that cite the same two documents translates into a more robust association [ 95 ]. This analysis says that two co-cited papers have a similar theme [ 94 ], thus identifying the most influential authors and their interrelationships inside a determined theme [ 97 ]. The co-cited papers are grouped into different clusters, considering the research area’s knowledge base and the similarity of themes [ 98 ]. The results of the top fifteen publications are presented in Table 6 .

Total citations (TC) and TC/year of the fifteen most cited publications.

Table 6 provides a citation analysis to identify the most influential articles on the subject of patient satisfaction. In addition to the total number of citations, the number of average citations per year is also included in this analysis, given that it provides an unbiased look at the impact of each article without prioritizing the year of publication [ 99 , 100 ]. When analyzing Table 6 , it is possible to conclude that the article with the highest number of citations is the most impactful and influential in the collection. The same happens for the following four articles. An example of an article with a ratio of TC/Year that is relatively high for the number of TC is Bjertnaes et al. [ 43 ], ranked on the second to last table position.

5.1.1. Documents’ Co-Citation Analysis Results

Figure 10 shows the network of the co-citation analysis (the graphs are constructed through VOSviewer software). Circles represent the items (documents, authors, sources, or keywords). The higher the number of citations or occurrences, the larger the size of the circle. Not all items are displayed; otherwise, they might overlap. The path length estimates the distance between items; the closer two items are, the stronger their relatedness. The connections between the items (lines) are called links. Link strength is a number associated with the link itself, which shows how secure the connection is between the items assessed. For instance, in the co-authorship links, the higher the link strength, the higher the number of publications the two authors developed [ 101 , 102 ].

Documents’ co-citation analysis network using VOSviewer software. Source: authors’ own construction.

Through association strength and considering publications with a minimum of seven citations, 32 articles within four clusters were found. It is important to note that the citations considered are local citations.

Cluster 1 comprises ten articles and is the second most cited cluster, with 102 citations and the most important regarding link strength (566). The studies included in this cluster are from the USA and are mainly reviews or academic settings. The main article within this cluster is Sitzia and Wood [ 103 ] with 24 citations and a 133-link strength. This cluster has the oldest set of collected articles, all ranging from the 1980s to the 1990s.

Cluster 2 is a collection of nine articles, with 103 citations and 485 link strength. Overall, it is the cluster with the highest number of citations but the second in link strength. These studies are all from the USA and create more profound research since they explore what factors influence patient satisfaction by developing patient surveys. The articles with the most impact in this cluster are Jackson et al. [ 11 ] with 18 citations and 94 link strength, and Jaipul and Rosenthal [ 104 ], with ten citations and 62 link strength. The majority of articles presented in this cluster are from dates after the 2000s.

Cluster 3 , in its turn, is composed of eight articles, with a total of 57 citations and 218-link strength. This cluster has the lowest number of citations and is the second to last regarding link strength. The articles focus on finding the factors that influence patient satisfaction, but are not as in-depth as the second cluster articles. Some articles with an academic setting are also present. The most important articles in this cluster are Andaleeb [ 32 ] with nine citations and 31 link strength; Parasuraman et al. [ 105 ], with nine citations and 23 link strength; and Otani et al. [ 106 ], with eight citations and 44 link strength.

Cluster 4 has five articles, with a total of 249 citations and 50 link strength. Regarding citations, this cluster is ranked in third place. It is the least important in terms of link strength. The articles from this cluster are all from the 1980s and 1990s, similar to the first cluster. Hall et al. [ 107 ], with 18 citations and 104 link strength, Cleary and McNeil [ 108 ] with nine citations and 39 link strength, and Kane et al. [ 109 ] with eight citations and 37 link strength, are the articles with the most impact in this cluster.

5.1.2. Authors’ Co-Citation Analysis Results

There are 8691 authors; thus, a restriction of a minimum of 20 citations per author was applied, and 30 authors were found divided into four clusters. Figure 11 shows the network of the author’s co-citation when the method of association strength is applied. The circles’ size represents the number of times the author is cited in the collection of 140 articles.

Authors’ co-citation analysis network using VOSviewer software. Source: authors’ own construction.

Cluster 1 includes fourteen authors and is the most crucial cluster, with the highest number of citations (449) and link strength (4775). The most relevant authors are Cleary (54 citations and 581 link strength), Hall (50 citations and 466 link strength), and Kroenke (45 citations and 535 link strength). These authors correspond to cluster 4 of Figure 10 , highlighting two of the above mentioned as the authors of two of the most cited documents of cluster 4 on the documents’ co-citation analysis.

Cluster 2 includes seven authors and is placed second on ranking the number of citations (213) and link strength (2147). Otani is the most relevant author in this cluster (50 citations and 485 link strength). This author is also the one with the most contributions to the collection, with a total of six articles, followed by Elliott (34 citations and 243 link strength) and Kurz (29 citations and 337 link strength), who collaborates with Otani on four of the six articles present in the database. Some of these authors have articles present on cluster 3 of the co-citation analysis.

Cluster 3 has five authors and placed third with regard to citation’ ranking (125) and link strength (969). Parasuraman (31 citations and 228 link strength), Donabedian (29 citations and 275 link strength), and Zeithaml (25 citations and 205 link strength) are the most influential authors in this cluster. The authors of this cluster are dispersed through clusters 3 and 4 of the documents’ co-citation analysis.

Cluster 4 is composed of four articles and is the least relevant cluster in citations (99) and link strength (823). The authors in this cluster are Aiken (31 citations and 236 link strength), Orav (25 citations and 185 link strength), Sloane (22 citations and 199 link strength), and Coulter (21 citations and 203 link strength). These authors are not present in Figure 11 due to the low number of local citations of their articles.

5.1.3. Sources’ Co-Citation Analysis Results

In Figure 12 , sources (journals) where the articles were published are assessed to find the frequency in which two sources are co-cited. It translates into the similarity between the scope of focus of the sources. Each circle represents a journal, and the size of the circle is proportional to the number of citations. Sources in the same cluster or that are connected have similarities.

Sources’ co-citation analysis network using VOSviewer software. Source: authors’ own construction.

From the 2278 sources present in the sample’s references, only sources with more than 20 citations were considered, making up a total of 29 sources. Through the method of normalization strength, three clusters were found. It is important to note that the sources in this analysis are the sources of documents cited by the collection of 140 articles and not the sources from the 140 articles in the collection.

Cluster 1 is composed of ten items and is the least important in citations (303) and link strength (4879). Despite being one of the most significant clusters, the sources included are not the most renowned. The International Journal of Health Care Quality Assurance (47 citations and 614 link strength), The Healthcare Manager (41 citations, and 496 link strength), and The Journal of Marketing (40 citations and 767 link strength) are the most relevant journals in this cluster. Cluster 1 is the only cluster of the analysis that included sources not only related to the medical field, such as the Journal of Marketing , Journal of Retailing, Journal of Services Marketing, Journal of the Academy of Marketing Science , and Managing Service Quality .

Cluster 2 has ten items and is the most important regarding citations (660) and link strength (9719), as can be seen by the differential size of the circles in Figure 12 . Social Science & Medicine (189 citations, and 2632 link strength), Medical Care (179 citations and 2450 link strength), and The Journal of General Internal Medicine (73 citations and 1217 link strength) are the most important sources regarding citations’ link strength in this cluster. Special attention goes to The British Medical Journal, Annals of Internal Medicine , and The Journal of the American Medical Association , which are the sources with a significant impact on the field. It is noticeable that the second cluster is the most important and impactful of the three.

Cluster 3 included nine sources and is in second place in citations (400) and link strength (4890). The International Journal for Quality in Health Care (86 citations and 995 link strength), BMC Health Services Research (59 citations and 683 link strength), and Health Services Research (56 citations and 756 link strength) are the sources with the most citations and link strength in this cluster. It is necessary to give a particular highlight to other sources integrated into this cluster, such as The New England Journal of Medicine and The Lancet , two highly influential journals in medicine.

5.2. Discussion on the Bibliometric Analysis Results

After conducting a bibliometric study with co-citation and bibliographic coupling analysis, it is possible to assess the changes and constants on patients’ satisfaction studies throughout the years and the most influential articles, authors, and journals in the field. The research topic is constant throughout the years, discovering the factors that influence patient satisfaction, those being explanatory variables or criteria that remain in focus. Being that this a worldwide study, articles from multiple countries and institutions were analyzed. However, the USA is the country with the highest number of published articles in this field and the largest number of institutions that executed the research. From a collection of 140 articles, 39% are from the USA, followed by China and Germany, with 8% each. The discrepancy between the number of articles in the USA and the remaining countries might be a result of their health system. Since most facilities are for-profit organizations, it is imperative to keep the customers (patients) satisfied, secure their loyalty, and, thus, ensure the organization’s success. Profitable loyalty and satisfaction need to be taken to a higher level where differentiation and competitive advantage are met [ 110 ].

As mentioned above, with bibliometric methods, it is possible to assess which articles, authors, and journals are the most important in the subject area. Combining the results from both co-citations and bibliographic coupling analysis ensures that a large number of items are evaluated and that definitive conclusions can be achieved.

For the co-citations and bibliographic coupling analysis of articles, the top five documents, considering citations and link strength are: Patient satisfaction: A review of issues and concepts by Sitzia and Wood [ 103 ], Predictors of patient Satisfaction by Jackson et al. (2001), Patient sociodemographic characteristics as predictors of satisfaction with medical care: A meta-analysis by Hall and Dornan [ 111 ], Patients’ experiences and satisfaction with health care: results of a questionnaire study of specific aspects of care by Jenkinson et al. (2002), and Service quality perception and patient satisfaction: A study of hospitals in a developing country , by Andaleeb [ 32 ]. From the decades of 1990 and 2000, these documents are the most influential with regard to the topic of patient satisfaction. It is important to note that from Table 6 , the document Patient safety, satisfaction, and quality of hospital care: Cross-sectional surveys of nurses and patients in 12 countries in Europe and the United States from Aiken et al. [ 12 ], is the article with the most citations, as well as the most influential article throughout the years. However, it was not included in the co-citation nor the bibliographic coupling analysis because it had no connection with the other articles, and thus zero link strength.

The authors’ co-citation and bibliographic coupling analysis revealed that the five most influential authors regarding citations and link strength are Cleary, Hall, Otani, Sjetne, and Aiken. Even though these authors do not have articles presented above that are mentioned as the most influential, these authors have written multiple articles on patient satisfaction and are thus essential and influential in the field.

The sources’ co-citation and bibliographic coupling analysis show that in terms of citations and link strength, Social Science & Medicine and Medical Care are the two most prestigious journals. The International Journal for Quality in Health Care, The Journal of General Internal Medicine , and The Journal of the American Medical Association are also influential journals in patient satisfaction.

6. Concluding Remarks and Future Directions for Research

This study reviewed studies published between 2000 and 2021 in three databases regarding patient satisfaction determinants. Firstly, a statistical analysis to discover the factors that influence patient satisfaction and the researchers’ methods was performed. Secondly, we executed a bibliometric analysis to find the most influential authors and documents within this theme.

The statistical analysis results yielded multiple determinants of satisfaction within diverse research areas, such as medicine, business, and the social sciences. Medical care, communication with the patient, and waiting time, patient’s age, perceived health status, and patient’s education are the factors that most influence patient satisfaction. Each one of these factors can create a positive or negative experience for the patient. Patient satisfaction directly connects to the loyalty of the patient towards the healthcare provider. Patient loyalty results in positive behaviors such as healthcare providers’ recommendations, compliance, and higher healthcare service usage, thus increasing profitability. With healthcare becoming an increasingly competitive market, measuring healthcare satisfaction and quality can help managers control, improve, and optimize several organizational aspects. While some markets and industries try to improve customer orientation, healthcare practitioners have to remain alert for the modifying behaviors of patient expectations [ 10 , 16 , 28 , 112 ]. An important market arising in the past few years has been that of medical tourism, and Ghasemi et al. [ 113 ] have comprehensively studied the impact of cost and quality management in patient satisfaction in this market. The authors verified the existence of a relationship of these three dimensions. Therefore, it becomes obvious that cost impacts satisfaction, a topic that has been overlooked by many researchers. Additionally, social media has been deemed as a relevant way for customers to express their satisfaction levels [ 114 , 115 , 116 ]. The influence of this route on the results should be better understood in future research.

The importance of the determinants of patient satisfaction can be assessed through several methods, as has been previously seen. Due to the ease of handling and computation, factor and regression analyses are the healthcare management methods. However, despite MUSA’s low usage rate, this is a useful method, with many advantages over the traditional customer satisfaction models. It considers the customers’ judgments in the way they are expressed in the questionnaires [ 25 , 67 ]. One must be careful when using ordinal data within models that are not suitable for the former; however, a lesson from our research is that some researchers are still struggling with those models despite the mathematical objections that these face. MUSA’s low usage in healthcare and its potential compared with other alternatives creates opportunities for broader dissemination of studies using that model. Other theoretically suitable alternatives should also be highlighted, including MUSA-INT, the integrated use of MUSA (or MUSA-INT) alongside benchmarking techniques (like DEA or the Benefit of Doubt), and some useful and appropriate multiobjective interval programming models.

Of course, the highlighted fact that patient satisfaction and health quality are not linearly related, and that the patient cannot always properly assess the provider’s performance, is a limitation that any satisfaction-based study faces. Despite the obvious need to study satisfaction determinants (and several reviews have tried to deal with this issue), the consistency of results is somehow absent. Perhaps the socio-economic differences among patients and even the characteristics of the health care systems in which they are part of may help to explain this lack of consistency. Future research should focus on this matter. Another possible limitation is the fact that we did not distinguish between inpatients and outpatients in this study. Although satisfaction may depend on the service itself, the same patient can be either an inpatient or an outpatient in different moments, meaning that the next experience (as an outpatient) will be somehow biased by the previous one (as an inpatient or vice-versa). It may help to justify the fact that often the term “patient satisfaction” seems more appropriate to study the healthcare facility as a whole, as it was one of the objectives in this study.

With regard to the second part of this study, an analysis of the literature through bibliographic methods featured the most important aspects of patient satisfaction research. Based on the article’s co-citation analysis, Patient satisfaction: A review of issues and concepts by Sitzia and Wood was the most relevant document. The published date of this article is 1997; thus, it was not included in our collection. However, when the reference list of the sample articles was analyzed, it was present on most of the lists. From the bibliographic coupling analysis, Predictors of Patient Satisfaction by Jackson et al. was the most critical document. This document was published in 2001, and thus it was included in our collection. These two articles are considered the most important and impactful in the area of patient satisfaction. The author’s co-citation and bibliographic coupling analysis revealed that Cleary and Otani are two of the most influential authors in this field. Both authors have collaborated in many articles in this research area; they are, thus, considered to be two of the most relevant researchers. Cleary’s articles related to patient satisfaction were published in the 1990s and are not included in our collection. Otani, however, is the author with the most documents in our collection. Despite the fact that his number of citations is not the highest, this author is influential because of the high number of collaborations in the field. From the journal’s co-citation and bibliographic coupling analysis, Social Science & Medicine and Medical Care were the most influential in patient satisfaction. However, considering general medicine/health, these journals were not the ones with the highest impact.

The knowledge obtained from this systematic review can be seen as an essential foundation for additional studies, and can be used to enhance further knowledge among healthcare practitioners, researchers, and scholars.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, D.C.F. and M.I.P.; methodology, I.V.; software, I.V.; validation, D.C.F., P.C. and M.V.; formal analysis, I.V. and M.I.P.; investigation, D.C.F. and I.V.; resources, D.C.F. and I.V.; data curation, I.V.; writing—original draft preparation, D.C.F. and I.V.; writing—review and editing, D.C.F., M.I.P., P.C. and M.V.; visualization, M.V.; supervision, M.I.P.; project administration, D.C.F.; funding acquisition, D.C.F. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare that they have no conflict of interest.

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• Volume 19, Issue 1
• Reviewing the literature
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• Joanna Smith 1 ,
• Helen Noble 2
• 1 School of Healthcare, University of Leeds , Leeds , UK
• 2 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
• Correspondence to Dr Joanna Smith , School of Healthcare, University of Leeds, Leeds LS2 9JT, UK; j.e.smith1{at}leeds.ac.uk

https://doi.org/10.1136/eb-2015-102252

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Implementing evidence into practice requires nurses to identify, critically appraise and synthesise research. This may require a comprehensive literature review: this article aims to outline the approaches and stages required and provides a working example of a published review.

Are there different approaches to undertaking a literature review?

What stages are required to undertake a literature review.

The rationale for the review should be established; consider why the review is important and relevant to patient care/safety or service delivery. For example, Noble et al 's 4 review sought to understand and make recommendations for practice and research in relation to dialysis refusal and withdrawal in patients with end-stage renal disease, an area of care previously poorly described. If appropriate, highlight relevant policies and theoretical perspectives that might guide the review. Once the key issues related to the topic, including the challenges encountered in clinical practice, have been identified formulate a clear question, and/or develop an aim and specific objectives. The type of review undertaken is influenced by the purpose of the review and resources available. However, the stages or methods used to undertake a review are similar across approaches and include:

Formulating clear inclusion and exclusion criteria, for example, patient groups, ages, conditions/treatments, sources of evidence/research designs;

Justifying data bases and years searched, and whether strategies including hand searching of journals, conference proceedings and research not indexed in data bases (grey literature) will be undertaken;

Developing search terms, the PICU (P: patient, problem or population; I: intervention; C: comparison; O: outcome) framework is a useful guide when developing search terms;

Developing search skills (eg, understanding Boolean Operators, in particular the use of AND/OR) and knowledge of how data bases index topics (eg, MeSH headings). Working with a librarian experienced in undertaking health searches is invaluable when developing a search.

Once studies are selected, the quality of the research/evidence requires evaluation. Using a quality appraisal tool, such as the Critical Appraisal Skills Programme (CASP) tools, 5 results in a structured approach to assessing the rigour of studies being reviewed. 3 Approaches to data synthesis for quantitative studies may include a meta-analysis (statistical analysis of data from multiple studies of similar designs that have addressed the same question), or findings can be reported descriptively. 6 Methods applicable for synthesising qualitative studies include meta-ethnography (themes and concepts from different studies are explored and brought together using approaches similar to qualitative data analysis methods), narrative summary, thematic analysis and content analysis. 7 Table 1 outlines the stages undertaken for a published review that summarised research about parents’ experiences of living with a child with a long-term condition. 8

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An example of rapid evidence assessment review

In summary, the type of literature review depends on the review purpose. For the novice reviewer undertaking a review can be a daunting and complex process; by following the stages outlined and being systematic a robust review is achievable. The importance of literature reviews should not be underestimated—they help summarise and make sense of an increasingly vast body of research promoting best evidence-based practice.

• ↵ Centre for Reviews and Dissemination . Guidance for undertaking reviews in health care . 3rd edn . York : CRD, York University , 2009 .
• ↵ Canadian Best Practices Portal. http://cbpp-pcpe.phac-aspc.gc.ca/interventions/selected-systematic-review-sites / ( accessed 7.8.2015 ).
• Bridges J , et al
• ↵ Critical Appraisal Skills Programme (CASP). http://www.casp-uk.net / ( accessed 7.8.2015 ).
• Dixon-Woods M ,
• Shaw R , et al
• Agarwal S ,
• Jones D , et al
• Cheater F ,

Twitter Follow Joanna Smith at @josmith175

Competing interests None declared.

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