customer service literature review

Customer-Focused Service Guarantees and Transparency Practices (2018)

Chapter: chapter 2 - literature review.

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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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Focusing the customer through smart services: a literature review

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• 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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Dreyer, S., Olivotti, D., Lebek, B. et al. Focusing the customer through smart services: a literature review. Electron Markets 29 , 55–78 (2019). https://doi.org/10.1007/s12525-019-00328-z

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3 Business Management and Marketing Department, Faculty of Business Sciences and Tourism, University of Vigo, 32004 Ourense, Spain

Amador Durán-Sánchez

4 Economy Department, Faculty of Economics and Business, University of Extremadura, 06071 Badajoz, Spain; se.xenu@nasnarudma

The objective of this research was to analyze the influence of the dimensions that enable the rating of service quality perceived by users of sport and health centers in the satisfaction they experience from the service received. In order to present the working hypothesis, a bibliographic review on the concept and dimensions of perceived service quality was carried out, as well as its relationship with satisfaction. The rating scale sports organizations (EPOD) was used as a measurement instrument. The application of a regression analysis was used to test the hypotheses. As a prior step, the measurement scales were validated and an exploratory factor analysis was applied to determine the structure of the variables considered. The regression models proposed show the joint influence of the dimensions used by the users to rate perceived service quality in their satisfaction. The results enabled us to observe that the dimensions considered in the model explained 75.7% of satisfaction, with the facilities and material, together with communication and activities, having the most significant influence on satisfaction. Meanwhile, dimensions that had less impact were the monitor and the staff. It is clear that there is a strong correlation between perceived quality and satisfaction with service.

1. Introduction

Currently, research shows that success and competitiveness in the management of sport and health centers requires more efficient management. In this sense, quality management, as one of the 25 most-used management tools [ 1 ], is essential [ 2 ]. Quality management is understood from two perspectives: The internal perspective (objective quality), focused on the standards of the service, and the external perspective (subjective quality), focused on quality as satisfaction of users’ expectations. The latter is currently the most-followed perspective in the service sector [ 3 ].

Thus, innovation and quality are the two key factors for business success [ 4 , 5 , 6 , 7 , 8 , 9 ]. Both concepts are linked in the sense that innovation is a part of continuous improvement [ 10 ] which, in turn, forms a fundamental part of quality. Porter [ 11 ] stated that the competitiveness of a country and, therefore, of its industrial and economic fabric, depends on the capacity to innovate and improve. With respect to organizations, innovation allows for economic sustainability and for their growth by generating competitive advantages [ 12 , 13 , 14 , 15 , 16 ].

Innovation is not exclusively associated with creativity and the generation of new products and services, but also refers to new forms of management and processes [ 17 , 18 ]. One of the most widely used definitions of innovation is provided by the Oslo Manual [ 19 ], which defines it as “the introduction of a new or significantly improved product (good or service), of a process, of a new organizational or marketing method, in the internal practices of the company, the organization of the workplace or external relationships”. Therefore, several types of innovation are distinguished: Product, process, organizational, and marketing innovation.

In this sense, the implementation of quality management systems is part of organizational innovation [ 20 , 21 , 22 , 23 , 24 ], since it involves the implementation of new organizational methods in the business. Therefore, the quality and, consequently, the implementation of quality management systems and the processes that are derived from it, become a tool for innovation and competitiveness in sport and health centers.

In the context of sports organizations, reference is made to service quality as “a differentiation strategy to increase productivity and profitability, as well as to improve the company’s image and achieve user loyalty” [ 25 ] (p. 250). In addition, service quality also enables knowledge of users’ perception of the quality of the service received, which is necessary to improve user satisfaction, as well as improve the competitiveness and viability of organizations. It should not be forgotten that satisfaction in the academic literature is considered a precedent for trust, mouth-to-ear communication [ 26 ], complaints [ 27 ], and loyalty [ 28 ].

In this research, carried out in the context of sport and health centers, the external perspective of quality is considered in which the client becomes the central axis of sports organizations. Therefore, it focuses on the concept of “perceived quality” of services, which is the way to conceptualize the predominant quality in the field of services.

In this sense, it must be taken into account that “a service is of quality to the extent that it meets or exceeds clients’ expectations” [ 29 , 30 , 31 ] and the concept is operationalized in practice by users comparing their expectations of the service with the perception that is formed once it is received [ 32 ]. In this way, quality ceases to be something objective (it focuses on the producer’s perspective) and instead becomes subjective, focusing on what the consumer says it is [ 29 ], as “only consumers judge quality: all other judgments are essentially irrelevant” [ 31 ] (p.18).

Research carried out on quality in sports services and consumer satisfaction has become important in recent years. According to Calabuig [ 33 ], it is mainly developed from three points of view in the sports sector: Psychosocial, the economic-business perspective, and the marketing perspective, focused on the consumer (studies based on SERVQUAL and alternative studies). This research follows the marketing perspective, whose research focuses, according to Pérez [ 34 ] (p.128), on “how to improve quality perception and the sense of satisfaction”.

Although several studies have been carried out following this perspective [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ], authors such as Tsitskari et al. [ 49 ] and Arías-Ramos et al. [ 50 ] (p.106) state that these types of studies are not sufficient; “there are many issues to be addressed, lines of research to be continued and uncertainties to be resolved on the assessment of perceived quality and user satisfaction in sports organizations”.

In this context, this research is aimed at analyzing the influence of the dimensions that enable the rating of the quality of service perceived by users in the satisfaction they experience with it, which enables to us observe whether the perceived quality of a sports service is directly related to the satisfaction level. The empirical study was carried out in a sport and health center with a sample of 206 clients. The measurement instrument known as the rating scale sports organizations (EPOD) was used.

This article is structured as follows. After the introduction, where the subject matter of the study is contextualized, the study is justified and the objective is presented. Section 2 contains the theoretical reference framework (concept of perceived quality of the service and its relationship with satisfaction) and the work hypotheses are presented. The methodology used (target population, measurement questionnaire, and data analysis) is described below. The results are collected in Section 4 , and finally, the conclusions obtained are discussed and presented.

2. Review of the Literature

2.1. perceived service quality concept.

The starting point to define the concept of “perceived service quality” is defining the terms “service” and “service quality”. In this sense, the definition of service provided by Grönroos [ 30 ] is one of the first definitions and delimits service as that activity or series of activities of a more or less intangible nature that normally, but not necessarily, take place through interactions between the client and the employees of the service company who try to solve the consumer’s problems.

This definition, together with the one provided from a different approach by Lovelok, approach the perceived service quality concept by taking into account the satisfaction of expectations. Lovelok [ 51 ] (p.491) understands customer service as activities aimed at a task that includes interactions between clients and the organization and seeks the mutual satisfaction of the expectations of both, so it must be designed with two objectives in mind: Customer satisfaction and operational efficiency.

With regard to the service quality term, its definition is very complex since the intrinsic characteristics of the services means, on the one hand, that the quality practices applied must be different from those for tangible products [ 49 , 52 ] and, on the other hand, a greater difficulty is involved when evaluating the quality of a service. In this sense, Parasuraman et al. [ 32 ] (p.36) states that “the difference between the evaluation of the quality of a service and that of a good by a consumer is not in the process, but in the nature of the characteristics on which the evaluation is performed”.

These characteristics were specified by Parasuraman et al. [ 32 ]: Intangibility, inseparability of production and consumption, or simultaneity, heterogeneity, or variability, expiration. In Parasuraman et al. [ 32 ] and Grönroos [ 53 ], a broad discussion of the differences between services and physical goods can be seen. According to Zeithaml [ 54 ] and Stanton et al. [ 55 ], the intangible aspects are difficult to identify and quantify and make it difficult to establish precise specifications to standardize their quality. On the other hand, they are susceptible to different evaluations by clients, which makes the measurement and evaluation of quality difficult [ 55 ]. The inseparability in the services of production and consumption, as well as the perishable nature and the potential heterogeneity or variability in the performance, make the precision of quality difficult [ 32 ] (p.35).

In this context, as already mentioned, there are many definitions in this regard [ 54 , 56 , 57 , 58 ] and their review provides two different views or approaches when defining the service quality term: Objective and subjective quality [ 53 ] (p.38). On the one hand, the objective quality or internal vision of quality focuses on the technical aspects [ 59 ] from the producer’s perspective, as well as the subjective quality or external vision of quality in which clients’ requirements are emphasized, thus emerging the “perceived quality” concept. This last concept was introduced by Gönroos [ 58 ] when considering the idea that clients compare their expectations with the service received, with the result of this process being the perceived quality of the service. This concept was developed later, both methodologically and empirically, by Parasuraman et al. [ 32 , 60 ]. An important aspect to mention is that these two visions gave rise to two schools of thought: The Nordic School and the North American School.

In the case of the Nordic School, its main representatives Grönroos [ 61 , 62 , 63 ], Gummesson [ 64 ], and Lehtinen and Lehtinen [ 65 ], focus on the concept of service quality from the point of view of the product, with efficiency being the basic objective for which standards are used for its control [ 65 ]. The Grönroos Service Quality Model [ 58 ] established two dimensions for service quality which interact between each other: Technical quality or design of the service, referred to as “what” service the client receives (result), being susceptible to be measured by the company and evaluated by the consumer; and the functional quality or performance of the service, which deals with “how” customer service (process) is provided. Both dimensions are compared with previous expectations by the client, which are influenced by the result of the service, by the way it is received, and by the corporate image [ 58 ]. Subsequently, this conceptual model of Grönroos, in which perceived quality is defined as a result of the comparison between the expected and received service, was moved to the United States and developed by Parasuraman, giving rise to the emergence of the North American School.

Bearing in mind that this last perspective is the one that best fits sports services, which is the scope of study in this research, it is the one that was developed in more detail. Thus, Parasuraman et al. [ 66 ] (p.3) defined perceived service quality by the client, as an overall assessment of the consumer regarding the superiority of the service resulting from the comparison made by clients between the expectations and perceptions regarding the performance of the service received. This definition of perceived quality became the most widely used way to conceptualize quality from the perspective of services and is the basis of the theoretical and methodological approach of Parasuraman et al. [ 32 ], in which the quality process in services is explained.

These authors posed the question of “What is service quality?” in their initial investigation. Thus, the concept of perceived quality [ 32 ] arised. They also determined the dimensions used by clients to rate services [ 66 ]. Finally, they developed a conceptual and empirical model to measure service quality: The SERVQUAL model, represented graphically by Zeithaml et al. [ 67 ] (p.26), and defined as “a summarized multiple-scale tool with a high level of reliability and validity that companies can use to better understand the expectations and perceptions that customers have regarding the service received”.

As shown, the two factors that determine perceived service quality are expectations and perceptions [ 66 ]. Expectations are defined by Parasuraman et al. [ 66 ] (p.17) as clients’ desires or needs and they are determined, as reflected in the conceptual model, by previous experiences, clients’ current needs and demands, the company’s external or formal communications, mouth-ear communication between clients, and the corporate image. Perceptions are defined as consumers’ beliefs regarding the service received, which will be determined by the dimensions which clients consider in order to rate the service.

In this regard, in the literature on the subject, there are divergences regarding these dimensions and there is no consensus. Garvin [ 68 ] consider eight dimensions (performance, characteristics, reliability, attachment, durability, service aspects, aesthetics, perceived quality), Lehtinen and Lehtinen [ 65 ] consider three dimensions (physical, corporate, and interactive quality), and Grönroos [ 58 ] takes into account the technical or result dimension, the functional or process dimension, and the corporate image.

However, the most-considered multidimensionality of service quality by researchers in this field is the one proposed by Parasuraman et al. [ 32 ], who consider that perceived quality is made up of 10 dimensions: Tangible elements, “appearance of physical facilities, equipment, staff and communication materials”; reliability, “ability to implement the service promised reliably and carefully”; responsiveness, “willingness to help customers and provide them with a quick service”; professionalism, “having the required skills and knowledge of the process of providing the service”; courtesy, “attention, consideration, respect and helpfulness of the contact staff”; security, “no dangers, risks or doubts”; credibility, “veracity, belief, honesty in the service provided”; accessibility, “accessible and easy to contact”; communication, “keeping clients informed using a language they can understand, as well as listening to them”; and understanding the client, “making the effort to know the clients and their needs” [ 67 ] (p.24). Subsequent research by these authors [ 66 ] reduced them to five dimensions: Tangible elements, reliability, responsiveness, security (including professionalism, courtesy, credibility, and security), and empathy (including accessibility, communication, and understanding of the user).

In summary, the concept of perceived service quality is a complex variable, with several definitions in this regard. This was observed by Díaz and Pons [ 69 ] (p.53), who, after analyzing the literature on perceived service quality, proposed two perspectives when defining the concept: From the perspective of customer perception [ 54 , 70 , 71 ] and from the perspective of customer expectations and perceptions [ 32 , 72 , 73 ]. However, in recent years, the most recurring perceived quality concept has been one which contextualizes quality in the field of services from the client’s perspective and is conceptualized by comparing the client’s expectations with the perceptions about the service received. According to Zeithaml et al. [ 31 ] (p.18), “only consumers judge quality: all other judgments are essentially irrelevant”.

The research conducted by Grönroos [ 58 ] and Parasuraman et al. [ 32 , 66 ], aimed at defining the concept of perceived quality, gave rise to two schools and their corresponding models of perceived service quality. As Gómez [ 74 ] (p.53) states, “to have a more complete vision and to finish understanding the concept of perceived service quality, it is necessary to know the different theoretical models based on this construct”. In the case of the North European or Nordic School, its integral models are the following: Models of quality of service or image [ 58 ], the Quality Model of Grönroos and Gummerson [ 53 ], augmented service offering [ 53 ], “Servuction” Model by Eiglier and Laneard [ 75 ], and the three-component model [ 76 ]. The North American School integrates six models: The SERVQUAL Model [ 32 ], Augmented Quality Service Model [ 77 ], SERVPERF Model [ 78 ], Multidimensional, Hierarchical Model [ 79 ], service quality model of Bolton and Drew [ 80 ], and Bitner service quality model [ 81 ].

2.2. Relationship between Service Quality and Consumer Satisfaction

In the previous section, the concept of perceived quality was broadly discussed, so the starting point of this section is to define the term “consumer satisfaction”. Two major lines of research in recent years, the cognitive model [ 82 ] and emotional model [ 83 ], have been integrated, leading us to consider satisfaction as a post-consumer response or assessment [ 84 ] susceptible to change in each transaction [ 85 ].

There is a great similarity between the concepts of perceived quality and satisfaction [ 86 ]. However, most researchers suggest that both concepts are different constructs and that service quality is a broader concept than satisfaction. Thus, Parasuraman et al. [ 66 ] refer to the differences between both concepts in relation to durability. Thus, perceived quality refers to an enduring attitude related to the superiority of a service, while satisfaction is a transitional assessment of a specific transaction in which a comparison is made with what was expected [ 85 ]. To Oliver [ 87 ], the differences are that when the consumer assesses the perceived quality, the predominant dimensions are those of a cognitive nature and, in the case of satisfaction, they are emotional in nature.

These differential characteristics, which are discussed in the literature, led Zeithaml et al. [ 86 ] to propose that the difference between both concepts is based on the fact that satisfaction involves an assessment made by the client for a specific transaction [ 88 ] and requires previous experience, since this assessment depends on the consumer’s previous expectations [ 76 , 89 ], whereas service quality can be perceived without the need for a direct experience with it [ 66 ].

There are many who affirm the existence of a relationship between both concepts [ 54 , 66 , 78 , 90 ]. However, they do not reach a consensus regarding the causal relationship between both concepts. Thus, Iacobucci et al. [ 91 ] state that there are two clearly differentiated positions: Those that support the idea that satisfaction is a consequence of perceived quality [ 66 , 78 , 92 , 93 , 94 , 95 ] and research that supports the inverse relationship, considering satisfaction as an antecedent of service quality [ 59 , 80 , 81 , 96 , 97 ]. However, there is also an intermediate position, in which satisfaction is considered both an antecedent and a consequence of the perceived quality of service. Representatives of this position are Parasuraman, Zeithaml and Berry [ 98 ], Rust and Oliver [ 76 ], and Martínez-Tur, Peiró and Ramos [ 85 ].

In this context, the following working hypotheses were proposed:

H1: The service quality dimensions have a positive influence on the satisfaction experienced by the users of sport and health centers.

H2: The service quality dimensions have a positive influence on the satisfaction with the facilities experienced by the users of sport and health centers.

H3: The service quality dimensions have a positive influence on the satisfaction with the organization of activities that the users of the sport and health centers experience.

H4: The service quality dimensions have a positive influence on the satisfaction with the activities experienced by the users of sport and health centers.

3. Methodology

3.1. universe, sample, and questionnaire.

The research was designed by organizing the collection of data in order to comply with the proposed objective through a structured questionnaire addressed to users of a sport and health center. To calculate the representativeness of the sample, only the subscribers were taken into account. Users who use the service occasionally, which represent a very small percentage, were excluded. Thus, the target population was made up of 1512 subscribers, and 206 users responded to the questionnaire (incomplete questionnaires were discarded), which represents a response rate of 13.62% and a margin of error of 6.35%, taking into account a 95% confidence level (Z = 1.96 p = q = 5).

The questionnaire was structured in three parts. First, to measure the perceived service quality, the rating scale sports organizations (EPOD) was created by Nuviala et al. [ 99 ], adapted to the sport and health centers where the study was carried out (29 items). This scale “is a tool for practical and direct application on the perception that users of sports services have of the sports organization and the services it provides” [ 99 ] (p.10). The original scale consists of 28 items grouped into four dimensions: Sports experts, facilities and material resources, activities, and image of the organization.

The second part of the questionnaire included the scale to measure user satisfaction with the service, which was divided into three dimensions: Satisfaction with the facilities (five items [ 100 ]) satisfaction with the organization (three items [ 99 ]), and satisfaction with the development of the activity (four items [ 101 ]). The last part of the questionnaire included the data that enabled us to define the sample profile. Five-point Likert measurement scales were used (1–totally disagree to 5–totally agree and 1–not at all satisfied to 5–very satisfied).

Regarding the profile of the user of the sport and health center, the user is between 18- and 40-years-old (77.6%), male (66.99%), student occupation (32.4%), or works in the private sector (31.07%), with a secondary education level (49.51%). This user usually attends the sport and health center three days a week (43.69%) on average, preferably in the afternoon (55.83%). The reasons for being a user of the center are: Proximity to home or work (20.55%), because of the treatment received (10.84%), and because of the range of activities desired (9.22%). The main reason for sports is entertainment in 40.29% of cases and for aesthetic reasons in 31.07% of cases.

3.2. Data Analysis Techniques

The statistical program SPSS 19.9 (IBM, Armonk, NY, USA) (Statistical Package for the Social Sciences) was used to perform the data analysis and was carried out in two phases. First, a descriptive study of the sample (mean and standard deviation) was carried out and the measurement scales were validated, taking into account the psychometric properties of reliability, validity, and unidimensionality [ 102 ]. To evaluate the reliability and delimit the number of items that measure each scale, Pearson’s item-total correlation coefficients were considered (they should not exceed 0.3 according to Nunnally [ 103 ]) and Cronbach’s α [ 104 ] was estimated (must be greater than 0.7).

The analysis of the unidimensionality enabled to us to find the structure of dimensions of the proposed scales. Prior to its performance, it was found that the data were adequate to perform the exploratory factor analyses: Analysis of the correlation matrix, Bartlett’s Sphericity test (χ 2 high and sig. > 0.05), the Kaiser-Meyer-Oklim (KMO) measure (>0.7, median: >0.8, good and 1> = KMO > 0.9, very good), and the sample adequacy measure were acceptable (unacceptable for values lower than 0.5, small values should be removed from the analysis). Unidimensionality was tested through the percentage of variance explained and the factor loadings of each indicator, for which an exploratory factor analysis of main components with varimax rotation was carried out [ 105 ].

Second, the multiple regression analysis was applied to contrast the hypotheses proposed. This process enabled us to study the relationship between a dependent variable (satisfaction) and its independent or predictive variables (dimensions of perceived service quality) through the estimation of the regression coefficients that determine the effect that the variations of the independent variables have on the behavior of the dependent variable. Prior to the regression analysis, the underlying assumptions on which this type of analysis is based were verified (linearity, independence, homoscedasticity, normality, and noncollaterality).

4.1. Validation of Measurement Scales

First, the internal consistency of the scale that measures the perceived quality of the service was analyzed through reliability analysis (item-total correlation and Cronbach’s α). Taking into account the item-total correlation, it was not necessary to eliminate any items, since all of them were above the recommended minimum of 0.3. Cronbach’s α that measures the reliability of each factor is higher than the recommended minimum 0.7 [ 103 ].

In order to analyze the unidimensionality of the scales, an exploratory factor analysis was carried out, which enabled us to group the items and identify five factors or dimensions that explain 70.28% of the total variance (it exceeds the minimum requirement of 50%) ( Table 1 ). The application of this analysis involved the elimination of the item “with this activity I obtain the results I expected” since the factor loading was less than 0.5 [ 106 ].

Perceived service quality: Descriptive statistics and exploratory factor analysis.

* N = 206; Likert scale = 1= least important /5 = most important; 1 Tests that show that the data obtained through the questionnaire were adequate to perform the factor analysis (requirements: Bartlett’s Sphericity Test χ 2 (sig. > 0.05), KMO > 0.7 median, MSA = unacceptable for values below 0.5); 2 S.D: Standard deviation; Source: Authors’ own data.

In the case of the satisfaction scale, the factor analysis resulted in three factors that were denominated (“satisfaction with the facilities”, “satisfaction with the organization of activities”, and “satisfaction with the activity”), which explain 73.77% of the total variance ( Table 2 ). The analysis of the item-total correlation did not involve eliminating any items, since they were higher than 0.3 in all cases.

Satisfaction: Descriptive and factorial exploratory analysis.

* N = 206; Likert scale = 1 = least important /5 = most important; 1 Tests that show that the data obtained through the questionnaire were adequate to perform the factor analysis (requirements: Bartlett’s Sphericity Test χ 2 (sig.> 0.05), KMO > 0.7 median, MSA = unacceptable for values below 0.5); 2 S.D: Standard deviation; Source: Authors’ own data.

From the results of the analyses carried out to corroborate reliability once the item “with this activity I obtain the results I expected” was eliminated in the scale that measures the perceived quality of the service, it can be concluded that the proposed scales are highly reliable, thus being free of random errors, and are able to provide consistent results.

4.2. Regression Analysis

Four multiple regression analyses were proposed in order to corroborate the objectives set. The models included six independent/predictive variables that corresponded to the dimensions included in the scale, enabling us measure the perceived quality of the service (monitor, facilities and material, activities, communication, and staff) with each of the dimensions of satisfaction that have been considered (dependent variable or criterion variable): Overall satisfaction, satisfaction with the facilities, satisfaction with the organization of activities, and satisfaction with the activities. Two control variables were incorporated into the model: Users’ sex and age.

First, the results obtained between the analysis variables in the correlation matrix were analyzed ( Table 3 ). Regarding the control variables, although no significant differences were found and the correlation coefficients are weak, it is observed that the age variable negatively affects the satisfaction with the facilities and with the organization and positively affects the satisfaction with the activities and overall satisfaction. The correlation coefficients allow us to affirm that the dimensions of the perceived quality of the service have a positive relationship with satisfaction (H1, H2, H3, H4), with strong and significant correlation coefficients at the p < 0.01 level.

Measurement scale correlations of the perceived quality of service and user satisfaction.

Note: * p < 0.001. Bilateral test; Source: Authors’ own data.

Prior to the regression analysis, the underlying assumptions on which this type of analysis is based were verified (linearity, independence, homoscedasticity, normality, and noncollaterallity). For the assumption of independence of residuals, the Durbin-Watson statistics was obtained, which, in the three regression models built, gave values between 1.5 and 2.5 ( Table 2 ). In all cases, it gave values lower than 2, which indicates positive autocorrelation.

In the case of collinearity, its diagnosis provided tolerance values between 0.302 and 0.556, which indicate noncollinearity. Therefore, none of the independent variables have correlations greater than 0.9. Moreover, it is possible to assume residual normality, since this tendency could be verified in the analysis of histograms and, in addition, it was confirmed by calculating the Kolmogorov-Smirnov test. Finally, regarding the homoscedasticity assumption, for each value of the independent variables in the scatterplot ( Figure 1 ), the residuals are distributed in a similar way (no relationship was observed between the forecasted values and the residuals).

Standardized waste. Source: Authors’ own data

In the regression analyses carried out, the measure of the goodness of fit of the model was estimated using the multiple correlation coefficient and the coefficient of determination, which is the square of the previous multiple correlation efficient and expresses the proportion of the variance of the dependent variable explained by the regression model. It is observed that the proposed models have an adequate goodness of fit. In this sense, the explanatory variables contained in the model explain 75.7% of overall satisfaction, 53.4% of the satisfaction with the facilities, 56.1% of the satisfaction with the organization of activities, and 56.0% of the satisfaction with the activity ( Table 4 ). In addition, the F statistic, which enables us to decide whether there is a significant relationship between the dependent variable and the set of independent variables taken together, provides a good adjustment to the point cloud (sig 0.000, indicates that there is a significant linear relationship).

Result of the regression analysis for the dimensions of the perceived quality of the service and satisfaction of the users.

Note: Cell entries are standardized coefficients; * p < 0.05; ** p < 0.001. Bilateral test; Source: Authors’ own data.

Second, the partial correlation coefficient of each explanatory variable was estimated, which indicates the specific relationship of the variable with the dependent variable assuming that the other independent variables remain constant. The sign of the correlation coefficient β makes it possible to determine the direction of the relationship and the F statistic, as well as the goodness of fit of the regression. The p -value (> or < that 1) indicates the significance level with the dependent variable.

The results obtained in the regression together with the correlations enable us to observe that in the case of “general satisfaction”, the dimensions of perceived service quality contribute significantly to explain satisfaction with high and significant β values at a p < 0.001 level. All dimensions, except for the monitor dimension ( p < 0.05), explain satisfaction at a p < 0.001 level. In this sense, the higher the perceived quality of each of the dimensions, the higher the satisfaction experienced by the users.

In this same line, the other three models of multiple regression were proposed with the objective of identifying which dimensions of perceived quality affect the satisfaction that users experience regarding the facilities, organization, and the activities, and to what extent. In the case of satisfaction with the facilities, it is observed that the monitor, the activities, and the staff do not contribute significantly to explain satisfaction (sig > 0.05). As expected, communication significantly contributes to explain satisfaction (β = 0.160, p < 0.05), along with the facilities dimension (β = 0.540, p < 0.001).

The regression model, which explains the satisfaction with the organization of activities, is significantly influenced by the organization of activities (β = 0.399, p < 0.001), staff (β = 0.180, p < 0.001), and communication (β = 0.149, p < 0.001), while the relationship with the facilities and the monitor is not significant (sig > 0.05). Finally, in the last model, which refers to satisfaction with the activities, all the variables are significant at the p < 0.001 level (communication and staff) or p < 0.05 level (monitor and facilities) except for the activities dimension.

Taking the results into account, the hypothesis H1 and, partially, H2, H3, and H4, are corroborated, since not all dimensions positively and significantly influenced satisfaction.

5. Discussion

First, note the results obtained related to the dimension structure of the scale, which enables us to measure the perceived quality of the service. In this research, the rating scale sports organizations (EPOD), developed by Nuviala et al. [ 99 ], was used. Since it was applied to a sample of users of an organization that provides sports services, but with different characteristics from the sample of the original scale, its reliability and unidimensionality were analyzed and studied.

In the case of reliability, Cronbach’s α, which measures the reliability for the total scale, is 0.962, which is very similar to that obtained by Nuviala et al. [ 99 ] of 0.916. If each one of the dimensions is taken into account, it is higher than 0.8 in all cases, corroborating the results obtained by Nuviala et al. [ 99 ] that obtained values between 0.799 and 0.885. Therefore, it was concluded that the scale is reliable.

Regarding the structure of dimensions in this investigation, the items were grouped into six dimensions: Monitor (six items), facilities (five items), sports equipment (four items), activities (nine items), communication (three items), and staff (three items). However, in research by Nuviala et al. [ 99 ], items were grouped into five dimensions (activities, sports experts, spaces, materials, image). The scale measuring satisfaction was divided into three factors or dimensions: Satisfaction with the facilities, with the organization, and with the activity, which correspond to the three scales proposed by Wicker et al. [ 100 ], Nuvialia et al. [ 99 ], and Graupera et al. [ 101 ], and which refer to satisfaction with three aspects or different areas of the sports center, corroborating its reliability.

Once the structure of the considered scales was discussed, the results obtained in this research were discussed relating to the four hypotheses that enabled us to observe the relationships with the quality they perceive and their satisfaction. According to the results, Hypothesis H1 was corroborated, which considered the positive relationship between the dimensions of perceived service quality and overall satisfaction (H1). It was confirmed that the relationship exists (in all cases, the standardized correlation coefficients are significant at the p < 0.001 level). These results, in the case of the relationship with overall satisfaction, are corroborated by empirical studies conducted by Bisschoff and Lotriet [ 107 ], Kyle et al. [ 108 ], Murray and Howat [ 94 ], Shonk and Chelladurai [ 109 ], and Nuviala et al. [ 110 ], which state that a greater level of quality service perception results in greater satisfaction.

Finally, in order to reinforce the validity of the hypotheses and study the relationship structure, different regression models were proposed, which included the perceived service quality dimensions as independent variables and overall satisfaction, satisfaction with the facilities, with the organization, and with the activities as dependent variables, with the purpose of evaluating the joint effects of the independent variables on satisfaction.

The first model showed that the hypothesis H1 was corroborated. All the dimensions of the perceived quality scale influenced satisfaction positively and significantly (on overall satisfaction, p < 0.001), with the variables included in the model explaining 75.7% of satisfaction. This clearly shows that the quality dimensions are closely related to satisfaction, with the most influential variables being facilities and material (β = 0.325, p < 0.001), followed by communication and activity (β = 0.225 and 0.214; p < 0.001). The least influential variables were to HR, monitor, and staff (β = 0.105 and 0.162, p < 0.001). Studies of a quantitative nature corroborate this result. For example, the study carried out by Nuviala et al. [ 110 ], included the perceived value, with 55.6% of satisfaction explained by their model, in addition to the perceived quality dimensions. On the other hand, the dimensions “activities and sports experts” were the most relevant in the regression equation, with β values of 0.347 and 0.266, respectively, with the “value and material” variables being the least important, with a β value of 0.074. These results differ from those obtained in this study. On the contrary, other in the study conducted by Mañas et al. [ 44 ], as well as in this study, it was found that tangible elements are important predictors of user satisfaction.

On the other hand, hypotheses H2, H3, and H4 were partially corroborated. It was observed that in the case of satisfaction with the facilities (H2), the facilities dimension (β = 0.540, p < 0.001) together with communication (β = 0.160, p < 0.05) were the only two dimensions that influenced satisfaction and explained 53.4%. These results show that a center interested in in improving its users’ satisfaction with its facilities must comply with the requirements and expectations of its clients regarding cleanliness, safety, temperature, and sports equipment. In addition, it should pay special attention to the communication mechanisms implemented in its organization. In this sense, the client expects the center to have a procedure of complaints and suggestions, and all those channels necessary to achieve adequate communication with its users.

If the regression model explaining satisfaction with the organization of activities is observed, the independent variables that positively and significantly influenced satisfaction were activities (organization and development), staff, and communication. The users did not consider the dimensions monitor and facilities and material when forming their satisfaction. In the case of satisfaction with the activities, four of the five dimensions influenced satisfaction to a greater or lesser extent, with the exception of the activity dimension, which was not significant (β = 0.095, p > 0.05), while the communication dimension was the most important. The comparison of these results with other studies is complex due to the differences between the measurement instruments used and the dimensions evaluated.

In short, it is observed that the dimensions of perceived quality related to HR (monitor and staff) were the least influential in the satisfaction experienced by the users of the sport and health center, with the dimension facilities and material the most important together with activities and communication, which show a very similar influence. The comparison of these results with other studies is complex due to the differences between the measurements used and the dimensions evaluated.

6. Conclusions

Before starting the presentation of the conclusions, note that this research work is novel since it aims, on the one hand, to fill a gap in research carried out in the sports organizations sector. In this sense, the relationship between perceived service quality and satisfaction has been studied extensively, and this has been corroborated [ 94 , 107 , 108 , 109 ]. However, there is still no consensus on the causality of the relationship, so it is necessary to continue conducting research in this regard [ 91 ].

There are many investigations that develop measurement scales of perceived quality. However, there are very few investigations that analyze which quality dimensions are the most important to form the client’s satisfaction. This research takes into account what was discussed by Szabó [ 111 ] and Tsitskari et al. [ 49 ], who state that the study of quality in the sports industry is in its early stages, so it is essential to continue doing research and deepening knowledge in this area [ 111 ]. In this sense, this research allowed the analysis and reinforcement of some of the conclusions already obtained in other studies.

The results of this investigation have significant academic implications and are of great interest to organizations that provide sports services, enabling the observation of how they jointly affect the dimensions of perceived service quality in the formation of their users’ satisfaction, becoming a key strategic element for any organization. In this sense, it is important to bear in mind that sport and health centers, like any other organization, must improve the quality of the services they provide in order to satisfy their users, and it is necessary to listen to users and measure their satisfaction. This will enable these organizations to adjust their service to the existing demand and to anticipate and adapt to the changes in users’ tastes since, as stated by Súarez et al. [ 112 ] (p. 30), “who determines the quality of a service is the user through his/her satisfaction”.

The first limitation of this study that the study was carried out in a single sport and health center. In future research, the studies should be extended to other sport and health centers, so the results should be extrapolated with caution. Another limitation of this study is its cross section.

Author Contributions

Conceptualization, Investigation, Methodology, Formal Analysis, Writing-Original Draft Preparation and Writing-Review & Editing, J.Á.-G., E.G.-V., M.d.l.C.d.R.-R. and A.D.-S.; Project Administration and Supervision, J.Á.-G. and M.d.l.C.d.R.-R.

This research received no external funding.

Conflicts of Interest

The authors declare no conflict of interest.

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