Customer's potential value: The role of learning

Komulainen, Hanna; Mainela, Tuija; Tähtinen, Jaana Article Customer's potential value: The role of learning jbm - Journal of Business Market Management Provided in Cooperation with: jbm - Journal of Business Market Management Suggested Citation: Komulainen, Hanna; Mainela, Tuija; Tähtinen, Jaana (2013) : Customer's potential value: The role of learning, jbm - Journal of Business Market Management, Freie Universität Berlin, Marketing-Department, Berlin, Vol. 6, Iss. 1, pp. 1-21, https://nbn-resolving.de/urn:nbn:de:0114-jbm-v6i1.400 This Version is available at: https://hdl.handle.net/10419/76797 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nd/3.0/ RESEARCH ARTICLE J Bus Mark Manag (2013) 1: 1–21 URN urn:nbn:de:0114-jbm-v6i1.400 Published online: 29.03.2013 ---------------------------------------- © jbm 2013 ---------------------------------------- H. Komulainen · Tuija Mainela · Jaana Tähtinen University of Oulu, Oulu, Finland e-mail: [email protected]i; [email protected]; [email protected] * The authors have contributed equally Customer’s Potential Value: The Role of Learning Hanna Komulainen · Tuija Mainela · Jaana Tähtinen* Abstract: Current views on value creation emphasize the role of the customer, mutual investments, and value co-creation. Nevertheless, at present the customerfocused research concentrates on value expectations and value experiences as outcomes but disregards the analysis of potential value that is dependent on the customer’s activity and learning in the process. The present study explores customer perceived value as a multidimensional phenomenon incorporating expected, realized, and potential dimensions. Using a real-life experiment, the study shows the role of customer learning particularly in realizing the potential value of novel technological services. To understand and achieve the potential value, customers need first to unlearn their current practices, second, to learn how to use the novel service, and third, to envision the best ways to use the novel service. Hence, a sacrifice made in the present day (i.e. learning efforts) will increase the potential value-in-use in the future. Keywords: Value co-creation · Interaction · Resource integration · Many-to-many networks · Service-dominant (S-D) logic · Relationship marketing Acknowledgements: The financial support of the Academy of Finland is gratefully acknowledged. The authors wish to thank the organizations whose invaluable collaboration has made this study possible. Customer’s Potential Value: The Role of Learning 2 Introduction The current views on service business, such as service logic (Grönroos, 2008; 2011) and the service dominant logic (Vargo & Lusch, 2004; 2008), emphasize need for mutual investments and the active roles of both parties in value creation, since that process determines the value-in-use for the customer (Edvardsson et al., 2005; Kowalkowski, 2011; Macdonald et al., 2011). Research has approached value as either a static concept (e.g., Liu et al. 2005, Menon et al. 2005; Ulaga 2003) or when temporal, focused on consumers’ past expectations and present experiences (e.g., Flint et al., 2002; Parasuraman, 1997; Woodall, 2003; Woodruff, 1997). However, recent research (Möller, 2006; Möller & Rajala, 2007) emphasizes the need to take into account the future orientation, too. Currently, little is known about the creation of potential value that may be realized only in the future. The present study explores customer perceived value as a multidimensional phenomenon incorporating expected, realized, and potential dimensions and focuses on that very dimension of potential value. The exploration will take place in the context of novel technological services for the following reasons. Firstly, the novel technological features of a business service make the value not only difficult to assess before the actual consumption of the service, but even during and after it, meaning that high levels of uncertainty are involved (Hogan, 2001). This emphasizes the need to envision the future and to conceive the potential value of the service. Secondly, such services are often launched while still under development, and they remain subject to near constant change as new versions are introduced throughout the lifecycle (e.g., Curran & Meuter, 2005). This means that once launched the service itself has not reached its full potentials yet and the customer needs to keep up with the development of technology and learn to use (i.e., operate) and utilize (i.e., fully exploit) new versions of the service. Hence, customer perceived value (CPV) might be very different at different points in time (see e.g., Green et al., 1996; Parasuraman, 1997). This makes the value of novel technological services dynamic, future-oriented, and dependent on learning. However, research has only recently begun to connect value creation and learning. Sanchez, Vijande and Gutierrez (2010) state that a supplier’s learning is a direct antecedent of its customer’s value creation capability, but customer’s learning efforts still await dedicated research on them. Although both benefits and sacrifices are commonly noted as playing important roles in customer value perception (see Ulaga & Eggert, 2005), sacrifices have been studied far less than benefits. Learning requires time and effort and therefore is a significant sacrifice. Moreover, this study explores learning related to its role in the temporality of CPV, in particular in the realization of potential value. Learning is viewed as an investment. Its form changes over time: today’s sacrifices in learning, although reducing the present net value of the service, may produce benefits tomorrow (see Woodall, 2003). Hence, we argue that value is a dynamic phenomenon that may be temporally past, present, or future oriented (see Stanley & Tyler, 2002) and that learning is an investment-type sacrifice that largely determines the potential value derived in the future. Some research has been conducted on the value of technological consumer services (e.g., Pura 2005, Heinonen 2004), but the research in business services Customer’s Potential Value: The Role of Learning 3 context is underdeveloped. The present study thus extends the exploration of temporality of value to the context of novel technological business services. The study contributes to research on value creation in two specific ways. First, the study conceptualizes CPV as a temporally changing phenomenon with expected, realized, and potential value dimensions. The introduction of the potential value concept adds future orientation to the current conceptualizations that focus primarily on the past and present. Second, the study discusses learning as a sacrifice that increases potential value achieved in the future. This questions the straightforward analysis of benefits as increasing value and sacrifices as decreasing value. Furthermore, the examination of the role of learning reveals the connectedness of the expected, realized, and potential value dimensions. The following theoretical section reviews value research, placing specific emphasis on time-sensitivity and the role of learning. The empirical setting of the study, the introduction and use of a novel technological business service, was chosen as it brings out the future-oriented, potential value dimension that remains under-researched. The methodological section describes a real-life experiment in which 40 companies using a novel technological service were observed and the representatives of 17 of the companies were interviewed on their experience. The findings from the empirical analysis are then presented as a learning-driven three-dimensional conceptualization of customer value. The final section discusses the theoretical and managerial implications and limitations of the study. Value Creation in Novel Technological Business Service Value in the Service Context Scholarly interest in value has shifted from an early focus on product-oriented value-in- exchange to value-in-use, in other words, onto customers’ value creation processes (e.g., Grönroos, 2008; Vargo & Lusch, 2004; Vargo et al., 2008). Accordingly, a service provider can create a value proposition, but the value is determined by the customer (Kowalkowski, 2011; Ulaga, 2011, Vargo & Lusch, 2008). This study adopts the value-in-use perspective and, following Lapierre (2000), Ulaga and Eggert (2006), and Woodall (2003), defines CPV as a subjective perception of the trade-off between multiple benefits and sacrifices, relative to the competition. Benefits and sacrifices stem from all service and relationship dimensions that customers perceive as facilitating or blocking attainment of their goals in the value creation processes (see Blocker, 2011; Woodruff, 1997). A number of studies (e.g., Eggert & Ulaga 2002, Lapierre 2000, Menon et al. 2005) categorize and classify CPV using the benefit-sacrifice approach. Within business services, value has been categorized by Lapierre (1997) into exchange and in-use elements, and in technological consumer services, Heinonen (2004) refers to two very similar dimensions as the outcome of the service interaction and functional aspects of the service delivery. However, these context-related categorizations do not discuss the relationship between benefits and sacrifices longitudinally. Grönroos (2009), in turn, divides sacrifices into short- and long-term variants. Short-term sacrifices include the Customer’s Potential Value: The Role of Learning 4 price paid for the service and long-term sacrifices refer to investments made in the relationship. Hence, this refers to the inevitable dynamism of value, which we will examine next and thereafter, proceed to examine learning as a primary type of longterm sacrifice. Value as a Time-Sensitive Concept Studies on CPV could be split into two types: static and temporal. Static studies lack explicit consideration of value evaluated at different times (e.g., Menon et al., 2005; Liu et al., 2005; Ulaga, 2003) whereas in temporal studies (e.g., Flint et al., 1997; Woodall, 2003; Woodruff, 1997) as well as service quality studies (e.g. Parasuraman et al., 1985) the evaluation takes place at the time of the purchase decision, during use, and afterwards. Hence, decision-making is based on a prediction of value, and only during (or after) use can a customer experience the value. Adding more detail, Parasuraman (1997) suggests that both the attributes customers use to judge value and the relative importance of those attributes may change soon after the purchase, but also during long-term use. This study adopts the temporal view and thus argues that both the benefits and sacrifices have past, present, and future dimensions. They may be expected, realized and/or potential and they change when evaluated at different points of time. Past, present, and future time act as reference frames for each other (Medlin 2004). Past time holds the memories and interpretations of events that are remembered. What we remember is likely to be based on what we need in order to understand the present and its attendant future possibilities (Mead 1932). Conversely, future time is full of many possible alternatives, of which only one can become the present (Luhmann 1979). However, these views of past and future as continuously changing contexts can only be interpreted in the present, thus making it a complex structure that relies on the existence of the past, in the form of learning, and the existence of the future, as intentions and expectations (Luhmann 1979). Next, the past, present and future aspects of time are considered against the expected, realized, and potential dimensions of value. First, expected value, i.e., the benefits and sacrifices customers expect to occur during the service use, even before they start using it, strongly influences the customer’s willingness to try a novel service (Komulainen et al., 2007). Parasuraman et al. (1985, 1991) suggest that customers have an implicit range of expectations for the service based on a “standard”. In the present study the service is, however, totally new and thus the customer has no real “standard” on which to base the expectations. Instead, the closest similar service is likely to be used as a point of comparison; in the case of m-advertising, that can be traditional mass-market advertising. As a result, the traditional point of comparison guides the expectations instead of the actual characteristics of the novel service. Thus, customer’s potentially distorted and unrealistic value expectations need to be taken into account specifically in the context of novel technological services. Flint et al. (1997) use a concept of desired value, referring to what customers want to happen and the benefits they seek. Here the concept of expected value is used to highlight that there are both expected benefits and expected sacrifices. Customer’s Potential Value: The Role of Learning 5 Second, the realized CPV refers to actual benefits and sacrifices customers perceive when evaluating the service during and after its use. The evaluation of the realized value takes place in the present time, although the target of the evaluation is what happened in the past. Similarly, Flint et al. (1997) write of a value judgment that reflects an assessment of what has happened in a specific use situation (in terms of benefits and sacrifices). Finally, as novel technological services are subject to continuous change and development, customers can picture the optimal and improved service in the future. This potential value is here seen as the trade-off between the benefits and sacrifices the customers expect from the service in the future, when it will provide the best possible value for them. Potential value thus refers not only to the improved future releases of the technological service that may provide even more benefits, but also the wide-spread use of the technology by other actors in the future. The latter may either increase the value for the customer or it may migrate, if the actor has not adopted the technological services with others (see Sharma 2002). The realization of potential value is thus influenced by the actions of the service provider, the customer, and the network actors instead of depending simply on the passing of time. At the core of these actions are complex learning processes (Möller 2006; Möller & Rajala 2007) that form an investment-type sacrifice required to realize the conceived potential value. Accordingly, if the customer sacrifices time and effort to learn to use the novel service skillfully, the benefits are expected to increase more than the sacrifices decrease the short term value. This follows the logic of Transaction Cost Analysis (TCA) (see Rokkan, Heide & Wathne, 2003; Gosh & John, 1999). Although TCA has more often been applied to the specific investments made by a supplier to create value for the customer, this study suggests that customer investments in learning – which are sacrifices made today – may increase or improve benefits derived in the future, and so result in higher CPV. Therefore, we will next take a closer look at learning and its role in CPV. Organizational Learning in Customer Perceived Value Learning refers to doing something better than before (Lewin, 1975 p. 65). It is a mechanism and a process that improves organizational understanding and performance (Bell et al., 2002; Senge, 1990) and thus represents a highly soughtafter, appreciated, and studied activity. Apart from at the individual level, learning can take place at intra-organizational (e.g., Senge, 1990; Slater & Narver, 1995), dyadic (e.g., Kodama, 2001), inter-organizational (e.g., Lane & Lubatkin, 1998), and network levels (e.g., Powell, Koput & Smith-Doerr, 1996). This study focuses on company-level learning and applies the concept of organizational learning. Organizational learning could be adaptive and/or generative (Slater and Narver 1995).The adaptive type is the most basic form of learning and occurs within a set of constraints both recognized and unrecognized that reflect the organization’s assumptions about its environment and itself. As a result, learning is focused on opportunities that are within the traditional scope of the organization’s activities. Generative learning occurs when an organization is willing to question its long-held Customer’s Potential Value: The Role of Learning 6 traditions or assumptions and focus on interrelationships and dynamic processes (see also, Senge, 1990). Learning is time-dependent as it refers to change (Lewin, 1975). It draws on experiences and affects future action. Although temporality is always implicitly present in the learning concept, existing research has not focused on the issue. An exception is the study of Styhre (2006) who sees workplace learning as taking place through continuous interaction, recollection, and anticipation of the past, present and future. Without a full understanding of how a certain practice is dependent on previous experiences and potential future events, learning (and changing the practice) becomes very difficult. This highlights the importance of active unlearning in situations when the organizational memory constrains generative learning (Slater & Narver, 1995; Bhatt, 2000). Organizational unlearning is viewed as “the discarding of old routines to make way for new ones, if any” (Tsang & Zahra, 2008). Without unlearning new effective routines are rejected within the organization, and the familiar ways of operating remain in place. To summarize, learning changes thoughts, attitudes and/or processes, requires effort and is based on experience. In addition, learning is firmly linked to value creation. Sanchez et al. (2010) suggest that learning by a supplier increases its capability for customer value creation. Taking a customer view, customer learning required to use a novel service and utilize its special features is a sacrifice made today that increases the benefits derived tomorrow. CPV inherently includes temporal dimensions that are here labeled expected value, realized value, and potential value. Previous research has discussed them as separate concepts and focused on the past and present (i.e., expected and realized) dimensions. In the context of novel technological business services, the importance of the future dimension (i.e., potential value) is heightened due to the novelty and constantly developing nature of the service. As the value changes over time, it is likely that learning will also take different forms. Specifically in terms of potential value, the role of learning is accentuated since learning in the two previous time dimensions (i.e. expected and realized value) influences the realization of the potential value in the future. Thus, it is important to empirically explore the role of learning in temporal value dimensions, its influence on the customer’s value perceptions over time and its connecting role for, and embeddedness in, the expected, realized, and potential value dimensions. Methodology Empirical Research Setting The technological business service that this study explores empirically is a mobile advertising (m-advertising) service through which retailers send advertisements (ads) to their customers’ mobile phones. The service enables sending unique, personalized, and customized mobile ads (m-ads) cost-effectively as well as engaging customers in personal real-time discussions and transactions with the retailer (Salo & Tähtinen, 2005). M-advertising is very different from traditional one-way advertising as it efficiently identifies the receiver of the message. Hence, it should be used in a Customer’s Potential Value: The Role of Learning 7 radically different way (see e.g., Choi, Stahl & Whinston, 1997; Salo & Tähtinen, 2005). Retailers cannot design and use m-ads in the same way as they might newspaper ads, and that presents a learning challenge. They need to change their ways of thinking about advertising and their practice to learn how best to apply madvertising. As such, the service offers a rich empirical setting to extend the theory of CPV. Research Method and Data Collection Due to the new, complex, and context-dependent nature of the phenomenon, a case study design (the m-advertising service being the case) and qualitative data collection methods were chosen. A case study is particularly useful in situations where the phenomenon is new and unknown and where current theories seem inadequate (Easton, 1995; Eisenhardt, 1989). It offers a means of developing theory by utilizing indepth insights on empirical phenomena and their contexts (Dubois & Gadde, 2002). The chosen research strategy follows an abductive theory-building approach (Peirce, 1957; Dubois & Gadde, 2002). The conceptualization presented is a result of a continuous interplay between theory and empirical observation and a research process that requires being open to new insights from either side to combine them systematically into the research findings (see Dubois & Gadde, 2002). The examination of value creation in m-advertising took place within a 7-week long field trial. In it, a research project organized an m-advertising service system (for SMS and MMS ads) and recruited 40 retailers to use it in the course of their daily business. The method placed the focus on the retailer’s views of value before, during, and after using the novel m-advertising service. The main data was gathered through thematic interviews with 17 retailers (see Table A1). The selection of the interviewees was a multi-stage process, in which all 40 m-advertisers were first interviewed by phone. Based on their willingness to use the service in the future the advertisers were categorized into ‘enthusiastic’, ‘doubtful’, ‘negative’, and ‘non-user’. The non-users had signed up for the trial but never sent a single m-ad. Second, to increase variety, retailers from each of the four categories were interviewed in person. The interviewees were selected to represent three usage types; users themselves, those enlisting the help of an advertising agency to use the service, and those who involved the research team. Thus, the 17 retailers represent a large variety of experience and attitudes towards m-advertising, and also various fields of retailing. This theoretical sampling aimed to maximize the differences between the interviewees’ perceptions of value (Glaser & Strauss, 1967; Spiggle, 1994). Two researchers conducted the interviews after the seven-week trial period. The thematic interviews covered five general areas: 1) Background information on the company 2) Objectives for and expectations of / assumptions about mobile advertising 3) Experience of mobile advertising (including quality of training and guidance, design and implementation of mobile ads, and use of the mobile advertising tool) 4) Effectiveness and utility of mobile advertising 5) Suggested improvements to the service. In addition, the specific experiences of each retailer were elicited by posing additional questions. The audiotaped interviews were transcribed verbatim, resulting in 171 pages. In addition, being involved in the recruitment and initial training of the Customer’s Potential Value: The Role of Learning 8 retailers and addressing their day-to-day problems during the trial allowed the researchers to observe and take notes on discussions, which also developed an understanding of the phenomenon. Data Analysis The interview transcripts formed the raw data of the analysis. The unit of analysis was the retailers’ perceptions and conceptions of the value of the novel technological service. The first interpretations of the data were based on multiple readings of each transcript. Thereafter, the original verbatim interview data were imported to the QSR N’Vivo software. The software facilitated the storing of the text, coding, searching, and retrieving of text segments and stimulated the researchers’ interaction with the data (see Dembkowski & Hanmer-Lloyd, 1995). The first multi-authored coding was based on the researchers’ theoretical and empirical pre-understanding using two basic coding categories; perceptions of value and learning. The concept of value was soon divided into temporal dimensions of expected, realized and potential value, since a single concept was insufficient to explain the variety in the retailers’ views. The analytical question of what connects and influences the temporal value perceptions led us to analyze retailers’ learning in greater detail. At this point, research on organizational learning was reviewed to create a basis for a multidimensional conceptualization. The concepts of adaptive learning, generative learning, and unlearning were thereafter used to approach the data. During the coding process, free nodes and memos were also created to store ideas that seemed to give meaning to the data. The refined definitions and interpretations of the concept were tied to specific words and lines within the transcripts, thus opening up the process to the scrutiny of all the researchers involved. Interpretations of parts (whether segments of a transcript or entire transcripts) were compared to each other following the constant comparative analysis method (see Glaser & Strauss, 1967; Spiggle, 1994; Strauss & Corbin, 1998). Finally, the categories describing value were crosschecked with categories of learning to create a temporal picture of value in relation to types and objects of learning. Findings Dynamic Nature of Customer Perceived Value Expected Value Dimension The expected value refers to the difference between the benefits and sacrifices that the retailer expects to experience when the service is used. The expected value influences a customer’s willingness to use a novel service. We had already requested offers, to find out what m-advertising would cost and how it can really be done, like in practice. … And then your project came along, and we, kind of, could do that without paying, and with your help and all that. So things, Customer’s Potential Value: The Role of Learning 15 The study makes two exciting theoretical contributions to the customer value discussion. These theoretical insights are connected to the context of the study: a novel technological business service. Taken together, the dimensions of expected, realized, and potential value help us to include the temporally loaded perceptions of customers assessing value. The concepts are connected to each other through the learning that varies at different points of time. Understanding the important role of temporality and learning in CPV enhances theory development and has implications for the providers of technological business services regarding how to manage the learning processes of their customers with the aim of improving the perceived value of a novel technological business service. As far as the authors are aware, this is the first study to connect learning in its different forms to a temporal view of CPV. In the area of customer expected value, customers distinguish certain elements of value that they look for in the service. Then they compare the new service to prior experiences and base the expectations of the service on those experiences. A type of learning that has a major role in expected value is unlearning the old attitudes and expectations. This is an innovative finding that adds to the value discussion and receives support from studies on learning organizations (Bhatt 2000, Slater & Narver 1995), which see unlearning as critical to the learning of any new process. In the case of expected value, the objects of unlearning are the prior expectations and attitudes based on an understanding of a different kind of service that is no longer relevant. In this study, m-advertising represents a novel service that enables more innovative and personal advertising. If the customer company is able to unlearn the prior experiences formed by using traditional advertising, it is possible to view a more realistic expected value of the novel service. If unlearning, however, does not take place it may distort both realized and potential value. In terms of the present, the expectations relate to the customer’s perceptions of realized value. Customers assess the value compared both to their past expectations, and to their experience of the use of the novel service. Extant studies have generally taken the view that benefits increase and sacrifices decrease CPV (e.g., Ravald & Grönroos, 1996), but we suggest that in order to perceive greater realized value, the customer must make sacrifices in the form of learning. The reason for this is that a customer who learns to use the service (i.e., the technical features of the service) can also learn to utilize the service (i.e., the special features of mobile advertising) and thus perceive the highest possible realized value. It follows that if the customer does not learn to use and then utilize the novel service, the value perceived is likely to be diminished. Therefore, we suggest that learning as a key sacrifice, is required from the customer if they are to perceive realized value. This follows the logic of value creation in TCA presented by Gosh and John (1999) and adds to Flint et al. (1997) and Ravald & Grönroos (1996) by raising the variable role of sacrifices; not only decreasing CPV but through their outcome, also increasing it. Finally, the potential value concept moves the realized value closer to what could be realized in the uncertain future but not yet. It is the best possible value the customer can imagine will be realized in future. The object of learning in the case of realized value is related, first, to the technological skills needed to use the service and second, to the specific features of the novel m-advertising service that must be Customer’s Potential Value: The Role of Learning 16 mastered to utilize the service effectively. The investments made in generative learning in the present, enable the customer to envisage the optimal service that would create potential value in the future. Related to potential value, learning is explorative and targets discovering new knowledge and ensuring future viability. Furthermore, we agree with Möller’s (2006) emphasizing the importance of learning by the entire network. For the potential value to become realized value in a novel technological business service context, it is essential that both customers and service providers are willing to invest in learning. Customers need to learn a new way of doing things and service providers need to listen to their customers and improve the service so that customers’ new ideas are incorporated into the service. Therefore, in a technological business service, potential value is achieved by mutual learning and adaptations undertaken by all parties (e.g., customers’ customers, content providers, and technology providers) in the network related to the service. Furthermore, it is important for the service provider to understand the expectations their customers hold for the service in the future. Only with such critical information, will they be able to develop the service and their customer relationships in a way that will ensure that a technological business service becomes a profitable business. Managerial Implications For managers working in the field of technological business services, the main implications of the study are as follows. First, it is important for service providers involved in a novel service development to get across to the first customers that they will need to make some sacrifices in order to learn to use the service, and in turn to derive significant value from that service. It is also crucial that the service provider helps customers to learn use the service. Customers should be encouraged to unlearn past expectations and assisted in absorbing the technological aspects and special features of the novel service. This requires that the service provider is aware of the customers’ absorptive capacity and other characteristics (e.g., technical resources, knowhow). Such awareness would help them recognize the specific needs of their customers and to adjust the support offered accordingly. If the service provider can manage the expectations of the customers, those customers will realize a greater value from the service and the chances of their continuing to use the service will improve. Understanding whether the customer’s learning is adaptive or generative is also important from the value creation perspective. In the case of adaptive learning, motivating the customers is more challenging since they will tend to utilize existing knowledge instead of actively pursuing new opportunities, which often results in a reduced perceived value. These types of customers also demand more effort of the service provider. Overall, understanding customers’ learning processes is vital to the service provider, since that understanding can determine whom its key customers are, how to serve different types of customers and how to motivate them to invest in their learning. Customer’s Potential Value: The Role of Learning 17 Limitations and Suggestions for Future Research When evaluating any study certain shortcomings can always be found. This paper draws heavily on interview data that has been acquired from retailers that used a novel m-advertising service for a relatively short time without making a monetary sacrifice. However, the aim of the paper was to conceptualize CPV, not to measure it or determine how valuable the service was for the retailers. Therefore, we feel that the cost issue and the field trial nature of the service setting do not significantly threaten the validity of the study. The rather short duration of the service use is an issue that might prompt calls for more longitudinal studies. In future, it would be interesting to explore expected, realized and potential value longitudinally at different stages of novel service development and compare how they change and relate to each other. This requires longitudinal research strategies, which of course are time and resource consuming. However, there may be a potential for such settings within large research projects. As this study deals with a novel m-advertising service that is was not in commercial use, it would be important to conduct research on CPV in commercialized technological services. That would enable a more comprehensive assessment of the role of sacrifices in the perception of value. It is also important to explore the dynamics of value in other empirical contexts, such as existing non-technological services. Moreover, it can be expected that in long-term relationships the temporal conceptualization of value suggested in this paper, would help us to understand the dynamics of the relationships as well. 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Appendix Table A1: Interview data Line of business Duration Interviewee(s) position Co-operative (groceries, clothing, a hotel, restaurants) 45 min Communications Manager Mobile applications 45 min Manager Art museum 35 min Press Officer and Assistant Advertising agency 30 min Assistant Leather goods 30 min Shop Manager Videos 15 min Shop Manager Mobile phones 30 min Shop Manager Health food 30 min Shop Manager Travel agency 25 min Customer Service Manager Shoes 40 min Shop Manager Furniture 30 min Owner Music store 25 min IT-support Oriental Restaurant 30 min Owner Telecommunication devices 40 min Office Manager Clothing 25 min Advertising Manager Clothing 30 min Administrative Manager Gifts and interior decoration 60 min Owners (two persons) Total 8 h 5 min 19 interviewees