From perceptions to propositions: Profiling customer value across retail contexts

Journal of Retailing and Consumer Services xx (xxxx) xxxx–xxxx

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From perceptions to propositions: Profiling customer value across retail contexts ⁎

Timo Rintamäkia, , Kaisa Kirvesb a b

University of Tampere, School of Management, Finland Finnish Institute of Occupational Health, Finland

A R T I C L E I N F O

A BS T RAC T

Keywords: Customer value Customer value proposition Retail contexts

The core claim made in the paper is that retailers wishing to identify and manage competitive customer value propositions succeed by measuring and modeling customer value perceptions with reference to specific contexts relevant for their competitive advantage. Hence, the purpose of the paper is to present development and validation of a scale for measuring and modeling customer value and to illustrate how contextual perspective contributes to the evaluation of customer value propositions. The findings, based on empirical data from Finland, Japan, and the U.S., validate a framework wherein customer value reflects economic, functional, emotional, and symbolic dimensions of value, associating with satisfaction and word of mouth effects. The customer value profiles generated on this basis provide analytical insight for evaluating how country, channel, product category, and competitive situation influence the criteria for contextual evaluation of customer value propositions.

1. Introduction Identifying and managing customer value propositions has established itself as a key topic in the management literature, because it aids companies in differentiation for competitive advantage (Payne and Frow, 2014). Hence, better-than-average companies need to “choose their value” and bring it to the core of their strategic management. This task is complicated by the fact that customer value often seems to be a moving target: it varies between customers, across contexts and situations, and over time (Holbrook, 1999). It requires analytics that assist companies in understanding what drives customer value as an outcome of their customer experience. Nevertheless, building an understanding of how value creation can have a positive effect on behavioral intentions is crucial for all industries. Contemporary retailing is a good example of an industry wherein the drivers of customer value are very context-dependent and a strong customer value proposition is crucial for competitive advantage. Many retailers operate with several product categories, carry competing brands, and cater for fragmented market segments in both offline and online environments. The core claim in this paper is that retailers wishing to identify and manage competitive customer value propositions succeed by measuring and modeling customer value with reference to specific contexts that are relevant for them. This calls for understanding the key dimensions of customer value from shoppers’ angle and for tools to measure the value created. Hence we conducted research with a twofold aim: to develop and test a model for customer ⁎

value in retailing and to illustrate how it can be profiled across retail contexts to identify and evaluate customer value propositions. To that end, a literature review is presented that builds on the concept of customer value. To test and validate the framework empirically and to illustrate the contextual effects of customer value profiles, an international survey was conducted. Both offline and online purchases of fashion and electronics items are examined as contexts of shopping value. The presentation of results and discussion is followed by conclusions and a few thoughts on implications. 2. Theoretical background Perceiving and proposing value represent two key perspectives to conceptual discussion surrounding customer value creation (Martelo Landroguez et al., 2013). While the former considers the customer’s angle and builds on literature on customer(-perceived) value, the latter is focused on customer value propositions as strategic tools for positioning and managing customer value creation. The perceptions of customer value reflect the outcomes of shopping, representing the key motivations for the customer to buy (Sheth et al., 1991). From the retailer’s perspective, customer value propositions are designed to resonate with these preferred outcomes. For our purposes, both perspectives have general theoretical roots that are usefully transplanted to the retailing context. More importantly, the contextual nature of customer value helps us understand why the relevancy of these dimensions of customer value varies with the person, time, and

Corresponding author.

http://dx.doi.org/10.1016/j.jretconser.2016.07.016 Received 22 October 2015; Received in revised form 2 June 2016; Accepted 24 July 2016 Available online xxxx 0969-6989/ © 2016 Elsevier Ltd. All rights reserved.

Please cite this article as: Rintamäki, T., Journal of Retailing and Consumer Services (2016), http://dx.doi.org/10.1016/j.jretconser.2016.07.016

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quality/performance, emotional value, and social value. Whereas the heritage of Sheth et al. (1991) can be seen in the PERVAL scale, other works, such as that on functional, social, emotional, and epistemic value by Ming-Sung Cheng et al. (2009), come even closer to the original conceptualization. Examples of studies inspired by Holbrook’s dimensions of value include Mathwick et al. (2001) providing evidence of online, catalog, and mall shopping environments’ effects on efficiency, excellence, aesthetics, and play; see also the work of Kim (2002) and of Keng et al. (2007). Integrating the dimensions of value discussed in earlier research and conceptualizing hierarchical models of customer value have gained popularity too. Davis and Dyer (2012) conceptualize and measure nine types of value: acquisition, transaction, efficiency, choice, aesthetics, exploration, self-gratification, social interaction, and social status. Rintamäki et al. (2006) model total shopping value as reflecting utilitarian, social, and hedonic dimensions, which have two sub-dimensions each: monetary savings and convenience; status and self-esteem; and entertainment and exploration, respectively. Combining hierarchical modeling and investigation of whether shopping-trip value is derived from the store or instead from products, Diep and Sweeney (2008) model utilitarian and hedonic value in terms of six sub-dimensions. Utilitarian shopping-trip value is derived from utilitarian store value and two types of product value: performance and value for money. Hedonic shopping-trip value is derived from hedonic store value and two other types of product value: emotional and social. Overall, a division between utilitarian and hedonic value seems to prevail in the discussion of dimensions of customer value in retailing, while the total number of dimensions varies. The utilitarian dimensions seem clustered around two key outcomes: value derived from the price paid and the value of the time and effort saved. Similarly, modeling of the hedonic dimensions of value is based on the feelings and emotions aroused by the shopping experience, alongside the various ways of expressing oneself that are rooted in the symbolic and social aspects of the shopping experience.

Fig. 1. Integrating perceptions and propositions with contextual customer value profiles.

situation (Holbrook, 1999). Understanding what constitutes poor, satisfactory, or excellent performance in terms of value created for the customer calls for contextual profiles of customer value (Fig. 1). 2.1. Perceiving customer value Understanding what customers value is a precondition for formulating competitive customer value propositions. This understanding of customer value is typically achieved through two routes: investigating the assessment of the tradeoff between benefits and sacrifices and conceptualizing the key outcomes of shopping represented by dimensions of customer value. The former approach is required when the structure of customer value and the dynamics of the resulting decisionmaking process are being assessed (Woodruff, 1997; Zeithaml, 1988). The latter approach, however, is the focus of this paper, because the key dimensions of customer value drive shopper behavior: they reflect the criteria that the customer uses in the evaluation process. In other words, modeling the key dimensions enables profiling customer perceptions of value for comparison of them to the intended value creation of the retailer. Theoretically, there are two general typologies of customer value that underlie most studies addressing the dimensions of customer value in retailing: functional, emotional, social, conditional, and epistemic value, conceptualized by Sheth et al. (1991) and Holbrook’s (1994, 1999) extensive axiological framework entailing eight types of value (efficiency, excellence, status, esteem, play, aesthetics, ethics, and spirituality). These studies acknowledge that value for the customer is about both utilitarian and hedonic outcomes. Both conceptualizations also address the role of social aspects related to value creation, which can be seen in how customers are perceived by others as a result of their consumption-related choices. In the retail context, the perceived shopping value (PSV) scale of Babin et al. (1994) is a widely cited way of measuring and modeling the utilitarian (task-related, instrumental, and rational) and hedonic (recreational, self-purposeful, and emotional) dimensions of customer value. Both the original and modified versions of this scale have been used across a variety of retail contexts. Another widely cited scale, PERVAL, by Sweeney and Soutar (2001), defines value in the consumer-goods context along four dimensions: price/value for money,

2.2. Proposing customer value Customer value propositions are created to reflect the customer value perceptions sought by the targeted customers. Having two key roles, customer value propositions position the company in the customers’ minds and align the organization around the strategic creation of customer value (Lanning and Michaels, 1988; Webster, 2002). Moreover, customer value propositions are an essential part of a measurement-based management system, integrating strategic goals with operational execution (Kaplan and Norton, 1996). The raison d'être of customer value propositions is hence to enhance competitive advantage. Retail-specific conceptualizations of customer value propositions are considerably scarcer than investigations of customer perceptions of value. Murray (2013), for instance, defines a retail value proposition with his “ESE” model, wherein environment, selection, and engagement represent key building blocks for strategic value creation. Environment encompasses the location, retail format, store-layout atmospherics, design elements, and use of technology. Selection is about the right mix of products, calling for expertise in supply-chain management, purchasing, tracking of trends, and category and shelfspace management. In essence, engagement has to do with customerrelationship management based on enticing customer experiences. The three components are then subjected to retail pricing, with a goal of balance between creating and capturing value. Focusing on identifying customer value propositions (for B2C and B2B customers but also developers) for business innovation, Lindic and Marques da Silva (2011) conceptualize a framework based on an Amazon.com case study, called “PERFA.” The “P” refers to performance, serving the customers as well as possible while being profitable. Next, ease of use involves reducing the effort of using a product or system, and reliability means the product delivering in line with its 2

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preferences varying across customers, times, and situations. Importantly, customer value is always preferential – even though the preferences may change, the perception of superior customer value leads to a preference. This interplay of relativistic preferences may result in situations in which the same customer perceives the value of a given offering differently as the situation changes.1 In addition to cognitive factors such as information, time, and the budget available, more hedonic motivations – seeking stimulation, relaxation, new ideas, etc. – may become important situational criteria (Arnold and Reynolds, 2003; Park et al., 1989). Holbrook concludes the definition of the nature of customer value with the idea that value is always based on assessment of holistic experience. Instead of an isolated transaction, past encounters and future learning alike constantly shape our perception of customer value. As Fiore and Kim (2007, 422) state in summary, a “[s]hopping experience entails consumer processes (e.g. product evaluation, attitude formation) and responses (e.g. satisfaction, or purchase behavior) affected by aspects of the shopping environment (e.g. brick-and-mortar retail store, shopping center, catalog, and online store), situation, and consumer characteristics.” More recently, Chandler and Vargo (2011) have addressed the role of contextuality from the service-dominant logic perspective. Their focus is on understanding how the co-creation of value is influenced by the context of the exchange. Accordingly, they conceptualize value-incontext, which may be manifested on three levels: micro-context level, within dyads; meso-context level, within triads; and macro-context level, within complex networks. In addition, they conceptualize a metalayer of context to emphasize how the three contextual layers evolve within service ecosystems. If the micro-context level represents an interaction between an individual customer and a certain retailer, there must be other levels of context that are relevant for understanding value creation and perceptions in retailing. For instance, recent advances in digitalization in general and mobile technologies in particular suggest that interactive evaluation processes may take place in several channels before, during, and after the purchase and entail several actors as sources of information (Larivière et al., 2013; Resmini and Rosati, 2011; Rigby, 2014). Besides service channels, there are other facets of context that may naturally orient the criteria for customer value. For instance, product categories may entail purchasing criteria that emphasize some dimensions of value over others. For retailers operating in heterogeneous markets, cultural differences might influence the evaluative processes, hence resulting in different perceptions of shopping value (Carlson et al., 2015). In summary, customer value is dynamic, varying in intensity with contextual factors; it is not an objective measure of a product or service or a stable characteristic of a customer but a predictive construct involving insight into how certain customers in certain situations evaluate the performance of certain products, retailers, and shopping experiences. We believe that identifying the key dimensions of customer value in retailing and understanding how contextual issues alter the perception of value are needed for understanding what drives shopper behavior and, consequently, how customer value propositions should be developed.

specifications. Flexibility, considered internally, is ability to reallocate and reconfigure the organizational resources, processes, and strategies when the company faces changes in its environment. The fifth element, affectivity, is focused on the customer and refers to the feelings or emotions associated with using the company and its products. Synthesizing research on perceptions of customer value in retailing into criteria for identifying competitive customer value propositions, Rintamäki, Kuusela, and Mitronen (2007) conceptualize four categories of customer value propositions: those focused on economic value (price), functional value (solutions), emotional value (customer experience), and symbolic value (meanings). Identifying the propositions is based on assessing the relevancy for creating value for the customer and competitive advantage for the retailer, in pursuit of the most viable value-creation strategy. Economic value results mainly from a decrease in monetary sacrifice and can be perceived as monetary savings, receiving products economically, and benefiting from sales offers or reduced prices. Functional value results mainly from a decrease in time- and effort-related sacrifice and can be perceived as getting the necessary products in one stop, quickly, conveniently, and with the products found easily. Emotional value stems mainly from the psychic benefits and can be perceived as enhanced mood, pleasure, enjoyment, and feelings of being comfortable. Symbolic value results mainly from an increase in meaning-related benefits and can be perceived as giving a positive impression or getting approval from others on the basis of one’s store and product choice. More recently, Saarijärvi et al. (2014) have employed these four dimensions in a case study bringing together the retail customer’s value criteria and executive priorities. As the examples above illustrate, most frameworks for customer value propositions are geared toward establishing a connection between customer value and company resources and capabilities. There are, however, differences in how customer and company perspectives are taken into account – the emphasis can be on the retailer offering or on customer perception. For the purposes of this paper, an approach that allows profiling the value in a way that integrates both perspectives – i.e., a conceptualization of customer value propositions that matches the strategic dimensions of customer value perceptions – is preferred (Sheehan and Bruni-Bossio, 2015). 2.3. Understanding the contextual challenge of profiling customer value The nature of customer value is inherently contextual, a moving target. The same goes for customer value propositions, which derive their meaning from the experiences that customers live through (Holttinen, 2014). This contextuality challenges both academics and managers to understand the match-making: bringing the right customers together with the right retailers and the right moments. Retailers that succeed in this match-making gain competitive advantage and stand out from their competitors. Context amplifies strategic differentiation by making some dimensions of customer value more relevant than the others (Moon, 2010), and some researchers suggest that companies should be able to think even in terms of the time of day when designing their value propositions (Dacko, 2012). Although some elements of contextuality have been considered a separate dimension of value, as seen in the “conditional value” of Sheth et al. (1991), alluded to above, or temporal and spatial value, as described by Heinonen (2006), we argue here that it is a core element of the concept and hence actually crosses all dimensions of value. For instance, Holbrook (1999) explains the contextuality of customer value by defining its nature as an interactive, relativistic preference experience. Interactivity results from encountering and evaluating products and services in a process wherein the characteristics of the object, subject, and evaluation process all potentially contribute to the outcome – i.e., the perception of value. The next two elements in Holbrook’s definition are the interlinked “relativistic” and “preference.” When we speak of the relativistic nature of customer value, we refer to

3. Methodology and the proposed framework As we have discussed above, the conceptualization of customer value is highly dependent on the perspectives selected – namely, the perception and proposition of customer value, and the context investigated. The methods applied for empirical research should there1 There is evidence of a link between demographics and customer value perception, exemplified by male gender being associated with greater likelihood to prefer utilitarian store environments and female with hedonic ones (see, for example, Borges et al., 2013). Our approach simply suggests that the contextual effects may sometimes override or at least balance these types of established relationships, an element that is essential in ascertaining and formulating competitive customer value propositions.

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combination of these. a) I’m evaluating recent shopping for fashion (e. g., clothing and shoes) b) I’m evaluating recent shopping for electronics (e.g., TV sets, cell phones, and appliances).” As can be seen from the above operationalization, the instructions emphasized “a satisfactory shopping experience that led to buying” in order to make sure the respondents evaluated shopping experiences that they preferred over others and that resulted in a purchase (indicating certain criteria to have been met in terms of perceived value). For further showing predictive validity inherent to the nature of customer value, we also included separate metrics for satisfaction and word of mouth for our model (see Fig. 2). To decrease the bias related to memory of the shopping experience, the evaluations were limited to the last three months. In addition, we asked the respondents to describe the channels they used for the purchase. 3.2. The framework and items used for operationalizing the key constructs

Fig. 2. The proposed framework and estimated model.

fore be based on choices that are supported well from these three perspectives.

The framework conceptualized but not operationalized by Rintamäki et al. (2007) was chosen as a basis for modeling the dimensions of customer value on account of its applicability for modeling both perceptions and proposition of value. In addition, it was designed to synthesize value dimensions in a way that is concise enough for measurement and modeling purposes. As argued in the literature review, economic and functional value capture the essential drivers of utilitarian value, whereas emotional and symbolic value reflect more hedonic, experiential, and social sources of value. Importantly, the selected framework also allows operationalization at the consequence level (i.e., the outcomes of shopping), which has recently been recommended for its predictive validity (Leroi-Werelds et al., 2014). External signs of validity for dimensions of customer value include satisfaction and word of mouth. These constructs are also essential from the retailer’s perspective, to link customer value creation to business goals. The items used can be seen in Table 1. We were not able to operationalize these four value dimensions on the basis of complete sets of items taken from earlier research; hence, we conducted a pilot

3.1. The research setting The contextual nature of customer value was taken into account in the crafting of the research setting. Ours being an evaluation of the outcomes of interactive, relativistic, and preferential shopping experience (Holbrook, 1999), we designed a setting for investigating how customer value differs between retail contexts. To anchor the evaluation of customer value in an earlier shopping experience, the respondents were asked to choose either fashion or electronics for further evaluation. The selection of these two contexts was based in the assumption that they are familiar to respondents but at the same time may differ when it comes to value perceptions. The operationalization was as follows: “Thinking about your recent experiences (within the last three months) in shopping for fashion or electronics, recall a satisfactory shopping experience that led to buying. Your assessment might be related to a store, a Web site, use of a mobile application, or a Table 1 Items in the study. Concept

Measure (preceded by “When shopping with this retailer”)

Key theoretical background

Economic value

I save money (1=Completely disagree … 10=Completely agree) I get the products at a good price I benefit from sale offers/discounts

Diep and Sweeney (2008), Rintamäki et al. (2006), Sweeney and Soutar (2001)

Functional value

I I I I

Emotional value

I end up in a good mood (1=Completely disagree … 10=Completely agree) It gives me pleasure I feel at ease I enjoy myself

Diep and Sweeney (2008), Rintamäki et al. (2006), Sweeney and Soutar (2001)

Symbolic value

I make a good impression on others through my choice of retailer (1=Completely disagree … 10=Completely agree) I find products that create a good impression of myself with others Others approve of my choice of retailer

Diep and Sweeney (2008), Rintamäki et al. (2006), Sweeney and Soutar (2001)

Satisfaction

How satisfied are you with this retailer? (1=Very dissatisfied … 10=Very satisfied) How well does this retailer match your expectations? (1=Not at all … 10=Completely) Imagine a perfect retailer. How close is this ideal to the retailer you named? (1=Not at all close … 10=Very close)

Established scale from Mägi (2003)

Word of mouth

I am likely to say good things about this retailer (1=Completely disagree … 10=Completely agree) I would recommend this retailer to my friends and relatives I will recommend this retailer to others

Established scale from Jones et al. (2006)

get all the products I need at one time (1=Completely disagree … 10=Completely agree) get all the products I need quickly get all the products I need conveniently find the right products easily

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Diep and Sweeney (2008), Rintamäki et al. (2006)

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Table 2 Data characteristics of the international sample datasets. U.S.

Finland

Japan

Table 3 The fit indices of the SEM models in the United States (n=776), Finland (n=858), and Japan (n=832). International sample, total

Gender Male Female

49.1% 50.9%

59.8% 40.2%

53.0% 47.0%

54.1% 45.9%

Age band 15–24 25–34 35–44 45–54 55–70 N

28.4% 25.6% 22.0% 16.1% 7.9% 776

20.4% 19.3% 21.7% 19.2% 19.3% 858

17.8% 32.3% 18.6% 15.4% 15.9% 832

22.0% 25.7% 20.8% 17.0% 14.6% 2,466

Model

χ2

df

p

χ2/df

TLI

RMSEA

SRMR

U.S. FIN JP

496.06 618.80 730.05

163 163 163

0.000 0.000 0.000

3.04 3.80 4.48

0.950 0.944 0.912

0.051 0.057 0.065

0.068 0.092 0.084

control variables are reported here (see Becker, 2005; Carlson and Wu, 2012; Spector and Brannick, 2011). 4. Results and discussion 4.1. Reliability and validity of the customer value scale

study in Finland (n=763) as a part of the scale-development process. The new customer value scale with four dimensions fitted the data well (χ2(71)=312.49, χ2/df =4.40, TLI (Tucker–Lewis index)=0.94, RMSEA (root mean square error of approximation)=0.08, SRMR (standardized root mean square residual)=0.05), and the statistically significant loadings varied between 0.52 and 0.97.

The results with the structural equation models for customer value, satisfaction, and word of mouth showed acceptable fit for the data in all three countries, as can be seen in Table 3. More specifically, in the U.S. data good fit was supported by all three fit indices. In the Finland and Japan data, however, the model fit was not fully supported by the SRMR, which marginally exceeded the threshold of 0.08. Nevertheless, it can be said that the overall fit of the model was acceptable for each country’s data. Thus, the construct validity of the customer value scale seems to be acceptable over three, quite different countries. Moreover, the standardized loadings were all statistically significant, rather strong, and consistent within the scale dimensions in all countries for the first-level factors, as can be seen in Table 4. However, on the higher level (i.e., that of customer value), the loadings were all statistically significant but differed between the countries. Interestingly, the customer value construct reflected more emotional and symbolic value in the Finnish as compared to the U.S. and Japanese data, in which the loadings were more equal between dimensions. This difference seems to be reflected in the explanatory power of customer value for satisfaction and word of mouth: the beta coefficients were smaller in the Finnish data than in the U.S. and Japanese data. Nevertheless, the metric for customer value showed criterion validity, since it was positively associated with satisfaction and word of mouth in each country as expected. In other words, higher customer value in terms of economic, functional, emotional, and symbolic value was related to higher satisfaction and word-of-mouth values. In addition to validity, the reliability (i.e., internal consistency) of the dimension of customer value was at a good level, with all the Cronbach’s alphas being ≥0.85.

3.3. Sampling An international sample was collected independently from three countries (the U.S., Finland, and Japan). The data collection took place as a part of a research project investigating channel usage in retailing. For the purposes of the project, a screening question was used that excluded individuals who did not have a smartphone. All datasets were collected online by a professional market-research company. Characteristics of the resulting datasets are presented in Table 2. 3.4. Modeling and methods of analysis To validate the new metric for customer value, structural equation modeling (SEM) was selected as the main method of investigation. Maximum likelihood with robust standard errors (MLR) estimation was applied in all three SEM analyses (i.e., separate analyses for each country), for its capability to process non-normally distributed data. The estimated model is presented in Fig. 2. The fit of the models was evaluated in light of the recommendations by Kenny (2014). The following indices and threshold levels were used: TLI≥0.90, RMSEA≤0.08, and SRMR≤0.08. Also, χ2/df is reported, although it is not used to evaluate the fit, because it has no universally agreed-upon threshold level. The analyses were carried out with Mplus, version 7.3 (Muthén and Muthén, 1998–2012). Furthermore, Cronbach’s alphas were calculated, to estimate the internal consistency of the metric. To investigate how customer value differs between contexts, contextual factors were taken into account. Firstly, country (the U.S., Finland, or Japan) was considered, for tapping into different cultural contexts. Secondly, the profiles of shoppers were derived from the data on the basis of the customers’ decision-making context (fashion or electronics) and choice of purchase channel (offline or online, including mobile purchase). The following four distinct profiles were identified: 1) fashion offline, 2) fashion online, 3) electronics offline, and 4) electronics online. Those participants whose use of purchase channel could not be unambiguously confirmed were excluded. Decisionmaking context and purchase channel were merged into a single variable, for avoidance of a three-way interaction analysis, with its inherent complexity of interpretation. To analyze the effect of the country and the shopper profile on customer value, 3×4 ANOVAs were conducted with SPSS, version 20. In pairwise comparisons, Bonferroni corrections were made. Analyses of variance were conducted also with controlling for gender and age, since these have been found to be related to customer value (Borges et al., 2013; Wiedmann et al., 2014). The results showed no changes; accordingly, the results without these

4.2. Customer value and different shopping contexts In addition to validation of the customer value scale, differences in customer value between contexts were examined. Figs. 3–5 and Tables 5 and 6 present the results. The main effects of country were evident for both the customer value dimensions and the outcomes (see Table 5). Of the respondents, the U.S. shoppers derived the most value from shopping as measured on all four dimensions of value: economic, functional, emotional, and symbolic. They were also the most satisfied and the most willing to recommend to others. Compared to the Japanese subjects, Finnish shoppers perceived more functional value and were more satisfied and more willing to recommend to others. The main effects of shopper profile (fashion offline, fashion online, electronics offline, electronics online) were also evident for both the dimensions of customer value and the outcomes (see Table 5). Among fashion shoppers, emotional value was highest, and fashion offline shoppers reported more symbolic value than the other shoppers did. Among electronics shoppers, functional value was highlighted, and electronic online shoppers especially often reported more economic value than fashion or electronic offline shoppers. Fashion offline and 5

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Table 4 Standardized loadings, correlations, and beta coefficients of the SEM models in the United States (n=776), Finland (n=858), and Japan (n=832) (all loadings, path coefficients, and correlation are statistically significant, at p < 0.05). U.S.

FIN

JP

Loadings Economic Item 1 Item 2 Item 3 Functional Item 1 Item 2 Item 3 Item 4 Emotional Item 1 Item 2 Item 3 Item 4 Symbolic Item 1 Item 2 Item 3

α=0.88 0.891 0.946 0.719 α=0.87 0.700 0.825 0.875 0.788 α=0.88 0.865 0.908 0.911 0.903 α=0.96 0.879 0.898 0.781

α=0.85 0.915 0.959 0.579 α=0.86 0.659 0.844 0.911 0.731 α=0.91 0.912 0.936 0.972 0.967 α=0.98 0.893 0.897 0.899

α=0.86 0.868 0.899 0.702 α=0.89 0.757 0.837 0.914 0.796 α=0.89 0.884 0.908 0.805 0.823 α=0.94 0.850 0.886 0.851

Customer value (VAL) Economic Functional Emotional Symbolic Satisfaction (SAT) Item 1 Item 2 Item 3 Word of mouth (WOM) Item 1 Item 2 Item 3

0.739 0.787 0.772 0.547 α=0.89 0.888 0.892 0.805 α=0.96 0.937 0.948 0.922

0.290 0.465 0.900 0.791 α=0.84 0.898 0.854 0.709 α=0.96 0.908 0.979 0.938

0.668 0.755 0.693 0.590 α=0.90 0.860 0.931 0.825 α=0.92 0.824 0.934 0.918

Fig. 4. Dimensions of customer value with the various shopper profiles in Finland.

Fig. 5. Dimensions of customer value for the various shopper profiles in Japan. Correlations SAT with WOM

0.601

0.555

0.370

Beta coefficients SAT on VAL WOM on VAL

0.828 0.803

0.547 0.586

0.692 0.749

were related to economic value, emotional value, and word of mouth. The aforementioned result that emotional value was perceived more often among fashion shoppers than among electronic shoppers is actually evident only in the U.S. and Finnish data (see Table 6 and Figs. 3–5). Moreover, these differences are smaller in the U.S. dataset than the Finnish one. In addition, the finding that economic value is especially strongly related to electronics online shopping echoed the situation in Finland, in fact. In contrast, the U.S. data showed no differences between the profiles in economic value, and it is clear that in Japan economic value was perceived the most not only by electronics online shoppers; the same proved true for fashion online shoppers. With regard to word of mouth, the overall result that fashion offline shoppers were the most willing to recommend their choice to others is evident only in the U.S. data (and the difference was found only between fashion offline and electronics offline shoppers). In Japan, there were no differences in word of mouth, and in Finland, fashion online shoppers together with electronic online shoppers were, in fact, the most willing to recommend to others. To illustrate how retailers can be profiled with customer value in mind, the three companies mentioned most often in the U.S. data with respect to electronics were selected for further examination. These are denoted as retailer A (n = 42), retailer B (n = 65), and retailer C (n = 134). Because of the small group sizes, it was not possible to consider the shoppers separately by purchase channel. The results (presented in Fig. 6) show that retailer A was given the highest evaluation and retailer C the lowest for economic, functional, and emotional value, along with satisfaction and word of mouth. Retailer B, in contrast, was close to retailer A for economic and functional value but in emotional value received a score similar to retailer C’s. Perhaps on account of this lower emotional value, retailer B did not reach retailer A’s levels for satisfaction and word of mouth.

“U.S.”=USA, “FIN”=Finland, “JP”=Japan.

Fig. 3. Dimensions of customer value for the various shopper profiles in the U.S.

electronics online shoppers were the most satisfied, and fashion offline shoppers were also the most willing to recommend to others. In the more complex conditions, with the effect of context considered with calculation of the interaction effects of country and shopper profile, there is a statistically significant effect for economic value, emotional value, and word of mouth (see Table 5). This implies that there are differences between the countries in how shopper profiles

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Table 5 Results of 3×4 ANOVAs. Country

Shopper profile

F test

U.S. (n=507)

Finland (n=759)

Japan (n=615)

1 (n=454)

2 (n=412)

3 (n=496)

4 (n=519)

Country

Profile

Country×profile

Economic

7.96 (1.77)

6.82 (2.18)

6.80 (1.93)

6.92 (2.32)

7.15 (1.94)

6.97 (2.10)

7.42 (1.83)

69.78*** U.S. > FIN, JP

12.04*** 1, 3 < 4

3.16**,a

Functional

7.90 (1.60)

7.31 (1.86)

6.94 (1.72)

7.05 (1.98)

7.28 (1.73)

7.49 (1.80)

7.53 (1.58)

47.43*** U.S. > FIN > JP

10.46*** 1 < 3, 4

1.60ns

Emotional

7.68 (1.84)

5.68 (2.51)

5.91 (1.81)

6.66 (2.42)

6.53 (2.01)

5.96 (2.37)

6.12 (2.25)

129.07*** U.S. > FIN, JP

14.63*** 1, 2 > 3, 4

3.54**,a

Symbolic

6.78 (2.07)

4.94 (2.36)

4.93 (1.93)

5.74 (2.36)

5.34 (2.22)

5.22 (2.33)

5.44 (2.26)

118.85*** U.S. > FIN, JP

5.96*** 1 > 2, 3

1.94ns

Satisfaction

8.14 (1.39)

7.70 (1.27)

7.23 (1.35)

7.75 (1.33)

7.48 (1.40)

7.58 (1.48)

7.81 (1.27)

68.10*** U.S. > FIN > JP

8.38*** 1, 4 > 2; 3 < 4

1.59ns

Word of mouth

8.28 (1.76)

7.52 (1.93)

6.36 (1.78)

7.72 (1.91)

7.01 (2.11)

7.28 (2.00)

7.35 (1.87)

130.56*** U.S. > FIN > JP

7.25*** 1 > 2, 3, 4; 2 < 4

3.13**,a

*p < 0.05, “U.S.”=USA, “FIN”=Finland, “JP”=Japan 1=Fashion offline, 2=Fashion online, 3=Electronics offline, 4=Electronics online. ** p < 0.01, *** p < 0.001 a = More in-depth breakdown of results provided in Table 6.

5. Conclusions and implications We developed and tested a model for customer value in retailing, and we illustrated how value perceptions can be profiled across retail contexts for purposes of identifying and evaluating customer value propositions. We have shown that customer value as a key performance indicator has many alternative interpretations, so it is clear that each company should choose the value that best serves its strategic goals when crafting customer value propositions and assessing success with them. With this paper, we sought to provide tools for understanding, measuring, and modeling customer value from this contextual and strategic angle. Acknowledging the limitations of our investigation, restricted to four contextual factors (country, product category, channel, and company level), we suggest the following conclusions and implications, with the associated avenues for future research. In our study, customer value was a reflective construct – i.e., a second-order factor (composed of economic, functional, emotional, and symbolic value factors) – that was positively associated with satisfaction and word of mouth. We modeled customer value as a reflective construct because we wanted to use a covariance-based SEM to test the theory and the metric developed (Wilcox et al., 2008). Moreover, the second-order factor of customer value provided a way to address the natural multicollinearity between individual dimensions of value (see

Fig. 6. Three illustrative retailer profiles for customer value, satisfaction, and word of mouth.

Bagozzi and Yi, 2012). Although the covariance-based SEM was the best option for assessment of the fit of the model, there was a drawback, in that we were unable to evaluate how the individual dimensions of customer value are related to satisfaction and word of

Table 6 Elaboration on country×shopper profile interaction. U.S.

Economic Emotional Word of mouth

Diff

1 (n = 135)

2 (n = 75)

3 (n = 177)

4 (n = 120)

8.15 (1.78) 8.05 (1.82) 8.66 (1.60)

8.17 (1.69) 8.01 (1.79) 8.32 (2.00)

7.69 (1.81) 7.23 (1.89) 7.95 (1.80)

7.99 (1.71) 7.72 (1.70) 8.31 (1.66)

– 1, 2 > 3 1>3

Finland

Diff

1 (n = 253)

2 (n = 89)

3 (n = 251)

4 (n = 166)

6.52 (2.35) 6.04 (2.55) 7.52 (1.89)

6.93 (2.29) 6.86 (2.32) 8.21 (1.81)

6.69 (2.15) 5.14 (2.45) 7.13 (2.06)

7.42 (1.78) 5.34 (2.38) 7.77 (1.68)

1, 3 < 4 2>1 > 3, 4 1 < 2, 2, 4 > 3

Japan

Diff

1 (n = 66)

2 (n = 248)

3 (n = 68)

4 (n = 233)

5.93 (2.20) 6.21 (1.75) 6.59 (1.77)

6.92 (1.78) 5.96 (1.69) 6.19 (1.83)

6.09 (2.06) 5.65 (1.71) 6.10 (1.56)

7.12 (1.85)

1=Fashion offline, 2=Fashion online, 3=Electronics offline, 4=Electronics online. “Diff”=statistically significant (p < 0.05). differences between groups

7

5.85 (1.99)

1, 3 < 2, 4 –

6.55 (1.76)

–

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T. Rintamäki, K. Kirves

shoppers perceived more emotional and symbolic value than did electronics shoppers. Interestingly, especially in the fashion context, the online channel proved an effective challenger to the offline experience in terms of emotional value, indicating that fashion retailers have succeeded better in bringing experiential elements into the digital shopping experience. This may be evidenced by the emotive and symbolic imagery used by many fashion e-tailers and also in the use of editorial contexts, social media, and co-creative practices as part of the shopping experience they offer. More research is needed on the relations of product category, channel, and customer value. For instance, future research could examine the transformation of the shopping experience (pre-purchase, purchase, and post-purchase) that online and mobile channels have brought for some product categories and at how it contributes to customer value. Performance relative to competitors’ determines the “goodness” of customer value profiles. A “good” profile reflects the favorable points of difference represented by a company’s strategically defined customer value propositions. Our comparison of the three companies illustrates the differences among established retailers’ customer value profiles for electronics purchasing in the U.S. market. Though our dataset’s size imposes limitations with regard to company-level comparison, we hope that it motivates both researchers and managers to recognize the potential relevance of the “softer” side of the customer value continuum for analysis of competitive advantage and success with customeroriented strategy.

mouth (see Carlson et al., 2015). Therefore, future studies could usefully apply the variance-based approach with a formative model. Measuring all attributes that drive customer value across various channels in the course of the shopping experience is virtually impossible in any parsimonious study, whether academic or not. Hence, focusing on the outcome level is strongly recommended. Once one understands how customer value perceptions are formed, this approach can be complemented with other methods, such as conjoint studies or use of qualitative techniques that aid in pinpointing which attributes (i.e., key value drivers) show success or failure to create the strategized value profile. In fact, future research would benefit from a set of techniques that help managers first establish their competitive customer value profiles, then identify which attributes best drive the differentiating customer value they have chosen, and finally analyze how well they succeed in their value creation strategies over time. This approach could be systematically extended across contexts, with, for instance, different attributes between online and offline channels that contribute on the same dimension(s) of customer value. Customer value is a strongly contextual concept – few demographic generalizations are available. Several context-linked customer value profiles are needed for in-depth understanding of the real dynamics of customer value propositions. Although earlier findings show that demographics affect perceptions of customer value, (e.g., Borges et al., 2013), our results indicate that matters of context such as product category, company/brand, and perhaps the situation framing the shopping experience are those that reign supreme for value perceptions. However, meta-level studies might prove helpful in the future for mapping the demographic variables that have good predictive validity. Our framework holds across the three countries investigated, but the customer value profiles and their roles differ with culture; for example, Carlson et al. (2015) present analogous findings. Response patterns across the three countries suggest that U.S. shoppers perceive more emotional and symbolic value than Finns or Japanese shoppers do. Further research is needed if we are to understand whether these differences reflect more the actual relative significance of individual customer value dimensions between these cultures, cultural response bias in surveys, or simply a shortcoming in our research setting. For instance, Harzing (2006) showed that respondents from the U.S. had a greater tendency toward acquiescence and extreme response style than Finnish and Japanese respondents did. Furthermore, the respondents from Japan tended to choose responses in the middle of the scale used. One possible explanation for such differences in response styles lies in differences between the countries in power distance, collectivism, uncertainty-avoidance, and extraversion. It seems that greater power distance, uncertainty avoidance, and extraversion (as seen in the U.S.) are related to higher acquiescence bias and extreme-response bias while a more collectivist leaning (as in Japan) is linked to higher middle-response bias. For researchers and managers alike, the challenge is to hone our techniques for understanding what is explained by response bias and which results represent a genuine difference in perceived value. Depending on the cause, the differences we found in value perceptions between markets could, from a multinational company’s perspective, support either applying different context-dependent strategies or using scaling procedures that take the “bias” into account. As Scarpi et al. (2014) conclude, utilitarianism and hedonism manifest themselves differently between the online and offline channels. Our results show that customer value perceptions differ across product categories and channels. The online channel outperformed the offline one in most instances with regard to economic and functional value. This is in line with the recent notions of e-tailers that constantly challenge their offline rivals by means of their pricing, wider selection, added convenience from decision-making tools, fast and free delivery, and no-cost product return. Electronics as a product category proved to be highly uniform, whereas the fashion shoppers’ customer experiences varied greatly in each of the three countries. In most cases, fashion

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