Understanding moderating effects in increasing share-of-wallet and word-of-mouth : A case study of Lidl grocery retailer

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY-NC-ND 4.0 https://creativecommons.org/licenses/by-nc-nd/4.0/ Understanding moderating effects in increasing share-of-wallet and word-of-mouth : A case study of Lidl grocery retailer © 2018 Elsevier Ltd. Accepted version (Final draft) Shaikh, Aijaz A.; Karjaluoto, Heikki; Häkkinen, Juho Shaikh, A. A., Karjaluoto, H., & Häkkinen, J. (2018). Understanding moderating effects in increasing share-of-wallet and word-of-mouth : A case study of Lidl grocery retailer. Journal of Retailing and Consumer Services, 44, 45-53. https://doi.org/10.1016/j.jretconser.2018.05.009 2018 Understanding moderating effects of increasing share-of-wallet and word-of-mouth: A case study of Lidl grocery retailer Aijaz A. Shaikh1 [email protected] Jyväskylä University School of Business and Economics P.O. Box 35, FI-40014 University of Jyväskylä, Finland Heikki Karjaluoto [email protected] Jyväskylä University School of Business and Economics P.O. Box 35, FI-40014, University of Jyväskylä, Finland Juho Häkkinen [email protected] Jyväskylä University School of Business and Economics P.O. Box 35, FI-40014, University of Jyväskylä, Finland 1 Corresponding author Understanding moderating effects in increasing share-of-wallet and word-of-mouth: A case study of Lidl grocery retailer ABSTRACT This study examines how five moderating variables (the length of the customer relationship, following a company in print media and on social media, remembering online advertisements, and the customer’s age) affect the relationships between perceived value and loyalty and satisfaction and loyalty in the grocery retailing sector. A series of hypotheses were developed and tested with a sample of 2,072 discount retailer customers in Finland. The results support all the direct effects hypotheses and show that perceived value and satisfaction both have a positive effect on loyalty, measured as a share of wallet and word of mouth and that the effect of perceived value tends to be stronger in the study context. Also, the five moderating variables have a positive moderating effect on the linkages between perceived value and eWOM and between satisfaction and eWOM. Theoretical and managerial implications, limitations, and future research directions are presented. Keywords: Perceived value; satisfaction; share of wallet; word of mouth; moderating effects. 1. Introduction Customer loyalty is an important business goal, a major driver of success, and a major strategic objective (Yang and Peterson, 2004; Peña et al., 2017; Mckercher et al., 2012). Customer loyalty is extensively measured by behavioral intention, which includes willingness to recommend and intentions to revisit places, such as retail outlets and tourist destinations (Kim and Park, 2017). High value is considered the primary motivation for a customer’s patronage or loyalty (Bolton and Drew, 1991; Yang and Peterson, 2004). Park et al. (2018) have supported the view that the perception of value is a better predictor of loyalty intentions than satisfaction or perception of the service quality. Customer perceived value, i.e., what consumers get for what they give (Park et al., 2018) and customer satisfaction, i.e., the consumer’s evaluation of the value derived from the shopping experience (Carpenter, 2008), play a significant role in retaining and increasing customer loyalty. Satisfaction has emerged as a primary aspect of the drivers of loyalty (García-Fernández et al., 2017); if a customer is satisfied, there are more possibilities for the client to have a positive perception of the organization, will demonstrate loyalty to the company, and will positively represent the company via WOM (García-Fernández et al., 2017). Similarly, customer satisfaction is widely considered a vital element of many favorable intentions and behaviors linked to increased customer loyalty and profits, lower operating expenditures, and high business success, thereby making it the focal point of business operations (Ratanavilaikul, 2018). Like perceived value, studies have found that customer satisfaction positively affects loyalty. For example, in the retailing context, Leppäniemi et al. (2017) found a significant positive relationship between satisfaction and loyalty. Specifically addressing the retail industry, only a few studies (e.g., Yang and Peterson, 2004; Peña et al., 2017) have considered the moderating effects on the value–loyalty and satisfaction–loyalty relationships. Prior research (e.g., El-Adly and Eid, 2016; Fazal-e-Hasan et al., 2018; Grosso et al., 2018; Nisar and Prabhakar, 2017) has also examined online retailers, upscale grocery retailers (e.g., super and hypermarkets), and shopping malls leaving discount grocery retailers scarcely researched. Considering these research deficiencies, the goal of this study is to first extend the knowledge of the relationships between customer perceived value–loyalty and customer satisfaction–loyalty in the discount retailer. Second, this study aims to contribute to the empirical literature about discount grocery retailers. It will achieve this by examining why customers shop in discount stores and by formally examining the indirect effects of customer demographics (relationship length, the customer’s age) and perceptions of advertising on the relationships between loyalty and its two chosen main drivers (satisfaction and perceived value). There are no scholarly works looking explicitly at the moderating effect (Peña et al., 2017) of customer demographics and perceptions of advertising on consumer behavior towards retailing firms. It is, therefore, beneficial to examine how different degrees to which customer demographics and perceptions of advertising are adopted may influence the satisfaction–loyalty and perceived value–loyalty relationships. Accordingly, this study tests the indirect effects of five moderator variables (relationship length, following a company in print media, remembering online advertisements, following a company on social media, and the customer’s age) on the relationships between the two drivers of loyalty (perceived value and satisfaction) and customer loyalty, measured by the terms share-of-wallet (SOW), word-of-mouth (WOM), and electronic word-of-mouth (eWOM). This paper proceeds as follows. Next, research setting and retail sector will be discussed. Subsequently, the research model and the hypotheses related to it will be presented, followed by the presentation of the research method and the results. A discussion of the findings, the contributions of this study, the limitations, and the recommendations for future research will conclude this study. 2. Research setting and retail sector 2.1 Research setting For the sake of this study, discount retail stores, which are different from retail department stores, has been postulated. According to Grace and O’Cass (2005, p.232), department retail stores are considered as “mass-merchandisers who highlight the quality image and high customer contact.” Discount retail stores, on the other hand, are considered “mass-merchandisers who emphasize selfservice and low prices.” The empirical context of this study is about the customers of one of the world’s largest discount grocery retailers, Lidl. The fundamental principles of Lidl are self-service and low prices in simplified shops. Lidl, a German grocery retailer, has over 150 stores in Finland and approximately 5,000 employees. Lidl’s share in the Finnish grocery industry is evaluated at 8.8% (Nielsen, 2017). Lidl is considerably different from its main rivals in Finland (Kesko and S-group), as it does not offer a loyalty card program, and the stores offer different selections and less floor space. Lidl was chosen as the target company for this study because it was possible to exclude any influence from a loyalty card program. 2.2 Retail sector Over the last three decades, the retail sector, including e-commerce, has gradually evolved in Europe and elsewhere and has developed into the most dynamic global economic sector (Pantano et al., 2017). According to e-Marketer (2017), the worldwide retail e-commerce sales reached USD 2.290 trillion last year in 2017, making up 10.1% of the total retail sales. This share will surpass 16% by 2021 when e-commerce sales hit USD 4.479 trillion. According to Corbet and McMullan (2018), the single European currency and the abolishment of cross-border tariffs increase of imports and exports across European borders, enabling suppliers to locate a broad variety of goods at lower prices. This changed the market behavior of the retailers, from using more inventive techniques to adopting cost-cutting efficiency, to offer high value and survive the competition created by low-cost retailers, such as Aldi and Lidl. Perhaps, perceived value for money may be more pertinent in the case of the discount retailer (Grace and O’Cass, 2005). Advances in information and communication technologies, the exponential growth in the use of portable devices and social media, and the options available to consumers for accessing, choosing, and purchasing merchandise and other items have evolved as well. With these innovative developments, a surge in the e-commerce business models is admissible and logical. Nevertheless, despite these innovative developments, brick-and-mortar retail, especially in the grocery shopping context, has remained the preferred alternative for the vast majority of customers. For instance, a market survey conducted by PwC (2015), involving more than 19,000 respondents, indicates that physical retail stores remain the primary destination for shoppers, despite escalating internet and online sales. Thus, the brick-and-mortar retail domain continues to have a rightful place in academic research (Bradley and LaFleur, 2016). 3. Research model and hypotheses The research model is presented in Figure 1. In conceptualizing the key terms of the study, this study follows the mainstream literature (e.g., de Matos and Rossi, 2008; Keiningham et al., 2005; Zeithaml, 1988). As the concepts of perceived value, satisfaction, SOW, and WOM are well-known, this study’s conceptual discussion focuses on the relationships among the variables and the proposed moderating effects (relationship length, following a company in print media, remembering online advertisements, following a company on social media, and the customer’s age). [Insert Figure 1 about here] 3.1 The effect of perceived value on loyalty (SOW, WOM, and eWOM) Understanding the sources of value of an offer is widely considered a key marketing strategy element (Gummerus, 2013; Mencarelli and Lombart, 2017). Perceived value is considered fundamental for all the marketing activities to maintain a competitive advantage (Shaikh and Karjaluoto, 2016). Obtaining value, from a consumer’s perspective, is a significant consumption goal for a successful shopping experience (El-Adly and Eid, 2016). In the retail industry specifically, perceived value is mainly investigated in the context of product and brand and is defined as the customer’s overall assessment of the utility of a product, which is based on their perceptions of what is received and what is given (Zeithaml, 1988). A few researchers have treated the perceived value as a multidimensional construct. For example, examining the perceived value in the context of department stores, Rintamäki et al. (2006) concluded that perceived value is a combination of utilitarian, hedonic, and social values. Similarly, Wang et al. (2004) also considered the perceived value to consist of several dimensions: perceived sacrifices, functional value, emotional value, and social value. About perceived value and its relationship to loyalty, research by Gruen et al. (2006) and Boonlertvanich (2011) established a positive link between these two constructs: the higher the perceived value a consumer derives from the provider, the higher the customer loyalty will be. For discount retailers, Kim and Lee (2010) define SOW as the share of a customer’s business that a particular retailer obtains in a product category. Prior research has identified three primary behaviors associated with customer loyalty: retention, SOW, and WOM (Bowman et al., 2000; Keiningham et al., 2007). Eggert and Ulaga (2010) noticed correlations between perceived value and SOW in business markets, finding that customer-perceived value may predict such outcome variables as purchase intentions, WOM, a reduced search for alternatives, and SOW. In a buyer-seller setting, Hughes et al. (2013) confirmed a strong positive effect of customer perceived value on SOW. A direct relationship, therefore, should exist between perceived value and SOW, and the value or benefits derived from a product or service will compel the consumer to spend more on that specific product or service. However, in the retailer or consumer context, this relationship has not been wellresearched. Therefore, this study will test this effect further and hypothesize: H1: Perceived value has a positive effect on SOW. WOM is considered a recognized dimension of loyalty (Kumar et al., 2013; Fuentes-Blasco et al., 2017). According to Westbrook (1987, p.261), WOM is defined as “all informal communications directed at other consumers about the ownership, usage, or characteristics of particular goods and services and/or their sellers.” WOM is considered an output of other constructs, such as perceived value, satisfaction, loyalty, quality, commitment, and trust (de Matos and Rossi, 2008). Both researchers and practitioners agree that WOM produces benefits for firms (Söderlund and Mattsson, 2015) and the perceived value has a positive effect on customers’ behavioral intentions, particularly on WOM (Hartline and Jones, 1996). Because of the advancements and innovations are seen in the internet and mobile technologies, traditional face-to-face WOM has changed into e-WOM, and now consumers can get information regarding products or services via eWOM (Hussain et al., 2017). According to Hussain et al. (2017) and Reichelt et al. (2014), research on eWOM is still in its initial phases and needs more attention to understand the power of eWOM communications. Hennig-Thurau et al. (2004) defined eWOM as any positive or negative statement made by a customer about a product, service, or company that is made available to a multitude of people and institutions via the Internet and social media. Prior research (Durvasula et al., 2004; Gruen et al., 2006; Hartline and Jones, 1996; Keiningham et al., 2007; McKee et al., 2006; Wang et al., 2004) has reported that perceived value is positively related with WOM and eWOM. McKee et al. (2006) argued that a customer who perceives high value tends to become more committed to the company or brand and seeks to recommend others to become loyal to the same company or brand. Thus, this study hypothesizes: H2: Perceived value has a positive effect on WOM. H3: Perceived value has a positive effect on eWOM. 3.2 The effects of satisfaction on loyalty (SOW, WOM, and eWOM) Customer satisfaction is defined as the customer’s overall evaluation of a product or service after he or she purchases it (Pham and Ahammad, 2017). Customer satisfaction has long been regarded as a vital determiner of long-term consumer behavior (Oliver, 1999; Yi, 1990) that results in customer retention, SOW, and profitability. The positive relationship between customer satisfaction and SOW has been supported across various industries, including both the business-to-business and the business-to-customer sectors (e.g., Buoye et al., 2016; Cooil et al., 2007; Hunneman et al., 2015). According to Keiningham et al. (2003), satisfaction is positively related to the share of business a customer conducts with a particular company. Hunneman et al. (2015) investigate the impact of consumer confidence on satisfaction and SOW. They confirm a positive relationship between satisfaction and SOW. Given the findings of various studies regarding the positive effect of satisfaction on SOW, this study hypothesizes: H4: Satisfaction has a positive effect on SOW. Both researchers and practitioners agree that customer satisfaction is an important component of a firm’s long-term success (Buoye et al., 2016). It is also well-known that satisfaction is an important post-purchase response that is commonly linked to relationship outcomes, such as positive WOM (Mittal et al., 1999). According to Anderson (1998), the relationship between satisfaction and WOM is asymmetric and nonlinear, and the level of satisfaction influences repurchase and WOM (de Matos and Rossi, 2008). Brown et al. (2005) suggested that higher satisfaction leads to greater levels of commitment and WOM intentions, and commitment leads to increased WOM behavior. Similarly, other studies have clearly documented a positive link between satisfaction and WOM (e.g. Brown et al., 2005; de Matos and Rossi, 2008; Heckman and Guskey, 1998; Hennig-Thurau et al., 2004; Maxham and Netemeyer, 2002; Mittal et al., 1999; Oliver and Swan, 1989; Sweeney and Swait, 2008). This relationship provides a great opportunity for companies to increase their market share by developing a positive WOM relationship with customers (Casaló et al., 2008). Moreover, through web-based social or online consumer opinion platforms, customers who are always online share their opinions on goods and services with a multitude of other consumers and communities. Yoo et al. (2015) reported that eWOM has a positive and significant impact on satisfaction in the marketplace. With these arguments in mind, this study hypothesizes: H5: Satisfaction has a positive effect on WOM. H6: Satisfaction has a positive effect on eWOM. 3.3 Moderating effect of length of the customer relationship Although the direct effects between perceived value and loyalty and between satisfaction and loyalty are well-established, less is known about the other factors contributing to the increase of SOW and positive WOM. The length of the relationship is an important variable to examine as it implies that a consumer has had sufficient service encounters with the service provider to acquire more information about the retailer over a period. This creates a logical relationship dynamic between the consumer and the retailer such that more encounters the consumer has with the retailer, the more he or she accumulates information about and experience with the partner. As the relationship extends, two important changes occur: customers become more experienced with both the service provider with which they are transacting and the market in which the provider competes (Bell et al., 2005). Empirical studies have validated this posture by elucidating that an increasing, as well as a sustainable relationship length, positively affect satisfaction and perceived value. Along the same lines, both Bolton (1998) and Verhoef (2003) argue that the longer the relationship, the higher tends satisfaction to be. Garbarino and Johnson (1999) found that the effects of satisfaction and trust depend on relationship orientation. Nonetheless, different views have also been outlined in other contexts. For instance, Karjaluoto et al. (2012) did not find a direct effect of relationship length on perceived value and loyalty in the telecommunications context. The question of how relationship length is related to the relationship between perceived value and the loyalty constructs used in this study (SOW, WOM, eWOM) and between satisfaction and the loyalty constructs has not been widely articulated in the literature, and therefore, the answer remains elusive. However, there are reasons to believe that the effects may increase with the length of the relationship between the customer and retailer. Raimondo et al. (2008) argued that because of the increasing information they have about their relationship with a provider, long-term customers usually recognize that they generate extra value for the provider through repeat purchases over time, favorable WOM, and increased cross-buying. In the service industry context, Verhoef et al. (2002) found that relationship length influences the strength of the relationship The findings of this study are valuable for managers operating in the retail industry. For example, the major takeaway from this study is that the perceived value and customer satisfaction are strong predictors of all three loyalty variables (i.e., SOW, WOM, and eWOM). Retailers should also note that, as confirmed by our empirical study, perceived value is a stronger predictor of customer loyalty variables than satisfaction is. Since the underline purposes of the companies’ marketing undertakings is to create customer loyalty for their products, services, ideas, or store (Vesel and Zabkar, 2009), our finding suggests that managers should offer increased value and monitor multiple measures of loyalty instead of just one and observe business performance to optimize in-store customer experience and achieve the best financial results. The results of the moderating effects indicate that advertising and use of social media pay off in the discount retailer context as they have positive effects on enhancing eWOM. However, it is worth noting that these indirect effects were not found to affect SOW or WOM. Therefore, online advertising and social media use seem to be more of a driver of eWOM, so businesses that do not currently employ ‘online’ and ‘social’ media strategies should highly consider doing so to increase eWOM. 6.3 Limitations and future research directions This study is not without limitations. First, the link to the survey questionnaire was published on Lidl’s official Facebook page; therefore, the survey sample is a convenience sample and biased to the Facebook population. Second, the study represents a cross-sectional snapshot of a point in time. A longitudinal study to investigate the relationships over time to understand how perceived value and satisfaction or other constructs might change is recommended. Third, this study focused on selfreported behaviors (SOW, WOM, and eWOM), and these are an incomplete proxy for actual behavior (Keaveney, 1995). They should be supplemented with behavioral measures in order to develop a composite index of loyalty (Dick and Basu, 1994). Finally, the research was conducted in Finland and covered only one company in the grocery industry. An extension of this study might take into account, for example, two other major players (S-Group and Kesko) in the Finnish grocery business. Future studies should also be undertaken in different countries in order to generalize the findings. References Anderson, E. W., 1998. Customer satisfaction and word-of-mouth. Journal of Service Research. 1 (1), 1–14. Baptista, G., Baptista, G., Oliveira, T., Oliveira, T., 2017. Why so serious? Gamification impact in the acceptance of mobile banking services. Internet Research. 27(1), 118–139. Bell, S. J., Auh, S., Smalley, K. 2005. Customer relationship dynamics: service quality and customer loyalty in the context of varying levels of customer expertise and switching costs. Journal of the Academy of Marketing Science. 33(2), 169–183. Bolton, R. N., 1998. A Dynamic Model of the Customer’s Relationship with a Continuous Service Provider: The Role of Satisfaction. Marketing Science. 17 (1), 45–65. Bolton, R. N., Drew, J. H., 1991. A multistage model of customers' assessments of service quality and value. Journal of Consumer Research. 17(4), 375–384. Boonlertvanich, K., 2011. Effect of customer perceived value on satisfaction and customer loyalty in banking service: The moderating effect of mainbank status. International Journal of Business Research. 11(6), 40–54. Bowman, D., Farley, J. U., Schmittlein D.C., 2000. Cross-National Empirical Generalization in Business Services Buying Behavior. Journal of International Business Studies. 31 (4), 667–686. Bradley, G. T., LaFleur, E. K., 2016. Toward the development of hedonic-utilitarian measures of retail service. Journal of Retailing and Consumer Services. 32, 60–66. Brown, T. J., Barry, T. E., Dacin, P. A., Gunst, R. F., 2005. Spreading the word: Investigating antecedents of consumers' positive word-of-mouth intentions and behaviors in a retailing context. Journal of the Academy of Marketing Science. 33 (2), 123–138. Buoye, A., Komarova Loureiro, Y., Kabadayi, S., Nejad, M. G., Keiningham, T. L., Aksoy, L., Allsopp, J., 2016. Is share of wallet exclusively about making customers happy or having more customers? Exploring the relationship between satisfaction and double jeopardy. Journal of Service Management. 27(4), 434–459. Carpenter, J. M., 2008. Consumer shopping value, satisfaction and loyalty in discount retailing. Journal of Retailing and Consumer Services. 15(5), 358–363. Carroll, B. A., Ahuvia, A. C., 2006. Some antecedents and outcomes of brand love. Marketing Letters. 17 (2), 79–89. Casaló, L. V., Flavián, C., Guinalíu, M., 2008. The role of satisfaction and website usability in developing customer loyalty and positive word-of-mouth in the e-banking services. International Journal of Bank Marketing. 26 (6), 399–417. Cooil, B., Keiningham, T. L., Aksoy, L., Hsu, M., 2007. A longitudinal analysis of customer satisfaction and share of wallet: Investigating the moderating effect of customer characteristics. Journal of Marketing. 71(1), 67–83. Corbet, S., McMullan, C., 2018. Stock market reaction to irregular supermarket chain behaviour: An investigation in the retail sectors of Ireland and the United Kingdom. Journal of Retailing and Consumer Services. 43, 20–29. Cronin, J., Brady, M., Hult, T., 2000. Assessing the Effects of Quality, Value, and Customer Satisfaction on Consumer Behavioral Intentions in Service Environments. Journal of Retailing. 76 (2), 193–218. De Matos, C. A., Rossi, C. A.V., 2008. Word-of-mouth communications in marketing: a meta-analytic review of the antecedents and moderators. Journal of the Academy of Marketing Science. 36 (4), 578–596. De Wulf, K., Odekerken-Schröder, G., Iacobucci, D., 2001. Investments in Consumer Relationships: A Cross-Country and Cross-Industry Exploration. Journal of Marketing. 65 (4), 33–50. Dick, A., Basu, K., 1994. Customer loyalty: Toward an integrated conceptual framework. Journal of the Academy of Marketing Science. 22 (2), 99–114. Durvasula, S., Lysonski, S., Mehta, S. C., Tang, B. P., 2004. Forging relation-ships with services: the antecedents that have an impact on behavioral outcomes in the life insurance industry. Journal of Financial Services Marketing. 8 (4), 314–326. Eggert, A., Ulaga, W., 2010. Managing customer share in key supplier relationships. Industrial Marketing Management. 39(8), 1346–1355. El-Adly, M. I., Eid, R., 2016. An empirical study of the relationship between shopping environment, customer perceived value, satisfaction, and loyalty in the UAE malls context. Journal of Retailing and Consumer Services. 31, 217–227. e-Marketer, 2017. Worldwide Retail and Ecommerce Sales: eMarketer's Estimates for 2016–2021. 〈 https://www.emarketer.com/Report/Worldwide-Retail-Ecommerce-Sales-eMarketersEstimates-20162021/2002090 〉. Fazal-e-Hasan, S. M., Ahmadi, H., Mortimer, G., Grimmer, M., Kelly, L., 2018. Examining the role of consumer hope in explaining the impact of perceived brand value on customer–brand relationship outcomes in an online retailing environment. Journal of Retailing and Consumer Services. 41, 101– 111. Fornell, C., Larcker, D. F., 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research. 18 (1), 39–50. Fuentes-Blasco, M., Moliner-Velázquez, B., Gil-Saura, I., 2017. Analyzing heterogeneity on the value, satisfaction, word-of-mouth relationship in retailing. Management Decision. 55(7), 1558–1577. Garbarino, E., Johnson, M. S., 1999. The different roles of satisfaction, trust, and commitment in customer relationships. The Journal of Marketing. 63 (2), 70–87. García-Fernández, J., Gálvez-Ruíz, P., Fernández-Gavira, J., Vélez-Colón, L., Pitts, B., Bernal-García, A., 2017. The effects of service convenience and perceived quality on perceived value, satisfaction and loyalty in low-cost fitness centers. Sport Management Review (In Press). Grace, D., O’Cass, A., 2005. An examination of the antecedents of re-patronage intentions across different retail store formats. Journal of Retailing and Consumer Services. 12(4), 227–243. Grosso, M., Castaldo, S., Grewal, A., 2018. How store attributes impact shoppers’ loyalty in emerging countries: An investigation in the Indian retail sector. Journal of Retailing and Consumer Services. 40, 117–124. Gruen, T. W., Osmonbekov, T., Czaplewski, A. J., 2006. eWOM: the impact of customer-to-customer online know-how exchange on customer value and loyalty. Journal of Business Research. 59 (4), 449–456. Gummerus, J., 2013. Value creation processes and value outcomes in marketing theory: strangers or siblings? Marketing Theory. 13(1), 19–46. Hartline, M. D., Jones, K. C., 1996. Employee performance cues in a hotel service environment: Influence on perceived service quality, value, and word-of-mouth intentions. Journal of Business Research. 35(3), 207–215. Heckman, R., Guskey, A., 1998. The Relationship Between Alumni and Uni-versity: Toward a Theory of Discretionary Collaborative Behavior. Journal of Marketing Theory and Practice. 6, 97–112. Hennig-Thurau, T., Gwinner, K. P., Walsh, G., Gremler, D. D., 2004. Electronic word-of-mouth via consumer-opinion platforms: what motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing. 18(1), 38–52. Henseler, J., Ringle, C. M., Sarstedt, M., 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science. 43(1), 115–135. Homburg, C., Giering, A., 2001. Personal characteristics as moderators of the relationship between customer satisfaction and loyalty—an empirical analysis. Psychology & Marketing. 18(1), 43–66. Hughes, D. E., Le Bon, J., Rapp, A., 2013. Gaining and leveraging customer-based competitive intelligence: the pivotal role of social capital and salesperson adaptive selling skills. Journal of the Academy of Marketing Science. 41(1), 91–110. Hunneman, A., Verhoef, P. C., Sloot, L. M., 2015. The impact of consumer confidence on store satisfaction and share of wallet formation. Journal of Retailing. 91(3), 516–532. Hussain, S., Ahmed, W., Jafar, R. M. S., Rabnawaz, A., Jianzhou, Y., 2017. eWOM source credibility, perceived risk and food product customer's information adoption. Computers in Human Behavior. 66, 96–102. Karjaluoto, H., Jayawardhena, C., Leppäniemi, M., Pihlström, M., 2012. How value and trust influence loyalty in wireless telecommunications industry. Telecommunications Policy. 36 (8), 636– 649. Keaveney, S. M., 1995. Customer switching behaviour in service industries: an exploratory study. Journal of Marketing. 59, 71–82. Keiningham, T. L., Cooil, B., Aksoy, L., Andreassen, T. W., Weiner, J., 2007. The value of different customer satisfaction and loyalty metrics in predicting customer retention, recommendation, and share-of-wallet. Managing Service Quality: An International Journal. 17(4), 361–384. Keiningham, T. L., Perkins-Munn, T., Aksoy, L., Estrin, D., 2005. Does customer satisfaction lead to profitability? The mediating role of share-of-wallet. Managing Service Quality. 15 (2), 172–181. Keiningham, T.L., Perkins-Munn, T., Evans, H., 2003. The impact of customer satisfaction on share of wallet in a business-to-business environment. Journal of Service Research. 6 (1), 37–50. Kim, H. Y., Lee, M. Y., 2010. Emotional loyalty and share of wallet: A contingency approach. Journal of Retailing and Consumer Services. 17(5), 333–339. Kim, K. H., Park, D. B., 2017. Relationships among perceived value, satisfaction, and loyalty: Community-based ecotourism in Korea. Journal of Travel & Tourism Marketing. 34(2), 171–191. Kumar, V., Pozza, I.D. Ganesh, J., 2013. Revisiting the satisfaction-loyalty relationship: empirical generalizations and directions for future research. Journal of Retailing. 89 (3), 246–262. Leppäniemi, M., Karjaluoto, H., Saarijärvi, H., 2017. Customer perceived value, satisfaction, and loyalty: the role of willingness to share information. The International Review of Retail, Distribution and Consumer Research. 27(2), 164–188. Maxham, J. G., Netemeyer, R. G., 2002. A longitudinal study of complaining customers’ evaluations of multiple service failures and recovery efforts. Journal of Marketing. 66 (4), 55–71. McKee, D., Simmers, C. S., Licata, J., 2006. Customer self-efficacy and response to service. Journal of Service Research. 8 (3), 207–220. McKercher, B., Denizci-Guillet, B., Ng, E., 2012. Rethinking loyalty. Annals of Tourism Research. 39(2), 708–734. Mencarelli, R., Lombart, C., 2017. Influences of the perceived value on actual repurchasing behavior: Empirical exploration in a retailing context. Journal of Retailing and Consumer Services. 38, 12–21. Mittal, V., Kamakura, W. A., 2001. Satisfaction, Repurchase Intent, and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics. Journal of Marketing Research. 38 (1), 131–142. Mittal, V., Kumar, P., Tsiros, M., 1999. Attribute-Level Performance, Satisfaction, and Behavioral Intentions over Time: A Consumption-System Approach. Journal of Marketing. 63 (2), 88–101. Moliner-Velázquez, B., Ruiz-Molina, M. E., Fayos-Gardó, T., 2015. Satisfaction with service recovery: moderating effect of age in word-of-mouth. Journal of Consumer Marketing. 32(6), 470–484. Naik, P., Peters, K., 2009. A hierarchical marketing communications model of online and offline media synergies. Journal of Interactive Marketing. 23 (4), 288–299. Nielsen, 2017. Nielsen Grocery Shop Directory. 〈http://www.nielsen.com/content/dam/nielsenglobal/fi/docs/Nielsen%20Press%20Release %2023%20March%202017.pdf 〉. Nisar, T. M., Prabhakar, G., 2017. What factors determine e-satisfaction and consumer spending in ecommerce retailing? Journal of Retailing and Consumer Services. 39, 135–144. Oliver, R. L., 1980. A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research. 17, 460–69. Oliver, R. L., 1999. Whence consumer loyalty? Journal of Marketing. 63, 33–44. Oliver, R. L., Swan, J. E., 1989. Equity and Disconfirmation Perceptions as In-fluences on Merchant and Product Satisfaction. Journal of Consumer Research. 16 (3), 372–383. Pantano, E., Priporas, C. V., Sorace, S., Iazzolino, G., 2017. Does innovation-orientation lead to retail industry growth? Empirical evidence from patent analysis. Journal of Retailing and Consumer Services. 34, 88–94. Park, S. H., Hsieh, C. M., Miller, J. C., 2018. Moderating effects of recreation specialization on the quality-value-loyalty chain: a case of the Taroko Gorge Marathon. International Journal of Tourism Sciences. 18 (1), 29–42. Peña, A. I. P., Jamilena, D. M. F., Molina, M. Á. R., 2017. The effects of perceived value on loyalty: the moderating effect of market orientation adoption. Service Business. 11(1), 93–116. Pham, T. S. H., Ahammad, M. F., 2017. Antecedents and consequences of online customer satisfaction: A holistic process perspective. Technological Forecasting and Social Change. 124, 332– 342. PwC, 2015. Total Retail 2015: Retailers and the Age of Disruption. 〈https://www.pwc.es/es/publicaciones/retail-y-consumo/assets/informe-total-retail-2015.pdf〉. Raimondo, M. A., " Nino" Miceli, G., Costabile, M., 2008. How relationship age moderates loyalty formation: The increasing effect of relational equity on customer loyalty. Journal of Service Research. 11(2), 142–160. Ratanavilaikul, B., 2018. Customer perceived value, satisfaction, and behavioral intentions in the logistic industry. AU Journal of Management. 10(2), 63–71. Reichelt, J., Sievert, J., Jacob, F., 2014. How credibility affects eWOM reading: The influences of expertise, trustworthiness, and similarity on utilitarian and social functions. Journal of Marketing Communications. 20, 65–81. Ringle, C. M., Wende, S., Becker, J.-M., 2015. SmartPLS 3. Available at; 〈http://www.smartpls.com 〉. Rintamäki, T., Kanto, A., Kuusela, H., Spence, M. T., 2006. Decomposing the value of department store shopping into utilitarian, hedonic and social dimensions: Evidence from Finland. International Journal of Retail & Distribution Management. 34(1), 6–24. Shaikh, A. A., Karjaluoto, H., 2016. Mobile banking services continuous usage-case study of Finland. In: Proceedings of 49th Hawaii International Conference on System Sciences (HICSS), pp. 1497–1506. Söderlund, M., Mattsson, J., 2015. Merely asking the customer to recommend has an impact on word-of-mouth activity. Journal of Retailing and Consumer Services. 27, 80–89. Srinivasan, S., Rutz, O. J. Pauwels, K., 2016. Paths to and off purchase: quantifying the impact of traditional marketing and online consumer activity. Journal of the Academy of Marketing Science. 44 (4), 440–453. Statistics Finland (2018). Suomen virallinen tilasto (SVT): Väestön ennakkotilasto [verkkojulkaisu]. Helsinki: Tilastokeskus: 〈http://www.stat.fi/til/vamuu/2018/02/vamuu_2018_02_2018-04-06_tie_001_fi.html〉. Sweeney, J., Swait, J., 2008. The effects of brand credibility on customer loyalty. Journal of Retailing and Consumer Services. 15 (3), 179–193. Tellis, Gerard J., 2009. Generalizations of Advertising Effectiveness in Markets. Journal of Advertising Research. 49 (2), 240–245. Vakratsas, D., Ambler, T., 1999. How advertising works: what do we really know? Journal of Marketing. 63, 26–43. Verhoef, P. C., 2003. Understanding the effect of customer relationship management efforts on customer retention and customer share development. Journal of Marketing. 67(4), 30–45. Verhoef, P. C., Franses, P. H., Hoekstra, J. C., 2002. The effect of relational constructs on customer referrals and number of services purchased from a multiservice provider: does age of relationship matter? Journal of the Academy of Marketing Science. 30(3), 202–216. Vesel, P., Zabkar, V., 2009. Managing customer loyalty through the mediating role of satisfaction in the DIY retail loyalty program. Journal of Retailing and Consumer Services. 16(5), 396–406. Walsh, G., Evanschitzky, H., Wunderlich, M., 2008. Identification and analysis of moderator variables: investigating the customer satisfaction-loyalty link. European Journal of Marketing. 42(10), 977– 1004. Wang, Y., Lo, H., Chi, R., Yang, Y., 2004. An Integrated Framework for Customer Value and CustomerRelationship Management Performance: A Customer-Based Perspective from China. Managing Service Quality. 14 (2-3), 169–182. Westbrook, R.A., 1987. Product/consumption-based affective responses and post-purchase processes. Journal of Marketing Research, 24 (3), 258–270. Yang, Z., Peterson, R. T., 2004. Customer perceived value, satisfaction, and loyalty: The role of switching costs. Psychology & Marketing. 21(10), 799–822. Yoo, C. W., Kim, Y. J., Sanders, G. L., 2015. The impact of interactivity of electronic word of mouth systems and E-Quality on decision support in the context of the e-marketplace. Information & Management. 52(4), 496–505. Zeithaml, V., 1988. Consumer perceptions of price, quality and value: A means-end model and synthesis of evidence. Journal of Marketing. 52 (3), 2–22. List of Figures: Figure 1: Research Model List of Tables: Table 1: Sample profile % N Gender Female 67 1,387 Male 33 685 Total 100.0 2,072 Age Under 18 2.0 42 18–25 11.9 247 26–35 25.3 524 36 – 45 22.2 459 46–55 18.7 388 56–65 13.4 278 Over 65 6.5 134 Total 100.0 2,072 Net monthly income (€) 500 or less 10.3 214 501 – 1,000 19.7 409 1,001–1,500 17.6 365 1,501–2,000 21.8 452 2,001 – 2,500 13.7 283 2,501–3,000 8.2 169 3,001–3,500 3.6 74 3,501 – 4,000 2.1 44 4,001 or more 3.0 62 Total 100.0% 2,072 Household size 1 27.3 565 2 38.4 795 3 15.2 315 4 11.5 239 5 or more 7.6 158 Total 100.0 2,072 Distance to the nearest Lidl store (km) Under 1 km 16.2 336 1–2 22.5 467 2–5 31.4 651 5 – 10 13.2 273 Over 10 16.7 345 Total 100.0 2,072 Relationship length Less than 6 months 1.8 37 6–12 months 2.8 58 1–3 years 13.7 284 3 – 5 years 28.0 580 Over 5 years a 53.7 1,113 Total 100.0 2,072 Notes: aLidl has operated in Finland since 2002. Table 2: Composite reliability (CR) and standardized loadings Factors CR Items Standardized Loadings (p< 0.001) PEVA model SAT model Perceived value (2 nd order) Perceived sacrifices 0.945 PVM1 0.864 PVM2 0.874 PVM3 0.826 PVM4 0.871 PVM5 0.872 PVM6 0.859 Functional value 0.889 PVF1 0.839 PVF2 0.888 PVF3 0.767 PVF4 0.767 Emotional value 0.920 PVE1 0.841 PVE2 0.802 PVE3 0.846 PVE4 0.808 PVE5 0.872 Social value 0.911 PVS1 0.873 PVS2 0.890 PVS3 0.877 Satisfaction 0.915 SAT1 0.781 SAT2 0.807 SAT3 0.806 SAT4 0.761 SAT5 0.862 SAT6 0.786 WOM 0.960 WOM1 0.933 0.933 WOM2 0.938 0.938 WOM3 0.913 0.912 WOM4 0.920 0.921 eWOM 0.937 eWOM1 0.906 0.908 eWOM2 0.919 0.920 eWOM3 0.911 0.908 SOW 0.948 SOW1 0.949 0.949 SOW2 0.933 0.933 SOW3 0.898 0.898