How store attributes impact shoppers’ loyalty in emerging countries: An investigation in the Indian retail sector

Journal of Retailing and Consumer Services 40 (2018) 117–124

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How store attributes impact shoppers’ loyalty in emerging countries: An investigation in the Indian retail sector

T

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Monica Grossoa, , Sandro Castaldob, Anjana Grewalc a b c

emlyon Business School, 23 Avenue Guy de Collongue, CS40203, 69134 Ecully CEDEX, France SDA Bocconi School of Management, Via Bocconi 8, 20136 Milano, Italy MISB Bocconi, 9th Floor, Hiranandani Knowledge Park, Powai 400076, Mumbai, India

A R T I C L E I N F O

A B S T R A C T

Keywords: India retail Store loyalty Satisfaction Perceived value Assortment Salespeople Store environment

This paper takes a first step towards verifying a loyalty building model in the Indian retail sector. The results show that the intensity of some core loyalty model paths in developed countries, are confirmed within the Indian retail sector. One such path refers to the relationship between store loyalty and its main driver, customer satisfaction. Satisfaction comes mainly from the store environment and the perceived value according to customers, which is influenced by the retailers’ product assortment decisions. Surprisingly, promotions don’t have an impact on the perceived value, while the perceived value has only a small and negative impact on store loyalty.

1. Introduction Western retailers in developed countries are increasingly facing pricing pressure, which is decreasing their margins and reducing customers’ loyalty. Hence, these retailers are looking for new ways of attracting shoppers to the store, increasing the number of purchases they make, and keeping them loyal to the store. This goal is particularly difficult to reach because of the parallel evolution of demand and the development of the e-commerce channel. Building intangible marketing resources has been identified as a possible means by which retailers can face the growing uncertainty in this broader scenario and address the challenges emerging from it. Nowadays, reinforcing the resources related to customer relationship management is considered crucial to gaining the needed flexibility for responding to the continuously changing retail context. Marketing literature has shown that loyal customers are less price sensitive and therefore more willing to pay a premium price, more likely to purchase more frequently, more willing to try the company's other product offerings, and to bring new customers to the firm (e.g. Reichheld and Sasser, 1990; Reichheld and Teal, 1996). Customer loyalty has been linked to the company's profitability (Reichheld and Sasser, 1990), particularly longer term one (Agustin and Singh 2005; Dewani et al., 2016) as this has emerged to be positively associated with customer revenue and customer retention, both of which drive customer lifetime value (CLV; Zhang and Wedel, 2009). In the western retail context, there are many examples of loyalty-based relationships in

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which customers have positive effects on firms’ performances. The wellknown UK-based retailer Tesco, for instance, has been able to build a strong, long-term relationship with its customers. This has allowed the company to develop a successful store brand. Tesco is currently one of the retail chains with the highest private label penetration. Further, leveraging its customers’ loyalty, the firm has extended its offer range to include financial services, insurance products, and tour packages. The main objective of retailers like Tesco is to deliver value to their customers and build a long-term and mutually beneficial relationship with them (e.g. Dick and Basu, 1994). Owing to its extensive experience in the UK, the company succeeded in leveraging customers’ loyalty in all the 12 countries in which it operates. In the US, companies annually spend about $50 billion on loyalty programs (McKinsey and Company, 2014). It is estimated that U.S. consumers may enroll in up to 29 loyalty programs, but 54% of memberships are inactive and 28% of customers have left a loyalty program before redeeming a single reward (COLLOQUY Loyalty Census, 2017). So, while customers overall might recognize the potential benefits of loyalty programs, there is a clear lack of engagement with the current value propositions, which suggests that companies should build loyalty on more and different actions than just developing a loyalty program. At the same time, western retailers look for new ways of developing their businesses beyond the modern countries where growth potential is limited. The geographic shifts in customer spending in developing countries has indeed prompted changes in the strategic priorities of many retailers (Diallo and Cliquet, 2016) facing more and more

Corresponding author. E-mail addresses: [email protected] (M. Grosso), [email protected] (S. Castaldo), [email protected] (A. Grewal).

http://dx.doi.org/10.1016/j.jretconser.2017.08.024 Received 1 April 2017; Received in revised form 8 August 2017; Accepted 25 August 2017 Available online 12 October 2017 0969-6989/ © 2017 Elsevier Ltd. All rights reserved.

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in the Indian retail sector to provide directions to retailers who are keen to venture into this interesting, but complex, landscape. Up to now research on store loyalty building has considered only a narrow set of tools that retail managers can leverage in order to foster customer relationships. We want to overcome this main limitation by considering a wider range of tools. Promotions and loyalty schemes are among the most investigated tools (e.g. Zhang and Wedel, 2009; Ramanathan and Dhar, 2010; Venkatesan and Farris, 2012), although their effects on relationship development are not immediately evident (e.g. Noble and Philips, 2004), are difficult to establish (e.g. Hart et al., 1999; Uncles et al., 2003), and not always positive (e.g. Ailawadi and Keller, 2004; Bucklin and Lattin, 1991). This is true, not only for western countries; for example, in China two thirds (66%) of the consumers currently belong to a membership program, 25% belong to three or more such programs, and 10% have had such memberships, but then allowed them to lapse (LoyaltyOne research, 2016). In a retailing context, other levers can also contribute to loyalty building, including customers’ relationships with salespersons, and the store environment (Guenzi et al., 2009). To date, very few studies have empirically tested comprehensive models of customer loyalty (Too et al., 2001). The aim of our study is therefore twofold, namely (1) to investigate the main drivers of store loyalty in the Indian market, and (2) to achieve this by developing a comprehensive model of customers’ loyalty. In doing so we will consider the possible range of levers available to the retail companies aiming to enter the Indian market in the near future. Our model is rooted in the extant literature and relies on empirical findings of studies related to store loyalty. We develop and analyze the model in two main stages. First, we focus on the two main antecedents (satisfaction and value for money), and we examine a core model (Stage 1) centered on store loyalty, which was widely accepted in the literature. Second, we broaden the perspective currently presented in the literature by simultaneously considering different store-level levers to gain further theoretical and managerial insights. This allows us to test an extended model (Stage 2) and thus to contribute to closing the research gap identified above. In the following pages we present our two-stage model and the results of our study that was aimed at testing it in the Indian market. The hypotheses identified in our model are derived from existing literature that emanates largely from studies conducted in Western countries. Our interest, therefore, is to investigate whether these findings are confirmed in the Indian market, and to ascertain which of the several drivers of store loyalty identified in the model are most relevant to Indian shoppers.

challenges in their traditional competitive landscapes. Rising incomes, improved infrastructure, institutional changes and fewer tariffs do make emerging markets more accessible and attractive (Cao and Pederzoli, 2013). In the developing world, the Indian retail market is a very dynamic sector which has achieved a volume corresponding to 10% of India's GDP, and offers employment to 8% of its population (www.ibef.org). It is one of the fastest growing retail markets in the world with an estimated size of USD 600 billion in 2015, projected to grow to USD 1.3 trillion by 2020 (www.ibef.org). At the beginning of 2017, India replaced China as the most promising retail market in the world (ATKearney report 2017), thanks to the relaxed FDI rules, the recent efforts to boot cashless payments, and the reform of their indirect taxation by introducing a nationwide goods and services tax. Therefore, it is not surprising that several Western retail players look at this market as a potential area of internationalizing their business. Burt and Mavrommatis (2006) stress that retailers who internationalize mostly attempt to transfer successful domestic offers to a foreign environment. This would imply that retail managers seeking to enter the Indian market will prioritize building strong loyalty basedrelationships with Indian customers. Their assumption is coherent with the results of a study conducted in four cities in Northern India which concluded that Indian customers prefer to establish a stable rapport with retailers, as for them, most of the interactions are oriented towards long term relationships (Khare et al., 2010). This, in fact, is the path selected by the above-mentioned retailer, Tesco, that replicates its loyalty building strategy by means of a loyalty card (Clubcard) in the foreign countries to which it expands. This loyalty scheme's success can, from the customers’ point of view be ascribed to its usage simplicity: customers just allow the cashier to swipe their card, upon which they are allocated points and special offers based on their taste as indicated by their purchases. This is enabled by a very complex data analysis system that Tesco outsourced to what at that time was one of its external subsidiaries, Dunnhumby. Tesco's internationalization process has been driven by Dunnhumby, who replicated the original model by using information gained from club card data as leverage in entering foreign countries. According to Franchising India (2017), brands such as Korres, Migato, Evisu, Pasta Mania, Lush, Melting Pot, Yogurt Lab, and Monnalisa are expected to enter the Indian market in the next few months, following the formulas that they have established and that work for them elsewhere, in the same way as big retail brands like Ikea, Uniqlo, GAP, Massimo Dutti and H & M have replicated their own formulas to enter this market recently. In spite of many success stories, rapid retail internationalization into emerging countries does not always lead to success (Diallo and Cliquet, 2016). International retailers entering emerging markets run into strong competition from local operators who often have store management experience (Swoboda et al., 2012) and can adapt their retail offer better to the local context (Diallo and Cliquet, 2016). In Western retail markets the balance between standardization and adaptation is considered to be a key factor in retail offers (Kaufmann and Eroglu, 1999; Cox and Mason, 2007). In emerging markets this balance is particularly critical and complicated because of their cultural difference from Western nations (Dholakia et al., 2012). It is therefore important to understand the key challenges in the Indian retail sector, as well as the key drivers of loyalty in the Indian market. One of the most critical phenomena currently characterizing the Indian retail market is its exponentially developing complexity, which has major effects on the behavior of economic actors. Retailing in India faces severe competitive pressures due to evolving structures in certain sectors that are shifting from a traditional to a contemporary landscape in a relatively short period of time. At present the formal sector is dominated by the informal sector which comprises 92%. The rapidly growing formal sector currently has an 8% share, which is expected to grow significantly in the next few years. This paper's objective is to present and test a loyalty-building model

1.1. Stage 1: The core variables 1.1.1. Store loyalty, satisfaction, and value for money The goal of this paper is to test a two-stage model that is centered on store loyalty in the Indian retail context. In this section, we focus on a widely recognized core theoretical model that links store loyalty to its main antecedents, namely satisfaction and value for money. Loyalty, at the service level, is defined as “the degree to which a customer exhibits repeat purchasing behavior from a service provider, possesses a positive attitudinal disposition towards the provider, and considers using only this provider when a need for this service arises” (Gremler and Brown, 1996; 173). This definition can easily be transferred to retailing as a specific service context. Marketing scholars widely consider loyalty to be a combination (e.g. Day, 1969; Dick and Basu, 1994) of attitudinal and behavioral factors, i.e. a combination of loyalty intentions and behavioral loyalty. In this paper, we focus on loyalty intentions which are defined as customers’ intention to engage in a diverse set of behaviors that will signal their motivation to maintain a durable relationship with the store. Loyalty intention, thus will be exhibited (e.g.) in recommending the store to a friend, regularly returning to the store, purchasing more from it, and 118

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characteristics and behaviors are a key component of customers’ overall evaluation of service quality, and that this influences their satisfaction with the selling firm. Extant research on the topic has verified that the quality of customers’ personal relationship with salespeople is positively associated with the quality of their overall relationship to the store (Beatty et al., 1996; Reynolds and Beatty, 1999b). Moreover, prior studies have emphasized the importance of the sales force's skills and behaviors in developing long-term relationships (e.g., Doney and Cannon, 1997; Swan et al., 1999). Two key elements are considered when evaluating the relationship between customers and salespeople, namely the salesperson's competence and trustworthiness. Previous studies have shown that customers’ and salespersons’ ability to communicate, interact, and build strong relationships leads to favorable outcomes in terms of satisfaction (Brown and Swartz, 1989; Crosby et al., 1990). Within the Indian retail sector, a study in the city of Surat (Patel and Desai, 2013) concludes that customer satisfaction is related to the quality of employees’ service delivery. We therefore hypothesize that:

being willing to repurchase from the store (Zeithaml et al., 1996; Sorihi et al., 1998; Nijssen et al., 2003). Many studies have focused on the impact customer satisfaction and perceived value have on loyalty intentions. Customer satisfaction is considered the key mediator in relationship marketing and service quality theory (Hennig-Thurau et al., 2002; Reynolds and Beatty, 1999a). It is considered a direct outcome of product or service performance in relation to customers’ expectations (Oliver, 1980). Several studies have observed and/or discussed relationships between consumers’ satisfaction and their loyalty intentions towards a service provider or a retailer on the basis of Oliver's (1980) individuallevel model of customer satisfaction (e.g. Anderson and Sullivan, 1993; Oliver and Swan, 1989; Reichheld and Sasser, 1990; Zeithaml et al., 1996). Based on the strong association between customer satisfaction and the formation of loyalty (e.g. Oliver, 1999), we therefore hypothesize that: H1: Customer satisfaction has a positive impact on intentional store loyalty.

H4: Salespeople's perceived competence has a positive effect on customer satisfaction.

Customers’ perceived value is conceptualized as the "tradeoff between the quality or benefits they perceive in the product relative to the sacrifice they perceived by paying the price” (Monroe, 1990; 46). In other words, it involves consumers’ perception of what they get for what they pay (Bolton and Lemon, 1999; Sirohi et al., 1998). Grewal, Levy, and Lehmann (2004) posit that, in retailing, perceived value should, and indeed does, drive customer loyalty. A number of researchers have reported that perceived value influences loyalty intentions (Sirdeshmukh et al., 2002) directly and indirectly via satisfaction (Cronin et al., 2000). Regarding the relationship between perceived value and satisfaction, Gallarza et al. (2011) have shown that value tends to be an antecedent of satisfaction rather than the other way around. Accordingly, we hypothesize that perceived value has both a direct and an indirect impact on satisfaction and customers’ loyalty intentions:

H5: Salespeople's perceived trustworthiness has a positive effect on customer satisfaction. The second store lever we consider, is the environment. Existing literature presents the environment as a relevant tool in conditioning customers’ behavior. In particular, studies related to environmental psychology (Mehrabian and Russel, 1974), store atmosphere (Donovan and Rossiter, 1982), and ‘servicescapes’ (Bitner, 1992) have analyzed the service environment's impact on various consumer responses. In a retail context, research on customer loyalty lacks a comprehensive model for analyzing the store environment's impact on customers’ responses (Baker et al., 2002). etal.,2009 have taken first steps developing a model relevant to a supermarket context. The authors argue that customers’ perceptions of the store environment could influence their cognitive and affective responses. They conceptualize and measure the store environment mainly on the basis of its layout. We follow this conceptualization, as previous research on the topic has shown that the store design does affect consumers’ responses more than other “ambient” factors do (Baker et al., 2002). In addition, environmental psychology research has suggested that the store environment's most important role is to be conducive to fulfilling customers’ needs (Canter, 1983). This essentially means that clients need to be able to move through the store efficiently. Thus, if the store's layout is optimally designed (Titus and Everett, 1995), it should contribute to the client's overall satisfaction with the shopping experience. A study undertaken in the Indian retail sector (Patel and Desai, 2013) confirms the role of physical contextual features on customer satisfaction. We therefore hypothesize that:

H2: Perceived value has a positive effect on store loyalty. H3: Perceived value has a positive effect on customer satisfaction.

1.2. Stage 2: The store levers antecedents In this section, we present the next stage which extends the existing model, which focuses on the main drivers of customer satisfaction and perceived value. Store managers can leverage these drivers (store levers) to increase store loyalty. To expand theoretical and managerial insights, we aim to determine which, if any, of the identified store lever variables influence customer satisfaction and perceived value the most. From a theoretical perspective, this will enable us to further investigate the store loyalty development process. From a managerial perspective, our findings will allow managers to activate policies to improve their customers’ satisfaction and perceived value and, accordingly, their overall loyalty. After an extensive review of the literature, we decided to focus on four key store lever variables, namely salespeople, store environment, merchandise assortment, and promotions. In the following paragraphs, we discuss our model.

H6: Store environment has a positive effect on customer satisfaction. Third, we focus on assortment, a key variable discussed in the retail literature. Fox et al. (2004) find that, in grocery stores, consumer expenditures correspond more to varying levels of assortment than to price. Assortment is also considered the central attribute of store image (e.g. Mazursky and Jacoby, 1986; Zimmer and Golden, 1988) and a predictor of consumers’ choice of shopping destinations (Oppewal et al., 1997). In a retail context, stocking only one particular brand is likely to be a discriminant factor that influences customers’ store choice. Because it is the base component of a retailer's offer, we argue that assortment is a main antecedent of the perceived value in that the quality of the assortment influences shoppers’ evaluation of the quality dimension of value; this, in turn, determines customers’ satisfaction with the store. The role of product variety on the customers’ satisfaction in the India has been shown for the city of Surat (Patel and Desai, 2013). We subsequently hypothesize that:

1.2.1. Store levers’ impact on satisfaction and perceived value First, we focus on salespeople, who are a key element in the retail context. In the service research literature, interpersonal relationships are considered a key element of the product offering (Berry, 1995; Czepiel, 1990). Research has shown that creating strong relationships between customers and service personnel has a positive impact on customer-to-firm relationships as a whole (Beatty et al., 1996; Reynolds and Beatty, 1999b). Sivadas and Baker-Prewitt (2000) demonstrate that sales personnel's 119

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category. The store had to be selected from the list of stores they had self-declared as ones where they had bought the category's products, and they were asked to specify it. The interviewees were not asked to select their preferred store, nor the store in which they do the majority of their shopping as this would have biased the results of the questionnaire towards loyal customers, while our objective was not to focus on loyal customers only. The store to which a respondent referred first, was used as a reference throughout the rest of the questionnaire. Depending on the various product categories, stores of different formats were included. This variance was deliberate, to avoid a restricted focus on a single store format. 2.2. Measures We designed the questionnaire based on a comprehensive literature review. The scales we found had mainly been used in the USA context, therefore we refined and adapted them to the Indian context. An expert panel of three professors and four top retail managers from varying backgrounds (e.g. cosmetics, groceries, fashion, and jewelry) was created to assist with this adaptation. The panel evaluated the scales that previous studies had used to measure the variables. The team was responsible for checking the content, scope, and purpose of the scales (content validity), as well as for ensuring their face validity regarding the specific Indian retail context. This enabled them to limit the construct and measurement inequivalence would affect the results (Hult et al., 2008). Following this evaluation, all the scales were taken directly or slightly adapted from existing scales (see sources in Tables 1 and 3 below). We did, however, allow for exceptions with scales measuring customer satisfaction and promotions, in which cases the team selected the best items from the available scales, and incorporated them into a new one. The expert panel suggested that only price related sales promotion be considered as this is the most common kind of promotion customers in the Indian market come across. Therefore, we operationalized promotions largely using a scale linked to price promotions.

Fig. 1. The hypothesized model and the constituting stages.

H7: Assortment has a positive effect on customer satisfaction. H8: Assortment has a positive effect on perceived value. Finally, similar to assortment, sales promotions are essential means by which retailers build and manage the relationship with their customer base. They are a key element of store image (e.g., Mazursky and Jacoby, 1986; Zimmer and Golden, 1988) and a traffic-building lever in attracting new customers to the store. Sales promotion cues motivate consumers to purchase not only the promoted brand but also ones not promoted, thus affecting the overall size and composition of consumers’ shopping basket (Ramanathan and Dhar, 2010). As they directly impact shoppers’ perceptions of price, we predict that these cues act as a determinant of the overall perceived value of a retailers’ offer, therefore we hypothesize: H9: Sales promotions have a positive effect on the perceived value. Fig. 1 depicts the conceptual model we developed, which highlights the hypothesized relationships between the main conceptual variables (Stage 1) and their antecedents (Stage 2). We tested this model in a study measuring the loyalty building processes of stores in India.

2.3. Sample Concerning the extent of our data, the final sample comprised 1651 questionnaires. The participants’ ages ranged between 13 and 64 years, with an average age of 30 (30.34) years. Parental approval was obtained for minors participating in the survey. In terms of gender, 75.9% of the respondents were male. Since this was an all India survey, 8 metros and mini metros (mega cities and million plus cities) were covered, and 48 smaller cities/towns (Class-1 Towns/cities) across N/S/E/W India. This sampling procedure overcomes the limitations of previous studies in the Indian retail sector that have been conducted in specific cities/centers in different parts of

2. Method 2.1. Data collection No secondary databases have specific data on all the variables required to test our conceptual model and the associated hypotheses. Only data on behavioral loyalty could be found in retailers’ sales records. The authors pre-checked the feasibility of collecting such real behavioral data before embarking on the study, to find that almost none of the contacted retailers were willing to disclose their data. Therefore, we collected intentional data from customers through a survey. To increase the generalizability of our findings and avoid biasing our results due to the retail sector selected for the study, we collected data from different product categories. Our sample consisted of two main macro-product categories: Fast-moving consumer goods (bought at grocery stores or pharmacies), and non-food products (bought at electronic and home decoration outlets). The questionnaire began with a question to assess the participants’ familiarity with each of the product categories using a seven-point Likert scale where one indicated “highly familiar” and seven indicated “not familiar at all”. The respondents were then randomly assigned to one of the categories that they had ranked as three or higher in familiarity. The respondents were asked to focus their attention on a particular store where they had at some stage bought the products in the assigned

Table 1 Stage 1: CFA results. Items SATISFACTION (new) I am completely satisfied with the shopping experience at the …. retailer (SAT1) Shopping at the …. retailer is a very delightful experience (SAT2) VALUE FOR MONEY (Adapted from Baker et al., 2002) In this store, products are economical (VFM1) In this store, compared to other stores, I can save money (VFM2) In this store, products have a high value for money (VFM3) LOYALTY INTENTIONS (Sirohi et al., 1998) In the future, I will use the store for more of my product/ category needs (ILOY1) I will recommend this store to a friend (ILOY2) I will continue shopping at this store (ILOY3)

120

Factor loadings

.940 .882

.880 .927 .931 .907 .888 .945

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the standardized factor loadings were large and significant (p < .001), supporting the convergent validity of the measures.

Table 2 Stage 1: Structural analysis results. Paths

Stand. coeff.

S.E.

C.R.

Hp.

Satisfaction →loyalty intentions Value for money → loyalty intentions Value for money → satisfaction

1.128

.044

23.808

H1: supported

−.132

.043

−3.025

.915

.020

49.193

H2: not supported H3: supported

3.2. Structural model To test our hypotheses for Model 1, we ran the structural model. The overall structural model showed a good fit (χ2(17) = 44.178, p < .001, GFI = .893, NFI = .956, IFI = .957, CFI =.957 and RMSEA = .162). Again, the χ2 was significant (p < .001) due to the large sample size. All the structural paths were significant (p < .001) (Table 2). This implies that our H5 (on the relation between salesperson trustworthiness and customer satisfaction) is not supported, which contradicts previous findings (Cooil et al., 2007). Satisfaction has a strong impact on intentional loyalty (H1), while value for money mainly impacts satisfaction (H3) rather than intentional loyalty (H2), on which it actually has a negative impact. This implies that our H2 is also not supported.

Table 3 Stage 2: CFA results. Items

SALESPEOPLE (two factors merged: competence - adapted from Hawes et al., 1993 - and trustworthiness - adapted from Swan et al., 1999) The store's salespeople can be trusted (SALE1) The store's salespeople are honest (SALE2) The store's salespeople are friendly (SALE3) The store's salespeople keep their promises (SALE4) The store's salespeople help me choose the right products by suggesting what is best for me (SALE5) The store's salespeople have my interests at heart (SALE6) The store's salespeople show a high product knowledge (SALE7) PROMOTION (new) In this store, price promotions are interesting (PROM1) In this store, price promotions make me want to shop (PROM2) In this store, price promotions are good deals (PROM3) In this store, price promotions are proposed with the right frequency (PROM4) STORE ENVIRONMENT (adapted from Guenzi et al., 2009) This store is easy to navigate (ENV1) In this store, I feel comfortable (ENV2) I like the layout of this store a lot (ENV3) In this store, the display of the merchandise is excellent (ENV4) In this store, signs and posters are very clear (ENV5) ASSORTMENT (adapted from Homburg et al., 2002) In this store, the number of different merchandise categories (breadth of products) is very high (ASS1) In this store, the number of stock keeping units within merchandise categories (depth of products) is very high (ASS2) In this store, the quality of the merchandise is very high (ASS3)

Factor loadings

4. Results for the extended model (Stage 2) .913 .920 .872 .929 .907

4.1. Assessment of measures We ran a confirmatory factor analysis (CFA) with structural equation modeling (Table 3). Again, the model was estimated using the MLE procedure. The overall measurement model showed an adequate fit (χ2(220) = 33.566, p < .001, GFI = .647, NFI = .882, CFI =.885 and RMSEA = .140). All the standardized factor loadings were high (> .719) and significant (p < .001), which supports the convergent validity of the measures. The items linked to salespeople converged in a unique factor.

.943 .932 .919 .804 .905 .901

4.2. Structural model

.944 .949 .957 .930 .721

The overall structural model showed a lower fit than the previous models, due to the increased complexity of the model (χ2(220) = 33.566, p < .001, GFI = .647, NFI = .882, CFI =.885 and RMSEA = .140). Again, the χ2 was significant (p < .001) due to the large sample size. All the structural paths were significant (p < .001). As Table 4 shows, Stage 2 confirms the results previously obtained in Stage 1. The only driver of perceived value was product assortment (), while the role of promotions (H9) was, surprisingly, not significant. Assortment showed an impact only perceived value (H6), while its impact on satisfaction was not significant (H7). The main drivers of satisfaction were the salespeople (H8), while the store environment was a weaker driver (H8) (Fig. 2).

.910 .937

.909

the country and for specific retail formats and categories. The results of each earlier study are therefore related to the center and to a particular format and category, while our study aims at being comprehensive in covering the country's perspective on a national level. Participants’ ethnicity was representative of the whole Indian subcontinent. Concerning income classification, individuals across the following income categories - given in the local currency, Rupees (Rs) were covered: 28% in < Rs 3 lakhs (1 lakhs equal 100,000) per annum (pa), 38% in Rs 3–5 lakhs pa, 26% in Rs 5–10 lakhs pa, 7% in Rs 10–30 lakhs pa, and 1% in > Rs 30 lakhs pa.

5. Discussion This paper takes a first step towards verifying a comprehensive loyalty building model in India. More specifically, our study contributes Table 4 Stage 2: Structural analysis results. Items

Stand. Coeff.

S.E.

C.R.

Hp.

Satisfaction → loyalty intentions Perceived value→ loyalty intentions Perceived value → satisfaction Salespeople→ satisfaction Environment → satisfaction Assortment →satisfaction

1.088

.040

25.557

H1: supported

−.095

.039

−2.398

.353 .545 .105 n.s.

.128 .055 .015

2.928 9.932 5.828

.983 n.s.

.016

56.415

H2: not supported H3: supported H4/5: supported H6: supported H7: not supported H8: supported H9: not supported

3. Results of the core model (Stage 1) 3.1. Assessment of measures We performed several analyses to assess our measures’ validity and homogeneity in the sub-samples, as will be indicated below. We conducted a confirmatory factor analysis (CFA). Owing to its robustness when using large sample sizes, we used the maximum likelihood estimate (MLE) procedure to estimate the model. The overall measurement model showed an adequate fit (χ2(17) = 44.178, p < .001, GFI = .893, NFI = .956, IFI = .957, CFI =.957 and RMSEA = .162). That χ2 was significant, is typically the case for large sample sizes. All

Assortment →perceived value Promotion → perceived value

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represent a social relationship marketing investments in their framework that can turn out to be linked to customers’ gratitude and consequently also to loyalty. The impact of store environment in the Indian context is supported by the findings of a study conducted for various store formats (Singh et al., 2014) which concluded that the key determinants of consumer buying patterns in a store were store atmosphere, store layout, customer service, visual communication, promotions, and the problemsolving ability of the store regarding returns. Surprisingly, the product assortment was found to have no significant impact on the satisfaction of customers. This could be explained by the findings of Rashmi's (2015) qualitative study on consumer motives and their profiles in the Indian context. In his study, the author profiled Indian shoppers of different grocery store formats and linked the profiles he obtained to their shopping motivations. According to the format, Rashmi found, customers did not regard sales promotions, display of merchandise, shopping experience, convenience, proximity, store environment, and the assortment composition as relevant in motivating their shopping behavior (Rashmi, 2015). In Stage 1, without considering the retail levers, the impact of value for money is higher than in Stage 2, because it encapsulates the lever's effect. More interestingly, only the product assortment component of the perceived value had a strong impact on satisfaction, while the promotions’ impact was negligible. This underscores the key role of the offer composition in terms of products in the retailing mix. This finding is supported by a previous study (Lysonski and Durvasula, 2013) which concluded that four of the eight established decision making styles in India had changed statistically between 1994 and 2009. Lysonski and Durvasula (2013) indicated increases in (i) brand consciousness, (ii) novelty-fashion consciousness, and (iii) impulsive-careless shopping, and a decrease in (iv) perfectionist-quality consciousness. This implies that retailers wanting to enter the Indian market should abandon the promotional logic they’re used to applying in western markets, and focus more on other levers. The need for testing the loyalty building model in this market is emphasized. To sum up, the main managerial implication of our study is that retailers who want to create a longterm relationship with their customers, should invest mainly in customer satisfaction. The role of salespeople as the main store level driver of satisfaction in achieving this, is clear. Retailers should therefore focus their investments in relationship marketing activities on the social investments as suggested by Dewani et al. (2016). The selection of suitable salespeople profiles in line with the positioning and the values of the retailer, as well as training in customer relationship management and in retail offer, will determine customers’ perceptions that their needs have been responded to. This will lead to customer satisfaction and therefore also develop loyalty. Retailers are advised to pay proper attention to their product offer, including assortment composition, which turned out to be the most important driver of perceptions regarding value for money. This represents the second antecedent of customer satisfaction. The selection of products for their assortment should be coherent with what the retailer promised in positioning itself. Customers should be able to find exactly what they have been led to expect in the store. The starting point in deciding what to offer should therefore be “what the customers want to buy” and not, as many western retailers erroneously did, “what the retailers want to sell.” Further, the store environment should be aligned to the retail positioning by creating an encouraging atmosphere and contributing to the in-store experience that enhances customers’ satisfaction. Retailers should harmonize all the levers of store environment management, such as store layout, shelf material, music, colors, visual merchandising tools, and windows. Finally, Indian retailers have the opportunity to avoid price cutting as a promotion gimmick which, in western countries, is creating price wars that erode retailers’ profit margins. We have found that price promotions do not have a significant impact on the perception of value for money, which implies that retailers can lever on

Fig. 2. Stage 2: The Two-Stage Loyalty Model for India.

to the existing literature by including the range of levers that retail managers can use in their stores to increase loyalty via a two-stage model. We present a core theoretical model (Stage 1) in which we test the impact of the two main antecedents of store loyalty, namely satisfaction and perceived value. The model was then extended by including the impact of four store levers – salespeople, the environment, product assortment, and promotions – in the loyalty building model (Stage 2). Stage 1 model confirms the main findings of previous studies – except for the relationship between value for money and store loyalty, which, surprisingly, is negative in the Indian market as we measured it. This is coherent with the results of a previous study conducted in the Indian context by Dewani et al. (2016) who investigated the impact relationship marketing investments (classified as social, structural or financial investments) have on long term relationships based on loyalty. Their results point to financial investments as the only ones that create a sense of obligation in the customer; such obligation is negatively associated with loyalty (Dewani et al., 2016). Financial investments are defined as “any tangible or intangible rewards provided by the donor which can be perceived in terms of monetary investments by the receiver” (Dewani et al., 2016, p. 144). These investments include any kind of monetary promotion which is a key component of the value for money perception. The negative impact of value for money on loyalty can therefore be explained as coherent with Dewani et al.’s (2016) study, as the financial part of retailers’ investments to create value (the price-linked component of value) could be predominant within the Indian context compared to the perception of benefit. This result highlights the importance for foreign retailers who are keen to enter the Indian market, of understanding the Indian sector and the loyalty building logic prevalent in the country, in particular considering how value building proceeds in this market. The evaluation of the two-stage model revealed the utility of including all store levers in the model, as, excepting promotion, they were found to be significant determinants of satisfaction and value for money. This model was particularly useful for investigating the drivers of satisfaction, which emerged as the key driver of intentional loyalty in Stage 1. In Stage 2, perceived value's impact on satisfaction was significantly lower than in Stage 1. In the second stage salespeople appeared to be the most relevant antecedents of satisfaction at an aggregate level, followed by the store environment, which had a weaker impact. Our results are coherent with a previous study in the city of Surat (Patel and Desai, 2013) which concluded that customer satisfaction is related to product convenience, employee service, shopping convenience, physical features, and pricing. This supports our conclusion that sales people in the store are the most relevant antecedents of satisfaction at an aggregate level, and is coherent again with Dewani et al.’s (2016) results as sales people can 122

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relationship to the technological environment (e.g. in considering variables such as age, income, or technological attitude) could assist retailers in better structuring their strategies according to their target shoppers. A second possible advantage of further investigation of technologies regards growing knowledge of loyalty building in Indian retail which moves towards an omni-channel vision. That would imply synergetic management of the numerous available channels and customer touchpoints, in such a way that the overall customer experience is integrated and optimized (Verhoef et al., 2015). In such a case, instead of comparing the experiences of customers across the different channels (physical and digital), the focus would be on an overall analysis of the customer journey that starts before the customer enters the retailers’ point of sale (store or website) and ends after he/she has exited. Everything that happens during the customer journey may have an impact on the relationship between the shopper and the retailer, and can therefore affect his/her loyalty. This indicates that retailers should know the impact of the different touchpoints on Indians’ perceptions and behaviors in all the phases of the customer journey. Such knowledge will assist in properly managing salespeople and that are under their control.

less costly variables rather than cutting prices to develop their customers’ loyalty. 5.1. Limitations and future research As is the case in all research, our study has a few limitations that should be pointed out, also to provide directions for future research on this topic. First, since the stores that were included in our analysis were selected by the respondents, our results are possibly biased. For example, it is possible that respondents selected their preferred store to reduce their cognitive effort. We tried to avoid this by not setting any criteria for their selection of a store. The responses we got, could anyhow be biased upwards in terms of loyalty. However, if this was the case, we can assume that it applies to all respondents and would have had little impact on the results of an analysis of the differences between stores (Sirohi et al., 1998). A second limitation of our study is the impossibility of collecting real behavioral data from retailers. We were obliged to rely on selfdeclared intentional behavior as a measure of loyalty. This could have led to common method bias issues due to common motive and/or social desirability. We tried to reduce such a bias as far as possible during the data collection and in controlling for ex post effects. We avoided temporal separation in our data collection, because it might have led to a lower response rate in the second data collection phase and would have made it impossible for us to control for any intervening variables that could have influenced the responses between the two data collection phases. We therefore structured the questionnaire to maximize the psychological and methodological separation of the questions referring to the variables used in our model. Some questions with different structures were added to the questionnaire with the pretext of mapping the overall shopping process at the store. Finally, we ran Harman's single-factor test ex post to control for common method bias. Nevertheless, these techniques cannot prove that our data was completely free from bias. Future research should compare our survey data with real behavioral data from retailers (i.e. sales data, all the better if from loyalty cards records). This would imply reducing the number of categories under investigation, and focusing only on a few retailers that would agree to sharing their databases with the researchers. This would, on the one hand, limit the generalizability of the study, but could, on the other hand, significantly improve our insights on the impact of various levers on behavioral loyalty. To this end, an observation of in-store behavior before the survey, and collecting behavioral data could provide the maximum level of detail on real in-store behavior. Finally, even though we included several retail levers in our model, it is still not exhaustive. An in-depth investigation of the cultural dimensions of shopping could provide insight as to which additional levers to include in future research. To conclude, interesting future research directions can be linked to the technological evolution that is re-shaping shopping behavior internationally. A first step in improving knowledge on loyalty building for retail companies would be to include online shopping behavior of customers in their study. The rapid evolution of e-commerce in India and its impact on store and customer loyalty is indeed an important area for further research. The impact of different decision-making styles identified in previous studies (e.g. Lysonski and Durvasula, 2013) should be incorporated. The current study could be replicated for ecommerce websites or mobile apps, adapting the traditional retail levers to web/mobile-specific ones. This could provide more impactful managerial implications to retailers that are now leveraging more and more by using brick-and-mortar strategies which merely add an e- and m-commerce part to the traditional practices of physical stores. This can hardly do more than point out similarities and differences in impact on loyalty in the digital and physical environment. In this context analyses of different customer segments’ behaviors, mainly regarding their

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