بررسی انگیزه های خریداران وفادار به فروشگاه از طریق معیارهای جایگزین وفاداری رفتاری
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|5137||2013||11 صفحه PDF||34 صفحه WORD|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : European Management Journal, Volume 31, Issue 4, August 2013, Pages 348–358
وفاداری به فروشگاه
تعریف وفاداری به فروشگاه
منافع فایده گرایانه
منافع لذت جویانه
سناریو و دادههای مطالعه
معیارهای وفاداری به فروشگاه
معیارهای انگیزه خرید
جدول 1: تحلیل توصیفی
تحلیل و یافتهها
جدول 2: توضیح سوالات و تحلیل عاملی اکتشافی
نتیجه گیری و دستاوردها
جدول 3: رابطه بین وفاداری به فروشگاه و منافع مورد نظر از خرید
This study establishes a theoretical framework and provides empirical evidence related to the motivations and benefits sought by store-loyal customers. From a theoretical perspective, the proposed framework distinguishes utilitarian benefits, such as monetary and time savings, from hedonic benefits, such as shopping enjoyment, innovativeness and impulsiveness. From a methodological perspective, this study suggests the appeal of considering different measures of store-loyal behaviour, particularly those based on consumers’ self-assessments, as alternatives to measures based on solely on their budget allocations. The empirical findings indicate moderate consistency between these measures; self-assessment measures are more closely related to consumers’ motivational profiles. They also indicate the greater explanatory power of motivational variables compared with socio-demographic variables for characterising store-loyal buyers. Finally, store-loyal buyers’ general profile is less price sensitive, more time and service sensitive, less concerned about entertainment and new experiences, more likely to feature planning and more brand loyal.
Customer loyalty remains a topic of great interest for firms (Kotler and Keller, 2009 and Reichheld, 1996), as well as a core element of relationship marketing (Berry, 1995). Relationship marketing emphasises the business benefits of increasing consumption among existing customers and preventing their loss, rather than working to attract new customers. Creating and maintaining customer loyalty thus constitute strategic requirements for modern business, particularly in the retail industry. Retailers offer various loyalty programs, including customer cards, discount coupons, special offers and promotions, with the main objective of retaining loyal customers and persuading less loyal consumers to spend more in their stores (Bustos-Reyes & González-Benito, 2006). A loyal customer base in turn exhibits several traits that are beneficial for retailers, including reduced sensitivity to other price and market offers and reduced proneness to seek other alternatives or switch stores (East et al., 2000, East et al., 1995 and Knox and Denison, 2000), increased spending and related sales (Knox & Denison, 2000) and significant communication potential through word of mouth (Bloemer et al., 1999 and Gounaris and Stathakopoulos, 2004). Such advantages translate into higher retailer profitability (Chaudhuri and Ligas, 2009, East et al., 1995, East et al., 2000 and Knox and Denison, 2000). But not all efforts devoted to increasing customer loyalty produce the expected results. Several studies indicate a clear customer tendency to disperse purchases across stores, especially for frequently purchased products such as food and household items (Ailawadi and Keller, 2004, Baltas et al., 2010, Flavián et al., 2001, Gónzalez-Benito et al., 2005, Knox and Denison, 2000 and Rhee and Bell, 2002). In short, consumers’ store choice is notably polygamous. The disappointing performance of loyalty programs might imply insufficient segmentation, because to increase loyalty, especially among their best customers, retail managers likely need to apply selective strategies to the customers with the greatest loyalty potential (Knox & Denison, 2000). Such a selective strategy is feasible only if potentially loyal customers share some common characteristics that make them identifiable and accessible (Baltas et al., 2010). Profiling (potentially) store-loyal customers is a high priority for managers. In response to this priority, we establish a theoretical framework and provide empirical evidence related to the profiling of store loyal customers. Both sides of the store loyalty equation are approached. On the one side, we focus on motivations of and benefits sought by store-loyal customers as determinants of store loyalty. They are classified into hedonic and utilitarian benefits. If we can identify motivations underlying their store-loyal behaviour, retailers might be able to apply more focused strategies, designed specifically to enhance the loyalty of target consumers. On the other side, we focus on alternatives measurements of behavioural loyalty derived from budget allocation patterns and consumers’ self-assessment. Our contribution is twofold, namely, theoretical and methodological. From a theoretical view, we focus on motivations and benefits sought in shopping and propose a framework that distinguishes utilitarian benefits, such as monetary and time savings or quality searches, from hedonic benefits, such as shopping enjoyment, innovativeness and impulsiveness (versus planning). Most previous studies of store loyalty focus on socio-demographic variables, which seem insufficient to identify loyal customers accurately (East et al., 2000 and Mägi, 2003). Less evidence is available regarding whether purchase motivations might help explain store loyalty, despite some suggestions and empirical evidence that such variables have more potential than socio-demographic variables (Konus et al., 2008 and Mägi, 2003). Segmentation by benefits sought offers a deeper sense of the motivational reasons and causal factors underlying consumption and therefore can help determine shopping behaviour more accurately than descriptive factors (Haley, 1995). Moreover, the few studies that relate motivations to store loyalty lack an overriding theoretical framework for integrating shopping motivations. Instead, they tend to study specific motivations (e.g., East et al., 2000 and McGoldrick and Andre, 1997), mainly as covariates to be controlled to isolate the effect of other determinants of store loyalty (Ailawadi, Pauwels, & Steenkamp, 2008). Only Mägi (2003) considers the relationship between shopping motivations and store loyalty, by analysing the effect of economic, apathetic and personalising shopping motivations. However, she also concludes that the consideration of other consumer characteristics and motivations might provide greater insight into the motivational drivers of loyal behaviour. In summary, the scarce attention and empirical evidence contained in previous literature strongly indicates the need to provide a more complete, comprehensive framework to analyse the motivational profile of store loyalty. From a methodological perspective, we highlight the appeal of measures of store-loyal behaviour based on consumers’ self-assessment as alternatives to measures based on budget allocation. Most previous research determines behavioural loyalty on the basis of allocations of budgets or visits across available stores, using objective data from consumer panels (e.g., Ailawadi et al., 2008, Gónzalez-Benito and Martos-Partal, 2012, Kau and Ehrenberg, 1984, Martos-Partal and Gónzalez-Benito, 2011 and Mägi, 2003) or subjective estimations from consumers (Baltas et al., 2010, Bustos-Reyes and González-Benito, 2006, Bustos-Reyes and González-Benito, 2008, East et al., 2000, Flavián et al., 2001 and McGoldrick and Andre, 1997). However, such an approach might obviate some loyalty behavioural patterns by failing to distinguish different shopping situations or specific product categories. For example, a consumer who always purchases dairy products in one store and vegetables in other could seem disloyal from an aggregated viewpoint, because his or her shopping budget is spread across multiple stores. In this case, the consumer’s subjective self-assessment of his or her behaviour would provide a more reliable measure of behavioural loyalty and therefore enhance the profile of store-loyal consumers. In the next section, we present our conceptual framework and review previous research to offer some theoretical support for our proposed hypotheses. After we describe the methodology for our empirical analysis, we present and discuss the findings. Finally, we outline our main conclusions and some implications for marketers.
نتیجه گیری انگلیسی
This article proposes a theoretical framework for characterising store-loyal buyers, together with empirical evidence related to such consumers in the Spanish retail context. Our first challenge has been to demonstrate the explanatory potential of consumers’ shopping motivations for characterising store-loyal consumers. Previous research has mainly focused on socio-demographic variables. To meet this challenge, we have classified consumers’ motivations according to utilitarian and hedonic benefits. Regarding utilitarian benefits, we consider monetary and time savings as well as service quality. Regarding hedonic benefits, we consider shopping enjoyment, innovativeness, impulsiveness and planning. Additionally, we consider brand loyalty as a mixed benefit. Our second challenge has been to find an appropriate way to measure store loyalty. In this regard, we compare two alternative approaches based on budget allocation and self-assessment, respectively. Previous research has mainly focused on budget allocation data from point of sale’ scanners or consumer panels. However such an approach may hide loyalty patterns within product categories or shopping occasions, and those patterns can be uncovered by considering self-assessment data from surveys. Our findings indicate moderate consistency between the measures, though the one based on self-assessment is much more related to the motivational profile of consumers. That result evidences that different approaches to measure store loyalty may lead to quite different views of that concept and its determinants. The empirical findings also indicate that store-loyal buyers’ general profile is less price sensitive, more time and service sensitive, less concerned about entertainment and new experiences, more likely to feature planning and more brand loyal. These results indicate the greater explanatory power of motivational variables compared with socio-demographic variables for characterising store-loyal buyers. If a retailer hopes to achieve a portfolio of loyal buyers, an effective strategy is to adopt a selective strategy focused solely on these buyers. To apply that strategy, the retailer needs to recognise common characteristics shared by the loyal customers. Whereas socio-demographic variables are useful to distinguish loyal customers, motivational variables are useful to decide what marketing strategies and tactics are more appropriate to get a better response from these customers. Our empirical evidence delineates a store-loyal buyer profile; retailers can use this information to segment their customers. The loyal segment contains utilitarian consumers who are very concerned about the opportunity costs of their time, more so than the pleasures of shopping. The loyal customer’s characteristics should help define the retailer’s strategy: Focus on ease of purchase and convenience to minimise the customer’s time loss, offer an assortment that includes brands to which the customer is loyal and ensure good service quality. This study also suffers some limitations that suggest further research directions. First of all, our models are incomplete. Our goodness of fit measures suggests that the explanatory power of the motivational determinants considered in our study is limited. However, we do not consider the effects of other store loyalty determinants. For example, other motivations, such as social interaction, or more precise measures of accessibility, such as store proximity (Bell, Ho, & Tang, 1998), might provide a better explanation of the spurious loyalty component. Additionally, our measures of benefits sought were computed from questions about general shopping. Since they might vary across product categories and retail sectors, the use of category-specific measures could improve their explanatory power. The use of longitudinal data (patronage data over time) might also help to capture differences across product categories and shopping occasions. Finally, our measure of share of wallet at the top 12 retailer chains ignores store loyalty to small retailers. Sample selection requirements derived from this limitation might involve some biases.