Does price sensitivity and price level influence store price image and repurchase intention in retail markets?

Journal of Retailing and Consumer Services 44 (2018) 201–213

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Does price sensitivity and price level influence store price image and repurchase intention in retail markets?

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Ana Paula Graciola , Deonir De Toni, Vinicius Zanchet de Lima, Gabriel Sperandio Milan University of Caxias do Sul, Business and Social Sciences, Department of Business Administration, 1130, Francisco Getúlio Vargas, Caxias do Sul, Rio Grande do Sul 95070-560, Brazil

A R T I C LE I N FO

A B S T R A C T

Keywords: Retail price image Repurchase intention Price level Price sensitivity

In today's competitive price environment, customers are more sensitive to store price image as a driver of the decision of where to buy. This paper addresses the impact of store price image on repurchase intentions in the context of retail markets in the south of Brazil. The moderating effects of price sensitivity and price level are also analyzed. A comprehensive model reports determinants of repurchase intentions. A descriptive quantitative research study is undertaken, based on an applied survey. Partial least squares–structural equation modeling (PLS–SEM) is used, supported by Smart-PLS 3.2.7. The model sample includes 207 customers, university students who had experienced retail purchases in different formats of supermarkets. The data analysis yields surprising findings. Results show that store price image positively impacted on customer repurchase intentions, with low and high price levels moderating these effects. Price sensitivity also presented moderating effects as another important variable acting on the relation between store price image and repurchase intentions for both low and high price sensitivity customers. The implications of these findings are discussed with suggestions for future research.

1. Introduction

better perceived shopping value (Diallo et al., 2015). Furthermore, the opening of new stores or new retailers in a specific marketplace may offer disruptive market price levels. In this perspective, with these perceptions of modified and varied market prices, customers may make cross-retail comparisons based on acquired market knowledge (Urbany et al., 1996). In this context, Zielke (2011) pointed out that future research should better comprehend the formation of emotions as part of a retailer's price image. Zielke (2014) presented the management implications of dealing with discount retailers in low-price environments. Future research should study in depth how customers interpret low or high prices, and to which causes they attribute these low and high prices (Zielke, 2014). So, in this study, low and high prices are posited as a moderator of the relations under investigation. Furthermore, customers tend to be sensitive to the dispersion of prices within a store's assortment, rather than just the overall price level (Alba et al., 1994; Hamilton and Chernev, 2013). This means that adding high prices can either raise or reduce a store's overall price image (Hamilton and Chernev, 2010). Thus, price advertising contributes to increasing price sensitivity by allowing the customers to focus more on prices than shopping (Kaul and Dick, 1995; Hamilton and Chernev, 2013). Overall store price image influences repurchase intentions (Chang

To achieve competitiveness, retail industry markets need to respond urgently with innovative strategies to recent modifications in customer behavior (Ferguson, 2014; Kim et al., 2014; Diallo et al., 2015). A few decades ago, the food retail market was dominated by high–low pricing strategies, with large and comprehensive stores offering wide assortments. Nowadays, emerging retail formats such as convenience stores and hard discount stores are modifying the retail industry (Dolbec and Chebat, 2013). In seeking to understand consumer behavior, retailers should observe their customers’ purchasing power, spending patterns, shopping trips, and increasing environmental awareness (Kim et al., 2014) to better fit the customers’ needs. Retailers’ evaluation of price image is an important tool to investigate their price positioning in the customers’ minds, to predict a set of customer priorities and, if necessary, derive appropriate measures to meet these priorities (Zielke, 2006). Customers assess a service or product through a sequence of evaluation and choice tasks. They have to compare the alternative price to a reference price, making it essential to understand how the reference price is shaped (Peschel et al., 2016). Also, in periods of crisis, customers look for low prices in association with good quality and positive social interaction, as an example of

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Corresponding author. E-mail addresses: [email protected] (A.P. Graciola), [email protected] (D. De Toni), [email protected] (V.Z. de Lima), [email protected] (G.S. Milan).

https://doi.org/10.1016/j.jretconser.2018.06.014 Received 2 November 2017; Received in revised form 10 May 2018; Accepted 25 June 2018 0969-6989/ © 2018 Elsevier Ltd. All rights reserved.

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(Zeithaml, 1988; Sànchez-Fernández and Iniesta-Bonillo, 2007). Seven dimensions have thus been identified as influencing retail price image, namely: quality, perceived value, price level perception, price fairness, positive emotions, negative emotions, and symbolic factors. Below, the constructs that make up the retail price image will be examined in turn.

and Wang, 2014). From this viewpoint, the main aim of this article is to verify the relation between store price image and the repurchase intention in the context of retail markets in the south of Brazil. Moderating effects of price sensitivity and price level are added to this framework. The remainder of this paper is organized as follows: the next section presents the theoretical framework in the fields of retail price image and repurchases intentions. An empirical survey based on customers (a student sample) as the method, detailed data analysis, and main results are the presented. Finally, the paper ends with a general discussion of the main findings, implications for theory and management, limitations, and the outlook for future research.

2.1.1. Quality The broadly accepted positive relation between perceptions of high price with good quality (Völckner and Hofmann, 2007; Zeithaml, 1988; Diallo et al., 2015). Conversely, when a service or product is cheap and offers poor value, customers may conclude that the store's service or product quality is low, or interpret it as a sign of negative relations between price and different quality aspects (Zielke, 2011, 2014; Diallo et al., 2015). Also, for Hellier et al. (2003), perceived quality is associated with the customer's overall assessment of the service delivery process. Customers believe that price and quality are positively related, and price may be seen as an indicator of quality in a competitive environment market (Grewal et al., 1998). From the service quality perspective, Parasuraman et al. (1988) define service quality as the bond between the customer's expectations of the service and their perception of the active experience (Kitapci et al., 2014).

2. Theoretical framework 2.1. Disentangling store price image multidimensionality Price is one of the most relevant market signs of retail competition (Lichtenstein et al., 1993; Zielke, 2006). The prominence of price is a given in all purchase situations. Moreover, price is a complex stimulus and many customers also perceive price more broadly, for example, customers use price to indicate product quality (Lichtenstein et al., 1993). Price image formation is a complex process, because it depends on different factors, such as limited knowledge and information mechanisms for obtaining prices; price evaluation being a subjective process, each price evaluation of a single product should be analyzed together with the overall price image of the whole store (Zielke, 2006). In this field, Zielke (2006) defined price image as a multidimensional attitude toward the retailer's price level, value, price fairness, and frequency of sales (Hamilton and Chernev, 2013). Dickson and Sawyer (1990) commented that consumers are heterogeneous when it comes to their attention and response to prices and price promotions. Price image is defined as a fundamental marketing phenomenon, with antecedents and consequences that influence consumer decision behavior. It is related to other constructs, such as reference price or store image, for example. Price image is often defined as the overall level of prices that customers match with a specific seller. In other words, price image is a unidimensional construct that reflects a customer's overall impression of the retailer's prices and is not analyzed in an isolated situation (Hamilton and Chernev, 2013). Another vein of thought says that store price image is developed by a dynamic process (Hamilton and Cherney, 2013; Alba et al., 1994). As customers visit a store and are exposed to and informed by the actual prices in that store, they gradually update their store price beliefs (Büyükkurt, 1986; Mägi and Julander, 2005; Nyström, 1970; Feichtinger et al., 1988; Lourenço et al., 2015). This kind of “learning process” may be understood when customers are looking for price cues that result in updating store price knowledge (Gauri et al., 2008; Urbany et al., 1996). A relevant implication about price image found by Hamilton and Chernev (2013) is that it is not defined by prices alone, but depends on all the marketing-mix variables. As a key marketing function, the provision of information is essential to a retailer's strategic positioning. For Büyükkurt (1986), customers assemble perceived product prices into an overall retail price image; thus, a positive product price perception can influence all price–value dimensions of retail (Diallo et al., 2015). The importance of price in retailing introduces a multidimensional retail price image concept as a latent variable (Zielke, 2011; Diallo et al., 2015). In this context of multidimensionality, Zielke (2006) recognized several price image dimensions, but price level and value perception seem to be the constructs that best represent price image. Others researchers have analyzed the theoretical structure of price image, with studies related to price image, brand, and retail environment (Zielke, 2006, 2010, 2010; De Toni et al., 2014), symbolic factors of price image (Allen, 2006; De Toni and Mazzon, 2013; De Toni and Mazzon, 2014), price fairness (Campbell, 2007) and perceived value

2.1.2. Perceived value For Emery (1969), value for money is the association between price and quality rating. Zeithaml (1988) points out that customer see value in different ways. Some of them define it simply as low price, others see only the benefits they receive, and still others evaluate quality as a value derived from the product or service that they paid for, or look at value as what they get for what they give. In this context, Dodds and Monroe (1985) clarify perceived value as the balance between perceived quality and sacrifice, and these dimensions are influenced by the products’ prices (Zielke, 2006). Perceived value is the overall evaluation of the net worth of the service, and is related to the customer's assessment of what is received (benefits) and what is given (costs or sacrifice) (Zeithaml, 1988; Hellier et al., 2003). Zielke (2011) states that value for money presents positive impacts of enjoyment and negative impacts of distress, anger, fear, contempt, and shame. In other words, low prices may reduce distress and anger if the perceived value is favorable; cheap prices may overcome anger and distress by offering value. Low prices may therefore increase shopping intentions if the customer's distress and anger are reduced by value (Zielke, 2011). 2.1.3. Price level perception Zielke (2010) found that price image is a multidimensional construct, with price level and price value image acting as the main dimensions of store price image (Zielke, 2006, 2010, 2014). However, price level image refers only to the amount of money paid for the same product or service, while the price value image reflects product quality and store attributes. A discount store can be seen as cheap in terms of price level, but not automatically in terms of value (Zielke, 2006). Retailers should pay more attention to price-level image, which means how cheap or expensive the store is in the customers’ view. Value for money is the relationship between the returns customers get for the prices they pay (Zielke, 2010). However, for Hamilton and Chernev (2013), customers can evaluate the same price as less favorable when it is related to a low store price image, compared with a high store price image. Also, sellers should be able to evaluate the price reference levels in the market; it is likely that the retailer's price image may adjust to comparisons of price (Hamilton and Chernev, 2013). 2.1.4. Price fairness In considering price level and price value image, the dimension that differentiates between them is price fairness. Price fairness is therefore commonly combined with an assessment of how fair a price adjustment 202

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Netemeyer, 1999; Zielke, 2006). When prices impress and/or surprise customers in a negative way, they may leave the establishment with a sense of coldness. Furthermore, when customers feel afraid of paying too much (Zielke, 2006), they can experience negative emotions, as will be discussed below.

is perceived to be. Furthermore, the fairness concept depends on the context of how price adjustments are implemented and whether differences in prices can be justified by the selling companies. Transparency of prices can be seen as part of price fairness, with customers often claiming that sellers try to mask price information (Zielke, 2006). The concept of price fairness has been defined as the judgment of a price as being reasonable, acceptable, or fair (Bolton et al., 2003; Xia et al., 2004). The cognitive definition of price fairness derives from comparisons to references or norms. People compare specific prices between two kinds of products or services and then consider the overall range of prices. The conceptual meanings they achieve can be understood from the several aspects of price fairness (Finkel, 2001; Xia et al., 2004). By experiencing a particular event, people may know what is unfair, but it is more difficult to distinguish what fairness really is (Xia et al., 2004). Xia et al. (2004) defined price fairness as the customer's associated emotions in comparing a retailer's price with others as reasonable, acceptable, or justifiable, having as references for comparison their own selves, other customers, or different retailers. Understanding price fairness is necessary to avoid the negative consequences of perceived unfair price policies. Price unfairness leads to negative consequences for the retailers, by provoking negative word of mouth, abandonment of the store, and so on (Campbell, 2007; Xia et al., 2004; Hamilton and Chernev, 2013). Moreover, price image can influence fairness judgments through comparative offers to customers who judge a price as unfair compared with goods or services at the same level, but with higher prices. In the same vein, customers can compare a price as being unfair if the comparison occurs between two sellers both with low store price images, as opposed to comparisons made between retailers with low store price image and high store price image (Hamilton and Chernev, 2013).

2.1.6. Negative emotions Narrowly defined, price perception represents the sum of money that needs to be delivered for particular goods or services. Higher prices generally negatively affect purchase probabilities. However, customers perceive price more broadly as a complex stimulus, rather than acting strictly just in a “negative role” in the disbursement of economic resources (Lichtenstein et al., 1993). For Diallo et al. (2015), pricing strategies should be exercised with attention, because the results for each regional service's or product's price perceptions may have negative effects on emotions toward the store. A lower price image may contribute to negative emotions (Diallo et al., 2015). Customers may be embarrassed to buy from cheap retailers, or even consider that cheap prices could be a result of unethical retailer policies (Zielke, 2011). In this context, emotions seem to be an obvious part of retail price image. Furthermore, customers’ own experiences may be associated with feelings like anger, excitement, and unhappiness that are related to particular retail prices (Zielke, 2011). In this vein, negative emotions may vary in type and intensity, and can be associated with price fairness (Xia et al., 2004). 2.1.7. Symbolic dimension Cognitive price image impacts on price-related emotions. These emotions also derive from social prestige and social responsibility, for example, from saving money (Zielke, 2011). As a symbolic dimension, cheap prices are related to saving money, but this may potentially be incongruent with the goals of social status and social responsibility if customers associate cheap prices with inferior quality and unethical practices of sellers (Roseman, 1984; Roseman et al., 1990; Zielke, 2011). Discounted prices can increase the congruence with status goals and also reduce negative feelings of contempt and shame by selling luxury items at very competitive prices. This attitude will attract customers from wider social classes to these retailers and raise the social acceptability of buying in such stores (Zielke, 2011). The assortment of items on offer has a relation with price image as a function of the degree to which products that compose the assortment can produce a specific symbolic meaning for customers. The assortment represents not only the variety of items, but expresses the retailer's identity (Aaker, 1999; Akerlof and Kranton, 2000; Hamilton and Chernev, 2013). Furthermore, products/services can be associated with customers’ self-expression when it comes to higher prices (Chernev et al., 2011).

2.1.5. Positive emotions Price image multidimensionality involves emotions, which form an essential part of this construct. Cognitive price image dimensions can influence several specific price-related emotions and analyzing these can contribute to understanding the customer's reactions to cognitive price perceptions (Zielke, 2011). Emotions play an important role in price-related contexts (O’Neill and Lambert, 2001); appraisals of situations or beliefs evoke a specific range of emotions (Roseman and Smith, 2001). Integrating emotions to understand retail price image is essential in perceiving a customer's reactions to the retailer's pricing activities. Also, retail price images are multidimensional latent variables and are defined as price-related emotions (Zielke, 2011). The multidimensionality of a price image is an “entire array of associations” related to an object; these associations may involve both cognition and emotions (Blackwell et al., 2001). In this direction, some researchers argue that price fairness, in a price comparison context, leads to both positive and negative feelings (Xia et al., 2004; Zielke, 2011). Emotional states and/or reactions are related to the pricing policies of sellers, and customers can feel happy, angry, or surprised when they perceive prices from retail stores (Zielke, 2006; O’Neill and Lambert, 2001). Positive emotions tend to result in positive shopping intentions and negative emotions lead to negative shopping intentions (Zielke, 2011). For Barlow and Maul (2000), customers’ positive experiences from retailers’ products/services are defined by emotional value (Ariffin et al., 2016). For Bagozzi et al. (1999), specific emotions may increase purchase intentions and customers may repurchase if they are satisfied (Xia et al., 2004). Future purchase intentions are related to customer satisfaction (Durvasula et al., 2004). As a positive emotion, the state of pleasure is experienced as a feeling of enjoyment and positive reinforcement. Excitement is rated on a single dimension, ranging from the sleep level to frantic excitement, and is related to the extent to which one feels unrestricted or free to act in different ways (Mehrabian and Russell, 1974; Bearden and

2.2. Relating store price image and repurchase intention Customers’ choices, their purchasing decisions and attitudes are guides in using holistic constructs to evaluate how cheap or expensive specific stores are (Arnold et al., 1983; Mazumdar et al., 2005; Hamilton and Chernev, 2013; Lourenço et al., 2015). Emotions as part of retail price image have an impact on goal-oriented behavior (Bagozzi et al., 1998). Also, the emotional dimension may have an essential impact on shopping intentions beyond price-level perceptions or value for money. Some researchers consider repurchase intention as subsequent repurchase behavior (Bemmaor, 1995; Mittal and Kamakura, 2001; Morwitz et al., 1993; Hellier et al., 2003). The concept of repurchase intention is approached by Lee et al. (2011) as the revisit intention that associates positive bonds with service quality and satisfaction (Kitapci et al., 2014). Furthermore, service quality perceptions are a precursor to word-of-mouth recommendations and repurchase intentions 203

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on a product's or service's attractiveness or utility. While product's price sensitivity is often observed individually, its impact has not been entirely explored when it comes to the location where the customer is situated (Shrivastva et al., 2016). A survey applied to Indian customers found out that price sensitivity is one of the strongest consumer behaviors. In India, there are many prudent customers who are sensible to the prices and will not be “ripped off” by the sellers. The young Indian male population presented more price sensitivity, a useful insight for marketing managers (Gupta, 2011). Price information can be related to customers who are more or less price-conscious (Lichtenstein et al., 1990; Wakefield and Inman, 2003). In the same way, researchers have studied the influence of demographics on price elasticity (Hoch et al., 1995) or psychographics psychographics (Urbany et al., 1996) on price sensitivity (Wakefield and Inman, 2003). However, the specific effect of social situations on customer's price sensitivity has not been examined by researchers. Wakefield and Inman (1993) examined the conjunction between consumption occasion (hedonic/functional) and social context (nonsocial/social) in boosting sensitivity. They found that customers are relatively less sensitive to price for hedonic products than for functional products. Also, customers with lower levels of price sensitivity are insensitive to either hedonic and functional services or goods. However, as the price sensitivity increases, the difference between functional and hedonic price sensitivity is remarkable (Wakefield and Inman, 2003). According to Hamilton and Chernev (2013), while consumer-specific factors can affect price image and lead to price sensitivity, this dynamic depends on the consumers’ style of processing information and their familiarity with market prices (Hamilton and Chernev, 2013; MSI, 2014). The focus of this paper is to therefore understand customers’ perceptions of supermarket environments with different levels of price (high and low) and with differing price sensitivity (high and low). When it comes to high and low price sensitivity, we posit that it has the following moderating effects:

(Anderson, 1998; Kitapci et al., 2014). Choi and Kim (2013), Wu and Chen (2014), and Ariffin et al. (2016) confirm that the customer's perceived quality positively impacts on repurchase intentions. In general, higher perceived quality leads to stronger repurchase intentions (Ariffin et al., 2016). Price image or overall store price image may thus become a critical issue for retailers (Chang and Wang, 2014). In that repurchase intentions are influenced by perceived price, which includes store price image, rather than by actual price only (Dodds et al., 1991). So, we propose: H1. Store price image impacts positively on repurchase intention.

2.3. Price level and price sensitivity as moderating variables Several studies show clearly that many consumers use price as an indication of product or service quality. Price level is perceived in a “positive role,” in other words, higher prices positively affect purchase intentions (Erickson and Johansson, 1985; Lichtenstein et al., 1988; Tellis and Gaeth, 1990; Zeithaml, 1988; Lichtenstein et al., 1993). The use of price as an indicator of product or service quality clearly varies depending on the situation (Monroe and Krishnan, 1985; Lichtenstein et al., 1993). Journal selections from MSI in 2014 (MSI, 2014) point to the framework developed by Hamilton and Chernev (2013) that shows the key drivers of price image formation and their consequences for consumer behavior. Furthermore, they highlight the overall level of prices, where a retailer can define a low price image even having high prices, or conversely, retailers can have a high price image despite the fact that they are offering a low price level for products and/or services (Hamilton and Chernev, 2013; MSI, 2014). Therefore, when it comes to high and low price levels, we hypothesize that they present moderating effects as follows: H2a. If the price level is low, then the relation between the store price image with the repurchase intention will be positive, but weaker.

H2c. If the price sensitivity level is low, the relation between the store price image and the repurchase intention will be positive, but weaker;

H2b. If the price level is high, then the relation between the store price image with the repurchase intention will be positive and stronger.

H2d. If the price sensitivity level is high, the relation between the store price image and the repurchase intention will be positive and stronger.

Price sensitivity can be seen as the weight and/or influence conjoined to price in a consumer's evaluation of a service or product (Erdem et al., 2002). Price sensitivity refers to the way that customers perceive and respond to changes or differences in products’ or services’ prices (Monroe, 1973; Wakefield and Inman, 1993) and influences routine decisions in response to changes in price (Bucklin, Gupta and Han, 1995; Wakefield and Inman, 2003). Erdem et al. (2002) point out that customers’ price sensitivity must have increased in recent years, because they are now more aware of substitutes (Erdem et al., 2002) and/or competitors. Furthermore, a firm's past customers may be more price sensitive because they believe that they deserve preferential treatment (Lee and Fay, 2017). A premium or high level price is associated with higher quality. The relationship between consumer price sensitivity and marketing mix has frequently been investigated (Erdem et al., 2002). For Erdem et al. (2002), price sensitivity may be moderated by the sensitivity of consumers to uncertainty about a given product's attributes. In other words, customers that perceived or expected higher quality may have their price sensitivity decreased; consistent higher perceived quality can lead to lower price sensitivity (Erdem et al., 2002). Shrivastva et al. (2016) find that in the ways customers perceive price gains and price losses, the impact of price sensitivity on consumption shows significant differences between populations, in this case, urban and rural. There is also variation in price sensitivity towards a product or service across different stores and chains. Consumer behavior related to price changes is an important dynamic input for companies’ strategic decisions. Furthermore, price sensitivities depend

2.4. Proposed theoretical model Based on the theoretical review, Fig. 1 shows the relations between constructs and their respective hypotheses. QL

MODERATORS Price sensitivity Price Level

PV

H2a (+) | H2b (+) H2c (+) | H2d (+)

PL

PF

Store Price Image

H1(+)

PE

NE

SY

Fig. 1. Proposed theoretical model. 204

Repurchase Intention

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Table 1 Sources of store price image scale. Source: Literature review by Scopel (2014). Constructs

Sources scales adapted from

Quality (QL) (four items) Perceived Value (PV) (five items)

Zeithaml (1988), Zielke (2010) Sweeney et al. (1999), Rust et al. (2001), Sirdeshmukh et al. (2002), Zeithaml (1988), Zielke (2006), Sheth et al. (1991), Cross and Dixit (2005), De Toni and Mazzon (2013) Zielke (2006, 2010, 2011), Winer (1986), Rajendran and Tellis (1994), De Toni and Mazzon (2013) Bolton et al. (2003), Xia et al. (2004), Munnukka (2006), Zielke (2010) Lazarus (1991), Zielke (2011) O’Neill and Lambert (2001), Sweeney and Soutar (2001), Peine et al. (2009), Lazarus (1991), Zielke (2011) O’Neill and Lambert (2001), Sweeney and Soutar (2001), Peine et al. (2009), Vinson et al. (1977), Levy (1981), Dichter (1985), Rucker and Galinsky (2008)

Price Level (PL) (six items) Price Fairness (PF) (four items) Positive Emotions (PE) (four items)

Negative Emotions (NE) (seven items)

Symbolic Dimension (SY) (four items)

Table 2 Sample characteristics. Variable Gender Female Male Missing Value Age (years old) 18–22 23–27 28–32 33–37 38–42 > 43 Missing Value Family monthy income < $ 500,32 $ 500,63–750,48 $ 750,79–1250,79 $ 1251,11–2501,59 $ 2501,90–3809,52 $ 3809,84–5003,17 > $ 5003,49 Missing Value Monthy spending on grocery shopping < $ 158,73 $ 159,05–253,97 $ 254,29–825,40 $ 825,71–1428,57 > $ 1428,89 Missing Value

3. Method and data

Frequency

Percentage

108 95 4

52.1 45.9 1.9

47 84 26 22 7 9 12

22.7 40.6 12.6 10.6 3.4 4.3 5.8

22 38 58 52 22 6 8 1

10.6 18.4 28.0 25.1 10.6 2.9 3.9 0.5

47 71 78 7 1 3

22.7 34.3 37.7 3.4 0.5 1.4

3.1. Procedure

3.2. Statistical technique

This study is based on a survey that was conducted among customers who had experienced retail purchases in different kinds of supermarkets, with high and low levels of price, within the last 12 months. We used a convenient customer sample of management undergraduate students from the south of Brazil. An important concern for the research is to obtain a sampling equivalence (Malhotra et al., 2012). Participants were invited to remember and register a recent purchase. First of all, the instrument was structured to measure the store price image, as a formative second-order factor, using the following constructs and measurement scales already pretested by Scopel (2014) in the Brazilian context (see Table 1). Furthermore, this study analyzed the relation of these constructs with the Repurchase Intention (RI) (five items) from Noyan and Simsek (2012). All these constructs include the moderation level of price (high and low), so customers answered a single research question for two types of supermarket, with low and high prices. These items can be seen in the scales described in the Appendix A. As another moderating construct, the customers were then asked to answer questions about their Price Sensitivity (PS) (five items), using a scale adapted from Gupta (2011) and Shrivastva et al. (2016). All the constructs’ scales range from 1 = strongly disagree to 7 = totally agree. Respondents were asked about their frequency of purchase. Finally, the respondents answered questions about their profile, such as gender, age, schooling, monthly income, and amounts spent on purchases in supermarkets. After the questionnaire was developed, content validation was performed to understand the relation of the content with the construct under analysis (Hair et al., 2010). The data collection instrument was submitted to a small group of experts who analyzed and checked the suitability of the indicators selected to represent the constructs discussed. Moreover, the pre-test was applied to 60 respondents through auto-fill.

The data were analyzed using the partial least squares–structural equation modeling (PLS–SEM) approach supported by SmartPLS® 3.2.7. PLS–SEM is a nonparametric method that minimizes the amount of unexplained variance. The technique differs from the method of maximum likelihood-based (CB-SEM), which requires a normal distribution of data and assumes normally distributed residuals (Ringle et al., 2015; Thiele et al., 2015; Hair et al., 2017).

4. Data analysis and results 4.1. Sample and measurement Table 2 shows the descriptive analysis of the demographic characteristics of the respondents. A total of 239 cases were obtained as the initial student sample. However, only 207 valid and usable questionnaires were considered. Thirty-two cases were eliminated, as they presented more than 5% of missing data. Mean value replacement with SmartPLS® 3.2.7 should only be used when the dataset exhibits extremely low levels of missing data, with less than 5% of values missing per indicator (Hair et al., 2017). Therefore, only cases with missing values below 5% were kept in this research. Out of this total of 207 cases, 108 (52.1%) were female and 95 (45.9%) were male; 84 (40.6%) were between 23 and 27 years old. Monthly family income varied from $750.79 to $1250.79, comprising 58 (28%) respondents. Fifty-two respondents (25.1%) had a family income from $1251.11 to $2501.59. Furthermore, 78 respondents (37.7%) had a monthly spend on grocery shopping from $254.29 to $825.40, while 71 (34.3%) respondents had a monthly spend from $159.05 to $253.97.

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stages were performed in PLS–SEM for the measurement of the reflective-formative model: evaluation of the measurement model and the structural model (Hair et al., 2017).

Table 3 Results for reflective measurement models. Variables

Quality (QL)

Perceived Value (PV)

Price Level (PL)

Price Fairness (PF)

Positive Emotions (PE) Negative Emotions (NE)

Symbolic Dimension (SY) Repurchase Intention (RI)

Items

QL1 QL2 QL3 QL4 PV1 PV2 PV3 PV4 PV5 PL1 PL2 PL3 PL4 PL5R PL6R PF1 PF2 PF3 PF4 PE1 PE2 PE3 PE4 NE1 NE2 NE3 NE4 NE5 NE6 NE7 SY1 SY2 SY3 SY4 RI1 RI2 RI3 RI4 RI5

Convergent Validity

Internal Consistency Reliability

Loadings

AVE

CR

> 0.70

> 0.50

Cronbach's alpha > 0.60

0.889 0.903 0.871 0.890 0.740 0.757 0.826 0.732 0.811 0.726 0.871 0.857 0.819 0.730 0.714 0.911 0.930 0.832 0.895 0.855 0.845 0.888 0.870 0.821 0.890 0.881 0.856 0.889 0.846 0.828 0.877 0.946 0.934 0.888 0.857 0.859 0.892 0.923 0.914

0.790

0.912

0.938

0.599

0.832

0.882

0.622

0.878

0.908

0.797

0.914

0.940

0.748

0.888

0.922

0.738

0.941

0.952

0.831

0.932

0.952

0.791

0.934

0.950

4.3. Evaluation of the measurement model

> 0.60

To validate a reflective measurement model, it is necessary to understand the internal consistency (Cronbach's alpha and composite reliability), convergent validity (loadings, average variance extracted) and discriminant validity (Hair et al., 2017). Internal consistency reliability of the measurement items was tested via Cronbach's alpha and all variables were higher than 0.70 (see Table 3). The composite reliability (CR) varies between 0 and 1, with higher values indicating higher levels of reliability. CR values between 0.70 and 0.90 can be regarded as satisfactory. Values above 0.95 are not desirable, because they indicate that all variables are measuring the same phenomenon (Rossiter, 2002; Hair et al., 2016). In this analysis, all values for CR are equal or below 0.95. Items that are indicators (measures) of a specific reflective construct should converge or share a high proportion of variance (Hair et al., 2016). Convergent validity was verified by examining the factor item loadings (standardized loadings) to ensure that all variables were above 0.70; and that all values of average variance extracted (AVE) (Fornell and Larcker, 1981), were higher than 0.50 (Malhotra et al., 2012; Hair Jr. et al. 2016) (see Tables 3 and 4). The results showed that convergent validity was achieved and that all measurement items well represented the respective variables. Discriminant validity is the extent to which a construct is truly distinct from other constructs by empirical standards (Hair et al., 2016). The comparison of the constructs in sharing variance (squared correlation) was performed by discriminant validity through the AVE of each construct (Fornell and Larcker, 1981). Table 5 shows that the square roots of the AVE (bold) were all distant from the diagonal correlation values, meaning that there was adequate discriminant validity. Another test for discriminant validity was performed, the heterotrait-monotrait ratio (HTMT), as shown in Table 6. This is an alternative, more reliable criterion for discriminant validity (Hair et al., 2016). It requires computing bootstrapping confidence intervals with 5000 resamples, as suggested by Hair et al. (2016). The columns designated as lower than 2.5% and greater than 97.5% represent the lower and upper bounds of the 95% (bias-corrected and accelerated) confidence interval. Consequently, as can be seen in Table 6, neither of the relations includes the value zero. For example, the relations between QL and Store Price Image are 0.070 and 0.160, respectively. This is consistent since the conservative HTMT threshold of 0.889 already supports discriminant validity (see Table 5) (Hair et al., 2016).

Note: Reliability and validity tests by using SmartPLS® 3.2.7.

4.2. Partial least squares–structural equation modeling The concepts related to these analyses are associated with the two stages of the application of PLS–SEM. In this study, two verification Table 4 Exploratory factor analysis for eight factors items loadings and cross loadings. Factor Items

Quality

Perceived Value

Price Level

Price Fairness

Positive Emotions

Negative Emotions

Symbolic Dimension

Repurchase Intention

QL1 QL2 QL3 QL4 PV1 PV2 PV3 PV4 PV5 PL1 PL2 PL3 PL4 PL5R PL6R PF1 PF2

0.889 0.903 0.871 0.890 0.352 0.398 0.393 0.264 0.205 − 0.031 − 0.040 − 0.013 − 0.037 − 0.212 − 0.164 0.218 0.215

0.360 0.402 0.363 0.329 0.740 0.757 0.826 0.732 0.811 0.321 0.392 0.330 0.348 0.148 0.198 0.458 0.453

− 0.106 − 0.016 − 0.115 − 0.119 0.328 0.209 0.218 0.248 0.426 0.726 0.871 0.857 0.819 0.730 0.714 0.596 0.603

0.221 0.272 0.219 0.213 0.433 0.393 0.430 0.291 0.454 0.352 0.554 0.563 0.569 0.436 0.424 0.911 0.930

0.063 0.157 0.077 0.075 0.436 0.363 0.373 0.372 0.490 0.600 0.659 0.605 0.545 0.345 0.378 0.503 0.505

0.025 0.013 0.069 0.034 − 0.117 − 0.014 − 0.121 − 0.044 − 0.189 − 0.004 − 0.223 − 0.177 − 0.231 − 0.430 − 0.379 − 0.306 − 0.294

0.165 0.229 0.275 0.323 0.252 0.266 0.218 0.329 0.196 0.217 0.068 0.045 0.050 − 0.161 − 0.142 0.125 0.114

0.338 0.357 0.324 0.271 0.442 0.319 0.395 0.382 0.342 0.292 0.414 0.459 0.471 0.325 0.285 0.470 0.489

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Table 4 (continued) Factor Items

Quality

Perceived Value

Price Level

Price Fairness

Positive Emotions

Negative Emotions

Symbolic Dimension

Repurchase Intention

PF3 PF4 PE1 PE2 PE3 PE4 NE1 NE2 NE3 NE4 NE5 NE6 NE7 SY1 SY2 SY3 SY4 RI1 RI2 RI3 RI4 RI5

0.308 0.207 0.062 0.114 0.089 0.113 0.067 0.026 0.061 0.034 0.016 0.068 − 0.046 0.316 0.261 0.233 0.191 0.371 0.356 0.257 0.324 0.306

0.522 0.437 0.406 0.424 0.451 0.537 − 0.083 − 0.149 − 0.095 − 0.071 − 0.167 − 0.092 − 0.122 0.280 0.308 0.291 0.288 0.532 0.499 0.338 0.415 0.352

0.423 0.580 0.560 0.503 0.597 0.654 − 0.219 − 0.269 − 0.238 − 0.203 − 0.286 − 0.308 − 0.218 0.060 − 0.000 − 0.010 − 0.046 0.445 0.394 0.451 0.422 0.433

0.832 0.895 0.426 0.386 0.475 0.533 − 0.255 − 0.285 − 0.257 − 0.215 − 0.311 − 0.312 − 0.267 0.132 0.121 0.099 0.103 0.510 0.471 0.472 0.449 0.445

0.398 0.487 0.855 0.845 0.888 0.870 − 0.123 − 0.120 − 0.090 − 0.084 − 0.106 − 0.100 − 0.066 0.248 0.214 0.185 0.260 0.388 0.320 0.341 0.345 0.341

− 0.265 − 0.271 0.024 0.007 − 0.152 − 0.238 0.821 0.890 0.881 0.856 0.889 0.846 0.828 0.232 0.231 0.218 0.284 − 0.116 − 0.169 − 0.215 − 0.189 − 0.170

0.132 0.079 0.228 0.253 0.221 0.174 0.193 0.236 0.223 0.267 0.230 0.214 0.236 0.877 0.946 0.934 0.888 0.204 0.132 0.065 0.138 0.076

0.435 0.497 0.283 0.278 0.345 0.428 − 0.113 − 0.146 − 0.122 − 0.125 − 0.206 − 0.229 − 0.195 0.157 0.123 0.112 0.118 0.857 0.859 0.892 0.923 0.914

Note: Bold values are items loadings higher than 0.700 at SmartPLS® 3.2.7.

Table 5 Correlation and average variance extracted (AVE). Constructs

1

2

3

4

5

6

7

8

(1) (2) (3) (4) (5) (6) (7) (8)

0.912 0.250 0.265 0.009 0.320 0.126 0.277 0.141

0.250 0.865 − 0.116 0.643 0.530 0.532 0.110 0.392

0.265 − 0.116 0.859 − 0.313 − 0.132 − 0.319 0.038 − 0.192

0.009 0.643 − 0.313 0.793 0.348 0.564 − 0.130 0.421

0.320 0.530 − 0.132 0.348 0.774 0.522 0.412 0.485

0.126 0.532 − 0.319 0.564 0.522 0.893 0.263 0.530

0.277 0.110 0.038 − 0.130 0.412 0.263 0.889 0.365

0.141 0.392 − 0.192 0.421 0.485 0.530 0.365 0.890

Symbolic Dimension Positive Emotions Negative Emotions Price Level Perceived Value Price Fairness Quality Repurchase Intention

Note: The average variance extracted (AVE) of each construct are values in diagonal, below the diagonal line are the shared values variances (r squared) and above the diagonal are the correlations values at SmartPLS® 3.2.7.

Table 6 Confidence intervals for HTMT.

H1

QL → Store Price Image PV → Store Price Image PL → Store Price Image PF → Store Price Image PE → Store Price Image NE → Store Price Image SY → Store Price Image Store Price Image → RI

lower than 2,5%

upper than 97,5%

Sig.

HTMT confidence interval does not include 1

0.070 0.215 0.282 0.265 0.227 − 0.264 0.038 0.528

0.160 0.273 0.342 0.312 0.278 − 0.076 0.151 0.672

0.000 0.000 0.000 0.000 0.000 0.001 0.001 0.000

Yes Yes Yes Yes Yes Yes Yes Yes

Table 7 Structural model assessment procedure.

H1

QL → Store Price Image PV → Store Price Image PL → Store Price Image PF → Store Price Image PE → Store Price Image NE → Store Price Image SY → Store Price Image Store Price Image → RI

Collinearity Assessment VIF

Significance of the path coefficients Hypothesized relationships

1.459 1.879 2.664 2.182 2.308 1.275 1.313 1.000

0.117 0.242 0.308 0.284 0.250 − 0.189 0.097 0.609

207

Level of R2 R2

f2 effect size f2

0.370

884.876 2950.008 3378.032 3508.424 2564.975 2657.396 684.335 0.588

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capabilities. In this way, the collinearity analysis tests for variance inflation factor (VIF) values below 5. In this study, all VIF values were below 5. The hypothesized relationships exist among the constructs, so standardized values between − 1 and 1 measure these relations, with estimated path coefficients close to + 1 representing strong positive relationships and vice versa for negative values.

4.4. Evaluation of the structural model The structural model's predictive capabilities and the relationship between the constructs were tested by running the coefficients of determination R2 (explained variance) and f2 (effect size) (Hair et al., 2016). Table 7 shows the steps to determine the model's predictive

Fig. 2. Model resolution by SmartPLS using PLS algorithm. 208

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The coefficient of determination (R2 value) represents a measure of in-sample predictive power (Rigdon, 2012; Sarstedt and Mooi, 2014; Hair et al., 2016). R2 values of 0.20 are considered high in disciplines such as consumer behavior (Hair et al., 2016). In this analysis, R2 presented a value of 0.370; this means that 37% of repurchase intention was explained by the store price image. The f2 effect size measures the strength of each variable in explaining endogenous variables; this means it represents the evaluation of the R2 values of all endogenous constructs (Hair et al., 2016). According to Hair et al. (2016), effect size values below 0.02 represent weak effects or indicate that there is no effect. Finally, Hair et al. (2016) suggest that the goodness-of-fit index should not be used to determine model fit. Fig. 2 presents the SmartPLS model and results yielded by PLS algorithm. This illustration relates to the findings of Tables 3–7. Table 8 shows the latent variable R2 for repurchase intention is 0.370 in the general framework, which means that store price image explains 37% of repurchase intention. In this vein, H1 is validated (β = 0.609, p = 0.000, 95% CI = 0.530–0.672). Furthermore, it was possible to verify that the dimension of store price image present significant correlations as a second-order construct.

low sensitivity respectively. This was tested with multi-group analysis in Smart-PLS 3.2.7, by splitting up the moderator into two dummy variables, which were then included in the model (Hair et al., 2016). Based on the sample of 207 cases, 83 (40.1%) customers presented low sensitivity, 82 (39.6%) customers had high sensitivity, and 42 (20.3%) customers were eliminated, resulting in a final sample of 165 respondents. This counterbalance was used in the research of Zaichkowsky (1985). So, using an average of a seven-point scale for each customer's scale sensitivity, the scale was divided into three parts. The first quintile (40% sample) represents customers with low sensitivity, the middle one (20%) was eliminated, and the third quintile (40% sample) represents customers with high sensitivity. This moderating variable allowed an understanding of the effects of customer price sensitivity on grocery environments as presented at Table 10. Interesting results were found in Table 10 about the moderating effect of price sensitivity. First, the store price image (low sensitivity) impacted on repurchase intention (β = 0.544, p = 0.000, R2 = 0.296, 95% CI = 0.288–0.659). Hence, H2c is validated. It was possible to verify that negative emotions (NE) toward the store price image did not present significant relations when moderated by the price sensitivity,

Table 8 General model resolution by SmartPLS using PLS algorithm and Bootstrapping. Hypotheses General Framework

H1

QL → Store Price Image PV → Store Price Image PL → Store Price Image PF → Store Price Image PE → Store Price Image NE → Store Price Image SY → Store Price Image Store Price Image → RI

Path Coefficient

Standard Error

t-value

p-value

0.117 0.242 0.308 0.284 0.250 − 0.189 0.097 0.609

0.115 0.240 0.306 0.282 0.248 − 0.184 0.096 0.608

5.010 16.394 19.311 24.173 19.931 3.393 3.443 17.242

0.000 0.000 0.000 0.000 0.000 0.001 0.001 0.000

R2

Confidence Intervals

Results

0.370

0.069|0.160 0.215|0.273 0.280|0.343 0.264|0.311 0.228|0.278 − 0.264|− 0.074 0.039|0.150 0.530|0.672

H1 Supported

Note: t-value should be greater than 1.96 (positive or negative) to be significant at < 0.05. Confidence interval neither of the relations includes the value zero.

for both high price sensitivity (β = −0.155, p = 0.061, 95% CI = − 0.254 to 0.068) and low price sensitivity (β = − 0.233, p = 0.229, 95% CI = − 0.395 to 0.238). In addition, the symbolic dimension (SY) did not present significant relations in the model when moderated by the price sensitivity for low price sensitivity (β = 0.081, p = 0.277, 95% CI = − 0.095 to 0.231). Furthermore, a significant link was found between store price image (high sensitivity) and repurchase intention (β = 0.704, p = 0.000, R2 = 0.495, 95% CI = 0.592–0.788). Thus, H2d is strengthened. For customers with both lower and higher price sensitivity, the store price image impacted on repurchase intentions. However, based on the R2 values for low and high price sensitivity, it was found that 49.5% of repurchase intention was explained by the store price image when customers presented high price sensitivity. On the other hand, 29.6% of repurchase intention was explained by the store price image when customers presented low price sensitivity.

4.5. The moderating effect of price level The moderating effect of price level was checked by splitting the sample into two groups, with high and low price levels respectively. This was tested by running analyses in Smart-PLS 3.2.7 (Hair et al., 2016). Each respondent replied for both price levels (low and high prices), so both groups comprised 207 customers. Table 9 presents all indexes. The moderating effect of price level, for Sobel (1982), is seen when the value for the critical ratio for the difference is above or below 1.96, p < 0.05, which means that there is an indirect effect of price level. Table 9 shows that only the negative emotions (NE) did not present significant differences associated with price level, for both low price level and high price level. However, the store price image (low price level) impacted on repurchase intention (β = 0.445, p = 0.000, R2 = 0.198, 95% CI = 0.304–0.565). Hence, H2a is validated. In addition to this, a significant link occurred between store price image (high price level) and repurchase intention (β = 0.650, p = 0.000, R2 = 0.422, 95% CI = 0.563–0.725). Thus, H2b is strengthened. So, customers at high price levels present stronger impacts on the relation between the store price image and repurchase intentions. Customers at low price levels decrease the impact, or present a weaker impact on the relation between the store price image on repurchase intentions, because the R2 value (R2 = 0.198) is lower in comparison with that of the high price level (R2 = 0.422).

5. General discussion 5.1. Theoretical contributions and managerial implications First, we identified the relevant findings of research related to direct effects within the theoretical framework. The price level and perceived value better explained the price image, as pointed out by Zielke (2006). Using the general framework of this study, the price level was found to be the most representative construct of store price image, as a reflectiveformative second-order construct, followed by price fairness, positive emotions, and the perceived value. The perceived value was the fourth construct that best explained the store price image. However, in the case of high price levels, perceived value better explained the store price image,

4.6. The moderating effect of price sensitivity The moderating effect of price sensitivity was checked by splitting the sample in two groups according to price sensitivity, with high and 209

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Table 9 Moderating effect of price level. Hypotheses H2a: Low Price Level QL → Store Price Image PV → Store Price Image PL → Store Price Image PF → Store Price Image PE → Store Price Image NE → Store Price Image SY → Store Price Image H2a Store Price Image → RI H2b: High Price Level QL → Store Price Image PV → Store Price Image PL → Store Price Image PF → Store Price Image PE → Store Price Image NE → Store Price Image SY → Store Price Image H2b Store Price Image → RI

Path coefficient

Standard Error

t-value

p-value

0.216 0.257 0.239 0.255 0.278 − 0.100 0.161 0.445

0.029 0.017 0.022 0.018 0.018 0.106 0.035 0.067

7.462 14.759 10.650 14.147 15.419 0.944 4.547 6.630

0.000 0.000 0.000 0.000 0.000 0.345 0.000 0.000

0.211 0.307 0.200 0.280 0.226 − 0.074 0.202 0.650

0.024 0.018 0.023 0.021 0.019 0.108 0.035 0.041

8.662 17.104 8.832 13.586 11.666 0.691 5.859 15.667

0.000 0.000 0.000 0.000 0.000 0.490 0.000 0.000

R2

Confidence intervals

Results

0.198

0.158|0.272 0.229|0.299 0.201|0.291 0.227|0.300 0.250|0.327 − 0.249|0.101 0.089|0.228 0.304|0.565

H2a Supported

0.422

0.170|0.268 0.279|0.351 0.157|0.248 0.247|0.328 0.195|0.269 − 0.245|0.102 0.127|0.259 0.563|0.725

H2b Supported

Note: t-value should be greater than 1.96 (positive or negative) to be significant at < 0.05. Confidence interval neither of the relations includes the value zero.

Table 10 Moderating effect of price sensitivity. Hypotheses H2a: Low Price Level QL → Store Price Image PV → Store Price Image PL → Store Price Image PF → Store Price Image PE → Store Price Image NE → Store Price Image SY → Store Price Image H2c Store Price Image → RI H2b: High Price Level QL → Store Price Image PV → Store Price Image PL → Store Price Image PF → Store Price Image PE → Store Price Image NE → Store Price Image SY → Store Price Image H2d Store Price Image → RI

Path Coefficient

Standard Error

t-value

p-value

0.162 0.258 0.275 0.300 0.270 − 0.233 0.081 0.544

0.060 0.247 0.039 0.026 0.032 0.194 0.073 0.086

2.694 6.514 7.092 11.468 8.367 1.203 1.111 6.312

0.007 0.000 0.000 0.000 0.000 0.229 0.267 0.000

0.101 0.238 0.316 0.276 0.237 − 0.155 0.095 0.704

0.028 0.019 0.021 0.016 0.015 0.082 0.033 0.052

3.640 12.763 15.110 17.075 15.597 1.877 2.886 14.336

0.000 0.000 0.000 0.000 0.000 0.061 0.004 0.000

R2

Confidence Intervals

Results

0.296

0.039|0.268 0.183|0.326 0.207|0.349 0.273|0.351 0.219|0.335 − 0.395|0.238 − 0.095|0.213 0.288|0.659

H2c Supported

0.495

0.046|0.155 0.203|0.273 0.281|0.364 0.251|0.318 0.211|0.271 − 0.254|0.068 0.033|0.162 0.592|0.788

H2d Supported

Note: t-value should be greater than 1.96 (positive or negative) to be significant at < 0.05. Confidence interval neither of the relations includes the value zero.

fairness in different ways for low and high price level stores. In parallel, positive emotions also presented a significant difference between both price levels, for low and high price stores. An interesting definition by Xia et al. (2004) for price fairness is that customers display emotions when a retailer's price is reasonable, acceptable, or justifiable, in comparison with those of competing retailers (Xia et al., 2004). As contributions of this research, these positive emotions could be related to price fairness. As noted by Zielke (2006), when customers feel afraid to pay too much, they may feel negative emotions. The first hypothesis examined the impact of store price image on repurchase intention. It tested the relations between perceived higher quality and stronger repurchase intentions (Choi and Kim, 2013; Wu and Chen, 2014; Ariffin et al., 2016). To understand the overall store price image may become a critical issue for retailers (Chang and Wang, 2014). The second hypotheses (H2a and H2b), examined the direct effects of store price image on repurchase intentions, with moderating effects that can provide a wider and more comprehensive vision of the consumer behavior under study. Thus, besides the hypotheses tested by the general framework, stores with low or high store price image presented significant further hypotheses, showing that overall store price image (low/high level) will impact positively on repurchase intentions. These

following by price fairness and positive emotions. Negative emotions did not impact on store price image at either low or high price levels. This result about negative emotions was also found by Scopel (2014). On the other hand, at low price levels, positive emotions best explained the store price image, following by price fairness and price level. Zielke (2011) indicated, for future research, the formation of emotions as part of a retailer's price image. Therefore, as a contribution to this research, positive emotions are seen as bonds that support both the general and the moderating frameworks. However, for negative emotions, the general framework showed relations being supported, while low price and high price levels did not support bonds. So, this result can be related to the idea supported by Lichtenstein et al. (1993), which highlights customers’ perception more broadly and in a more complex way than playing just a “negative role.” This study sought to answer the research questions proposed by Zielke (2014) and provide greater depth in understanding how customers respond to low and high prices. As feedback on this issue, the moderating price level is seen to present different values for low and high levels of prices. Specially, the relations between store price image and price fairness, and store price image and positive emotions, showed significant differences between the multi-group analyses. According to these findings, it is possible to support the idea that customers perceived price 210

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findings are corroborate in the literature; for Dodds et al. (1991), repurchase intentions are conducted by perceived value, more than just the actual price, while for Chang and Wang (2014), the dimensions of price image, as price value, price fairness, and price pleasure, impact on repurchase intentions. However, the main finding of this research is that customers with low price level perceptions decrease this impact, presenting a weaker impact on the relation between the store price image on repurchase intention, in comparison with customers with high price level perceptions. With regard to the moderating effect of price sensitivity, the second hypotheses (H2c and H2d), show that the store price image will impact on repurchase intention with weaker or stronger relations, depending on the moderating effects of price sensitivity. Also, as pointed out by Hamilton and Chernev (2013) and MSI (2014), the customers’ information processing style and familiarity with market prices is one of the factors that influences the relation with price sensitivity. Possession of information can contribute to the customer having more or less price sensitivity. As argued in the theoretical section—by Erdem et al. (2002), for example—low price sensitivity can be associated with higher perceived quality; customers who expected higher quality may exhibit less price sensitivity. Furthermore, experienced higher quality can lead to lower price sensitivity (Erdem et al., 2002). This means that a customer who is easily influenced by higher quality impressions tends to have lower price sensitivity.

price image, as argued by Hamilton and Chernev (2013). Furthermore, a qualitative approach may ask customers about overall price level, price image, comparative judgment, and so on. Going further, future studies could examine customers’ acquisition of store price image beliefs in the context of conflicting information from retailers (Hamilton and Chernev, 2013). In addition, future research should consider the use of a longitudinal approach, to better understand how the store price image evolves over time; this approach could also contribute to retailer managers better understanding changes in customer behavior. Furthermore, a bibliometric review of store price image could be produced, to better comprehend new research possibilities, fresh construct relations, and also new signs of research gaps. In this vein, future research could focus on brand retail credibility as related to price sensitivity. Beyond that, others studies may investigate strong and/or weak brand valuations and their relation to price premiums. For this reason, Erdem et al. (2002) suggested that consumers with higher price sensitivity are consistent across brands in their behavior; however, stronger brands tend to command premium prices. From this study, stronger retail brands will be interpreted as operating with higher store price images that command premium prices. Another research line that should be broadened and deepened is the focus on the impact of negative emotions on store price image, to understand better why these bonds were not supported in this research for both low and high price level stores. In this vein, Lichtenstein et al. (1993) argued that negative emotions are a complex stimulus, with customers perceiving price through a broad range of influences.

5.2. Future research and limitations Future research could focus on understanding the formation of store Appendix A

Variable

Scale items

Quality

QL01–This supermarket has a good quality QL02–The quality of this supermarket is perfectly acceptable QL03–The quality of this supermarket is better compared to other supermarkets QL04–This supermarket has high quality VP1–The money I spend at this supermarket is well spent. VP02–The old saying “you get what you pay for” is true for this supermarket VP03–The set of benefits in the supermarket is compatible with all sacrifices/ actual costs VP04–The benefit you get buying at this supermarket is very high VP05–The price of this supermarket is appropriate to what I get for my money PL01–The price of this supermarket is very low PL02–This is a cheap supermarket PL03–The price of this supermarket is lower compared to other supermarkets PL04–This is a low level supermarket PL05R–The price of this supermarket is very high PL06R–The price of this supermarket is expensive PJ01–This supermarket offers a fair price PJ02–This supermarket offers an acceptable price PJ03–The price in this supermarket is justifiable PJ04–This supermarket offers a reasonable price PE01–I am excited about the price of supermarket PE02–The price of this supermarket makes me feel happy PE03–I am very satisfied with the price of supermarket PE04–I like the price of this supermarket NE01–The price of this supermarket makes me feel sad NE02–I feel depressed when I think about the price of supermarket NE03–I feel sad when I think about the price of supermarket NE04–The price of this supermarket makes me feel unhappy NE05–I feel angry when I think about the price of this supermarket NE06–I am afraid to pay too much for the price of this supermarket NE07–The price of this supermarket makes me angry

Perceived value

Price level

Price fairness

Positive emotions

Negative emotions

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Symbolic dimension

Repurchase intention

Price sensitivity (moderator)

SB01–This supermarket will make me feel more powerful SB02–This supermarket will allow me a prominent position in society SB03–This supermarket will help to increase my status SB04–This supermarket will improve favorably the perception of others regarding myself RI01–I plan to do most of my future shopping in this supermarket RI02–If I go shopping today, I will go to this supermarket again RI03–I do most of my shopping in this supermarket RI04–When I go shopping, I consider this supermarket first RI05–When I go shopping, this supermarket is my first choice PS01–I buy as much as possible sale/discounted prices PS02–Supermarkets with the lowest prices are usually my choice PS03–I am willing to put in extra effort to find lower prices PS04–I usually go and check the products and their prices in several supermarkets before buying PS05–Price is more important than the supermarket brand

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