Abstract
In this study we aimed to investigate the effects of satisfaction, perceived value and value for money on the relationship between store attributes and loyalty. This research is based on an Indian study that presents controversial results on the theory of marketing, namely: value for money has a low and negative impact on loyalty and sales promotion does not influence the customer’s perception of value for money. This theory inspired our study which inserts two additional variables to the Indian model, for a better understanding of the results. The aim of this study was to investigate the background of consumer loyalty in conjunction with value for money, perceived value and retail satisfaction, in an emerging country: Brazil. To achieve the objective, a survey was conducted, answered by 516 retail customers in the city of São Paulo and the data were analyzed using Structural Equation Modeling (SEM). The results show that value for money, as well as satisfaction and perceived value, impacts loyalty. It was also found that all the retail attributes tested in this research contribute, in some way, to establishing loyalty of retail customers, even in a retail context of an emerging market (Brazil).
Keywords:Brazilian retail,Attitudinal loyalty,Satisfaction,Perceived value,Value for money,Store attributes.
Received: 13 February 2020 / Revised: 116 March 2020 / Accepted:21 April 2020/ Published: 30 April 2020
Contribution/ Originality
This study contributes to existing literature by investigating the effects of satisfaction, perceived value and value for money on the relationship between store attributes and loyalty.
1. INTRODUCTION
Loyalty can be defined as a deep commitment by the consumer to favor or repurse a product or service in the future (Oliver, 1999). Several authors mention important variables that influence the development of consumer loyalty: perceived quality (Parasuraman, Zeithaml, & Berry, 1988) satisfaction (Dick & Basu, 1994; Oliver, 1999) perceived value (Sirdeshmukh, Singh, & Sabol, 2002; Yang & Peterson, 2004) and trust (Garbarino & Johnson, 1999; Sirdeshmukh et al., 2002).
Despite being consolidated in the academic context, consumer loyalty still requires examination, both to comprehend the concept of loyalty and to understand the variables that influence and precedes its formation. The study by Grosso, Castaldo, and Grewal (2018) performed in Indian retail, presented a controversial result. In that study, the higher the value for money ratio, the lower the consumer loyalty intention. In addition, unexpectedly, the sales promotions construct did not contribute to building consumer loyalty (Grosso et al., 2018).
The provocative results in the study by Grosso et al. (2018) are the motivation for a new investigation of these constructs, to understand whether this phenomenon applies only to the context of Indian retail or can be extended to other realities of retail, such as in Brazil. Grosso et al. (2018) addressed promotion only from a monetary perspective (for example, prices). However, other types of promotion from a non-monetary perspective (for example, prizes, bonus packages, samples and sweepstakes), can add value to the product (Campbell & Diamond, 1990; Diamond & Sanyal, 1990) and affect brand loyalty (Mendez, Bendixen, Abratt, Yurova, & O’Leary, 2015) and were not considered in their research. In addition, the perceived value, a relevant construct for building loyalty (Lai, Griffin, & Babin, 2009) was not considered in the study by Grosso et al. (2018) which addressed only the value for money related to the price of the product / services.
Given the importance of testing the findings by Grosso et al. (2018) in another retail context, and also the opportunity to understand the effects of non-monetary promotion and the perceived value on loyalty, this research performed a new investigation, using the conceptual model of the study carried out in Indian retail study with the addition of these two new constructs.
2. THEORETICAL FRAMEWORK
2.1. Consumer Loyalty
Consumer loyalty can be defined as a high degree of commitment to prefer or repurse a brand in the future, evidenced by repeatedly pursing the brand or the same set of brands (Oliver, 1999). Loyalty can be expressed as the likelihood of the consumer recommending, repursing or, depending on the context, re-visiting the retailer (Anderson & Sullivan, 1993; ndrashekaran, Rotte, Tax, & Grewal, 2007; Cronin, Brady, & Hult, 2000; Gustafsson & Johnson, 2004). Marketing literature considers consumer loyalty from two perspectives, namely: attitudinal and behavioral, which would be the repurse of a product or service (Dick & Basu, 1994). In this article, we approach loyalty only from an attitudinal perspective, that is, of loyalty intentions, as a set of behaviors that signal the customer’s motivation to maintain a lasting relationship with the store (Dick & Basu, 1994; Sirohi, McLaughlin, & Wittink, 1998; Zeithaml, Berry, & Parasuraman, 1996).
Loyalty can be formed by several factors that impact consumer buying behavior and is guided by mental elements shaped by customers (Garbarino & Johnson, 1999). Trust (Garbarino & Johnson, 1999; Morgan & Hunt, 1994) satisfaction (Dick & Basu, 1994; Grosso et al., 2018; Hogreve, Iseke, Derfuss, & Eller, 2017; Oliver, 1999; Yang & Peterson, 2004) perceived value (Sirdeshmukh et al., 2002; Yang & Peterson, 2004) and perceived quality (Hogreve et al., 2017; Parasuraman et al., 1988) are global assessments that associate consumers’ knowledge and experiences with a supplier, directing its actions and choices consecutively. Thus, we propose the first hypothesis of this research.
H1. Customer satisfaction has a positive impact on attitudinal loyalty.
The value for money ratio is measured by the customer’s assessment from the perspective of what he paid and what he received in return (Rajaguru, 2016; Ryu, Han, & Kim, 2008). That is, when the customer has the perception that he is saving money or is taking advantage of a transaction, he tends to perceive value (Sirohi et al., 1998) to be satisfactory (Cronin et al., 2000) and this can positively affect the intention of loyalty (Grosso et al., 2018; Oliver, 1999).
Thus, we assume that:
H2a. Value for money has a positive impact on satisfaction.
H2b. Value for money has a positive impact on attitudinal loyalty.
H2c. Value for money has a positive impact on perceived value.
Perceived value is the exnge between sacrifices and the benefits that the client perceives (Sirohi et al., 1998; Zeithaml et al., 1996). The perceived value can also be defined as a trade-off between the benefits perceived by customers and the perceived costs (Hallak, Assaker, & El-Haddad, 2018; Sweeney & Soutar, 2001). According to Zeithaml (1988) the perceived value is the general utility received from products and services. However, the sacrifices undertaken by the consumer go beyond the monetary value, additionally encompassing time and physical and psychological exhaustion (Moeller, 2010; Zeithaml, 1988). Perceived value predicts consumer satisfaction (Cronin et al., 2000), and may generate an intention to indicate or repurse the product / service (Cronin et al., 2000; Sirohi et al., 1998).
Thus, we hypothesize that:
H3a. Perceived value has a positive impact on satisfaction.
H3b. Perceived value has a positive impact on attitudinal loyalty.
2.2. Store Attributes as Driving Loyalty Antecedents
In the study by Grosso et al. (2018) four store attributes were used: sales personnel (competence and trust), layout of the store environment, assortment of merndise, and sales promotion. In this study, non-monetary promotions (hedonic promotions through prizes, bonus packages, samples and sweepstakes, which can add value to the product / service) were included (Campbell & Diamond, 1990; Diamond & Sanyal, 1990) in the model, as it is understood that this attribute differs from monetary (utilitarian) promotions. Consumers may prefer non-monetary promotions, whereas in other contexts, monetary promotion is preferred (Sinha & Verma, 2017).
Thus, it is essential to address the two aspects of sales promotion, not only in a utilitarian way, but also in a hedonic way. In this document, we analyzed five store variables, four from the Indian study and one variable that we added: reliability and competence of salespeople, layout of the environment, product assortment, monetary and non-monetary sales promotions.
2.3. Store Sellers
Store sellers are considered key elements in the retail context (Grosso et al., 2018). Studies indicate that employees have a positive impact on the store environment and consumer buying behavior (Aldousari & Elsayed, 2018). When the relationship between employee and customer is fostered, in a personalized way, customers tend to have a more favorable attitude towards the product / service (Berry, 1995; Minnema, Bijmolt, & Non, 2017).
Thus, one of the most important retail attributes, when it comes to retail consumer satisfaction, is store employees (Paul., Sankaranarayanan, & Mekoth, 2016). Store managers must always be attentive by controlling behavior and forming a friendly sales team who is willing to help the customer, consequently enabling consumer satisfaction at the time of purse (Yip, n, & Poon, 2012).
Studies performed in an Indian market concluded that customer satisfaction is related to the quality of the services provided by employees (Patel & Desai, 2013) and that the competence and reliability of store salespeople has a positive effect on consumer satisfaction (Grosso et al., 2018; Liu & Leach, 2001). Therefore, based on these concepts, we elaborate the following hypotheses:
H4a. The competence and reliability of store salespeople has a positive effect on consumer satisfaction.
H4b. The competence and reliability of store salespeople has a positive effect on the value perceived by the consumer.
2.4. Store Environment
The store environment can also be understood as the store atmosphere (Kotler, 1973) and the consumers’ buying behavior can be affected by light, odor, temperature, color and the convenience of the store layout (Baker, Parasuraman, Grewal, & Voss, 2002; Bakker., van der Voordt, Vink, & de Boon, 2014; Russell & Mehrabian, 1974).
Russell and Mehrabian (1974) offer a theoretical archetype that describes the effect of the environment on people. Through functionalist psychology, which recognizes the interaction between humans and stimuli (Choi & Kandampully, 2019) the researchers adopted the SOR - stimuli, organism and response - paradigm for understanding the influence of the environment on human behavior.
Although SOR was originally developed to understand human behavior affected by the environment, several marketing researchers have adopted the paradigm for understanding consumer behavior in a pursing environment (Bakker. et al., 2014).
The store environment can affect the consumer’s buying behavior. Emotions associated with consumption are formed by responses to a specific assessment made by the consumer. The perceived atmosphere is positively associated with hedonic and utilitarian assessments of the value of retail purses (Rayburn & Voss, 2013). Thus, it was possible to assume that:
H5a. The layout of the store environment has a positive effect on the value perceived by the consumer.
Environmental psychology indicates that the most important function of a space (for example, interior of the store) is the ability to satisfy the occupants’ goals (Canter, 1983). Convenience is one of the main objectives of a customer, which includes entering and leaving the store quickly and finding the product they want without difficulty (Baker et al., 2002). The ideal store layout for the target audience can facilitate the movement of customers (Titus & Everett, 1995) and contribute to the general satisfaction of the consumer. From this review, we elaborate the following hypothesis.
H5b. The layout of the store environment has a positive effect on consumer satisfaction.
2.5. Assortment of Products
The assortment, or variety of products available to the customer in a store (Martínez-Ruiz, Blázquez-Resino, & Pino, 2017) is considered an essential attribute of the store (Zimmer & Golden, 1988) and this construct is seen as more relevant, even compared to the price. Fox, Montgomery, and Lodish (2004) in their econometric model, concluded that in supermarkets, consumer spending is attributed more to assortment than to price. Assortment can also be a predictor of consumers’ choice of shopping destinations (Oppewal, Timmermans, & Louviere, 1997) and the intention to repurse (Koo, 2006).
Assortment of products can satisfy customers looking for variety (Kahn & Wansink, 2004). Assortment influences the assessment of buyers on the dimension of quality, which, in turn, influences the perception of value, and can satisfy store consumers (Grosso et al., 2018; Pizzi & Scarpi, 2016). Thus, we develop the following hypotheses:
H6a. The assortment of products has a positive effect on consumer satisfaction.
H6b. The assortment of products has a positive effect on the value perceived by the consumer.
H6c. The assortment of products has a positive effect on the value for money perceived by the consumer.
2.6. Sales Promotion
Sales promotions are vital elements of the store’s image and drivers of building traffic to attract new customers to the store (Zimmer & Golden, 1988). Sales promotions not only motivate consumers to buy the promoted products, but also those that are not promoted, thus affecting the size and general composition of the consumers’ shopping basket (Ramanathan & Dhar, 2010).
Grosso et al. (2018) reveal that monetary promotion (promotion by price perception) directly affects the price perception of buyers, which could influence the perception of the store’s overall value. However, the study neglected non-monetary promotion (prizes, bonus packages, samples and sweepstakes), which can add value to the product / service (Campbell & Diamond, 1990; Diamond & Sanyal, 1990) in addition to affect brand loyalty (Mendez et al., 2015).
Sales promotion can be seen from two perspectives. Monetary promotions are designed to reduce prices and can be perceived as a financial savings by the customer. Non-monetary promotions are more difficult to be related to the price of the product / services, as they are linked to the gain (Liao & Ze, 2006). Monetary promotions, despite boosting sales and generating more flow to the store (Zimmer & Golden, 1988) can decrease the perceived quality by the consumer, unlike non-monetary promotions, which can add global value (Sinha & Verma, 2017). Given this framework, we present the hypotheses:
H7a. Promotion of non-monetary sales has a positive effect on the value perceived by the consumer.
H7b. Promotion of non-monetary sales has a positive effect on the value for money perceived by the consumer.
Campbell and Diamond (1990) and Diamond and Sanyal (1990) indicate that monetary and non-monetary promotions are related to the reference price, through gains and financial savings. In this way, the customer can perceive a gain on the transaction of a product / services, that is, the consumer perceives that he obtained benefits superior to his expenses (hedonic and or utilitarian). Thus, we propose that:
H8. Monetary sales promotion has a positive effect on the value for money perceived by the consumer.
Figure 1 shows the conceptual model we developed, highlighting the hypotheses along with the constructs added to the original model by Grosso et al. (2018).
Figure-1. Conceptual model
Source: Adapted from Grosso et al. (2018)
3. METHOD
3.1. Data Collection Procedures
This survey was conducted directly with retail consumers, based on scales previously validated and adapted (for example, supermarket to store) to the context in which it was applied.
To increase the generalization of the results and avoid influences on the results due to the choice of the data collection location, we collected customer data from different retailers (clothing stores, supermarkets, convenience stores, etc.). The collection instrument began with questions to filter the respondents in order to exclude some profiles to avoid interference with the results, namely: the respondents could not be researchers / doctors, employees of retail stores, retail managers, nor could they work in fields related to marketing and they would need to be the decision makers of their own purses and be 18 years of age or older. If the respondent indicated any of these pertained to them, the questionnaire would end with a message of thanks. Otherwise, the respondent moved on and we asked him to choose a store in which he had made a purse in the last 15 days, any store. At the end of the questionnaire, respondents indicated the store they evaluated.
The questionnaire was developed in the QuestionPro software and was applied in three ways: (1) addressing retail consumers in person and asking them to answer the collection instrument, using a Tablet; (2) sharing on social networks; (3) requesting the completion, through a link, to students at a university in the city of São Paulo on three different campuses. In all cases, respondents did not identify themselves, thus guaranteeing their anonymity.
3.2. Scales for Data Collection
The research instrument was developed with the scale used in the Indian study, and we added questions for the constructs perceived value and non-monetary promotion. All scales were previously translated by three specialists in the English language and adapted to the context of stores (, Kim, & Erlen, 2007). After validating the questionnaire, a pre-test was performed with a control group (Hair, Black, Babin, & Anderson, 2014). The constructs were measured using a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree).
The questionnaire followed the following configuration: the first part addresses questions regarding the attributes of the store, satisfaction, perceived value, value for money and, finally, attitudinal loyalty. The last part is relevant to demographic issues (gender, age, marital status, education and family income), for analyzing the profile of buyers in retail stores.
We used the scale translated from the study by Grosso et al. (2018) to measure loyalty, value for money, trust and commitment of store sellers, store environment, promotion of monetary sales, and product assortment constructs. To measure the constructs added to the study, we used the scale of perceived value, translated and adapted to the context (for example, supermarket to store) by Lai et al. (2009) and Levesque and McDougall (1996). We used the scale by Reid, Thompson, Mavondo, and Brunsø (2015) for measuring non-monetary promotions.
3.3. Sample
The sample was composed of 516 respondents, 56.5% of whom were female and 36.6% of whom were male, with the remainder (almost 7%) preferring not to answer. In addition, 73.0% of respondents were aged between 18 and 30 years and 44.8% of respondents had a monthly income of up to R$ 1,874.00 (above class C2 -Criterion Brazil-ABEP, [sd]), and 55.7% had incomplete higher education. In addition, 87.2% were college students and another 9.8% were retailers who responded via an online panel. 2.9% were consumers who answered the survey within the store on their own electronic device. Respondents were asked to indicate a store that was available, however, this item was not mandatory. Of the evaluated stores, the result was scattered. The stores that were chosen the most were: 9.4% Renner; 7.0% of C&A; 6% Lojas Marisa; 5% Lojas Americanas; 4.0% Riachuelo and Carrefour Supermarket; 3.0% Kalunga; 2.7% Extra Supermarket; Magazine Luiza 2.3%.
3.4. Data Analysis Procedure
We chose to use Structural Equation Model Analysis (SEM) for data analysis. SEM was conducted as indicated by Hair.., Ringle, and Sarstedt (2011) and Ringle, Da Silva, and Bido (2014).
SmartPLS 2.0 M3 software Ringle et al. (2014) analyzed the collected data. The convergent validity and discriminating validity of each variable of the structural model were evaluated. For Chin, Marcolin, and Newsted (2003) the method treated by the PLS (Partial Least Square), evaluated latent variables as exact linear combinations of the observed measures. In this way, one can avoid the problem of indeterminacy and generate an exact definition of the component scores.
4. DATA ANALYSIS
The data were submitted to the Kolmogorov-Smirnov normality test, with Lillefors significance correlations, using the SPSS software, for samples of more than 100 elements, according to the guidance of Hair., Sarstedt, Ringle, and Gudergan (2017).
The results showed a significant p-value (<.001), indicating that there is no normality in the sample. Thus, it was confirmed that PLS is the appropriate estimation method for regressive data analysis, since it is an appropriate method for non-parametric samples (Ringle et al., 2014). The data analysis procedure was performed using the SmartPLS 2.0 software, following the appropriate guidelines by Hair. et al. (2017) and Ringle et al. (2014) and was carried out in two stages, for both models. In the first stage, the quality of the measurement model was analyzed, which demonstrates the relationship between the constructs and their indicators. In the second stage, the structural model was analyzed, which measures the relationship between the constructs (Hair. et al., 2017; Ringle et al., 2014). However, before this procedure, a descriptive analysis of the sample was conducted.
4.1. Analysis of Model I Results
SmartPLS 2.0 measured the relationships proposed in this model. The first analysis is concerning Algorithm PLS, which generated the structural model. According to the criteria of Fornell and Larcker (1981) the values of the strokes must be greater than .50. Thus, it was necessary to eliminate only one variable from the environment construct (ENV5), as the stroke showed a result below .50 (.482).. In the second evaluation step, the internal consistency was checked using the values of composite reliability (CR) and Cronbach’s Alpha (CA). For Hair et al. (2014) the values should be (> .60) for Cronbach’s Alpha and (> .70) for the composite reliability. The variables Satisfaction (CA= .568) and Assortment (CA = .557) presented Cronbach’s Alpha less than .60. However, Ringle et al. (2014) state that the most appropriate indicator for this analysis is composite reliability and, therefore, the two models presented adequate results, since all composite reliability values were greater than .70, according to Table 1.
Table-1. Reliability and Validity Indicators - model I.
| Constructs | AVE |
Composite Reliability |
R2 |
Cronbach’s Alpha |
Redundancy |
| Environment | .526 |
.815 |
.699 |
||
| Value for money | .647 |
.846 |
.431 |
.729 |
.278 |
| Loyalty | .585 |
.807 |
.425 |
.645 |
.093 |
| Monetary promotion | .588 |
.850 |
.766 |
||
| Satisfaction | .697 |
.822 |
.502 |
.568 |
.254 |
| Assortment | .529 |
.770 |
.557 |
||
| Sellers | .507 |
.877 |
.839 |
For the discriminating analysis, the method used respected the parameters indicated by Fornell and Larcker (1981). It can be observed that the values of the square roots for the two models, in the strokes, are higher than the correlations Table 2. Thus, it can be said that the models have discriminant validity.
Table-2. Discriminant validity of the Fornell-Larcker method of model I.
| Constructs | 1 |
2 |
3 |
4 |
5 |
6 |
7 |
| (1) Environment | .725 |
||||||
| (2) Value for money | .316 |
.804 |
|||||
| (3) Loyalty | .624 |
.443 |
.765 |
||||
| (4) Monetary promotion | .434 |
.656 |
.553 |
.767 |
|||
| (5) Satisfaction | .644 |
.370 |
.608 |
.489 |
.835 |
||
| (6) Assortment | .578 |
.310 |
.546 |
.437 |
.501 |
.727 |
|
| (7) Sellers | .528 |
.377 |
.467 |
.466 |
.565 |
.517 |
.712 |
Note: The highlighted diagonal cells show the square root of the construct’s stroke.
Once the discriminant validity was guaranteed and the measurement adjustments concluded, the structural models were analyzed (Ringle et al., 2014). In this step, we analyze Pearson’s coefficients (R²), which indicate the level of variance of endogenous variables, explained by the structural model. The Person coefficient varies from 0 to 1, the more distant from 0, the higher the model’s predictive accuracy (Hair. et al., 2017).
Cohen (1988) suggests that for research in the area of social and behavioral sciences the classification should be made as follows: R² = 2% small effect, R² = 13% medium effect and R² = 26% large effect Ringle et al. (2014). All R² values in the model showed a large effect, as shown in Table 3.
Also in Table 3, when comparing models I [model tested by Grosso et al. (2018)] and replicated in this study (model I with addition of the perceived value and non-monetary promotion constructs), the results showed a significant improvement in all constructs of the alternative model (II).
Table-3. Person’s Coefficient (R²) compared to other models.
| Constructs | R² model I |
R² model II |
| Value for money | .431 |
.438 |
| Loyalty | .425 |
.451 |
| Satisfaction | .503 |
.522 |
| Perceived value | ------ |
.557 |
To analyze the significance of the relationships, we used the bootstrapping calculation (SmartPLS resampling technique). Thus, we analyzed the relationship between the constructs, with n = 516 and samples (resampling) of 1000 Ringle et al. (2014). The report generated shows that of all the relationships, only one was not significant (>.05), the relationship between assortment and value for money ratio (.659). Table 4 shows the path coefficients, t-test and the significance of the relationships obtained in the structural model.
Table-4. Evaluation of hypothetical relationships.
| Relations | Original Load | Average Load Bootstrap |
Standard Deviation | t-Test | Sig. |
| Environment -> Satisfaction | .423 | .424 | .044 | 9.618 | *** |
| Value for money-> Loyalty | .311 | .312 | .038 | 8.109 | *** |
| Value for money-> Satisfaction | .113 | .111 | .037 | 3.045 | *** |
| Monetary prom. -> Value for money | .643 | .646 | .037 | 17.477 | *** |
| Satisfaction -> Loyalty | .515 | .514 | .035 | 14.724 | *** |
| Assortment -> Value for money | .029 | .030 | .044 | .671 | --- |
| Assortment -> Satisfaction | .095 | .097 | .046 | 2.084 | ** |
| Sellers -> Satisfaction | .252 | .254 | .045 | 5.604 | *** |
Note: critical limits for t-test (>=120). 1.65 = p<.10*; 1.96 = p<.05**; 2.53 = p<.01***.
For the analysis of the model’s quality, the GoF (Goodness-of-Fit) was calculated; obtaining a value of .501, which indicates that the model is well adjusted (Wetzels, Odekerken-Schröder, & Van Oppen, 2009). Even though there is no critical limit, unofficially, GoFs greater than 0.36 are considered suitable for studies in applied social sciences (Ringle et al., 2014). In this way, it can be said that the structural model I has acceptable adjustment and explanatory power. The GoF index of model I can be seen in Table 5.
Table-5. Goodness-of-Fit (GoF) of model I.
| Constructs | AVE |
R2 |
AVE x N° |
| Environment | .526 |
- |
2.106 |
| Value for money | .647 |
.431 |
1.943 |
| Loyalty | .585 |
.425 |
1.756 |
| Monetary promotion | .588 |
- |
2.355 |
| Satisfaction | .697 |
.502 |
1.395 |
| Assortment | .529 |
- |
1.587 |
| Sellers | .507 |
- |
3.549 |
Note: GoF = Geometric mean of means R² AVE = .506.
4.2. Analysis of Results - Model II
To measure the relationships proposed in model II, the model with the addition of the non-monetary promotion and perceived value variables, we used the same criteria as in model I, namely SmartPLS 2.0. The first analysis was the Algorithm PLS, which generated structural model I, without the added variables.
According to the criteria of Fornell and Larcker (1981) the values of the strokes must be greater than .50. Thus, for model II, it was necessary to exclude two variables, one variable from the non-monetary promotion construct (PMN5) and the variable from the environment construct (ENV5), as the AVE showed a result below .50 (.466) and (.482). Following the guidelines of Hair. et al. (2017) and Ringle et al. (2014) we eliminated the variables PMN5 and ENV5, which had the lowest value among the variables of the non-monetary promotion (.452) and environment (.625) constructs.
The analysis of the internal consistency of model II was performed using the values of composite reliability (CR) and Cronbach’s Alpha (CA). For Hair et al. (2014) the values should be (>.60) for Cronbach’s Alpha and (>.70) for the composite reliability.
The variables Satisfaction (CA = .568) and Assortment (CA = .557) in model II, as well as in model I, presented Cronbach’s Alpha less than .60. However, Ringle et al. (2014) state that the most appropriate indicator for this analysis is composite reliability. Thus, model II also showed adequate results, since all values were greater than .70. The results for the model II reliability and validity indicators can be seen in Table 6.Table-6. Reliability and validity indicators for model II.
| Constructs | AVE |
Composite Reliability |
R2 |
Cronbach’s Alpha |
Redundancy |
| Environment | .526 |
.816 |
.699 |
||
| Value for money | .647 |
.846 |
.438 |
.729 |
.277 |
| Loyalty | .585 |
.807 |
.451 |
.645 |
.081 |
| Monetary promotion | .588 |
.850 |
.766 |
||
| Non-monetary promotion | .521 |
.844 |
.774 |
||
| Satisfaction | .697 |
.821 |
.522 |
.568 |
.230 |
| Assortment | .528 |
.769 |
.557 |
||
| Perceived value | .597 |
.855 |
.557 |
.775 |
.176 |
| Sellers | .508 |
.878 |
.839 |
For the discriminating analysis, method II also respected the parameters indicated by Fornell and Larcker (1981). It can be observed that, for the value of square roots for model II, the strokes are superior to the correlations Table 7. Thus, it can be said that model II showed discriminant validity.
Table-7. Discriminant validity of the Fornell-Larcker method - model II.
| Constructs | 1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
| (1) Environment | .725 |
||||||||
| (2) Value for money | .318 |
.804 |
|||||||
| (3) Loyalty | .623 |
.444 |
.765 |
||||||
| (4) Monetary promotion | .434 |
.656 |
.553 |
.767 |
|||||
| (5) Non-monetary promotion | .190 |
.242 |
.172 |
.239 |
.722 |
||||
| (6) Satisfaction | .644 |
.371 |
.608 |
.489 |
.192 |
.835 |
|||
| (7) Assortment | .581 |
.311 |
.545 |
.436 |
.235 |
.504 |
.727 |
||
| (8) Perceived value | .624 |
.431 |
.549 |
.464 |
.250 |
.620 |
.544 |
.772 |
|
| (9) Sellers | .524 |
.379 |
.464 |
.465 |
.327 |
.559 |
.518 |
.637 |
.713 |
Note: The highlighted diagonal cells show the square root of the construct’s stroke.
This table shows the correlations between the latent variables of the constructs.
Once the discriminant validity was guaranteed and the measurement adjustments concluded, the authors analyzed the structural model (Ringle et al., 2014). In this step, we analyze the determination coefficients (R²), which indicate the level of variance of the endogenous variables, explained by the structural model. Therefore, for this model, all R² values also had a large effect, according to Ringle et al. (2014) and is illustrated in Table 8.
We use the same procedure as in model I, to analyze the significance of relationships, following the guidelines of Hair. et al. (2017) and Ringle et al. (2014). Thus, the report generated shows that of all the relationships, two were not significant (>.05), the relationship between assortment and value for money ratio (.385) and between non-monetary promotion and perceived value (.749).
Table-8. Evaluation of hypothetical structural relationships.
| Relations | Original Load |
Average Load Bootstrap |
Standard Deviation |
t-Test |
Sig. |
| Environment -> Satisfaction | .425 |
.427 |
.045 |
9.531 |
*** |
| Environment -> Perceived value | .316 |
.318 |
.041 |
7.704 |
*** |
| Value for money -> Loyalty | .283 |
.284 |
.040 |
7.147 |
*** |
| Value for money -> Satisfaction | .114 |
.113 |
.039 |
2.959 |
*** |
| Value for money -> Perceived value | .159 |
.158 |
.038 |
4.192 |
*** |
| Monetary promotion -> Value for money | .628 |
.631 |
.039 |
16.308 |
*** |
| Non-Monetary prom. -> Value for money | .088 |
.089 |
.035 |
2.513 |
** |
| Non-monetary prom.-> Perceived value | .023 |
.025 |
.031 |
.749 |
--- |
| Satisfaction -> Loyalty | .401 |
.402 |
.048 |
8.326 |
*** |
| Assortment -> Value for money | .016 |
.015 |
.043 |
.385 |
--- |
| Assortment -> Satisfaction | .097 |
.097 |
.046 |
2.089 |
** |
| Assortment -> Perceived value | .136 |
.136 |
.044 |
3.055 |
*** |
| Perceived value -> Loyalty | .302 |
.301 |
.043 |
6.954 |
*** |
| Perceived value -> Satisfaction | .222 |
.218 |
.052 |
4.241 |
*** |
| Sellers -> Satisfaction | .244 |
.243 |
.045 |
5.429 |
*** |
| Sellers -> Perceived value | .340 |
.339 |
.044 |
7.754 |
*** |
Note: Critical limits for t-test (>=120). 1.65 = p<.10*; 1.96 = p<.05**;2.53 = p<.01***.
Table 9 shows the path, t-test coefficients and the significance of the relations obtained in the structural model. We calculated the GoF (Goodness-of-Fit) for the analysis of the model’s quality, obtaining the value of 0.526, which indicates that the model is well adjusted (Wetzels et al., 2009). In this way, it can be said that structural model II has acceptable adjustment and explanatory power.
Table-9. Goodness-of-Fit (GoF) - model II.
| Constructs | AVE |
R2 |
AVE x N° |
| Environment | .526 |
------ |
2.107 |
| Value for money | .646 |
.431 |
1.940 |
| Loyalty | .585 |
.425 |
1.756 |
| Non-monetary promotion | .521 |
------ |
2.609 |
| Perceived value | .597 |
.557 |
2.389 |
| Monetary romotion | .588 |
------ |
2.355 |
| Satisfaction | .697 |
.502 |
1.395 |
| Assortment | .528 |
------ |
1.585 |
| Sellers | .508 |
------ |
3.558 |
Note: GoF = Geometric mean of the means - R² and AVE = .526.
4.3. Hypothesis Analysis
The hypothesis test was proposed in two stages, with bootstrapping and structural paths being analyzed in the first step to confirm the hypotheses (H1, H2a, H2b, H2c, H4a, H5a, H6a, H6c) of model I. In the second stage, the same analyses were made with the hypotheses of the structural model II, which is composed of all the hypotheses of the model I, plus the additional hypotheses (H3a, H3b, H4b, H5b, H6b, H7a, H7b, H8) from study two. Table 10 presents the hypotheses of model I and model II simultaneously.
5. GENERAL DISCUSSIONS
This study aimed to investigate the relationship of store attributes and retail consumer loyalty in an economically emerging country, Brazil. Based on the study performed in India, by Grosso et al. (2018) the purpose of this work was to replicate the Indian study and add additional variables, in search of finding theoretical and managerial advances in marketing.
The principal motivating factor for the replication of the study was the negative result found between the value for money and loyalty of the Indian consumer. Such results greatly diverge from other theories (Li & Green, 2011). Were we facing a theoretical finding in the context of consumers in emerging countries? Or would this effect be unique to Indian retailers?
Table-10. Results of studies conducted.
| Hypothesis | Proposition | Results - Study I | Results - Study II |
| H1 | Satisfaction has positive impacts on consumer loyalty. | Accepted | Accepted |
| H2a | Value for money has a positive impact on perceived value. | Accepted | Accepted |
| H2b | Value for money has a positive impact on consumer loyalty. | Accepted | Accepted |
| H2c | Value for money has a positive impact on consumer satisfaction. | Accepted | Accepted |
| H3a | Perceived value has a direct and positive effect on consumer satisfaction. | Non tested | Accepted |
| H3b | Perceived value has a direct and positive effect on consumer loyalty. | Non tested | Accepted |
| H4a | The store’s salespeople (competence and reliability) have a positive effect on satisfaction. | Accepted | Accepted |
| H4b | The store’s salespeople (competence and reliability) have a positive effect on the perceived value. | Non tested | Accepted |
| H5a | The layout of the store environment has a significant positive effect on satisfaction. | Accepted | Accepted |
| H5b | The layout of the store environment has a significant positive effect on perceived value. | Non tested | Accepted |
| H6a | The assortment of products has a significant positive effect on satisfaction. | Accepted | Accepted |
| H6b | The assortment of products has a significant positive effect on the perceived value. | Non tested | Accepted |
| H6c | The assortment of products has a significant positive effect on value for money. | Rejected | Rejected |
| H7a | Promotion of non-monetary sales has a positive effect on value for money. | Non tested | Accepted |
| H7b | Promotion of non-monetary sales has a positive effect on the perceived value. | Non tested | Rejected |
| H8 | Promotion of monetary sales has a positive effect on value for money. | Non tested | Accepted |
To study this further, the first part of the study was proposed, that is, the exact replication of the Indian study. We raised the same hypotheses, applying the same scale, this time, in another emerging country (Poushter, 2016) Brazil. For the antecedents of loyalty, we tested the direct and indirect effects of value for money on loyalty via satisfaction (Cronin et al., 2000; Sirdeshmukh et al., 2002).
Unlike the Indian study, our study indicates that value for money, even if only price-related statements are addressed, has a positive effect on consumer loyalty. In other words, value for money for Brazilian retail consumers, as well as for most other countries that have previously tested this construct, remains a key construct for the direct and indirect construction of consumer loyalty (Cronin et al., 2000; Hogreve et al., 2017; Sirdeshmukh et al., 2002).
As expected, in both studies, consumer satisfaction had the greatest impact in forming consumer loyalty (Meesala & Paul, 2018; Oliver, 1999).
With satisfaction addressed, we investigated store attributes, similarly to the Indian study. Once again, the studies diverged because in the Indian study, salespeople were the antecedents that generated the greatest impact on consumer satisfaction, followed by the store environment. The assortment of products had a non-significant effect.
In this research, salespeople, the store environment and the assortment of products contributed to explaining consumer satisfaction. For Brazilian consumers, the most relevant store attribute for explaining loyalty is the store environment. Thus, the satisfaction of Brazilian consumers is more linked to the layout of the store environment, the ease of navigation, and appropriate signs and posters (Baker et al., 2002; Kahn & Wansink, 2004; Titus & Everett, 1995).
Despite being less important in the analysis, reliable, honest and helpful salespeople are essential attributes for a long-term relationship (Crosby, Evans, & Cowles, 1990) and to satisfy the consumer (Grosso et al., 2018; Patel & Desai, 2013). The same is true for the variety of products offered in the store (Kahn & Wansink, 2004; Pizzi & Scarpi, 2016). Thus, the relationship with consumer satisfaction did not present any non-significant results for the Brazilian study.
As a value for money, the results between studies differed. For the Indian study, the only predictor of value for money was the assortment of products, also pointing out that price promotions do not affect the perception of value for money of the Indian consumer Grosso et al. (2018).
For this study, promotions affected the consumer’s perception of value for money, which justifies the constant price wars, due to the high number of competitors in various retail formats. Thus, price reductions, when perceived by consumers, generate a feeling of gain over the store, which can satisfy the consumer and improve the intention to buy products / services (Ghezelbash & Khodadadi, 2017).
For model II, we added two variables to the study: perceived value and non-monetary promotions. The value perceived by customers addresses not only issues related to price, but also the hedonic context, such as pleasure in relation to costs incurred (time, energy and effort spent) in the transaction (Babin, Darden, & Griffin, 1994; Lai et al., 2009). Thus, the inclusion of this construct is justified as it influences consumer loyalty (Cronin et al., 2000; Hallak et al., 2018).
As expected, perceived value was of great importance for the direct and indirect formation of consumer loyalty. Perceived value was the antecedent that most explained consumer loyalty and satisfaction, when compared to value for money (Gallarza., Del Chiappa, & Arteaga, 2018). In addition to presenting higher results, when compared to the value for money, perceived value improved the model, and further explained satisfaction and loyalty of retail consumers.
To study the value perceived by the customer, the same store attributes as model I were used, with the addition of non-monetary sales promotions. As the perceived value deals with hedonic issues (Babin et al., 1994; Lai et al., 2009) we propose relationships with the sellers and the store environment. The attribute that most contributed to impact the value perceived by the customer was the relationship with the seller (Naylor & Frank, 2000) followed by the store environment, with a small difference in values (Rayburn & Voss, 2013).
The non-monetary sales promotion attribute was related to the construction of value for money and perceived value. However, this attribute was only relevant in relation to perceived value, presenting non-significant values in relation to value for money. A possible explanation for this result is that value for money addresses issues related to perceived gain related to price (Babin et al., 1994). In relation to the perceived value (hedonic) (Babin et al., 1994; Lai et al., 2009) the results were significant, demonstrating that it is important to work not only on price promotions, but also promotions related to awards and sweepstakes (Liao & Ze, 2006).
6. THEORETICAL AND MANAGEMENT CONTRIBUTIONS
The present study brings rich contributions to the marketing field and in particular, the behavior of consumer retailers in emerging countries. It also brings important contributions to retail store managers.
Although this study only reinforces what the theory already said, it is important to investigate possible results that demonstrate different paths from those already known and established. Thus, the greatest contribution of this study is to revalidate that the value that the customer perceives contributes strongly in establishing consumer satisfaction and loyalty.
In this study, another important contribution was to understand the perceived value of retail customers. For a better understanding of this construct, it is necessary to address more than the perceived value of price reductions. Thus, this document provides a better understanding of consumer satisfaction and loyalty, when we address the value that the customer fully perceives, that is, when we compare the benefits that the consumer receives versus his time spent, effort, energy and money.
Marketing managers who intend to adopt a customer loyalty strategy need to be aware of more than the prices and discounts granted. The results of this study indicate that marketing managers need to adopt measures that save the consumers’ time, as well as their energy, to decrease their effort. In this way, customers will perceive a high value in the shopping experience, which can lead them to a state of satisfaction and, consequently, increase the nces of customer loyalty.
Another interesting result that this study brings is the fact that the assortment of products affected only the perceived value, but not the value for money. Such results corroborate the results found for the perceived value. That is, when the store’s assortment of products is adequate, the consumer tends to spend less time, effort and energy. In this way, he perceives a high value, but does not realize that he obtained advantages related to the price.
Marketing managers must always be aware of their stocked merndise. The variety of brands in stock, as well as the maintenance of product displays, can contribute to customer satisfaction and improve customer loyalty. A store with a wide variety of brands can reduce time spent looking for the desired product, while also slowing down the energy and effort spent in finding the desired products.
As expected, we found results demonstrating the relevance of salespeople to generate a high value perceived by the customer, and this attribute can directly satisfy consumers. Thus, when retail customers trust salespeople and / or perceive competence in the services provided, they tend to perceive value and increase satisfaction.
Therefore, marketing managers should pay attention to the service of salespeople, as customers value friendly, honest, and trustworthy salespeople, who are ready to help and answer questions. In addition, marketing managers should train their sales team, so that they understand the products and services offered in the store. This study demonstrated that customers tend to perceive value and are satisfied with salespeople who know the products and services offered in the store. Another important item for a sales team is to always fulfill what was promised to the customer.
This research also endorses the importance of the layout of the store environment, which once again demonstrated the importance of reducing time, energy expended and customer effort to improve perceived value and satisfaction. Thus, marketing managers who wish to satisfy their customers, as well as increase perceived value, need to be attentive to the proper display of goods, to the comfort provided to the customer, improving the circulation of people and the visual communication of the store. The aspects mentioned are directly linked to the time customers spend, as well as their energy and effort.
This study demonstrated the importance of both types of sales promotions: monetary and non-monetary. However, promotions showed positive effects only on the perception of gain or superior value for money on the prices rged by retailers. Thus, marketing managers need to pay attention to the store’s objective, since sales promotions affect only the value for money, that is, the perceived value related to the price. Regardless of whether the promotions are related to price or awards, the customer perceives a gain through price reduction. Depending on the strategy and the target audience, this can be a means to improve customer satisfaction and can increase loyalty.
Finally, this proposed model, in addition to demonstrating the importance of perceived value in building consumer satisfaction and loyalty, has shown that it is more robust and can predict more than 50% of consumer satisfaction, and almost 50% of loyalty.
7. LIMITATIONS OF RESEARCH AND FUTURE STUDIES
This research contributed to the promotion of knowledge in the marketing area, more specifically for the retail sector. However, like all studies, it has limitations. The sample of this study was composed of three distinct groups: those that were collected inside a retail store (2.9%), a panel with retail customers (9.8%) and with university students (87.2%). Despite the methodological rigor, the difference in the size of the n of the samples could have been smaller.
The scale used for the non-monetary promotion construct, despite presenting good results, differs from the other scales. Thus, the generic format of the questions may be a limitation for this study. A scale aimed at the retail store can improve the nomological validity of the construct. In this research, we addressed some of the main store attributes. However, we do not address others, such as product quality, advertising and convenience. These attributes can, in some way, improve the explanation of the model.
For future research, we understand that, with the strong advance of omninnel, it is necessary to research store attributes in hybrid sales nnels. Therefore, future research could evaluate the different attributes of stores in the physical and online environment. Another possibility for future studies is to include other store attributes in the model to test the importance of all attributes in developing store satisfaction and loyalty.
This model could still be tested in other countries, to understand the effect of the Indian study. Another possibility is the promotion of cross-country research, research in different countries and comparison of results in emerging and developed economies. Another possibility is to carry out a meta-analysis, addressing all the effects of perceived value on loyalty. Finally, as an indication for future research, it is proposed to conduct experiments with attributes of physical and online stores. Finally, it is understood that this work, in addition to answering the research question and carefully following all the proposed objectives, also answers the question proposed in the title. It has been proven that yes, loyalty is still the same.
Funding: This study received no specific financial support. |
Competing Interests: The authors declare that they have no competing interests. |
Acknowledgement: All authors contributed equally to the conception and design of the study. |
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