Abstract
Purpose
This study aims to understand customer’s assessments of neighborhood stores during the COVID-19 pandemic through the influence of in-store environmental factors on patronage intention.
Design/methodology/approach
Online survey with 528 participants about the last shopping trip in neighborhood retail. The authors performed data analysis using structural equation modeling techniques.
Findings
High-perceived spatial crowding negatively influences shopping experience value perceptions, while human crowding influences patronage intentions through increased perceived hedonic value.
Research limitations/implications
Results suggest that purchase experience at well-known neighborhood stores during a sanitary crisis is becoming less convenience-oriented and a substitute for leisure activities due to social distancing.
Practical implications
The findings elucidate the social function of neighborhood convenience retailing during the COVID-19 pandemic. The results emphasize that a pleasant shopping experience arising from a good relationship with shopkeepers and other customers is more influential on patronage intention than a good product assortment and store layout.
Social implications
This paper contributes to the survival of small neighborhood businesses during the financial crisis installed due to Covid-19 by helping businesses become more attractive to their consumers and competitive in the new context.
Originality/value
The combined context of the health crisis due to COVID-19 and neighborhood retail of an emerging country raises the need for tests to better understand established marketing theories. Based on this rationale, this work intends to replicate and extend selected previous findings to the new environment dictated by the pandemic.
Keywords
Citation
Moll Brandão, M., França Sarcinelli, A., Bisi Barcelos, A. and Postay Cordeiro, L. (2023), "Leisure or work? Shopping behavior in neighborhood stores in a pandemic context", RAUSP Management Journal, Vol. 58 No. 2, pp. 97-110. https://doi.org/10.1108/RAUSP-02-2022-0064
Publisher
:Emerald Publishing Limited
Copyright © 2023, Marcelo Moll Brandão, Arthur França Sarcinelli, Ananda Bisi Barcelos and Luiza Postay Cordeiro.
License
Published in RAUSP Management Journal. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
The health crisis caused by the new coronavirus impacted the consumption patterns of retail customers on a global scale and at a moment’s notice (Prentice et al., 2021). As social isolation is one of the most effective ways to prevent the spread of the virus (Farooq et al., 2020), government authorities have implemented lockdown measures to decrease the number of infected people. Thus, going to physical stores was limited, impacting the consumer’s shopping experience (Leite et al., 2020). From this perspective, it is inferred that individuals more prone to threat and danger perceptions of COVID-19 will present changes in their purchase decision-making process and different behavior in situations of high crowding (Sheth, 2020).
Understanding the real impact of this pandemic on business survival and the degree of change in consumer behavior has become a recent challenge for management academics (Donthu & Gustafsson, 2020). There is a development of research published in relevant journals seeking the development and refinement of theories that explain the impacts suffered by retailers (Roggeveen & Sethuraman, 2020; Lee & Goldsmith, 2022). A research front headed by Das et al. (2021) reconciles findings from studies within the consumer behavior field before and during the COVID-19 pandemic to argue that health crisis contexts alter societal macroforces that culminate in changes in the marketing mix of retailers and service providers as a reactive posture.
Therefore, changes in consumer behavior have generated the need for agile responses by retailers. Successful retail strategies before the COVID-19 pandemic may fail to have an effect during and after the health crisis (Roggeveen & Sethuraman, 2020). In addition, client agglomeration can influence buying behavior at physical stores when social distancing is recommended. In studies conducted prior to the COVID-19 pandemic, Wang and Ackerman (2019) found that the infectious disease threat can influence the perception of people density. Closed and small spaces, such as neighborhood stores, can have many products and layouts that contribute to a greater feeling of crowding.
The discussion about crowding dimensions is based on social and non-social agglomeration (Blut & Iyer, 2020). The former relates to the number of people and their interactions within a designated area and comprises the human crowding construct, whereas the latter indicates the individual evaluations of the physical space and is known as spatial crowding (Eroglu et al., 2022). Previous studies revealed that when the other clients are similar to the consumers themselves, people agglomeration has a positive effect (Huang et al., 2018; O’Guinn et al., 2015). For example, in the case of neighborhood stores, the neighborhood residents end up meeting when going on shopping trips, usually to buy convenience products. This influence from other consumers adds to the influence of retail employees as human variables that impact buying behavior (Turley & Milliman, 2000).
In the context of social distancing, the perception of value attributed to purchasing may vary according to shopping values. Recently, when one had easy access to restaurants and malls, shopping trips were perceived as pleasant moments, that is, places with a predominance of purchases with a high perception of hedonic value. On the other hand, utilitarian value is perceived as rational behavior, such as quickly and efficiently going to the nearest grocery store to buy a missing ingredient. However, with the decrease in access to leisure and pleasure purchasing, the consumption of convenience products made at the neighborhood stores seems to supply part of the need for distraction previously experienced by purchases with a high perception of hedonic value (Babin et al., 1994).
In contexts considered “normal”, purchases seen as convenience are not expected to be perceived for their hedonic value. However, in a study performed by Griffin et al. (2000), purchases seen as utilitarian and convenience during the fall of the Soviet Union and the beginning of Russian economic reopening were simultaneously evaluated as having both utilitarian and hedonic value. Therefore, in contexts of crisis where there is a shortage of shopping environment options, even convenience purchases can assume hedonic value.
Faced with the “new normal” experienced with COVID-19, businesses must review established consequences of crowding and other consumer behavior attributes in retail (Eroglu et al., 2022). Baker et al. (2002) mention that the influence of store environmental factors on patronage intention is evident in the perceptions of hedonic and utilitarian values as mechanisms that explain the positive effect of appropriate stimuli in the internal store environment on the image of the store formed by the individual. That is, stimuli in the store environment, directly and indirectly, impact the construction of the mental image of what the store is in general (de Cosmo et al., 2022). Conversely, the reality of a crisis experienced during the pandemic, in addition to the neighborhood retail context of an emerging country, raises the need for tests to better understand the theory by rethinking marketing practices in new contexts (Babin et al., 2021; Hubbard & Carriquiry, 2019; Sheth, 2011; Das et al., 2021; Sheth, 2020).
Given the changes in the retail scenario arising from the context of social distancing amid a sanitary crisis, this article seeks to analyze and test how the perceptions of hedonic and utilitarian values explain the influence of social and spatial aspects of the store environment (i.e. comfort and perceived crowding) on store image and patronage intention by the consumer in neighborhood retail stores.
2. Hypothesis development
The central premise of this study is that consumer responses to neighborhood brick-and-mortar environments are a function of social and spatial factors at the point of purchase (Machleit et al., 1994). Therefore, customers’ overall assessments of the local retailer are primarily based on convenience and unplanned purchase experiences to address their daily needs.
Drawing from the Attachment Theory (Bowlby, 1969), consumer comfort is characterized as a psychological effect shaped by the social relationship with a service provider (Turley & Milliman, 2000; Baker et al., 2002); a positive emotion associated with feelings of relaxation, tranquility and decreased anxiety (Spake et al., 2003); and an emotional level that varies according to the consumer’s connection to familiar people or significant belongings (Dunn, 1977). Thus, being comfortable within store boundaries is desirable for establishing a long-term consumer–retailer relationship through risk perception reduction.
We argue that understanding consumer comfort is an important predictor of neighborhood store purchasing behaviors because feelings of relaxation and convenience benefit consumers’ evaluation of their shopping experience (Seiders et al., 2007). A relaxed state of mind will lead to increased patronage intention through better assessments of the brick-and-mortar service encounter.
Despite the application of social distancing measures, consumers continue to make purchases of essential products (for example, food, beverages, medicine) in physical outlets, even though the frequency of purchase and the attributes determining a good shopping experience have changed. However, how consumers derive value from their shopping experience remains grounded in two dimensions: hedonic and utilitarian (Babin et al., 1994). The former consists of an evaluation based on leisure, pleasure and fun factors during the purchase, while the latter focuses on economic and pragmatic aspects of the purchasing task (Baker & Wakefield, 2012).
Thus, it is understood that higher levels of comfort provide better hedonic value perceptions in neighborhood retail shopping situations because individuals are more familiar with operational aspects of the point of sale, such as sales staff, product assortment and store layout. Being more familiar with the shopping situation in a health crisis context contributes to achieving a more relaxed emotional state and being unconcerned about the risk of infection, enabling those with a hedonic motivation to take advantage of going to the store. In addition, consumers with utilitarian purchase motivation also benefit from higher levels of comfort. Feeling calmer and less stressed in a shopping situation that must be avoided because of contagion contributes to achieving the set shopping goals and better non-monetary cost-benefit perceptions such as time savings, touchless interaction with salespeople, quick purchase of products without delivery. Hence, the following two hypotheses are stated:
Comfort (CFT) positively influences hedonic value perceptions (HV).
Comfort (CFT) positively influences utilitarian value perceptions (UV).
A second predictor variable of shopping experience evaluations is perceived crowding, conceptualized as a psychological evaluation of the supply and demand of store space stimuli (Eroglu & Machleit, 1990), subdivided into two dimensions: human and spatial (Machleit et al., 1994).
Human crowding is understood as an individual’s experiential assessment of the number of people and intensity of social interactions in a given physical space (Machleit et al., 2000). Therefore, the variation in the number of customers and employees inside a store interferes with the individual’s perception of how desirable it is to be in this place, and this line of reasoning is consistent with the S-O-R paradigm (Mehrabian & Russell, 1974).
Current literature indicates conflicting results on the impacts of human crowding on consumer behavior (Blut & Iyer, 2020; Mehta, 2013). Previous studies suggest that crowded stores have good products and prices, positively influencing consumer satisfaction and patronage intention (Oliveira et al., 2017; Machleit et al., 1994). In addition, they can lead to greater feelings of exploration (Das & Varshneya, 2017), willingness to interact with other customers (Eroglu et al., 2005), greater pleasure (Li et al., 2009) and increased behavioral intentions (Machleit et al., 1994).
However, increasing levels of perceived human crowding can also lead to higher stress and less shopping enjoyment (Baker & Wakefield, 2012), adoption of avoidance behaviors (Grewal et al., 2003; Noone & Mattila, 2009), higher employee risk perception (Chang, 2021) and willingness to interact with automated service robots (Hou et al., 2021). Therefore, there are indications that the store context is an essential condition in defining the outcome of crowding perceptions (Eroglu et al., 2022), and it is inferred that the human crowding dimension impacts differently according to consumer value perception (Hui & Bateson, 1991).
We draw from the Optimal Social Contact Theory (Allport, 1954) to posit that the increase in perceived human crowding in neighborhood retailing in a pandemic context directly and positively influences the increase in perceived hedonic value. Thus, brief contact with local retailers and other consumers in the neighborhood becomes an escape from the rules of isolation and a moment of pleasure via social contact with familiar people and objects.
On the other hand, human crowding is expected to negatively influence the perceived utilitarian value of neighborhood retail customers in times of health crisis. A high concentration of customers inside the store makes this shopping situation undesirable for a utilitarian-motivated individual since the high number of customers not only increases their perceived risk of contagion but also decreases the cost-benefit of the purchase (time savings, less commuting, and agility to make the purchase). Thus, H2a and H2b are presented:
Human crowding (HC) positively influences hedonic value perceptions (HV).
Human crowding (HC) negatively influences utilitarian value perceptions (UV).
Spatial crowding is conceptualized as a psychological perception of the geographic area available in a store. This evaluation indicates how much the layout and the usable area of a store interfere with sensations of mobility restriction and thus impact the consumer’s approach or avoidance behavior (Mehta, 2013).
Previous studies also indicate contrasting outcomes of the consequences of perceived spatial crowding (Dion, 2004; Santini et al., 2020). For example, a spacious, organized and easy-to-traffic store can elicit greater perceptions of refinement and sophistication (O’Guinn et al., 2015), whereas stores that are small in size, disorganized and with a low circulation of products and people invite a low-quality store image (Kim & Runyan, 2011), which decreases on-site purchase intention (Eroglu et al., 2022). However, a store with high perceived spatial crowding can also be a highly cost-effective indicator for more price-sensitive people (Baker et al., 1994).
It is inferred in this paper that higher perceptions of the shopping experience value come from lower perceptions of spatial crowding. Furthermore, through the understanding that a high degree of disorganization of the store environment generates greater perceptions of contamination (Gupta & Coskun, 2021); the perceived risk of contamination by disease is related to perceptions of crowding and leaves individuals in a state of alert (Chang, 2021); and finally the social distance between customers in the store is a determinant of consumer protection (Pantano et al., 2021), we realize that consumers will decrease their perception of hedonic value. Besides, as neighborhood tenants are often unaware of the importance of a well-planned servicescape and, for financial reasons, open stores in small properties, there is a higher probability of an increased lack of control over the purchasing situation (Hui & Bateson, 1991), which leads to feelings of discomfort, mobility restriction and stress. Thus, a purchase focused on the leisure and pleasure of the store visit experience becomes risky in high spatial density.
In addition, smaller environments with cramped spaces can limit movement and cause a feeling of turmoil. Such a perception results in discontent for those who want to shop without having their movements constrained by a lack of space (Blut & Iyer, 2020; Eroglu & Machleit, 1990). Therefore, it is hypothesized that the degree of familiarity with the neighborhood store does not diminish the effect of poor store layout, disorganization and lack of space on perceived utilitarian value. H3a and H3b are thus formulated:
Spatial crowding (SC) negatively influences hedonic value perceptions (HV).
Spatial crowding (SC) negatively influences utilitarian value perceptions (UV).
The store image can be understood as the set of perceptions experienced by the customer based on the physical and psychological aspects (Borges et al., 2016). It is how the store is defined in the mind of each consumer. As analyzed in this study, the store image involves both hedonic and utilitarian aspects. When positive, it can influence the patronage intention satisfactorily, reflecting the customers’ willingness to trust the store (Bao et al., 2011).
The patronage intention, related to the customer’s cognitive assessment, can be stimulated by several factors, considering the value assessments attributed to the purchasing experience, whether it is predominantly hedonic or utilitarian. Dimensions, such as pleasure and cost/benefit, can influence the perception of this variable (Mehta et al., 2013; Baker et al., 2002). Patronage intention may have different motivations according to the consumer’s perception, which, through a set of attributes, determines the consumer’s attitude toward the store (Pan & Zinkhan, 2006). Thus, an increase in perceived value, regardless of the motivation, improves a store’s perceived image. The store image is understood as part of the lived experience or perceptions developed during the consumer’s contact with the store. Then, we formally state:
Hedonic value perceptions (HV) positively influence store image (SI).
Utilitarian value perceptions (HV) positively influence store image (SI).
The store image plays a key role in the positioning strategy, in which the store image is reflected concerning the type of retailer and competitors to generate consumers’ reactions that are preferable (Burlison & Oe, 2018). Doyle and Fenwick (1974) stated that the store image has a critical impact on a store’s appeal while reaching out to their customers and, over time, the store image affects customers’ patronage behavior (Mazursky & Jacoby, 1986), which can ensure customer loyalty (Kunkel & Berry, 1968). The store image is the element that influences a customer to choose to frequent a particular shop and is a crucial component of store patronage in many studies (Sirgy et al., 2000). Its construct has been described as a set of perceptions that consumers hold about a store’s functional and psychological characteristics (Chebat et al., 2006), which makes a store feel different from others. Stores contain various cues that customers perceive, ultimately influencing their purchase intention (Baker et al., 2002). Considering that store image is a consequence of perceived value, and the relationship between store image and consumer behavior is positive and consistent with current literature, the following hypothesis is stated:
Store image (SI) positively influences patronage intention (PI).
3. Method
We performed data collection through an online survey developed on the LimeSurvey platform. We emailed potential respondents from a Brazilian university database of students, professors, and employees, inviting them to participate in this research. Finally, we used Portuguese translations of reliable and validated scales in the retail context to measure all constructs (see web appendix).
Upon opening the survey link and agreeing to participate, respondents provided basic demographic information (e.g. age, gender and place of residence). Next, they were explicitly primed with the following header: “Think about your last in-person purchase at a small food or variety retailer in your neighborhood (e.g. bakery, mini-market, grocery store, snack bar, pharmacy, convenience store, etc.)”, followed by questions related to the date of last purchase, store location using a seven-point scale ranging from “very close” to “very distant”, the amount spent, type of products purchased and size of basket measured with a seven-point differential semantic scale ranging from “way smaller than planned” to “much larger than planned”. Next, the survey presented questions regarding independent variables at random: human and spatial crowding (Machleit et al., 1994; Mehrabian & Russell, 1974), comfort (Spake et al., 2003), hedonic and utilitarian value (Babin et al., 1994), store image (Pan & Siemens, 2011), patronage intention (Grewal et al., 2003) and the COVID-19 perceived severity (adapted from Laato et al., 2020). After data collection, the structural equation modeling (SEM) technique was used following Hair et al. (2021) guidelines to test our hypotheses.
4. Results
The authors collected data between February 11 and March 9, 2021, from a total of 573 respondents. We discarded 55 participants that did not fit our answering criteria (inattentive, incomplete or incorrect answers), resulting in 528 valid responses. Our overall sample is composed mainly of females (N Female = 321) between the ages 18 and 83 (M age = 37.38; σ = 12.81), that live near their reported local brick-and-mortar retailer (M distance = 2.79; σ = 1.82) and perceive the COVID-19 pandemic as severe (M perceived severity = 6.18; Median = 6.33; σ = 0.95).
Regarding the physical store itself, 45.45% answered that their last shopping trip was at a single-store retailer located in a residential area, whereas 51.51% reported that their last shopping experience was at a local brick-and-mortar retailer on a commercial street, with only 3.03% of respondents reporting that they went to shopping centers. In addition, most shopping trips were related to grocery items (N grocery = 490), and only 7.2% were not related to food purchasing. In addition, 47.72% of participants buy products at least once a week in their neighborhood store, 11.55% buy less than once a month and only 2% never bought in their local brick-and-mortar retailer before. Their average time spent in the store is 16.76 min and the average amount spent is 289.11 BRL.
This data indicates that most respondents usually shop for groceries at local neighborhood retailers, meaning that individuals go to nearby neighborhood retailers to make unplanned, convenience purchases in familiar surroundings (i.e. stores where the retail environment, frontline employees, store owners and other costumers are well-known).
We performed the reliability, convergent validity and discriminant validity analyses to ensure the measurement model was robust. After removing the defective items (i.e. HV2, HV7 and UV3), the results (Table 1) are under the expected values of internal consistency (Cronbach’s alpha and composite reliability greater than 0.6) and convergent validity (AVE greater than 0.5) indicated by the literature (Hair et al., 2021).
Cross-loading analysis shows discriminant validity since the comparison of outer loadings between the constructs always indicated a higher loading of the indicator on its correct theoretical construct. The Fornell–Lacker criterion corroborates this result as the value of the square of the AVE of a given construct is greater than the correlation between the other latent variables (Fornell & Larcker, 1981). In addition, we inserted discriminant validity assessment by heterotrait-monotrait ratio, which values were under the threshold of 0.85. Thus, the constructs are appropriately valid and distinct from each other.
After checking the validity and reliability of the measurement model, we performed the structural model analysis step. Based on Hair et al. (2021) guidelines, we used the bootstrapping procedure with 5,000 subsamples via SmartPLS 3.0 to ascertain the significance of the proposed structural paths. We present the summary of these results in Table 2.
The significance analysis of the structural paths indicates that all proposed relationships are significant at the 95% confidence level. Furthermore, the results found the relationship between consumer–employee interaction and value perceptions to be statistically significant since the positive influence of comfort on hedonic (β1 = 0.298; σ = 0.04; p < 0.01) and utilitarian value (β2 = 0.2; σ = 0.06; p < 0.01) was evidenced. Therefore, the higher the degree of familiarity of the individual with the neighborhood store, the higher the probability of establishing a healthy and trustful interaction with the shopkeeper, making the trip to the store both a pleasurable experience (i.e. leisure modality) and an objective and task-focused shopping situation. This confirms H1a and H1b.
Regarding the dimensions of perceived crowding, human crowding has a positive influence on hedonic value (β3 = 0.164; σ = 0.043; p < 0.01) and a negative influence on utilitarian value (β4 = −0.144; σ = 0.04; p < 0.01). This means that high human density in a neighborhood store is understood as a way to socialize with friends and close neighbors for hedonic consumers, whereas utilitarian consumers perceive overcrowding as an impediment to making a responsive and convenient purchase at their neighborhood retailer. Different reactions to human crowding based on consumer orientation, even in social distance situations, corroborate H2a and H2b.
In addition, we found a negative impact of spatial crowding on perceived hedonic (β5 = −0.422; σ = 0.028; p < 0.01) and utilitarian values (β6 = −0.142; σ = 0.044; p < 0.01). This result signifies that a cluttered store environment and poorly designed store layout restrict the consumer’s perception of mobility and freedom of movement to the point of strongly interfering with their perceptions of leisure of the shopping trip and, to a lesser degree, of the utilitarian purchase objective, thus, confirming H3a and H3b.
We also performed the test of the impact of consumers’ value perceptions on store image formation, indicating that both hedonic (β7 = −0.514; σ = 0.402; p < 0.01) and utilitarian value (β8 = −0.256; σ = 0.058; p < 0.02) positively impact store image. Thus, the retailer image in the consumers’ individual and collective consciousness is a function of the value perceptions derived from the shopping experience. Research data confirm H4 and H5 by highlighting the more significant impact of hedonic value perceptions over utilitarian value in the composition of the store image during the COVID-19 pandemic. This result indicates that, since the enforcement of social distancing measures limited the frequency of physical human interactions, going to the neighborhood store became an essential source of socialization at the expense of purely objective shopping. In addition, data supports H6 as the store image strongly influences the patronage intention (β9 = −0.657; σ = 0.035; p < 0.01). This conveys that retailers who invest in building a positive store image, even during a pandemic, will have positive returns on their revenue by generating a greater incentive for their customers to increase repurchase frequency and intention to recommend the store to other neighbors.
With these results, the proposed model has high predictive power (R2 of the dependent variable = 0.43), mainly composed of the store image and hedonic value constructs.
The results portray the positive impact of comfort on the perception of hedonic consumers, in addition to mediating the positive relationship between store image and the perceptions of utilitarian and hedonic values, in agreement with the literature (Babin et al., 1994; Borges et al., 2016; Bao et al., 2011). Furthermore, besides agreeing with the theory on the subject, the hypothesis tests showed larger effect sizes for the indirect relations that pass through the perception of hedonic value compared to the indirect relations that pass through the perception of utilitarian value.
The perception of utilitarian value would be expected to be more significant in a model that explains the shopping behavior in a neighborhood store. Results suggest that the shopping experience at well-known stores close to home may be a possible substitute for leisure activities in times of social distance. The average amount spent by respondents (BRL$289.11) also seems to be higher than what is necessary for an average convenience purchase.
5. Conclusions
The results identified that the pandemic resulted in behavioral variation in the sample. High-perceived spatial crowding negatively influences shopping experience value perceptions. A store environment with a high perception of crowding causes a feeling of agglomeration (in terms of the number of customers) and congestion (in terms of physical space disposition). This perception negatively influences store image and customer repurchase intention.
Conversely, human crowding influences patronage intentions through increased perceived hedonic value. In addition to agreeing with the theory on the subject (Babin et al., 1994; Borges et al., 2016; Bao et al., 2011), we reveal that the more significant effect sizes for the indirect relations regarding the perception of hedonic value evidence of a possible change in the characteristic of the purchasing moment. During the pandemic, the purchase for convenience in neighborhood stores began to include hedonic aspects, as a way to replace leisure activities in urban areas, whose operation changed due to social distancing. Now, in a post-pandemic condition, neighborhood stores may have a new relationship between hedonic and utilitarian attributes. The possibility of confusing value perception and purchasing objectives at this new moment can be compared to post-crisis situations previously experienced in retail (Griffin et al., 2000). The indicators obtained reinforce the evidence about changes in consumer behavior in pandemic scenarios, moments that drive unusual purchasing and alternative motivations (Laato et al., 2020).
Faced with this situation in which medium and long-term impacts remain uncertain, the retail market must adapt, seeking ways to overcome the challenges imposed by the health crisis and maintain the necessary sales levels. Thus, this article contributes to the survival of small neighborhood businesses during the financial crisis due to Covid-19 by helping businesses become more attractive to consumers and competitive in the new context. During periods of social distancing, we recommend that a shopkeeper resort to constructing a good relationship between employees and consumers to ensure the customer is more comfortable, the main factor in evaluating the shopping experience is leisure. This assessment would guarantee higher repurchase intention and store recommendation.
The suggestion is to maintain the store as an organized environment, with a spacious and airy layout. In addition, physical aspects involving a good pace of assortment distribution are relevant factors for maintaining a positive purchasing moment perceived by the consumer.
Although our conclusions may apply to other scenarios, it is essential to test this study in others markets, including those that address the coronavirus pandemic differently from Brazil. Here, recommendations for social isolation may last longer than in other countries with advanced disease control. As a result, it is necessary to analyze the possible consequences of this continuity in the national reality and investigate ways to reduce the negative impacts on retail stores in the long term, providing solutions for marketing amidst a health crisis. In addition, another limitation of this study is that we collected the data online for research that is dedicated to studying neighborhood retail. In another context, other than the social distancing that affected the period, face-to-face data collection is more appropriate.
Model validation
Construct | CR | Cronbach’s α | AVE | R² | Fornell–Lacker criterion | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | |||||
1 - Comfort | 0.94 | 0.93 | 0.54 | – | 0.885 | ||||||
2 - Hedonic value | 0.85 | 0.79 | 0.57 | 0.366 | 0.494 | 0.733 | |||||
3 - Human crowding | 0.84 | 0.77 | 0.72 | – | −0.354 | −0.102 | 0.756 | ||||
4- Patronage intention | 0.88 | 0.80 | 0.54 | 0.565 | 0.514 | 0.335 | −0.083 | 0.845 | |||
5 -Spatial crowding | 0.80 | 0.65 | 0.57 | – | −0.602 | −0.538 | 0.381 | −0.394 | 0.728 | ||
6 - Store image | 0.96 | 0.95 | 0.57 | 0.194 | 0.822 | 0.550 | −0.250 | 0.657 | −0.594 | 0.893 | |
7 - Utilitarian value | 0.80 | 0.63 | 0.80 | 0.218 | 0.337 | 0.137 | −0.269 | 0.328 | −0.317 | 0.327 | 0.757 |
General significance test
Structural paths | β | SD | T value | p-value | Sig. |
---|---|---|---|---|---|
Comfort → Hedonic value (β1) | 0.298 | 0.040 | 7.395 | 0.000 | yes |
Comfort → Utilitarian value (β2) | 0.200 | 0.066 | 3.029 | 0.002 | yes |
Human crowding→ Hedonic value (β3) | 0.164 | 0.043 | 3.799 | 0.000 | yes |
Human crowding→ Utilitarian value (β4) | −0.144 | 0.042 | 3.390 | 0.001 | yes |
Spatial crowding→ Hedonic value (β5) | −0.422 | 0.028 | 18.377 | 0.000 | yes |
Spatial crowding→ Utilitarian value (β6) | −0.142 | 0.044 | 5.869 | 0.000 | yes |
Hedonic value→ Store image (β7) | 0.514 | 0.042 | 10.020 | 0.000 | yes |
Utilitarian value→ Store image (β8) | 0.256 | 0.058 | 2.423 | 0.015 | yes |
Store image→ Patronage Intention (β9) | 0.657 | 0.035 | 18.994 | 0.000 | yes |
Source: Elaborated by authors (2021)
Author contributions: Marcelo Moll Brandão: conceptualization (Lead), formal analysis (Equal), investigation (Equal), methodology (Equal), project administration (Lead), supervision (Lead), writing – original draft (Equal), writing – review & editing (Equal); Arthur França Sarcinelli: corresponding author, conceptualization (Supporting), data curation (Equal), formal analysis (Equal), methodology (Equal), writing – original draft (Equal), writing – review & editing (Equal); Ananda Bisi Barcelos: conceptualization (Equal), formal analysis (Equal), investigation (Equal), methodology (Supporting), writing – original draft (Equal), writing – review & editing (Equal); Luiza Postay Cordeiro: investigation (Supporting), methodology (Supporting), writing – original draft (Supporting), writing – review & editing (Supporting).
Supplementary material
Supplementary data can be found online.
References
Allport, G. W. (1954). The nature of prejudice, Reading, MA: Addison Wesley.
Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: Measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20(4), 644–656. doi: 10.1086/209376.
Babin, B. J., Ortinau, D. J., Herrmann, J. L., & Lopez, C. (2021). Science is about corroborating empirical evidence, even in academic business research journals. Journal of Business Research, 126, 504–511. doi: 10.1016/j.jbusres.2020.06.002.
Baker, J., Grewal, D., & Parasuraman, A. (1994). The influence of store environment on quality inferences and store image. Journal of the Academy of Marketing Science, 22(4), 328–339. doi: 10.1177/0092070394224002.
Baker, J., Parasuraman, A., Grewal, D., & Voss, G. B. (2002). The influence of multiple store environment cues on perceived merchandise value and patronage intentions. Journal of Marketing, 66(2), 120–141. doi: 10.1509/jmkg.66.2.120.18470.
Baker, J., & Wakefield, K. L. (2012). How consumer shopping orientation influences perceived crowding, excitement, and stress at the mall. Journal of the Academy of Marketing Science, 40(6), 791–806. doi: 10.1007/s11747-011-0284-z.
Bao, Y., Bao, Y., & Sheng, S. (2011). Motivating purchase of private brands: Effects of store image, product signatureness, and quality variation. Journal of Business Research, 64(2), 220–226. doi: 10.1016/j.jbusres.2010.02.007.
Blut, M., & Iyer, G. R. (2020). Consequences of perceived crowding: A meta-analytical perspective. Journal of Retailing, 96(3), 362–382. doi: 10.1016/j.jretai.2019.11.007.
Borges, L. A. S., Carvalho, D. T., & Miranda, C. S. (2016). Antecedentes da intenção de compra de marcas próprias: Um estudo no mercado emergente. RAE - Revista De Administração De Empresas, 56(6), 611–625. doi: 10.1590/s0034-759020160604.
Bowlby, J. (1969). Attachment, New York, NY: Basic Books.
Burlison, J., & Oe, H. (2018). A discussion framework of store image and patronage: A literature review. International Journal of Retail & Distribution Management, 46(7), 705–724. doi: 10.1108/IJRDM-11-2017-0275.
Chang, T. S. (2021). Social distancing in retail: Influence of perceived retail crowding and self-efficacy on employees’ perceived risks. Journal of Retailing and Consumer Services, 62, 102613. doi: 10.1016/j.jretconser.2021.102613.
Chebat, J. C., Sirgy, M. J., & St-James, V. (2006). Upscale image transfer from malls to stores: A self-image congruence explanation. Journal of Business Research, 59(12), 1288–1296. doi: 10.1016/j.jbusres.2006.09.007.
Das, G., & Varshneya, G. (2017). Consumer emotions: Determinants and outcomes in a shopping mall. Journal of Retailing and Consumer Services, 38, 177–185. doi: 10.1016/j.jretconser.2017.06.008.
Das, G., Jain, S.P., Maheswaran, D., Slotegraaf, R.J., & Srinivasan, R. (2021). Pandemics and marketing: Insights, impacts, and research opportunities. Journal of the Academy of Marketing Science, 49(5), 835–854.
de Cosmo, L. M., Piper, L., Nataraajan, R., & Petruzzellis, L. (2022). The effects of circular format on store patronage: An Italian perspective. Journal of Business Research, 140, 430–438. doi: 10.1016/j.jbusres.2021.11.013.
Dion, D. (2004). Personal control and coping with retail crowding. International Journal of Service Industry Management, 15(3), 250–263. doi: 10.1108/09564230410540926.
Donthu, N., & Gustafsson, A. (2020). Effects of COVID-19 on business and research. Journal of Business Research, 117, 284–289. doi: 10.1016/j.jbusres.2020.06.008.
Doyle, P., & Fenwick, I. (1974). How store image affects shopping habits in grocery chains. Journal of Retailing, 50(4), 39–52.
Dunn, J. (1977). Distress and comfort, Cambridge, MA: Harvard University Press.
Eroglu, S. A., & Machleit, K. A. (1990). An empirical study of retail crowding: Antecedents and consequences. Journal of Retailing, 66(2), 201.
Eroglu, S. A., Machleit, K., & Barr, T. F. (2005). Perceived retail crowding and shopping satisfaction: The role of shopping values. Journal of Business Research, 58(8), 1146–1153. doi: 10.1016/j.jbusres.2004.01.005.
Eroglu, S. A., Machleit, K. A., & Neybert, E. G. (2022). Crowding in the time of COVID: Effects on rapport and shopping satisfaction. Journal of Retailing and Consumer Services, 64, 102760. doi: 10.1016/j.jretconser.2021.102760.
Farooq, A., Laato, S., & Islam, A. N. (2020). Impact of online information on self-isolation intention during the COVID-19 pandemic: Cross-sectional study. J. Med. Internet Res, 22(5), e19128. doi: 10.2196/19128.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50. doi: 10.2307/3151312.
Grewal, D., Baker, J., Levy, M., & Voss, G. B. (2003). The effects of wait expectations and store atmosphere evaluations on patronage intentions in service-intensive retail stores. Journal of Retailing, 79(4), 259–268. doi: 10.1016/j.jretai.2003.09.006.
Griffin, M., Babin, B. J., & Modianos, D. (2000). Shopping value of russian consumers: The impact of habituation in a developing economy. Journal of Retailing, 76(1), 33–52. doi: 10.1016/S0022-4359(99)00025-1.
Gupta, S., & Coskun, M. (2021). The influence of human crowding and store messiness on consumer purchase intention–the role of contamination and scarcity perceptions. Journal of Retailing and Consumer Services, 61, 102511. doi: 10.1016/j.jretconser.2021.102511.
Hair, J. F., Jr, Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook, Springer Nature: Berlin.
Hou, Y., Zhang, K., & Li, G. (2021). Service robots or human staff: How social crowding shapes tourist preferences. Tourism Management, 83, 104242. doi: 10.1016/j.tourman.2020.104242.
Huang, X., Huang, Z., & Wyer, R. S., Jr, (2018). The influence of social crowding on brand attachment. Journal of Consumer Research, 44(5), 1068–1084. doi: 10.1093/jcr/ucx087.
Hubbard, D. W., & Carriquiry, A. L. (2019). Quality control for scientific research: Addressing reproducibility, responsiveness, and relevance. The American Statistician, 73(sup1), 46–55. doi: 10.1080/00031305.2018.1543138.
Hui, M. K., & Bateson, J. E. (1991). Perceived control and the effects of crowding and consumer choice on the service experience. Journal of Consumer Research, 18(2), 174–184. doi: 10.1086/209250.
Kim, J. H., & Runyan, R. (2011). Where did all the benches go? The effects of mall kiosks on perceived retail crowding. International Journal of Retail & Distribution Management, 39(2), 130–143. doi: 10.1108/09590551111109085.
Kunkel, J. H., & Berry, L. L. (1968). A behavioral conception of retail image. Journal of Marketing, 32(4), 21–27. doi: 10.2307/1249333.
Laato, S., Islam, A. K. M. N., Farooq, A., & Dhirt, A. (2020). Unusual purchasing behavior during the early stages of the COVID-19 pandemic: The stimulus-organism-response approach. Journal of Retailing and Consumer Services, 57, 102224. doi: 10.1016/j.jretconser.2020.102224.
Lee, A. Y., & Goldsmith, K. (2022). Looking back and looking forward:(re) interpreting consumer insights in a time of transition. Journal of the Association for Consumer Research, 7(1), 1–7. doi: 10.1086/718146.
Leite, H., Hodgkinson, I. R., & Gruber, T. (2020). New development: ‘healing at a distance’ – telemedicine and COVID-19. Public Money & Management, 40(6), 483–485. doi: 10.1080/09540962.2020.1748855.
Li, J. G. T., Kim, J. O., & Lee, S. Y. (2009). An empirical examination of perceived retail crowding, emotions, and retail outcomes. The Service Industries Journal, 29(5), 635–652. doi: 10.1080/02642060902720121.
Machleit, K., Eroglu, S. A., & Mantel, S. P. (2000). Perceived retail crowding and shopping satisfaction: What modifies this relationship? Journal of Consumer Psychology, 9(1), 29–42. doi: 10.1207/s15327663jcp0901_3.
Machleit, K., Kellaris, J. J., & Eroglu, S. A. (1994). Human versus spatial dimensions of crowding perceptions in retail environments: A note on their measurement and effect on shopper satisfaction. Marketing Letters, 5(2), 183–194. doi: 10.1007/BF00994108.
Mazursky, D., & Jacoby, J. (1986). Exploring the development of store images. Journal of Retailing, 62(2), 145–165.
Mehrabian, A., & Russell, J. (1974). An approach to environmental psychology, 1st ed., Cambridge, MA: The MIT Press.
Mehta, R. (2013). Understanding perceived retail crowding: A critical review and research agenda. Journal of Retailing and Consumer Services, 20(6), 642–649. doi: 10.1016/j.jretconser.2013.06.002.
Mehta, R., Sharma, N. K., & Swami, S. (2013). The impact of perceived crowding on consumers’ store patronage intentions: Role of optimal stimulation level and shopping motivation. Journal of Marketing Management, 29(7–8), 812–835. doi: 10.1080/0267257X.2012.729075.
Noone, B. M., & Mattila, A. S. (2009). Consumer reaction to crowding for extended service encounters. Managing Service Quality: An International Journal, 19(1), 31–41. doi: 10.1108/09604520910926791.
O’Guinn, T., Tanner, R., & Maeng, A. (2015). Turning to space: Social density, social class, and the value of things in stores. Journal of Consumer Research, 42(2), 196–213. doi: 10.1093/jcr/ucv010.
Oliveira, A. S., Silva, D., Brandão, M. M., & Lopes, E. L. (2017). Influência do crowding na lealdade mediado pela satisfação do consumidor em processos de compras no varejo. Revista De Administração Da Universidade Federal De Santa Maria, 10(4), 614–631. doi: 10.5902/1983465916176.
Pan, Y., & Siemens, J. C. (2011). The differential effects of retail density: An investigation of goods versus service settings. Journal of Business Research, 64(2), 105–112.
Pantano, E., Pizzi, G., Bilotta, E., & Pantano, P. (2021). Shopping with (out) distancing: Modelling the personal space to limit the spread of contagious disease among consumers in retail stores. Journal of Marketing Management, 37(17-18), 1–19.
Pan, Y., & Zinkhan, G. M. (2006). Determinants of retail patronage: A meta-analytical perspective. Journal of Retailing, 82(3), 229–243.
Prentice, C., Altinay, L., & Woodside, A. G. (2021). Transformative service research and COVID-19. The Service Industries Journal, 41(1-2), 1–8.
Roggeveen, A. L., & Sethuraman, R. (2020). How the COVID-19 pandemic may change the world of retailing. Journal of Retailing, 96(2), 169.
Santini, F. D. O., Ladeira, W. J., Sampaio, C. H., & Perin, M. G. (2020). Effects of perceived retail crowding: A meta-analytic study. The International Review of Retail, Distribution and Consumer Research, 30(4), 411–436.
Seiders, K., Voss, G. B., Godfrey, A. L., & Grewal, D. (2007). SERVCON: Development and validation of a multidimensional service convenience scale. Journal of the Academy of Marketing Science, 35(1), 144–156.
Sheth, J. N. (2011). Impact of emerging markets on marketing: Rethinking existing perspectives and practices. Journal of Marketing, 75(4), 166–182.
Sheth, J. N. (2020). Business of business is more than business: Managing during the covid crisis. Industrial Marketing Management, 88, 261–264.
Sirgy, M. J., Grewal, D., & Mangleburg, T. (2000). Retail environment, self-congruity, and retail patronage: An integrative model and a research agenda. Journal of Business Research, 49(2), 127–138.
Spake, D. F., Beatty, S. E., Brockman, B. K., & Crutchfield, T. N. (2003). Consumer comfort in service relationships: Measurement and importance. Journal of Service Research, 5(4), 316–332.
Turley, L. W., & Milliman, R. E. (2000). Atmospheric effects on shopping behavior: A review of the experimental evidence. Journal of Business Research, 49, 193–211.
Wang, I. M., & Ackerman, J. M. (2019). The infectiousness of crowds: Crowding experiences are amplified by pathogen threats. Pers Soc Psychol Bull, 45(1), 120–132.
Further reading
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879. doi: 10.1037/0021-9010.88.5.879.