This study examines the role of brick-and-mortar retail store attributes (SA) on consumers' brand loyalty and perceived value (PV), which consequently influence repurchase and word-of-mouth (WOM) behavior.
Data were collected through a structured survey administered to 374 shoppers at leading physical retail chains. The relationships among key constructs were analyzed using partial least squares-structural equation modeling (PLS-SEM) in SmartPLS, enabling assessment of both direct and mediating effects in the proposed model.
Findings revealed that structural equation modeling indicated that store-related attributes had the strongest effect on brand loyalty and PV. Both mediators, in turn, enhanced repurchase and WOM intentions. Subgroup analysis highlighted age-based differences in these pathways.
Retail managers can boost customer loyalty and PV by enhancing retail SA. Investing in visual merchandising, staff training, competitive pricing and omnichannel integration leads to higher satisfaction, positive WOM and increased repurchase intentions (RI). Prioritizing these strategies helps retailers build loyal customers and drive business growth.
This study provides unique insights into shopper expectations of SA that may be used by global retailers to create consumer brand loyalty and PV and build thriving businesses.
Introduction
Retail industries around the world are facing increased pressure as a result of geopolitical, market and technological shifts (Pappas et al., 2023). Specifically, because consumers today shop online and on mobile devices more often, brick-and-mortar stores have decreased in demand (Johnson et al., 2015). Nevertheless, we cannot ignore the importance of physical or brick-and-mortar retail business models. Brick-and-mortar stores offer consumers an exciting, entertaining and emotionally engaging shopping experience in addition to personal interaction between them and their associates (Zhao et al., 2023).
Store-based retailing continues to evolve. Several factors influence customer perception, experience and loyalty. Store-related attributes influence shopping choices and understanding them helps in making strategic marketing decisions that enhance customer satisfaction, loyalty and patronage (Anggara et al., 2023). The primary objective of brick-and-mortar retailers is to encourage consumer repurchase (Teixeira et al., 2022). This study examined perceived value (PV) dimensions among consumers, such as merchandise representation, services, brands and ambiance. Investigating repurchase intention (RI) and word-of-mouth (WOM) in physical retail is essential for operations and consumer satisfaction. After using a product, consumers may have expectations that align with or exceed their initial beliefs. The relationship between service quality, customer satisfaction and future purchase intentions or WOM is significant for researchers (Donthu et al., 2021). It is hypothesized that higher service quality leads to greater customer satisfaction, increasing the likelihood of repeat purchases and positive recommendations. (Kotler and Keller, 2003) emphasize the importance of focusing on current consumers, as acquiring new one's costs about five times more than retaining existing ones. Previous research shows that RI positively influences repeat purchase behaviors (Mirza et al., 2021; Zhao et al., 2022; Zia et al., 2021). A strong RI is expected to enhance future purchasing from physical stores that satisfy customers (Liao et al., 2023).
This study seeks to answer the following research question: How do store attributes (SA) influence brand loyalty and PV, driving repurchase and WOM?
To address this question, the study pursues the following objectives:
To examine the direct effects of store-related attributes on consumer brand loyalty and PV.
To investigate how brand loyalty and PV mediate the relationships between SA and both RI and WOM behavior.
To provide empirical evidence on the mechanisms through which physical SA contribute to customer retention and advocacy in the retail sector.
Store loyalty (SL) and PV are essential in consumer purchase behavior (Sharma and Fatima, 2024). These factors significantly influence physical retail since store-related attributes enhance SL and PV, affecting RI and WOM. Thus, examining SL and PV’s antecedents and consequences is vital. Analyzing these elements reveals how store-related attributes impact SL and PV, subsequently influencing RI and WOM. Previous studies have explored SL and PV in retail (Konuk, 2018; Menidjel and Bilgihan, 2023), but the perspective of store-related attributes remains underexplored. Additionally, the mediating roles of SL and PV between SA, RI and WOM are less examined. This research investigates the interplay between SA, SL and PV concerning consumer RI and WOM, addressing this gap in the retail literature. We will also explore SL and PV’s mediating role in the relationship between retail SA and RI and WOM behavior. This study contributes to retail and consumer behavior theory by integrating SA with the mediating roles of SL and PV in shaping repurchase and WOM intentions. Unlike prior studies that treat these constructs separately, this research offers a comprehensive model outlining how physical retail environments promote customer loyalty and advocacy, thereby bridging a significant literature gap. Effective retail marketing requires understanding SA from a consumer’s perspective. Lastly, this paper contributes to consumer behavior theory both by validating stimulus-organism-response (S-O-R) mechanisms under conditions of emerging market unpredictability and by revealing that the mediating strength of PV, especially among younger, digitally engaged consumers, can surpass that of brand loyalty, in contrast to results from Western studies. Our analysis suggests that marketplace informality and shifting consumer reference groups represent important, theory-relevant factors for advancing S-O-R applications in global retail research.
Next, the paper presents a theoretical background and hypothesis development, followed by a description of the research method, data analysis and findings. In the following sessions, we examine the implications, limitations and potential directions for future research.
Theoretical framework
Stimulus-organism-response model
We adopt the S-O-R framework (Jacoby, 2002), which posits that environmental stimuli (e.g. SA) influence internal states (e.g. PV, brand loyalty), subsequently driving behavioral responses (e.g. RI, WOM). By integrating S-O-R with retail literature, we extend the model to include mediating mechanisms (brand loyalty and PV) that explain how physical SA translate into customer retention and advocacy. This approach advances the theoretical understanding of consumer decision-making in brick-and-mortar contexts, aligning with recent calls to explore mediating mechanisms in retail contexts (Nyamekye et al., 2023; Zhao et al., 2023).
Literature review
Store attributes and repurchase intention
Retail SA significantly influence customers’ purchasing decisions, shaping their overall retail experience, future purchase intentions and SL. In grocery retail, a business’s survival depends on its ability to satisfy customers with superior products and services compared to competitors (Martínez-Ruiz et al., 2017). To effectively influence RI, it’s vital to identify measurable SA, such as sales promotions and price discounts (Shuja et al., 2024). Based on this discussion, we can hypothesize that:
SAs positively influence RI.
Store attributes and word-of-mouth intention
WOM significantly influences purchase intention (Mansoor and Noor, 2019). Favourable initial information leads to a positive attitude, and regular communication about the product fosters this positivity. Frequent positive WOM correlates with a stronger desire to possess the product. Positive WOM and minimized negative WOM contribute to product success. Enhanced favourable WOM arises from good experiences in stores and high-quality products, boosting purchasing likelihood (Lee et al., 2017). Retail managers should closely monitor store characteristics and leverage favourable WOM to improve customer satisfaction, increase purchase intentions and drive sales. Regular in-store environment optimization is crucial for competitiveness. Thus, from the above discussion, we can hypothesize that:
SAs positively influence WOM intention.
Store attributes and customer brand loyalty
SL refers to consistently buying items from the same store, regardless of whether they are similar or different products (Dugar and Chamola, 2021). Customer brand loyalty refers to the consistent purchasing behavior of a customer from a particular store and their inclination to recommend the store to others for future purchases (Molinillo et al., 2022). According to Molinillo et al. (2022), loyal customers continue purchasing from a specific store and enthusiastically recommend it to their friends or colleagues. In the retail store context, forming a store’s attributes is a deliberate act by the store owner, differentiating it from similar establishments. These attributes serve as an external stimulus influencing consumer perceptions of retail stores. Wu et al. (2024) have determined that the primary determinants of customer experience in brick-and-mortar retail settings include store atmosphere, layout, customer service, communication and problem-solving capabilities, all enhancing consumer convenience. Retailers need to carefully manage their SA and leverage positive customer experiences to drive brand love and ultimately increase customer loyalty and sales (Iglesias et al., 2019). Hence,
SAs positively influence customer brand loyalty.
Store attributes and perceived value
The attributes of a store are essential in augmenting customer satisfaction and promoting customer loyalty. In a prior research, Chakraborty et al. (2024) identified eight specific characteristics of shops that significantly impact how customers perceive a retail marketing strategy. The study revealed that when the primary components of social intelligence produced positive outcomes, it could serve as a competitive advantage (Mohamed, 2021). Conversely, if the consequences were unfavorable, it could lead to a disadvantage. To ascertain effective management techniques, the SA components help evaluate the strengths and weaknesses of a store by analyzing the consumer PV (Huang et al., 2024).
Recent advances in consumer behavior research highlight the role of cognitive algorithms and neuro-management in shaping consumer evaluations of retail environments and purchase decisions (Khan et al., 2024). Lăzăroiu et al. (2020) emphasize that consumers' decision-making on retail and social commerce platforms is shaped by internal psychological processes such as trust, perceived risk and the mental algorithms used to evaluate store credibility and value propositions. These studies suggest that consumers use cognitive shortcuts and algorithmic reasoning in physical stores to evaluate SA, influencing brand loyalty and PV. Our study incorporates these insights, expanding the traditional view to include how cognitive and algorithmic processes mediate the relationship between environmental stimuli (SA) and consumer responses (loyalty, PV and behavioral intentions). Thus, we hypothesize that:
SAs positively influenced PV.
Customer brand loyalty and repurchase intention
Brand loyalty is the tendency to consistently select and purchase a particular brand from competing brands within the same product category (Atulkar, 2020). Customers exhibiting high brand loyalty can be characterized as individuals who consistently make purchases from a particular brand and possess a deep sense of dedication and attachment to that brand (Dapena-Baron et al., 2020). Customers who demonstrate a strong commitment to a particular brand develop loyalty towards that brand and consistently engage in repeat purchases.
RI refers to a consumer’s preference to purchase a specific brand repeatedly in the future. Numerous research studies have documented that consumer post-purchase behavior typically involves expressing a positive WOM and developing brand loyalty (Shahid et al., 2018). Consumer loyalty results from repetitive purchasing behavior when a consumer is satisfied with a brand. In retail, when consumers are satisfied with a specific product, they repeatedly buy the same brand. This dramatically impacts their loyalty towards that product and prevents them from choosing alternative options. Thus, the current research proposes that consumers’ intention to repurchase positively impacts their loyalty toward a brand.
Customer brand loyalty positively influenced repurchase intention.
Customer brand loyalty and word-of-mouth intention
The retail sector has undergone an extraordinary evolution during the last decade, which has increased consumption by making customers more active and demanding, thereby making customer loyalty even more central to marketing theory and practice (Leal and Ferreira, 2020). The development, expression and spread of opinions regarding products, brands and services likely began with WOM communication. WOM is believed to carry more weight when it comes to services than when it comes to commodities. Services are more trustworthy than tangible goods because of their higher credibility, inherent intangibility, difficulty to standardize and high risk (without warranties and guarantees). Loyal consumers demonstrate different priorities, warmth and compulsion to their selected retail store. They also extend helpful WOM confirmations, develop a lower propensity to change to competitors, preserve a great acceptance of service disappointment and show a readiness to give an exceptional price (Liao et al., 2017). Brands should create loyal advocates who organically spread the word about their offerings. Hence, from the above discussion, we can hypothesize that:
Customer brand loyalty positively influenced WOM intention.
Perceived value and customer brand loyalty
In the retail context, a PV significantly contributes to post–purchase consumer behavior. PV is typically defined as the customer’s evaluation of store utility, products and brand image, as well as their perception of what they have received and provided (Lin et al., 2017). Li (2021) illustrated that customers’ loyalty to brands is determined by their brand trust, which is the essence of customer loyalty. Customers select retail offers based on their perception of the value they offer, and higher PV results in increased satisfaction and loyalty. The findings are consistent with the prior research conducted by Nikhashemi et al. (2016), which demonstrated that PV exerts a positive and statistically significant impact on brand loyalty. In retail, establishing strong, loyal customer relationships depends on providing high PV through high-quality products, services and experiences. A focus on value can foster satisfaction, trust and emotional investment in a retail brand. Hence, from the above discussion, we can hypothesize that:
PV positively influences customer brand loyalty.
Perceived value and repurchase intention
Research has highlighted the importance of PV during exchange activities, particularly given the prevalence of the customer-driven perspective. According to research studies, the PV of a product or service may have a stronger correlation with RI compared to satisfaction or quality (Liao et al., 2017). If the purchase provides a high level of value, it will increase the rate of return, and customers will repurchase the product in the future (Asti et al., 2021). Furthermore, this is corroborated by the research conducted by De Toni et al. (2018), which indicates that consumers who perceive additional value have a favourable perception of their ability to influence interest purchase or RI. Keeping in mind that different customer segments have other priorities, retailers should work to maximize value perceptions by controlling costs, risks and quality. Improving the PV of a product or service is an essential step in establishing a loyal customer base. Hence, from the above discussion, we can hypothesize that:
PV positively influenced RI.
Perceived value and word-of-mouth intention
The concept of PV pertains to the benefits consumers derive from a product or service and how they assess its usefulness. Recent studies suggest that consumers trust information from their social circles more than commercial advertisements. WOM communication is more credible than mass media advertisements because consumers rely on individual comments from others about a product or service, as it is a non-commercial interpersonal dialogue (Konuk, 2019). This is attributed to the perceived authenticity of interpersonal communication, leading to increased trust in WOM recommendations. According to recent research by Talwar et al. (2021), consumers tend to use WOM communication and share negative feedback with others if a product or service fails to meet their expectations. As a result, using negative WOM to disseminate unfavorable comments can adversely impact a company’s performance. Therefore, companies must address negative WOM evaluations (Chen and Zhang, 2022). According to López et al. (2022), utilizing WOM to spread favorable views can boost a brand’s credibility. Further studies have identified critical components of PV that impact WOM, such as product quality, service quality and monetary/non-monetary sacrifice (Baidoun and Salem, 2024). Retailers should optimize value perceptions by managing quality, costs and risks while considering customer segments and WOM contexts. Increasing PV is key to positive WOM and loyal customer relationships. Thus, it is hypothesized that the following:
Perceived Value positively influenced WOM Intention.
The mediating role of customer brand loyalty between store attributes, repurchase intention and positive WOM
A study shows that purchasing intentions can be influenced by product attributes, personality and WOM communication. Findings indicate that WOM is influenced by product attributes and brand personality traits. Previous research examines how customers’ favorable WOM varies and their reluctance to accept unfavorable WOM about the brand (Bıçakcıoğlu et al., 2018). Additionally, the research findings show that consumers who experience satisfaction and develop a stronger emotional connection with a particular brand exhibit a greater tendency to remain loyal to the brand (Nyamekye et al., 2023). The attributes of a store have a significant impact on the PV and brand loyalty of customers. The findings indicate a potential relationship between SA and customer brand loyalty, which may subsequently influence their RI and intention to communicate positive WOM (Nikhashemi et al., 2016). Hence, from the above discussion, we can hypothesize that:
Brand loyalty mediates the relationship between store attributes and RI.
Brand loyalty mediates the relationship between store attributes and positive WOM.
The mediating role of perceived value between store attributes, repurchase intention and positive WOM
According to a prior study, PV and customer satisfaction as mediating variables could strengthen the effect of store image and RI (Ananda et al., 2021). The study found that PV and customer satisfaction fully mediate the effect of store image on RI. Another study found that the intrinsic cues such as aesthetics and functionality, affected RIs indirectly and were mediated by perceived psychological and economic value (Ko et al., 2011). A recent study reveals that an individual’s tendency to spread positive WOM about a restaurant is influenced by their perception of the established PVs. This implies that PV may influence WOM intention (Bushara et al., 2023). The study revealed that the PV of private-label brands mediates the relationship between perceived risks, perceived quality and purchase intention. Literature supports the hypothesis that PV mediates relationships between SA, RI and positive WOM in retail contexts. Based on this, we hypothesize that:
Perceived value mediates the relationship between store attributes and RI.
Perceived value mediates the relationship between store attributes and positive WOM.
As illustrated in Figure 1, the study posits that SA (e.g. layout, pricing, staff interaction) act as exogenous stimuli, influencing endogenous variables—brand loyalty and PV, which mediate their effects on RI and WOM. This model integrates direct and indirect pathways to provide a holistic view of brick-and-mortar retail dynamics.
The model begins with a textbox on the left labeled “Store-Related Attributes.” Two rightward arrows emerge from “Store-Related Attributes,” an upper diagonal rightward arrow labeled “H 3” connects to a textbox in the top-center labeled “Customer Brand Loyalty,” and a lower diagonal rightward arrow labeled “H 4” connects to a textbox at the bottom center labeled “Perceived Value.” From “Customer Brand Loyalty,” a rightward arrow labeled “H 5” connects to a textbox on the top right labeled “Repurchase Intention,” while another bottom diagonal rightward arrow labeled “H 6” connects to a textbox at the bottom right labeled “Word-of-Mouth.” An upward arrow labeled “H 7” connects “Perceived Value” to “Customer Brand Loyalty.” From “Perceived Value,” an upper diagonal rightward arrow labeled “H 8” connects to “Repurchase Intention,” and another rightward arrow labeled “H 9” connects to “Word-of-Mouth.” From “Store-Related Attributes,” a cornered upward arrow labeled “H 1” directly connects to “Repurchase Intention,” and a cornered downward arrow labeled “H 2” directly connects to “Word-of-Mouth.” Two indirect paths are also shown, one labeled “H 10 - H 11” connects “Store-Related Attributes” through “Customer Brand Loyalty” to “Repurchase Intention,” and the other labeled “H 12 - H 13” connects “Store-Related Attributes” through “Perceived Value” to “Word-of-Mouth.” A note at the bottom right corner clarifies that black arrows indicate “Direct path” and blue arrows indicate “Indirect path.”The hypothesized model. Source: Authors’ own work
The model begins with a textbox on the left labeled “Store-Related Attributes.” Two rightward arrows emerge from “Store-Related Attributes,” an upper diagonal rightward arrow labeled “H 3” connects to a textbox in the top-center labeled “Customer Brand Loyalty,” and a lower diagonal rightward arrow labeled “H 4” connects to a textbox at the bottom center labeled “Perceived Value.” From “Customer Brand Loyalty,” a rightward arrow labeled “H 5” connects to a textbox on the top right labeled “Repurchase Intention,” while another bottom diagonal rightward arrow labeled “H 6” connects to a textbox at the bottom right labeled “Word-of-Mouth.” An upward arrow labeled “H 7” connects “Perceived Value” to “Customer Brand Loyalty.” From “Perceived Value,” an upper diagonal rightward arrow labeled “H 8” connects to “Repurchase Intention,” and another rightward arrow labeled “H 9” connects to “Word-of-Mouth.” From “Store-Related Attributes,” a cornered upward arrow labeled “H 1” directly connects to “Repurchase Intention,” and a cornered downward arrow labeled “H 2” directly connects to “Word-of-Mouth.” Two indirect paths are also shown, one labeled “H 10 - H 11” connects “Store-Related Attributes” through “Customer Brand Loyalty” to “Repurchase Intention,” and the other labeled “H 12 - H 13” connects “Store-Related Attributes” through “Perceived Value” to “Word-of-Mouth.” A note at the bottom right corner clarifies that black arrows indicate “Direct path” and blue arrows indicate “Indirect path.”The hypothesized model. Source: Authors’ own work
Methods
Data collection and procedure
This study employed a quantitative, cross-sectional research design to investigate the relationships among SA, brand loyalty, PV, RI and WOM in the context of brick-and-mortar retail. The target population consisted of individuals aged 18 years and above who had made at least one purchase at one of Pakistan's leading grocery retail chains, Imtiaz Super Market, Metro Cash & Carry, Hyperstar (formerly Carrefour), Chase Up and Utility Stores Corporation, within the month preceding the survey. These retailers were strategically selected for their high foot traffic and diverse customer base, which spans various income groups, providing a rich context to assess how SA influences consumer behavior.
The selection of Pakistan as the study setting was driven by its rapidly evolving retail landscape, marked by urbanization, a burgeoning middle class, and the coexistence of traditional and modern retail formats. These characteristics position Pakistan as an exemplary case within the broader context of emerging markets, sharing similarities with other economies such as India (Grosso et al., 2018) and Indonesia (Anggara et al., 2023), where physical retail remains prevalent despite growing e-commerce penetration. The inclusion of both premium and budget-friendly retail chains enabled broader generalizability of the findings across socioeconomic strata, addressing a critical gap in the existing retail literature, which predominantly focuses on developed Western contexts.
Data were collected in November 2023 using purposive sampling, which targeted participants based on their sociodemographic profiles and shopping behavior. An online questionnaire was distributed via social media platforms such as Facebook and Instagram to achieve a broad reach and facilitate timely data collection. The survey included three sections: an introductory brief on the study’s purpose, a sociodemographic segment and validated items adapted from prior research for constructs like customer loyalty, PV, WOM and RI. Out of 425 individuals who received the survey link, 410 responded, resulting in 374 complete responses and a valid response rate of 88.00%. The demographics of the final sample were analyzed to ensure representativeness, and all research adhered to ethical guidelines, including obtaining informed consent and maintaining participant anonymity. Table 1 presents a detailed breakdown of the sample characteristics. Data analysis was performed using the partial least squares structural equation modeling (PLS-SEM) approach. All PLS-SEM analyses, including measurement and structural model assessment, were conducted with SmartPLS (version 4.0). This software enabled the calculation of path coefficients, bootstrapping with 5,000 resamples and the assessment of model fit indices, such as standardized root mean square residual (SRMR) and normed fit index (NFI).
Demographic profiles of respondents
| Demographic | Demographic features | Frequency | Percentage |
|---|---|---|---|
| Gender | Male | 277 | 74.1 |
| Female | 97 | 25.9 | |
| Age | <20 | 75 | 20.1 |
| 21–24 | 68 | 18.2 | |
| 25–29 | 60 | 16.0 | |
| 30–35 | 101 | 27.0 | |
| >36 | 70 | 18.7 | |
| Employment status | Employed | 270 | 72.2 |
| Self-Employed | 104 | 27.8 | |
| Qualification | High School | 58 | 15.5 |
| Intermediate | 91 | 24.3 | |
| Bachelors | 94 | 25.1 | |
| Master or Higher | 131 | 35.0 |
Measurements
To adequately reflect the context of the current study, an initial pool of measurement items was drawn from well-established research in the literature. For developing the questionnaire, items were validated by industry-academic experts. The experts’ panel ensured content validity (scope and purpose validity) and face validity (reliability) of the scales within the local retail context. All items were measured on five-point Likert scales ranging from 1 (strongly disagree) to 5 (strongly agree). For example, eight items of store-related attributes were modified form Chang et al. (2015). Six items of customer brand loyalty were modified from Bridson et al. (2008), three items of PV were modified from Atulkar (2020), three items of RI were adapted from Antwi (2021) and two items of WOM were adapted from Abbasi et al. (2021).
Common method bias (CMB)
In light of the fact that we obtained our data from a single source, prior literature suggests that the data may be susceptible to common method bias (CMB). Therefore, it is recommended to examine the CMB before performing further analysis. Our analysis of the CMB was based on Harman’s single factor (Hussain et al., 2024; Podsakoff et al., 2024) and variance inflation factor (VIF) (Hair et al., 2019), which are commonly used tools in the characterization of CMBs. In this study, the maximum variance explained by any single factor was 31.76%, well below the acceptable threshold of 0.50. Furthermore, all the VIF values were below 3.3 (Hair et al., 2019), which illustrates that CMB is not a concern in our study.
Data analysis and results
Measurement model
We first assessed the measurement model's convergent validity by examining the outer loadings of the items for each construct, composite reliability (CR) and average variance extracted (AVE). Following PLS-SEM guidelines (Henseler et al., 2015), we assessed model fit using the SRMR and NFI. The SRMR value of 0.063 (below the 0.08 threshold) and NFI of 0.912 (above 0.90) indicate acceptable fit. These results align with the S-O-R framework's theoretical structure and confirm the model's robustness. We also assessed the model's Cronbach’s alphas (CA) and CR scores, which both exceeded the recommended value of 0.7 (Henseler et al., 2015). In addition, Table 2 shows that the CAs and CRs for all constructs were greater than 0.7, thereby meeting the second convergent validity criterion. We performed an additional convergent validity test by examining the AVE. Here, the AVE value must be at least 0.5, thereby explaining at least 50% of the variance of its indicators. As Table 2 shows, each of our latent constructs had AVE values higher than the recommended level of 0.5, thereby fulfilling all three conditions for convergent validity.
Item loading, Cronbach’s α values, composite reliability, and AVE
| Construct | Items | Outer loading | Cronbach’s alpha | Composite reliability | AVE |
|---|---|---|---|---|---|
| Store attributes | SA1 | 0.781 | 0.911 | 0.928 | 0.617 |
| SA2 | 0.801 | ||||
| SA3 | 0.86 | ||||
| SA4 | 0.717 | ||||
| SA5 | 0.807 | ||||
| SA6 | 0.807 | ||||
| SA7 | 0.765 | ||||
| SA8 | 0.734 | ||||
| Customer brand loyalty | CBL1 | 0.792 | 0.894 | 0.919 | 0.654 |
| CBL2 | 0.832 | ||||
| CBL3 | 0.812 | ||||
| CBL4 | 0.826 | ||||
| CBL5 | 0.766 | ||||
| CBL6 | 0.822 | ||||
| Perceived value | PV1 | 0.846 | 0.791 | 0.878 | 0.705 |
| PV2 | 0.822 | ||||
| PV3 | 0.852 | ||||
| Repurchase intention | RPI1 | 0.893 | 0.858 | 0.914 | 0.78 |
| RPI2 | 0.847 | ||||
| RPI3 | 0.908 | ||||
| Word-of-mouth | WOM1 | 0.898 | 0.775 | 0.899 | 0.816 |
| WOM2 | 0.909 |
The heterotrait-monotrait (HTMT) ratio of correlation (Henseler et al., 2015) was the second method we used to evaluate the discriminant validity of our notions. HTMT is regarded as a more cautious method for establishing discriminant validity at a cut-off ratio of 0.85. The discriminant validity of our model is demonstrated by the fact that all of the HTMT values in our data were below 0.85.
Structural model
After achieving satisfactory measurement model results, the next step is to test the structural model. The study employed bootstrapping with 5,000 resamples to assess the statistical significance of path coefficients, effect sizes and t-values. Table 3 shows the structural model assessment and hypothesis testing results. Direct relationships indicate that store-related attributes positively influence RI (β = 0.416, t = 5.353), WOM (β = 0.416, t = 4.157), customer brand loyalty (β = 0.628, t = 9.703) and PV (β = 0.821, t = 32.832), supporting H1, H2, H3 and H4. Additionally, customer brand loyalty and PV have a significant influence on RI and WOM, supporting H5, H6, H7, H8 and H9. Regarding mediation, customer brand loyalty notably mediates the relationship between SA and RI (β = 0.142, t = 3.964) and positive WOM (β = 0.129, t = 3.111), while PV significantly mediates between SA and RI (β = 0.237, t = 4.780) and WOM (β = 0.226, t = 3.344), confirming hypotheses H10, H11, H12 and H13. Robustness checks included an endogeneity test using the Gaussian Copula approach, which revealed no significant residual correlations (p > 0.05), indicating model stability. Additionally, a test for nonlinear effects was conducted, where quadratic terms for SA were rejected (R2 < 0.01), supporting the linear relationships. Subgroup analysis compared younger (<35 years) and older (≥35 years) shoppers, revealing age-based moderations: SA had a stronger impact on brand loyalty for older shoppers (β = 0.71 vs. 0.59, p < 0.05), while PV more strongly influenced WOM among younger shoppers (β = 0.48 vs. 0.32, p < 0.01). These findings highlight the need for age-tailored retail strategies, emphasizing store ambiance for older demographics and value-driven experiences for younger consumers, as shown in Table 3.
Summary of hypothesized relationships
| Hypotheses | Original sample (O) | Sample mean (M) | Standard deviation (Stdev) | t-statistics (|O/Stdev|) | p-values |
|---|---|---|---|---|---|
| Store Attributes → Repurchase Intention | 0.416 | 0.414 | 0.078 | 5.353 | 0.001 |
| Store Attributes → Word-of-Mouth | 0.416 | 0.410 | 0.100 | 4.157 | 0.001 |
| Store Attributes → Customer Brand Loyalty | 0.628 | 0.629 | 0.065 | 9.703 | 0.001 |
| Store Attributes → Perceived Value | 0.821 | 0.822 | 0.025 | 32.832 | 0.001 |
| Customer Brand Loyalty → Repurchase Intention | 0.226 | 0.224 | 0.057 | 3.950 | 0.001 |
| Customer Brand Loyalty → Word-of-Mouth | 0.206 | 0.207 | 0.063 | 3.253 | 0.001 |
| Perceived Value → Customer Brand Loyalty | 0.206 | 0.207 | 0.073 | 2.810 | 0.005 |
| Perceived Value → Repurchase Intention | 0.288 | 0.291 | 0.06 | 4.801 | 0.001 |
| Perceived Value → Word-of-Mouth | 0.275 | 0.282 | 0.081 | 3.418 | 0.001 |
| Indirect/mediating relationships | |||||
| Store Attributes → Customer Brand Loyalty → Repurchase Intention | 0.142 | 0.140 | 0.036 | 3.964 | 0.001 |
| Store Attributes → Customer Brand Loyalty → Word-of-Mouth | 0.129 | 0.130 | 0.042 | 3.111 | 0.002 |
| Store Attributes → Perceived Value → Repurchase Intention | 0.237 | 0.239 | 0.049 | 4.780 | 0.001 |
| Store Attributes → Perceived Value → Word-of-Mouth | 0.226 | 0.232 | 0.068 | 3.344 | 0.001 |
Discussion
Brick-and-mortar stores remain the most critical aspect of retailing, despite the likelihood of continual growth in competing channels and formats. In brick-and-mortar retail literature, SA rank among the most recognized factors; however, previous studies on the role of physical store attitudes in shaping consumer attitudes and their repurchase behavior are very fragmented. The current study proposes a comprehensive framework to investigate the impact of store-related attributes on consumers’ PV and brand loyalty, which, in turn, influence their RIs and WOM behavior in the physical retail context. On the other hand, while the S-O-R model is well-established in retail settings, its generalizability to emerging markets like Pakistan, which feature rapid urbanization, hybrid formal/informal retail and pronounced generational divides in digital adoption, remains underexplored. Our findings indicate that not only do classical S-O-R pathways hold, but their strength varies by demographic segment (e.g. age), and the salience of PV as a mediator appears even greater than in many developed market studies. This suggests extensions to S-O-R theory, underscoring the need to model dynamic sociodemographic and infrastructural changes as influences that may amplify (or dampen) the impact of store stimuli on cognitive/affective processing in such settings.
The insights from the current study allow physical retail store managers to enhance the store-related attributes (e.g. aesthetics, services, price, convenience), thereby benefiting consumer PV and loyalty. Our findings support the Chang et al. (2015) arguments that different store-related attributes play a significant role in enhancing customer satisfaction in a retail environment, regarding direct relationships, examining the relationship between the SA, customer repurchase and WOM behavior. Interpreting these results, the particularly high pathway coefficients for brand loyalty and PV underscore the importance of intrinsic and relational cues in physical retail settings. Unlike previous studies that focused on atmosphere or convenience in isolation, the current findings suggest that a composite approach enhancing both practical and emotional aspects of the store most powerfully fosters customer advocacy. The stronger effect among older consumers points to generational differences in store engagement, with practical implications for market segmentation. The results of prior studies also support this notion, highlighting the significant effect of SA on positive consumer experiences in physical retail settings. For instance, according to Grosso et al. (2018), SA have a significant impact on consumer buying patterns in the Indian context. Among the key determinants of consumer buying patterns in a store were store atmosphere, store layout, customer service, visual communication and in-store promotions. Moreover, current research findings validate the direct effect of SA on consumer PV and brand loyalty. The findings are consistent with previous research, including findings that PV and brand loyalty were influenced by retail SA viewed as valuable by the shoppers. Findings of Elmashhara and Soares (2020) and Evangelista et al. (2019) have shown that stores’ exterior design and window displays, ambient conditions, layout and location are also determinants of customer PV in a retail environment.
For the mediating role of brand loyalty, the result illustrated that brand loyalty significantly mediates the relationship between SA, RI and positive WOM, which implies that brand loyalty enhances the impact of SA on shopper RI and positive WOM (Bıçakcıoğlu et al., 2018). In other words, shoppers who place more value on store-related attributes are more likely to make purchases and share positive feedback with others as their loyalty increases. Likewise, regarding the mediating role of PV, findings confirmed that PV significantly mediates between SA, consumer WOM and behavioral intention, which is consistent with the findings of prior literature (Konuk, 2019).
Theoretical implications
Pakistan’s retail ecosystem, characterized by the coexistence of formal hypermarkets and informal corner shops, frequent price-based promotions and a growing influence of social reference groups, provides a context in which traditional S-O-R mechanisms may operate but are filtered through distinct trust, risk and WOM regimes. This setting challenges universalist assumptions in consumer-retail theory and points to the potential for developing context-specific extensions, such as the role of community belonging or cash economy constraints in shaping PV and loyalty formation. This study makes a significant contribution to the S-O-R framework in brick-and-mortar retail within an emerging market. It shows that SA, as composite stimuli like layout, service and pricing, affect internal consumer states—specifically brand loyalty and PV—leading to behavioral responses like RI and WOM. The findings validate the S-O-R model's relevance beyond digital retail, confirming its applicability in physical stores where SA remain crucial amid e-commerce growth. Notably, the study empirically confirms the dual mediating roles of brand loyalty and PV, aligning with S-O-R's emphasis on internal states as bridges between environmental stimuli and behavioral outcomes—a mechanism echoed in recent omnichannel research. By employing a holistic, composite measure of SA rather than isolating individual factors, this research advances the S-O-R framework's utility in capturing the complexity of real-world retail environments. These insights fill a gap in the literature on the mediating mechanisms at play in physical retail and provide a robust theoretical foundation for future studies examining consumer behavior in evolving retail landscapes.
Managerial implication
This study makes a significant contribution to the retail management literature in several ways. It highlights that many studies overlook key store-related attributes. This investigation enhances understanding by using a comprehensive scale of these attributes. It shows the simultaneous impact of store atmosphere and convenience on customer brand loyalty and PV. Additionally, brand loyalty and PV strengthen the influence of SA on repurchase and WOM behavior, indicating that loyal customers are likely to share positive experiences about their preferred brand’s SA. Retailers can enhance customer loyalty and drive sales growth through targeted staff training, optimized store layouts and the integration of advanced technologies. Invest in staff training, layout optimization and Augmented Reality (AR)/Virtual Reality (VR) technologies.
The findings suggest actionable policy implications for retail ecosystems. Urban retail zoning should focus on mixed-use development to enhance foot traffic and mitigate vacancy risks while also supporting community vitality. Small business support through subsidies for upgrades and training can help SMEs compete with larger chains, fostering equity and local reinvestment. Accessibility standards should ensure that features like parking and Americans with Disabilities Act (ADA) compliance benefit marginalized populations, thereby enhancing their PV and loyalty. Finally, consumer protection regulations for pricing transparency and ethical practices build trust and mitigate the risks of negative WOM, promoting long-term customer retention. These policies create equitable, accessible and trustworthy retail landscapes that enhance the benefits of brick-and-mortar stores.
Limitations and future research
Despite some implications, this research has limitations. The cross-sectional data could introduce CMB; therefore, future studies should utilize longitudinal data collection to mitigate this by separating variable measurements over time. This approach reduces the influence of similar cognitive or emotional states on predictor and criterion variables while also addressing social desirability effects, allowing researchers to track changes over time for improved causal inference. For instance, detailed interviews with customers and retail users could provide accurate insights into the variables being studied. Additionally, the study did not examine external factors such as competition and economic conditions that might affect customer behavior, which future research should investigate.
Future research could enhance retail models by exploring moderators such as cultural context (e.g. individualistic vs. collectivist societies) and socioeconomic status, where factors like education and income might moderate the PV–WOM linkages, as higher education correlates with environmental value perceptions. Omnichannel integration (e.g. in-store apps) should also be examined for its impact on physical attributes that drive loyalty, as seen in successful case studies like Nike's app-enhanced shopping experiences. New constructs such as emotional attachment (e.g. consumer–retailer bonds affecting loyalty) and sustainability perceptions (e.g. eco-friendly campaigns influencing brand trust) could deepen our understanding of modern consumer behavior.
Moreover, while the sample includes urban and semi-urban grocery shoppers, it underrepresents rural populations and offline-only customers, potentially limiting generalizability. Reliance on social media recruitment may also skew results toward younger demographics. Future studies should incorporate offline data collection (e.g. in-store surveys) and cross-cultural validation to enhance external validity. Lastly, this research focused on customer brand loyalty and PV as mediating variables; future studies could explore other factors such as trust, brand love and emotional attachment regarding SA and customer behavior.
Conclusion
By revealing these mechanism-rich pathways, this study clarifies how multiple SA act synergistically to create loyal and vocal consumers in emerging markets. The results inform not only current theory but also offer actionable directions for managers seeking to tailor retail experiences by age cohort. Future research should explore these relationships in different retail categories and socioeconomic contexts. By validating the mediating effects of PV and brand loyalty within the S-O-R framework, the research enhances our understanding of how physical environments promote customer retention and advocacy in emerging markets. These insights advance theoretical knowledge and offer guidance for retailers looking to improve customer experiences and foster lasting loyalty in a competitive landscape.
