Evaluating the quality of service in shopping experiences: an adaptation of the retail service quality (RSQS) model

The Indian retail landscape is rapidly expanding on a global scale, projected to become the fifth largest retail destination worldwide, reaching an estimated $2 trillion by 2032 (Boston Consulting Report, 2023). India was ranked second in the 2021 Global Retail Development Index and is expected to become the third largest retail market by 2030 (Deloitte Report, 2023). The retail sector contributes 10% to India's GDP and employs approximately 35 million people, which is about 8% of the workforce. By 2030, it is expected to create an additional 25 million jobs (Kearney Report, 2023). A.T. Kearney's report forecasts that the Indian retail sector will grow at a compound annual growth rate (CAGR) of 25% from 2019 to 2030, increasing revenue from $779 billion in 2019 to $1,407 billion by 2026, potentially surpassing $1.8 trillion. Between 2010 and 2022, the sector's revenue grew steadily, surpassing $2.77 billion. In the next five years, the growth rate is expected to accelerate from 15% to 20%, positioning India as the fifth most promising retail market. Although organized retail penetration remains low, the sector has the potential to expand, contributing 14% to India's GDP by 2032 (Kearney Report, 2023).

The disparity in spending between residents of larger cities and smaller towns has diminished due to increased media exposure, higher literacy rates and improved access to consumer goods. This shift has significantly transformed the retail sector, which is the fastest-growing sector in India. The increasing middle class, a large population and a significant demographic in their 30s present numerous opportunities for entrepreneurial growth in retail. Fast-growing segments include food and beverages, electronics and apparel. Government reforms promoting Foreign Direct Investment (FDI) in various sectors have generated positive sentiments in the retail sector. Notably, small, owner-managed shops make up over 90% of India's retail landscape. Retailing is divided into organized and unorganized sectors, with organized retail expanding at an annual rate of 45–50%. By 2030, organized retail is expected to quadruple, capturing a 34% share of all retail trade. The global economy will benefit from the influx of FDI in these sectors. Over the past two decades, FDI has become a crucial component of development strategies in both developed and developing countries. Increased purchasing power, rising consumerism and the entry of foreign retailers have all contributed to the modernization of Indian retail (IBEF, Boston Consulting Report, 2023).

Technological advancements and mobile applications have significantly impacted customer satisfaction in organized grocery retail outlets (Rashid & Rokade, 2021). Digital retail, driven by technology, enhances the customer experience, with seven e-services quality attributes differentiating leading retail e-brands (Kalia & Paul, 2021). Customers interacting with different social media platforms assess quality differently, and service quality attributes significantly affect supermarkets (Li et al., 2023). Numerous studies on service quality have been conducted both in India and abroad on consumer's perception towards service quality and customer satisfaction (Teas, 1993; Brown, Churchill, & Peter, 1993; Parasuraman, Berry, & Zeithaml, 1991; Abu, 2004). Before 1996, the most popular model used in these studies was SERVQUAL (Parasuraman, Zeithaml, & Berry, 1988). However, since then, many studies have applied the Retail Service Quality Scale (RSQS) model (Dabholkar et al., 1996). Delivering high-quality services is crucial for any organization's success as it promotes customer satisfaction and loyalty. Numerous studies have explored different facets of service quality and its impact on various industries.

The SERVQUAL model (Parasuraman, Zeithaml, & Berry, 1985) identified five dimensions of service quality: tangibles, assurance, empathy, responsiveness and reliability. Findings demonstrate that these dimensions significantly influence customers' perceptions of service quality, with meeting or exceeding expectations enhancing customer satisfaction and loyalty. Another study by Babakus and Boller (1992) and Zeithaml and Bitner (2003) explored how customer perceptions of service quality are related to service encounters. They found that customers' assessments of service quality are based on their interactions with service providers, directly affecting their overall satisfaction. Additionally, Grönroos (2000) emphasized the importance of client involvement in service delivery, highlighting that businesses must actively engage customers to improve service quality and foster positive experiences. Fan et al. (2017) focused on patients' perception of service quality in China and the results proved that all six dimensions of service quality showed a negative relation. This body of research underscores the importance of service quality in promoting customer satisfaction and loyalty. Companies prioritizing service quality by focusing on assurance, responsiveness, empathy and tangibility are more likely to provide satisfying customer experiences and foster enduring client relationships. Dabholkar et al. (1995) also highlighted the strong influence of service quality on customer satisfaction and loyalty, emphasizing the importance of assessing and enhancing service quality in retail environments.

In developing contexts, studies by Yeap Ai Leen and Ramayah (2011) found that while certain dimensions of retail service quality (RSQ) were validated, aspects such as reliability, problem-solving and policy were considered insignificant. The RSQ scale has been effectively applied in various contexts, including grocery stores and online retail, proving to be a useful tool for assessing and improving customer perceptions of service quality in the retail sector. Most research on service quality in specific retail formats has been conducted in urban areas. There is a lack of research comparing service quality among different retail formats in South Indian cities. Therefore, conducting this study is crucial. Additionally, India's booming retail industry has attracted numerous multinational corporations, increasing competition within the sector. This influx of FDI has prompted Indian retailers to enhance service quality, which is vital for shaping customer perception- and satisfaction, and distinguishing between inferior and superior service offerings.

The key focus of the study was on the application of RSQ scale on hypermarkets and supermarkets in south India. Analyzing these two formats separately is essential for formulating effective marketing strategies. Research findings pertinent to the rapid-access, convenience-oriented supermarket model may not be applicable to the large-scale, comprehensive hypermarket model and vice versa (Pauline & Selvi, 2018). Supermarkets and hypermarkets require different research approaches because they operate with distinct scales, customer behaviors and operational priorities. Supermarket research focuses on localized marketing and managing perishable goods, while hypermarket research deals with complex logistics, broader marketing campaigns and diverse product categories. Recognizing these differences helps generate more accurate insights, such as emphasizing store atmosphere for supermarkets and product assortment or navigation for hypermarkets (Jayasankaraprasad, 2014).

The reason for selecting Southern India for the study being that South India acts as the hub of modern retail (hyper-markets and supermarkets), which is driven by high concentration of middle, high-income and wealthy population. Major metros and emerging cities together contribute over $7 billion in retail activity accounting for about 10 % of India's GDP and 8% of employment. South India is standing out to this industry because of its unique, robust, tradition-driven market that offers vast potential and hosts many highly successful regional brands (Kumar, 2020). Anarock, one of the leading real estate companies has reported that in 2022, 98 new malls were established in India covering 48 million sq. ft., with the highest additions in the South (34 malls). South India's mall expansion closely follows the West India (37) and significantly outpaces the North, which add 21 new malls (Kumar, 2020). The other factors that differentiate South India from rest of the country include higher literacy rates, lower population growth rates, higher urbanization offers unique opportunities and untapped potential for retailers in South India. Further, the people in the South speak different languages, have different ethnic backgrounds and have different cultural traditions, as well as the fact that buyers are more sensitive to price and brand, makes retailers use diverse retail strategies in different regions of the South. Strong customer engagement practices and advanced backend technologies further drive the region's retail success.

Since most Indian consumers are shifting their purchasing behavior from local retail outlets to supermarkets and hypermarkets, understanding their service quality expectations is essential for effectively targeting this growing customer base. An in-depth understanding of service quality dimensions can help supermarket and hypermarket operators improve their services and effectively respond to competitive pressure from multinational organized retail formats. Thus, there is a need to investigate service quality elements that affect the expectations and perceptions of the customers using Retail Service Quality Scale (RSQS). This study aims to evaluate shoppers' perceptions of service quality dimensions offered in hypermarkets and supermarkets in South India. More specifically the study attempts to accomplish the following objectives:

1. To examine shoppers' perceptions of the key dimensions of service quality in hypermarkets and supermarkets, and to identify which dimensions contribute most to overall service quality.

2. To examine how supermarkets and hypermarkets differ in shaping shoppers' perceptions of quality.

Research on retail format development and consumer shopping behavior in India has largely concentrated on metropolitan areas, leaving small and emerging cities relatively underexplored (Krishnamurthy & Venkitachalam, 2023; Hiremath, Panda, & Pasumarti, 2023). As India continues to evolve as a significant emerging economy in the retail sector (Dholakia, Dholakia, & Chattopadhyay, 2018), shifting consumer preferences from traditional to modern retail formats have accelerated the expansion of organized retailing in South Indian cities (Goyal & Aggarwal, 2007; Krishnamurthy & Venkitachalam, 2023).

Studies in smaller Indian cities highlight the growing importance of hedonic and utilitarian values in shaping consumer attitudes. Research conducted in the northern region revealed that both value dimensions significantly influence consumers' attitudes toward shopping malls and that age differences further moderate responses to mall attributes (Khare, 2011; Khare & Kautish, 2023). Similar observations in the Philippines indicate that hedonic and utilitarian values, alongside SERVQUAL dimensions, positively shape satisfaction and behavioral intentions (Ong, Prasetyo, Vallespin, Persada, & Nadlifatin, 2022). Mishra, Srivastava, and Srivastava (2025) found that perceived product quality and shopping flexibility drive Indian consumers' adoption of digital storefronts by brick-and-mortar retailers, while delivery timeliness concerns act as key deterrents. These findings affirm that in-store service quality attributes remain central to consumer decision-making even within digitally integrated, omnichannel retail environments.

Service quality remains a critical determinant of shopper satisfaction and positive retail experiences across diverse formats. Multiple studies in India demonstrate its broad significance. In fast-food service settings, SERVQUAL and SERVPERF were applied to identify diagnostic service quality dimensions among consumers in New Delhi (Jain & Gupta, 2004). In life insurance services, product knowledge, empathy, reliability and trust emerged as key antecedents of favorable consumer behavior (Gera, 2011). In the context of departmental stores and hypermarkets in New Delhi and NCR, RSQ was found to comprise ambience and layout, salespeople, merchandise, convenience, services, prices and customer care (Verma & Duggal, 2015). Service quality expectations have been shown to vary continuously across India's diverse cultural contexts (Singh, 2024). Similarly, in logistics services, service quality dimensions positively influence customer satisfaction (Gupta, Singh, Mathiyazhagan, Suri, & Dwivedi, 2023). International evidence from South Africa supports these patterns, showing that atmospherics and reliability significantly affect supermarket shopping frequency (Musasa & Tlapana, 2023). Consumers with a higher appreciation for store design exhibit a comparatively lower satisfaction-to-commitment translation (Faria, Carvalho, & Vale, 2022). This finding highlights that the tangible and aesthetic dimensions of in-store experience complement service quality in shaping retail outcomes.

Store-level operational and experiential factors also play a central role in customer evaluations of organized retail formats. Findings from tier-II cities show that in-store logistics, product assortment, shelf arrangement, store environment, employee interaction and ease of returns significantly drive customer satisfaction (Kumar Singh et al., 2022) and subsequently foster customer loyalty (Bhatia & Bhatt, 2023). In restaurant settings in Delhi and Bangalore, tangibility and assurance have been found to strongly influence customer satisfaction (Biswas & Verma, 2023), reinforcing the broader relevance of service quality across retail and service domains. Rashid and Rokade (2023) established that RSQ significantly shapes customer satisfaction, trust and loyalty in Indian organized retail, with retailer personality mediating this relationship. The study found that a humble and sophisticated retailer personality amplifies positive customer outcomes, while disingenuous traits deteriorate them. Cross-national RSQS validation evidence further confirms that dimension weights vary by format and cultural context, with Pham and Nguyen (2024) finding broad RSQS applicability in Vietnam and Sharma and Yadav (2024) identifying only physical appearance, personal interaction and problem-solving as significant satisfaction drivers in mixed-format Indian retail.

With rapid digitalization in India, understanding online retail service quality (ORSQ) has become increasingly important. Studies indicate that ORSQ significantly enhances customer satisfaction and engagement in the Indian online retail landscape (Singh, Jasial, Misra, & Bansal, 2024). Research from the industrial buyer perspective also reveals that e-service quality within e-marketplaces positively influences online purchase behavior, as demonstrated using Straussian grounded theory (Nandankar, Sachan, Adhikari, & Mukherjee, 2023). Further, service quality in technology-driven platforms such as FinTech services has been investigated through an integrated SERVQUAL–TAM model. The findings show that perceived usefulness and service quality significantly shape user attitudes and behavioral intentions toward digital payment technologies (Sharma, Jangir, Gupta, & Rupeika-Apoga, 2024). Despite the rapid growth of digital retail channels, a significant proportion of consumers continue to favor in-store experiences alongside digital touchpoints, reinforcing the enduring role of face-to-face service quality in driving loyalty across both online and offline retail presences (Sharma, 2024).

Overall, the literature indicates that India's evolving retail landscape – across physical, online and hybrid formats – is shaped by a combination of shopping values, service quality factors, operational attributes and technology-driven experiences. Given the continuous expansion of retailing into smaller cities and the dynamic expectations of consumers, a deeper understanding of shopping behavior across formats is essential for designing effective marketing strategies.

The RSQS, adapted from the SERVQUAL scale (Dabholkar et al., 1996), is a widely used and validated tool for assessing service quality in the retail industry. The RSQS model includes dimensions such as personal interaction and problem-solving, which significantly impact customer satisfaction and loyalty (Kim & Jin, 2002). A study by Hong, Park, and Jeon (2021) validated the RSQS in the online retail domain, revealing that it demonstrates good validity and reliability in estimating service quality online. These findings indicate that the RSQS is effective in evaluating service quality in both traditional brick-and-mortar and online retail settings. Lassar, Manolis, and Winsor (2000) applied the RSQS to grocery store consumers, finding that it accurately measures service quality in this context. The multidimensional RSQS model has five dimensions: problem solving, interpersonal interaction, physical aspect, reliability and policy (Dabholkar et al., 1995). The model is shown in Figure 1. Each dimension's influence on shopper satisfaction with respect to the retailer is discussed as follows:

The term “physical aspects” refers to the availability of facilities, the appearance of the store, the personnel, equipment and visible materials in various retail store formats (Parasuraman et al., 1985; Dabholkar et al., 1995; Siddiqi, 2011; Mersha, Sriram, Yeshanew, & Gebre, 2012). Some studies suggest that physical aspects alone cannot address customer dissatisfaction problems, though they are used by customers to evaluate service outcomes (Brady and Cronin, 2001; Mukherjee et al., 2003). Another study highlights the fact that the tangible dimension has little impact on customer service and satisfaction in Tunisia and Canada (Ladhari, Ladhari, & Morales, 2011). However, a few studies have found that the significance of tangible constructs is evident in predicting shopper impressions, satisfaction and attitudes (Wong & Sohal, 2003; Petridou, Spathis, Glaveli, & Liassides, 2007; Beneke, Greene, Lok, & Mallett, 2012; Kitapci, Taylan Dortyol, Yaman, & Gulmez, 2013). Thus, the following research hypothesis is put forth:

Reliability in retail formats measures a store's ability to consistently deliver on its promises and provide high-quality service to customers without errors (Parasuraman et al., 1988; Kim & Jin, 2002; Abu Khalifeh & Som, 2012; Beneke et al., 2012). In countries such as Korea, Canada, Tunisia (Ladhari et al., 2011) and India (Choudhury, 2014), reliability enhances customer confidence in the store (Wong & Sohal, 2003; Yuen & Chan, 2010). Additionally, reliability is the strongest predictor of customer satisfaction (Wolfinbarger & Gilly, 2003; Kumar et al., 2010). It ensures accurate billing and proper entry of each item at the final checkout (Zhang, Yao, Sun, & Tay, 2019; Zia, 2020; Musasa & Tlapana, 2023). Consequently, reliability is crucial in assessing service quality across different retail formats. Based on this understanding, the study proposes the following hypothesis:

Personal interaction is used to gauge shoppers' perceptions regarding helpful employee service (Kim & Jin, 2002; Zhang et al., 2019). Employees play a critical role in providing personalized assistance and various services during shopping. Furthermore, effective interaction helps resolve customer issues, ensuring that product returns are handled efficiently and grievances are addressed (Zia, 2020). The attention given to personal interaction by employees is positively correlated with customer satisfaction in the store (Gounaris, 2008; Ozdemir & Hewett, 2010; Beneke et al., 2012; Reynaers, 2020). A positive personal interaction strategy significantly improves the problem-solving approach. Therefore, the following hypothesis is proposed for the study:

In general, policies encompass a range of decisions related to services offered to customers, including the quality of assortments, credit facilities, operating hours, convenient parking, payment options and other services (Kim & Jin, 2002; Beneke et al., 2012; Zia, 2020). Effective retail policies encourage customers to visit stores more frequently (Yuen & Chan, 2010) and support the need for convenient purchases and after-sales service (Briggs, Deretti, & Kato, 2020). Therefore, well-crafted policies positively influence customer satisfaction. Consequently, the following hypothesis is proposed for the study:

Accessibility refers to providing convenient services that enable customers to smoothly purchase items (Eriksson & Nilsson, 2007). Innovations in service accessibility play a significant role in customer satisfaction and retention (Bahia & Nantel, 2000). The ultimate goal of accessibility is to enhance shopper satisfaction (Mahlangu & Makhitha, 2019; Das Nair, 2018; Mielmann, 2019). Therefore, the hypothesis stated for this study is:

Problem-solving skills in a store are crucial for managing potential issues such as customer complaints and exchanges (Swanson & Kelley, 2001; Beneke et al., 2012; Zia, 2020). Effectively resolving customer complaints leads to increased satisfaction, enhanced credibility and positive perceptions of the store layout (Beneke et al., 2012; Caruana, 2002; Minh, Ha, Anh, & Matsui, 2015). Therefore, the hypothesis for the study is confirmed as follows:

This study employs a quantitative methodology to assess the service quality of shopping experiences among Indian consumers. The targeted demographic comprises shoppers aged 16 to above 55 years. Data were collected through purposive sampling, incorporating both primary and secondary sources. Purposive sampling was employed to ensure that only respondents who actively shop at supermarkets or hypermarkets and fall within the targeted age group were included. This technique facilitates the collection of data from respondents with first-hand, relevant and recent experience of the retail environment under study (Tajik, Golzar, & Noor, 2025), thereby improving the relevance and accuracy of the research results (Thomas, 2022).

A standardized self-administered questionnaire was used for collecting primary data. A pilot study was conducted across three supermarkets and one hypermarket in Tirupati city, with a sample of 42 respondents. Shoppers were approached immediately after completing their purchases, and the feedback obtained was used to refine the questionnaire for clarity and respondent convenience. The study was restricted to South India, specifically the cities of Chennai, Hyderabad, Bangalore and Tirupati. Questionnaires were distributed equally across supermarket and hypermarket settings, with 100 surveys administered in each format, yielding a total of 200 completed responses. After screening for incomplete and invalid entries, 170 responses were finalized for analysis. This sample size is consistent with recommendations for survey-based retail research, where a minimum of 100 to 200 responses is considered adequate for meaningful statistical inference (Krejcie & Morgan, 1970; Cochran, 1977). According to the US FMI industry association, supermarkets offer a full line of groceries ranging from 15,000 to 60,000 SKUs across a floor space of 750 to 3,500 square meters, while hypermarkets typically cover 5,000 to 15,000 square meters and carry more than 200,000 merchandise brands at any given time. The collected data were analyzed using IBM SPSS 25, employing correlation and regression analysis to assess the impact of service quality dimensions on shopper satisfaction.

The RSQS, developed by Dabholkar, Thorpe and Rent in 1996, was used to measure the differences between customers' perceptions and expectations. This instrument is a key element of the gap model, which focuses on customer-focused quality. The RSQS identifies six dimensions of RSQ: personal interaction, policy, physical aspects, problem solving, accessibility and reliability. Consumers regularly use these metrics to assess services and gauge their level of satisfaction. Table 1 indicates RSQS model consisted of 21 questions, empirically tested using a seven-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree), to measure shopper perceptions towards services provided in supermarkets and hypermarkets. The research tool is a series of structured questions designed to assess different facets of service quality while accounting for customer expectations and perceptions. A seven-point Likert scale was used for the scoring system; 1 represented no expectations for quality to 7 unimaginable expectations, 1 represented no qualities to 7 unimaginable qualities and 1 represented strongly dissatisfied to 7 highly satisfied.

Since this study relies on self-reported data collected through a single questionnaire, the potential for CMB was addressed through both procedural and statistical remedies (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Procedurally, respondent anonymity was assured, predictor and criterion variables were separated across distinct questionnaire sections, unambiguous language was used and respondents were informed that no right or wrong answers existed thereby minimizing social desirability bias and evaluation apprehension. Statistically, Harman's single-factor test was conducted by entering all study variables into an exploratory factor analysis; the first unrotated factor explained 25.8% of the total variance, remaining well below the 50% threshold, indicating that CMB is unlikely to pose a serious concern or substantially distort the relationships examined in this study (Harman, 1976; Podsakoff et al., 2003).

The Cronbach's alpha values are used to test the internal consistency of constructs. It is observed from Table 2 that the Cronbach's alpha values are more than 0.6 (Nunnally, 1978). According to Hair, Anderson, Tatham, and Black (2010), validity measures the extent to which the set of indicators accurately represent the construct. The average variance extracted (AVE) values are more than the threshold value 0.5 (Fornell & Larcker, 1981). Hence, the AVE results indicated convergent validity.

Table 3 indicates that out of the 170 respondents, 92 (54%) were male, while the remaining 78 (46%) were female. 52 respondents (31%) were between 36 and 45, followed by 38 respondents (22%) were between 26 and 35, 30 respondents (18%) were between 46 and 55, 26 respondents (15%) were above 55 and 34 respondents (14%) were in the 16–25 age group. A total of 124 respondents (73%) are married and 46 respondents (27%) are unmarried. Further these 170 respondents, 133 (78.4%) visit on weekly basis, 26 (15.2%) visit on monthly basis and remaining 11 (6.4%) visit on daily basis to retail stores.

To address objective 1 of the current study, descriptive statistics measures of mean and standard deviation (SD were calculated for both hypermarkets and supermarkets. The results of the descriptive statistics are presented in Table 4 (Indicator wise Mean and SD) and Table 5 (Construct Wise Perceived Service Quality Score). The results for hypermarkets in Table 4 indicate that the retail layout is clean, attractive and convenient has highest mean (4.99) in physical aspects construct. Customers who are able to get products and services when they require them has highest mean (5.26) in Reliability construct. Employees are consistently polite with customers has highest mean (5.24) in Personal interaction construct. Retail store offers high-quality merchandise has the highest mean (5.26) in Policy construct. The queues are quickly moving during transactions have highest mean (4.99) in Accessibility construct. Store shows sincere interest to solve its customers' problem has highest mean (4.9) in Problem solving construct

The results for supermarkets reveal that the store keeps items for customers easy to find what they need has highest mean (5.00) in physical aspects construct. Customers use to get error free sales transactions and records has highest mean (5.26) in Reliability construct. Employees are able to answer customers' questions has highest mean (5.38) in Personal interaction construct. Store provides convenient parking space for customers has (5.60) in Policy construct. The goods are placed on the shelves easily accessible has highest mean (4.92) in Accessibility dimension. Store shows sincere interest to solve its customers' problem has highest mean (5.00) in Problem solving construct. The SD of all the items measuring respective constructs is less than 1 for both hypermarkets and supermarkets, which infers that the mean scores exhibit less dispersion and are closely clustered around the mean. It indicates that the data are more stable and uniform.

The results presented in Table 5 reflect the perceived service quality score for both hypermarkets and supermarkets on the select quality parameters. The service quality score was obtained on a 7-point rating scale. The results decipher that perceived service quality score of all the dimensions for hypermarkets vary between 4 and 5.5. The highest score for service quality was obtained for physical aspects (5.26) followed by accessibility (5.24). Personal interaction in hypermarkets was rated as lowest on perceived service quality with a mean score of only 4.78. Respondents feel that physical aspects along with accessibility to the products in hypermarkets are the most critical parameters of retail operations. The overall perceived service quality score for hypermarkets is reported at 5.01 with standard error (SE) of 0.70.

The results further reveal that accessibility (4.79) is perceived as the strongest service quality dimension in supermarkets followed by physical aspects (4.77). However, personal interaction is perceived as the weakest service quality determinant with a mean service quality score of 4.39. The supermarkets received overall score of 4.58 for all the service quality dimensions with S.E 0.88.

Furthermore, it can be observed from the results displayed in Table 5 that hypermarkets exhibit the highest service quality score against supermarkets. The highest mean difference between hypermarkets and supermarkets was observed in physical aspects (0.49) and reliability (0.48). It indicates that these two dimensions are perceived as very critical and contribute most towards superior quality of hypermarkets. Hypermarkets seem to attract and satisfy customers by their huge servicescape elements and accessibility to diverse product assortments with greater product depth, which supermarkets do not possess by their nature. It can be deducted from the observable differences that customers perceive hypermarkets deliver better service quality than supermarkets. Although, hypermarkets exhibit higher score across all RSQ dimensions, the differences in mean service quality are not statistically significant.

Table 6 reveals that the accessibility dimension is having comparatively high correlation (0.47) with satisfaction followed by physical aspects (0.37), reliability (0.30), problem solving (0.26), policy and personal interaction (0.24).

Before conducting regression analysis to test the underlying hypotheses proposed for hypermarkets and supermarkets, collinearity diagnosis was conducted. The collinearity between the independent constructs was examined by obtaining the VIF score. The results presented in Table 7 reveal that the VIF score for all the independent construct is less than the acceptable threshold limit of 5 (Hair et al., 2010). Hence, no multi-collinearity issue was reported and data are considered fit for further examination.

The hypotheses testing results are presented in Table 7. The exogenous variables regressed with the endogenous construct have obtained an R-square value of 0.408, which deciphers that the variables explain around 40% variance in satisfaction. For studies where the motive is to explain behavioral outcomes like intention, motivation or satisfaction, the R-square value greater than 0.20 is considered as substantial (Hair et al., 2010).

The regression results for hypermarkets revealed that physical aspects, problem solving and accessibility have a significant positive impact on satisfaction with β = 0.49, 0.35 & 0.57 respectively and p < 0.05. Based on these results hypotheses, H1a, H5a and H6a are supported. However, the relationship between reliability, personal interaction, policy and satisfaction was reported as statistically insignificant. The size of the regression coefficient reveals that accessibility is the strongest determinant of shopper satisfaction for hypermarkets followed by physical aspects of the store. The inferences drawn confirmed that there is contextual significance with respect to accessibility, physical aspects and problem solving in the evaluation of service delivered in the hypermarkets. These results are consistent with the findings of Das, Kumar, and Saha (2010), Khudri and Sultana (2015) and Zia and Khan (2018). Researchers such as Das Nair (2018) and Mahlangu and Makhitha (2019) have also deciphered that the ultimate aim of accessibility is to improve satisfaction among shoppers. Moreover, the findings of Beneke et al. (2012) and Kitapci et al. (2013) also support the results of the current study. They have reported that tangible aspects of shopping centers are imperative for predicting shoppers' satisfaction. Lastly, the results that problem solving is a significant predictor of satisfaction find support from the studies of Beneke et al. (2012) and Minh et al. (2015), who found that effective problem solving or complaint resolution mechanisms enhance satisfaction and lead to higher credibility of the store.

Table 8 shows positive correlation between the dimensions and Shopper's Satisfaction. Among all dimension reliability is having comparatively high correlation (0.54) with satisfaction followed by accessibility (0.51), policy (0.40), physical aspects (0.30), personal interaction (0.24) and problem solving (0.23).

The collinearity diagnosis results presented in Table 9 reveal that VIF values for all the independent constructs is less than the threshold limit of 5. It indicates that no multicollinearity issues persist among the study variables, and the data are fit for regression analysis.

The results of the regression analysis for supermarkets reveal that all the variables regressed with satisfaction have obtained an R-square value of 0.408. It infers that the independent variables together explain around 40% of variance in satisfaction, which is considerably large for behavioral studies.

The results presented in Table 9 further reveal that reliability, personal interaction and accessibility have a significant positive impact on satisfaction among supermarket shoppers with β = 0.41, 0.29 and 0.34 respectively and p < 0.05. Based on these results, hypotheses H2b, H3b and H6b are supported. It can be observed from the regression coefficients that reliability (Kumar et al., 2010) is perceived as the strongest determinant of satisfaction by supermarket shoppers followed by accessibility. The results of the study corroborate with the findings of Das et al. (2010), Ozdemir and Hewett (2010), Khare (2013), Khudri and Sultana (2015) and Osakwe (2022), who argued that these service quality dimensions have a significant impact on the customer purchase intention and satisfaction in different retail formats. The results are consistent with the findings of Choudhury (2014) who argued that reliability in India enhances customer confidence in the store. Reliability is ensured through safe billing mechanisms and proper checkouts (Zia, 2020; Musasa & Tlapana, 2023). Lastly, the study found that personal interaction plays an important role in enhancing satisfaction for supermarkets. The results are in line with the findings of Beneke et al. (2012) and Reynaers (2020), who deciphered that personal interaction and attention given by employees to shoppers enhances satisfaction levels. The study conducted by Zia (2020) also reported that effective interactions help to resolve customer issues and make the shopping experience comfortable.

Furthermore, the results decipher that there is an insignificant impact of physical aspects, policy and problem solving on satisfaction among supermarket shoppers. Unlike hypermarkets, supermarkets do not possess large physical environments that can allure customers. There are structural constraints which limit the physical aspect of supermarkets. Customers largely perceive supermarkets as means of easy shopping solutions, without paying much attention to the size of its servicescape. To enhance the shopping experience, supermarkets should focus on enhancing the store ambiance of the limited shopping space.

The objectives of the study are to examine shopper perceptions and identify the critical service quality dimensions influencing satisfaction in hypermarkets and supermarkets in South India. The findings reveal that shoppers' perceptions of physical aspects and reliability are more favorable compared with other service quality dimensions in hypermarkets. Furthermore, there is a substantial difference found for overall quality expected and perceived by the shoppers, indicating hypermarkets are more customer oriented. According to the regression coefficients value, accessibility is the most critical factor in determining a shoppers' satisfaction, followed by physical aspects and problem-solving dimension. The results of the study support the studies of Das et al. (2010), Khudri and Sultana (2015), Zia and Khan (2018), Zia and Hashmi (2019). Among supermarket shoppers, accessibility is observed to rank first with the highest mean score, followed by physical aspects, problem-solving, reliability, policy and personal interaction. The regression output reveals that reliability is the major critical dimension, followed by accessibility and personal interaction as per the coefficients value for shopper's satisfaction in supermarkets. The results of the study confirmed and supported the work of Ozdemir and Hewett (2010), Khare (2013), Khudri and Sultana (2015), Osakwe (2022). The findings also indicate that there is a significant difference between the overall quality expected and perceived, suggesting that supermarkets should focus more on service quality in order to compete with hypermarkets.

This study makes several meaningful contributions to retailing and service quality literature, particularly in the context of organized retail formats in South India. First, by empirically testing the RSQS across supermarkets and hypermarkets, the study extends the applicability and robustness of the RSQS model within emerging market settings. The findings demonstrate that the relative importance of service quality dimensions varies across retail formats, thereby reinforcing the argument that service quality is context-specific rather than universal. This contributes to the growing body of cross-cultural and cross-format RSQS validation studies by providing empirical evidence from an underexplored South Indian context.

Second, the study enriches theoretical understanding by identifying accessibility as a dominant determinant of shopping experience in hypermarkets and reliability as the most influential factor in supermarkets. This differentiation advances existing service quality frameworks by highlighting how format characteristics (store size, customer flow and assortment complexity) shape consumer evaluations of service quality in ways that generic models may fail to capture. Future theoretical models of RSQ should therefore incorporate format heterogeneity as a moderating variable.

Third, by linking RSQS dimensions directly to shopping experience, the study bridges the gap between service quality theory and experiential consumption literature. The findings suggest that functional service elements such as physical aspects, problem-solving mechanisms and personal interaction play a crucial role in shaping experiential outcomes, particularly in brick-and-mortar retailing. This connection is especially significant given the growing discourse on omni-channel retailing, where physical store service quality continues to anchor consumer loyalty even as digital touchpoints proliferate.

Finally, the study provides a valuable theoretical reference for future academic research in South Indian cities and similar emerging economies, offering a validated framework for comparative studies across retail formats, regions and consumer segments. Policymakers and researchers can also utilize these insights to better understand how service quality dimensions influence consumer welfare and retail competitiveness in rapidly urbanizing markets.

The findings offer several actionable insights for retail managers, practitioners and policymakers operating supermarkets and hypermarkets in South India and comparable emerging market contexts. For hypermarkets, the study highlights accessibility, physical aspects and problem-solving mechanisms as critical drivers of shopping experience. Managers should prioritize improving retail accessibility by ensuring faster queue movement at billing counters, providing clearer in-store signage, offering better parking facilities and maintaining systematic shelf arrangements to help customers locate products easily. Since accessibility emerged as the strongest determinant, operational efficiency during peak shopping hours should receive immediate managerial attention.

The physical aspects of hypermarkets also significantly enhance customer experience. Retail managers should focus on maintaining store cleanliness, adopting convenient and intuitive layouts, investing in modern equipment and fixtures and creating visually appealing store environments. Ensuring that products are easy to locate and stores are aesthetically pleasing can positively influence customer perceptions, dwell time and purchase intentions - making store aesthetics a strategic lever for competitive differentiation.

In addition, an effective problem-solving mechanism is essential for improving shopping experience in hypermarkets. Managers should train employees to show sincere interest in resolving customer issues, handle returns and exchanges efficiently and address customer complaints promptly. Empowering employees with the authority and skills to resolve problems at the point of interaction can significantly strengthen customer trust and reduce dissatisfaction-driven attrition.

For supermarkets, the study identifies reliability as the most critical determinant of shopping experience, followed by accessibility and personal interaction. Managers should ensure consistent availability of goods and services, maintain error-free billing and record-keeping, deliver consistent product quality and honour return and exchange policies as promised. Reliability in routine service transactions builds cumulative trust, which is particularly important for retaining the repeat-purchase customer base that underpins supermarket profitability.

Lastly, personal interaction plays a vital role in supermarkets. Retailers should focus on recruiting and training staff with strong product knowledge, courteous behavior and a customer-oriented attitude. These aspects can be effectively embedded into HR policies related to recruitment, training and performance evaluation. In smaller-format stores where customer–employee contact is more frequent and intimate, the quality of personal interaction can serve as a meaningful point of differentiation.

From a broader strategic perspective, both supermarket and hypermarket operators should recognize the fact that service quality investment is not a cost centre but a source of sustained competitive advantage, particularly as organized retail continues to expand into tier-II and tier-III Indian cities. Policymakers and retail associations can leverage these findings to develop sector-specific service quality benchmarks and training standards that support the professionalization of organized retail in emerging urban markets. Future investment decisions regarding store expansion, workforce development and operational redesign should be informed by the format-specific service quality priorities identified in this study.

The present study, while generating meaningful insights into RSQ across supermarkets and hypermarkets in South India, is subject to several limitations that simultaneously open productive avenues for future research. Geographically, the study was restricted to four South Indian cities, limiting the generalizability of findings to other Indian states, tier-II and tier-III cities and broader emerging economies. Future research should extend this framework to other regions of India and comparable markets in Southeast Asia, Africa and Latin America, enabling cross-cultural comparisons of service quality perceptions across diverse consumer populations and strengthening the external validity of the RSQS model.

Methodologically, the cross-sectional quantitative design captures consumer perceptions at a single point in time. Future research would benefit from longitudinal designs to track how service quality perceptions evolve in rapidly changing retail environments. Mixed-methods approaches (integrating quantitative surveys with in-depth interviews, focus group discussions or ethnographic observation) could yield richer insights into experiential dimensions of RSQ that structured questionnaires may not fully capture. The study examined direct relationships between RSQS dimensions and shopper satisfaction without incorporating moderating or mediating variables. Future research should explore the moderating roles of demographics (including age, gender, income and education) alongside shopping frequency and motivation (utilitarian versus hedonic). Mediating variables such as customer trust, perceived value, store image and emotional satisfaction could further illuminate the mechanisms through which service quality translates into loyalty and patronage intentions. The sample size of 170 respondents, while adequate for the present analysis, constrains statistical power; future studies should employ larger samples to enable more sophisticated techniques such as structural equation modelling and multi-group analysis.

Additionally, future research should broaden the scope to include the retailer's and employee's perspectives, investigate security as an emerging RSQS dimension and comparatively examine service quality across online, offline and omni-channel retail formats. Finally, integrating the Technology Acceptance Model (TAM) with RSQS frameworks would allow researchers to examine how technological infrastructure (including self-checkout systems, digital payments and AI-assisted navigation) shapes service quality perceptions and user intentions, particularly in tier-II and tier-III Indian cities where digital retail adoption is accelerating.