How service operations, perceived benefit, and psychological ownership enhance customer retention in retail - evidence in Vietnam supermarkets

Abstract In a world with enormous opportunities and challenges from the 4.0 revolution and the lingering COVID-19 pandemic, customer retention is more important than ever for retailers. While marketing and advertising can be more or less limited during the pandemic, retailers pay more attention to the supply and service operations of products as salvage to satisfy the essential demands of customers. However, few scholars discuss the effects of service operations on customer retention in retail because it is lower consumer awareness and challenging to measure accurately and adequately. Therefore, with the foundation of commitment-trust theory, this study examines service operations’ direct and indirect effects on customer retention through perceived benefit in omnichannel retailers. Simultaneously, it assesses how psychological ownership affects customer retention and moderates the effect of perceived benefit on customer retention in the Vietnamese supermarket as empirical evidence. The combination of a qualitative method (with 32 in-depth interviews) and a quantitative method (through a survey conducted with 374 shoppers) is implemented. Partial least-squares structural equation modelling with SmartPLS software is utilized for data analysis and hypothesis testing. From the findings, the study offers an operations perspective and a customer view of how to store service operations contribute to customer perception of benefits and customer retention. Interestingly, the study discovered that psychological ownership is not only a critical antecedent of customer retention but also enhances the effect of perceived benefit on customer retention as its moderating role.

commitment-trust theory, this study examines service operations' direct and indirect effects on customer retention through perceived benefit in omnichannel retailers. Simultaneously, it assesses how psychological ownership affects customer retention and moderates the effect of perceived benefit on customer retention in the Vietnamese supermarket as empirical evidence. The combination of a qualitative method (with 32 in-depth interviews) and a quantitative method (through a survey conducted with 374 shoppers) is implemented. Partial leastsquares structural equation modelling with SmartPLS software is utilized for data analysis and hypothesis testing. From the findings, the study offers an operations perspective and a customer view of how to store service operations contribute to customer perception of benefits and customer retention. Interestingly, the study discovered that psychological ownership is not only a critical antecedent of customer retention but also enhances the effect of perceived benefit on customer retention as its moderating role.

Introduction
In the new global economy, there is evidence that retailing, the chain of activities related to purchasing and providing goods and services to consumers (Cox & Brittain, 2004), plays a crucial role in the supply chain (Lysons & Farrington, 2020). Furthermore, retailing is a critical sector in any economy, which rapidly changes markets, businesses, and consumer shopping behaviour (Hübner et al., 2018). As an inevitable trend of the 4.0 technology revolution, it deals with novel challenges and complexities (Caro et al., 2020;Mou et al., 2017). The last decades have witnessed a growing trend toward modern retail with significant transformations and innovations. Now, in the mature stage, retailers have various formats, such as convenience stores, conventional supermarkets, superettes, hypermarkets, food-based superstores, combination stores, box (limited-assortment store), hard discount stores, warehouse stores (Berman et al., 2018;Gauri et al., 2021;Zentes et al., 2017). The diversity of retail channels and store formats brings customers more and more experiences and benefits (Alexander & Cano, 2020;Dunne et al., 2011) and contributes a significant global sales proportion.
Regarding the trigger of retail innovations, the breakthrough of ICT generates the introduction of e-shopping as one of the most important achievements in the retail industry (Shankar et al., 2021). Although physical stores account for most worldwide retail sales, e-retailers or non-store retailers rapidly encroach the retail market worldwide. At the tipping point of innovation, the omnichannel retailer can be recognized as superior to create a more substantial competitive advantage with the excellent combination of in-store and online shopping, bringing more benefits to consumers (Alexander & Cano, 2020;Turner & Gardner, 2014). Moreover, the COVID-19 pandemic strongly negatively impacts people's lives and the global economy, and indeed, retailing is also severely damaged by it (Roggeveen & Sethuraman, 2020). While marketing campaigns and advertising activities are limited during the pandemic outbreak and social distance, omnichannel retailers are seeking short-term operating approaches (e.g., delivery, inventory, and supply chain aspects) to ensure customers' essential needs and simultaneously attempt to predict customer behaviour after pandemic as likely new-normal (Gauri et al., 2021;Hwang et al., 2020;Roggeveen & Sethuraman, 2020). Therefore, store operating performance with a customer-centric approach is a considerable long-term solution for supermarkets to increase customer benefits and retention (Gupta & Ramachandran, 2021;Roggeveen & Sethuraman, 2020).
As a critical demand, scholars and omnichannel retailers should investigate customer retention and its antecedents from an operations perspective in a rough context with the negative impacts of the severe pandemic. Therefore, inspired by the gaps between academic knowledge and business practice, this study aims to discover whether supermarket service operations, in terms of operational service factors, increase PB and CR, with the Vietnamese retail context as empirical evidence. The study also predicts whether the psychological ownership of the customer affects their shopping behaviour and attitude when repurchasing in supermarkets.
In sum, the research questions are presented below: a. Which service operational factors support customer benefits and customer retention in supermarkets?
b. Does the perceived benefit generated by operational service factors support customer retention in supermarkets?

c. Does psychological ownership increase customer retention and moderate the relationship between perceived benefit and customer retention in supermarkets?
To answer these questions, the objectives are set to (i) identify the operational service factors that directly affect CR and indirectly affect CR through BP, (ii) examine the mediating role of PB in the relationships between the service operational factors and CR, and (iii) investigate the impact of PO on CR and its moderating role on the effect of PB on CR. Based on the commitment-trust theory (Morgan & Hunt, 1994), a conceptual model to represent the direct and indirect effects through PB of the service operational factors on CR is formulated. Simultaneously, the model presents the moderating role of PO on the effect of PB on CR, which allows us to predict whether the relationship between PB and CR can change due to PO. A combination of Qualitative and Quantitative research is deployed. Partial least-squares structural equation modelling (PLS-SEM) with SmartPLS is utilized for data analysis and hypothesis testing.
The remaining structure of this study takes the form of five sections, consisting of Related literature, Conceptual framework and hypothesis formulation, Research methodology, Research findings and Discussions, and Conclusion.

Retailing and service operations
While various definitions of the term retailing have been suggested, Cox and Brittain (2004, p. 3) indicate that "Retailing is the sale of goods and services to the ultimate consumer for personal, family, or household use". Similarly to any business, a retailer has three core functions: Operations, Finance, and Marketing (Stevenson, 2018), in which Operations function is central because its performance of products and services delivery positively affects customer satisfaction and customer loyalty in the long term (Kumar et al., 2011;N. C. Slack et al., 2016). Indeed, what customers experience during the shopping journey results from numerous efforts of relevant functions, mainly operations (Hübner et al., 2018).
Mentioning retail operations, Kandampully (2012) claims that all aspects of modern retail business primarily concern product-related and service-related issues, from procurement to stakeholder relationship establishment, and stores are where goods and services are delivered. However, the service operations, which significantly contribute to the loyalty of the customers and substantially improve CR (Bojei et al., 2013;Reddy et al., 2011;Stevenson, 2018), are invisible to supermarkets customers (Ray & Chiagouris, 2009). Regarding input resources of services, Johnston et al. (2021) and N. C. Slack et al. (2016) identify two groups: (i) Transforming resources (e.g., staff, facilities, technology, knowledge, etc.) and (ii) Transformed resources (e.g., materials, information, customers, etc.). As a result, based on the retail operations perspective, combined with merchandise, services create retail synergy to bring more customer values and benefits and increase customer retention and loyalty (Johnston et al., 2021). Johnston et al. (2021) argue that retailing is a special industry with essential services, of which input resources consist of People (staff), Locations (premises and facilities), Technology (ICT systems), and Service processes (knowledge) to create the "products" in terms of customers' experience, benefits, emotions, judgment, and purchase intentions.

Customer retention and its importance in retail
Theoretically, customer retention (CR) has been an exciting topic for scholars since the mid-1990s (Ang & Buttle, 2006) and a more critical issue of any business organization than ever (Cambra-Fierro et al., 2021), especially in retail (Dunne et al., 2011). Many scholars claim that CR can be understood as the capacity of a company or the ability of a product to maintain a continuous business relationship with customers for the long term (Vroman, 1996). According to R. L. Oliver (1997, p. 392), CR means "Deeply held commitment to rebuy or repurchase a preferred product or service consistently in the future, despite situational influences and marketing efforts having the potential to cause switching behaviour". This definition is close to those of Buttle (2004Buttle ( , 2009) and Alberts et al. (2018), who define CR as a company's effort to promote a sustainable long-term business relationship with its customers, or the continuity of relationships between the organization and the customers. In particular, Srivastava et al. (2018) have a new definition of usable in retail that CR is the tendency or attitudinal measurement of existing customers for repurchasing.
Based on previous studies, Buttle and Maklan (2019, p. 95) practically define CR as follows: Customer retention is the number of customers doing business with a firm at the end of a financial year expressed as a percentage of those who were active customers at the beginning of the year.
Regarding the customer lifecycle, Buttle (2004), Maklan (2015, 2019), Zentes et al. (2017) claim that it is of three main stages: (i) customer acquisition, (ii) customer retention, and (iii) customer development, in which customer retention is the stage that every enterprise wants to prolong as much as possible. CR is always one of the retailers' most crucial business concentrations (Ahmad & Buttle, 2002;Aspinall et al., 2001;Buttle, 2009;Dunne et al., 2011;Kanwal & Rajput, 2014). Additionally, Payne et al. (1998) argue that strong correlations exist between company profitability, service quality, and CR. They further explain that the company benefits from increased customer expenditure from high repurchase frequency, less price-sensitive, willingness to pay a price premium, and significant sources of referrals. In contrast to other researchers, Bojei et al. (2013) argue that loyalty can measure CR, and loyal customers usually stay with the firm and repurchase; however, customers who make a repeat purchase or retain are not necessarily loyal to the firm.
Additionally, CR rates can significantly influence company profitability, while customer satisfaction does not always guarantee customer loyalty (Stevenson, 2018); therefore, retailers should design a retention strategy to keep customers shopping as much as possible. In the context of aggressive competition among retail channels, the concept of patronage in retailing is developed by Blut et al. (2018) as the interchange between retailers and shoppers, in which retailers offer services to obtain their shoppers' positive attitude and behaviour for long-term retention. Additionally, in empirical research, Kamran Disfani et al. (2017) also emphasize that the satisfaction-loyalty link in retail is more important than ever with store format enhancements.
Regarding CR antecedents, generally, most studies focus only on customer satisfaction, customer loyalty, and perceived values from a marketing point of view. These common antecedents have been shown to directly result in CR or mediate between other factors and CR. Additionally, many scholars have discussed the possible impact of operations on CR (Ahmad & Buttle, 2002;Huarng & Yu, 2020;Kanwal & Rajput, 2014;Ray & Chiagouris, 2009) in the retail industry in recent years. While the main remains just partially reflect an operational prerequisite role in delivering perceived benefits for customer satisfaction or loyalty and retention (Aparna et al., 2018;Blut et al., 2018;Bojei et al., 2013;Hanaysha, 2018;Huarng & Yu, 2020;Julian et al., 2015;Sharmeela-Banu et al., 2012).
According to Ahmad and Buttle (2002), Aspinall et al. (2001), and Maklan (2015, 2019), historical data and related metrics, in terms of Key Performance Indicators (KPIs), such as customer retention rate, profit-adjusted retention rate, cost of Customer retention or Costeffectiveness of Customer retention tactics, are usually used to measure CR in practice. As a result, CR measurements in retail from an operations perspective are very different. However, they are also based on three main approaches: Behavioral measurements, Attitudinal measurements, and Composite measurements (Bowen & Chen, 2001;Oliver, 1999). In contrast, Aspinall et al. (2001) discover that managerial employees usually use the metrics, e.g., trends in sales, sales and sales at the individual level, percentage of customers buying and frequency as behavioural measurements, and measure of declared loyalty/commitment, customer attitude, and product preferences as attitudinal measurements.

Perceived benefit in retail
While a variety of opinions on Perceived benefit (PB), one definition is close to those of Chandon et al. (2000) and Liu et al. (2013), who define PB as the beliefs of positive outcomes associated with behaviour, usually in a specific transactional context or for an individual's perception. Also, PB is presented as a positive value as customers' expectation of the quality of products or services (Gan & Wang, 2017;Goraya et al., 2020;Hult et al., 2019;Zeithaml, 1988), one of the results of the service operations process (Johnston et al., 2021;N. C. Slack et al., 2016). Especially in retail, PB is the customer's perception of the best fit their expectation when shopping through variety, convenience, trust, risk, etc. (Zhu et al., 2018). In other words, PB is the beneficial effect of a specific transaction, a positive outcome, or an advantage of shopping (Hasim et al., 2019). For e-retailing, PB is described as an expected reward for customers (Komalasari et al., 2021;Koohikamali et al., 2019) or as the total benefit that satisfies consumer needs and wants (Tanadi et al., 2015).
"Today's value-conscious customers are neither impressed by the best product nor persuaded by the lowest price alone. Instead, consumer purchase decisions are often guided by a careful assessment of what benefits they obtain" (Mazumdar, 1993, p. 29). In other words, customers pay more attention to their shopping benefits and cost-effectiveness to save money, time, and effort and maximize their experience and satisfaction (Y. K. Kim & Kang, 1997;Y. K. Kim et al., 2002Y. K. Kim et al., , 2014Zeithaml, 1988). Therefore, the perceived benefit of the customer becomes significant for retailers to satisfy customers and lead to greater loyalty of customers to stores (Alreddy et al., 2019;Kyguoliene et al., 2017). When discussing shopping benefits based on the marketing view, Y. K. Kim et al. (2014) present that customers seek the benefits or outcomes of purchasing, such as pleasure and convenience from stores and quality merchandise at a reasonable price. Hence, many scholars conclude that offering more benefits and value to the customer's service experience beyond the core products can allow firms to benefit from loyal customers, lead to brand commitment and brand equity (Goraya et al., 2020;J. Oliver, 2014;Zhu et al., 2018). Also, Pal and Byrom (2003) argue that operational factors (i.e., Stock, Space, Staff, Systems, and Standards) can contribute to retailers' customer benefits. Alberts et al. (2018) define that Customer benefits show how the customer may benefit from the products and services; hence, Retailers should understand customer needs to provide what exceeds their expectations for survival and success (Pal & Byrom, 2003).
Concerning the antecedents of PB, scholars provide various concepts depending on industry specifications, distribution channel format, or perspectives of marking or operation. Typically, J. Oliver (2014), Kyguoliene et al. (2017), Steyn et al. (2010) claim that loyalty programs or marketing strategies generate PB. In contrast, other scholars argue that PB is derived from perceived product quality and price (Sweeney & Soutar, 2001;Yeh et al., 2020;Zeithaml, 1988), service quality, innovation (Yeh et al., 2020), store image, price image (Diallo, 2012;Yeh et al., 2020). Particularly in the retail sector, Mishra et al. (2012) claim that PB's dimensions are Saving, Quality, Convenience, Value expression, Exploration, and Entertainment derived from sales promotion. H. -Y. Kim et al. (2013) and Kyguoliene et al. (2017) argue that retailers' loyalty programs can create customer benefits such as money-saving, convenience, entertainment, recognition, and social benefits. Unlike previous researchers, Goraya et al. (2020) and Zhu et al. (2018) believe that offline and online channel integration generates customer benefits, including perceived variety, convenience, and risk reduction. However, not many researchers have paid particular attention to the effects of operational service factors on PB. Mentioning PB's consequences, positively and surprisingly, almost all scholars agree that they are commonly patronage (or patronage intention), purchase intention, satisfaction, retention, or loyalty (Gan & Wang, 2017;Goraya et al., 2020;H. -Y. Kim et al., 2013;Kyguoliene et al., 2017;Zeithaml, 1988;Zhu et al., 2018).

Psychological ownership and consumer behaviour
Recent studies show that psychological ownership (PO) is closely related to consumer behaviour, especially in Revolution 4.0 and intense competition among retail channels (Morewedge et al., 2021;Peck & Shu, 2018). The concept of PO is derived from the extension of self-theory (Peck & Shu, 2018) and described as "the state in which individuals feel as though the target of ownership (material or nonmaterial in nature) or a piece of it is 'theirs' (i.e., 'It is mine!')" (Pierce, Pierce et al., 2001, p. 299, 2003. In other words, PO is defined as the individuals' feelings of ownership or sense of possessiveness toward a target thing (Blut et al., 2018;Julian et al., 2015;Peck & Shu, 2018). Furthermore, Pierce et al. (2003) also explain that PO refers to the sense of possession that happens even without legal ownership. According to Peck and Shu (2018), PO can predict positive consumer attitudes or behaviour, such as willingness to pay more for a product or service, word-ofmouth, or purchase intention. Understanding the motives and routes of PO can help marketers and managers predict customers' behaviours; therefore, it is recommended that retailers in the technology-driven evolution preserve customer PO as a priority of business strategy.
In a study not so long, Q. Zhao et al. (2016) illustrate that PO significantly affects customer loyalty, while J. H. Kim et al. (2021) claim that PO impacts consumers' behavioural outcomes and influences their satisfaction. In recent research, I. T. Lee et al. (2021) demonstrate the relationships between PO and customer trust, satisfaction, perceived benefit, and perceived risks in the service industry. Additionally, Kirk and Swain (2018) demonstrate that digital technologies can improve PO by giving opportunities to consumers and showing or developing their feelings of ownership in the diverse digital environment (e.g., websites, virtual worlds, or social networks). Thus, omnichannel retailers should offer a practical and convenient shopping environment for customer experience or attractive imagery design on websites or any media channel for enhancing PO to obtain customer trust, satisfaction, perceived benefit, and CR.

Conceptual framework and hypothesis formulation
When reviewing the studies of trust, Paluri and Mishal (2020) mention that trust is described as the belief that a party's promise is trustable and that party will carry out its responsibilities in an exchange relationship (Schurr andOzanne, 1985, cited by Paluri &Mishal, 2020). In contrast, commitment is when the trading parties are willing to devote energy to sustaining the relationships (Dion et al., 1992, cited by Paluri & Mishal, 2020. A commitment between parties can be understood as the willingness of buyers and suppliers to exert effort based on their relationships (Paluri & Mishal, 2020). Morgan and Hunt (1994) claim that long-term relationships between organizations and individuals are impossible without trust and commitment. Interestingly, Liang and andWang (2006) and Mahmoud et al. (2018) imply that building customer trust is one of the critical strategies to ensure customer retention. At the same time, Wong and Sohal (2002) and Evanschitzky et al. (2006) suggest that commitment of service providers and customers positively affects CR.
This study is an opportunity to examine the retail industry's commitment-trust theory (Morgan & Hunt, 1994). Hypothetically, this theory can be implied in the retailing context that retailers, as trading parties or service providers, desire to maintain long-term relationships with customers by performing their responsibilities as commitments to bring the most benefits to customers for trust creation. Customers, as committed parties, trust retailers through PB and may engage in retention or patronage in-store or online. Therefore, the conceptual model is formulated to illustrate the effects of operational service factors, including People, Premises, ICT systems, and Customer services, on PB and CR. Furthermore, as a new development, the model shows the integration of (i) the mediating role of PB between operational service factors and CR and (ii) the moderating role of PO on the effect of PB on CR, which can be seen in Figure 1.
According to Johnston et al. (2021), N. C. Slack et al. (2016), and Stevenson (2018), the input resources of service operations consist of People (staff), Locations (premises and facilities), Technology (ICT systems), and Service process (knowledge). In this research, these dimensions, also-called service operational factors, are the basis for developing the research hypotheses. As the conceptual model, the service operational factors, so-called independent variables, are redefined in terms of People, Premises, ICT systems, and Customer services with relevant indicators developed from the existing studies in the retail and service sectors. The constructs and their relevant indicators' descriptions can be found in Table 1.
Regarding the target variable of the model, the CR construct is adopted and developed from previous work-related literature in the retail and service sectors, including six indicators related to customer retention and the intention of patronage or repurchase. CR can be reflected by Preference, Frequency increase, Feeling loyalty, Word-of-mouth, Recommendation, and Involvement, which are detailed in Table 2.
Because service operational factors are theorized to have effects on CR as in the conceptual model, the hypotheses are proposed as follows: H1a: People (Employees) in supermarkets have direct positive effects on CR.

H1b: Premises of supermarkets have direct positive effects on CR.
H1c: ICT systems in supermarkets have direct positive effects on CR.

H1d: Customer services of supermarkets have direct positive effects on CR.
Additionally, PB is hypothesized to mediate between service operational factors and CR in this research. The proposed dimensions of PB are Instant gratification, Saving, Convenience, and Enjoyment, which are probably generated from service operations based on previous studies in retail. Besides, perceived risk is described as a component lessening PB as per the perceived value concept (Zeithaml, 1988); hence, Risk reduction can be considered a positive dimension in improving PB in this research. The descriptions of PB indicators are detailed in Table 3.    So far, this is the first time the integration of mediating effect of PB and moderating effect of PO on CR has been examined in retail. Therefore, PO's dimensions are adopted and developed from the studies of Peck and Shu (2018) and Peck and Luangrath (2018), and PO's indicators are purposively selected and modified to be suitable for the research context. Although many scholars, e.g., Emrich et al. (2015) and Q. Zhao et al. (2016), deeply study the mediating role of PO, in this paper, PO is hypothesized to positively affect CR and moderates the effect between PB and CR. The proposed scales are presented with detailed descriptions in Table 4.

Based on the conceptual model and the review of previous studies, the hypotheses are formulated as follows
H3a: PO has a direct positive effect on CR.

Research methodology
The qualitative method is utilized in this study because it can offer an effective way to develop a theory. It is more beneficial to gain insight into store service operations and discover the gaps between academic studies and business practice (Saunders et al., 2019). In-depth interviews with store managers are conducted to collect expert opinions to assess and verify the proposed constructs, and select the most suitable and relevant indicators of these constructs in the conceptual model.  Sampling for the interview is convenient, non-probability, and purposive to collect the best enable answers to meet the research objectives, based on knowledge of the research problem, that fit particular criteria of exploratory and pretesting a questionnaire. According to Saunders et al. (2019), there is no rule to determine the suitable sample size for non-probability sampling; hence, they recommend twelve to thirty participants for a heterogeneous group. Interviews are conducted with 32 store managers from the five biggest omnichannel retailers from Vietnam (2 companies), Japan, Korea and Thailand, of which stores are located in Ha Noi and Ho Chi Minh City (Vietnam). The criteria to select interviewees are store operations managers who have at least three years of work experience in the target stores. In an attempt to make each interviewee feel as comfortable as possible, the semi-structured interview protocol (in the form of questions and relevant probes) and interview schedules will be sent to interviewees for reference and preparation before the interview is conducted. The interview can be face-to-face or online through Google Meet or Microsoft team. Simultaneously, a commitment letter will be signed between the author (as interviewer) and store managers (on behalf of interviewees) to ensure that information collected will be disclosed if only the store manager's permission is obtained. Because the results of qualitative research are the foundation for designing the survey questionnaire in quantitative analysis in the following stage, the indicators with an agreement rate of less than 60% will not be included and developed in the questionnaire.
Additionally, Quantitative research is deployed in this study to examine the relationships between variables and incorporate controls to ensure data validity in experimental research (Saunders et al., 2019). Together, frequency analysis is used to assess the shoppers' psychology, shopping frequency, attitudes, beliefs, prejudices, preferences, motives, opinions about the store service operations, and their perception of the benefits of shopping in supermarkets.
For data collection, a 5-point Likert scale questionnaire is designed based on the results of qualitative research. The questionnaire in Google Docs, answerable via a PC or mobile device, will be sent to the target respondents who do shopping at least once a month in any store or on the websites of the top five largest supermarkets in Vietnam with the assistance of the store managers. The data collected with the suspicious response (inconsistent, illogical, or straight-lining, e.g., the same score for all) were removed before data analysis.
According to Hair et al. (2017, p. 24), the minimum sample size is proposed to be equal to "10 times the largest number of structural paths directed at a particular construct in the structural model" in PLS-SEM. However, Barroso et al. (2010), Cordeiro et al. (2009), in Vinzi et al., (2010,

Table 4. PO's measurements and descriptions
Descriptions Sources (PO_1) Sense of store or its website ownership: When shopping in a supermarket or on its website, customers feel like it is theirs. Avey et al. (2009); Kirk and Swain (2018); J. Lee and Suh (2015) (PO_2) Sense of product ownership: Although customers have not purchased the product(s), they feel that the products displayed on shelves or quoted on the website are theirs. Fuchs et al. (2010); Kirk and Swain (2018) (PO_3) Familiarity: Customers have a strong sense of familiarity or connection with the supermarket or its website, so they can know how it is structured and how its products are displayed. Garson (2016), and Hair et al. (2017) agree that the larger sample size will increase the statistical power, precision, consistency, and reliability of PLS-SEM estimations, and PLS-SEM also works very well with a large sample size (Hair et al., 2019). Furthermore, with data sets of 250 or more, the results of CB-SEM (covariance-based structural equation modelling) and PLS-SEM are similar with four or more appropriate indicator variables used to measure each construct (Hair et al., 2017). Therefore, the minimum sample size in this research is 250. With a target population of 1,000 shoppers and an estimated response rate of 25%, the expected sample size will be at least 250.
Especially, Harman's one-factor test (1976) is used to detect bias response through Common Method Variance (CMV). In order to suggest that the quantitative results are non-bias responses, the value of CMV must be less than 50%.

Key research findings
Generally, the qualitative research results were positive. Most participants agreed with all proposed constructs and relevant indicators by at least 75% of interviewees (compared to the target of 60%), although they provided many different and considerable definitions and explanations. The result supported the conclusion that the proposed constructs and indicators were verified. Furthermore, it could be a prerequisite to answer research question one that service operational factors, including People, Premises, ICT systems, and Customer services, may contribute to PB and CR from a retailer's point of view. Taken together, these results provide significant insights into store service operational factors and the point of view of store managers on PB, CR, and PO of customers in the retail context. Based on the results of qualitative research, the questionnaire has been developed, of which each question is derived from a relevant indicator that customers will assess through a survey for quantitative analysis.
For the questionnaire survey of Quantitative research, of 1,000 shoppers of the supermarket chains, who were sent the questionnaire, 429 returned the responses. Positively, the 374 responses to this survey satisfied the quantitative research requirements (much larger than the minimum sample size as required). Table 5, the female response rate is 60.96%, a notable higher than that of the male group of 39.04%. It allows the implication that the majority of shoppers are females. The response rate is almost evenly divided among age groups or generations, as defined by Avey et al. (2009). Notably, Babyboom and Generation X (41-55 years old or older), Y (26-40 years old) and Z (25 years old or under), as generation definitions of McCrindle and Wolfinger (2009), 1 accounted for 35.83%, 36.10% and 28.07% of the total number of respondents, respectively.

As shown in
Regarding education, 62.57% of the total respondents have undergraduate and post-graduate degrees. Therefore, not so surprisingly, the majority of respondents are Staff/Worker/Officer (30.21%), Professional/Lecturer/Teacher (28.61%), and Management/Business Owners (17.11%), whose monthly net incomes are commonly in the range from VND10 million to VND25 million. In comparison, the rests account for 24.6%, including Students, Freelancers/Housewives/Retired, and others, with relatively lower incomes than the major groups.
Interestingly, when asked about the frequency of shopping at supermarkets, 62.57% of the respondents answered that they usually do shopping at least once a week, of which 30.48% said that they do shopping in supermarkets at least five times a month. The results are consistent with the reports of PWC (2021) and Statista (2021a;2021b;2022c).

For hypothesis testing, the process is performed in two stages: (i) Evaluating the measurement models and (ii) Evaluating the structural model with relevant metrics computed by SmartPLS.
As shown in Table 6, the convergence validity of the conceptual model is built when the relevant metrics are under the thresholds. Mainly, indicators' internal loadings are between 0.790 to 0.893, significantly higher than the threshold of 0.708, and constructs' average variance extracted (AVE) values are from 0.685 to 0.752 while the threshold is 0.5 (Hair et al., 2017(Hair et al., , 2019. Moreover, the results in Table 6 also indicate that the internal consistency reliability of the model is confirmed when the values of composite reliability, Cronbach's alpha coefficients, and rho-A are in the range of 0.897 to 0.938, 0.847 to 0.920, and 0.853 to 0.922, within the given thresholds respectively (Hair et al., 2017(Hair et al., , 2019. Moreover, the Heterotrait-Monotrait Ratio (HTMT) is used to examine the discriminant validity of the model. Given that the figures of all the constructs in Table 7 are less than 0.9, the discriminant validity of the model has been checked (Hair et al., 2017). Therefore, it can be concluded that the measurement models are validated. Furthermore, with CMV value of 48.50%, it can be concluded that the data collected are non-bias.
In Table 8, the value of Root mean square residual covariance-RMS theta of 0.109, less than 0.120, implies that the model is well-fitting (Hair et al., 2017). Furthermore, with the Coefficients of determination-R 2 of PB and CR of 0.769 and 0.737, respectively, it can be suggested that the model has moderate and substantial explanatory power (compared to the threshold of 0.5 for moderate and 0.75 for substantial as Hair et al., 2017Hair et al., , 2019. Furthermore, the Predictive relevance value-Q 2 values of PB and CR are 0.573 and 0.522, higher than the threshold of 0.35, and it can be concluded that the model has large predictive accuracy. Detailed in Table 8, the findings show the direct positive effect of PEO, PRS, ICT, CUS, PB, and PO on CR with Path coefficients in the range of 0.109 to 0.265 and significant p-values less than 0.05 (or < 5%). Simultaneously, the figures show the indirect contributions of service operational factors (i.e., PEO, PRS, ICT, CUS) to the effect of PB on CR (detailed in the range from 0.036 to 0.084) that make up the total effect of PB on CR of 0.265. Consequently, it can be concluded that hypotheses H1a, H1b, H1c, H1d, H2a, H2b, H2c, H2d, H2e and H3a are accepted. Therefore, the model can be estimated as shown in Figure 2a.
To examine the moderating role of PO on the effect of PB on CR, the construct PO*PB, which represents the interaction effect of PB and PO, is added (see Figure 2b).
The Structural model with PO*PB effect on CR with figures detailed in Table 9 and Figure 2b demonstrate that PO significantly and positively moderates the effects of PB on CR. As a result, it makes R 2 of CR increase from 0.737 to 0.742 (i.e., 0.68%). Out of expectation, the moderating role of PO on the relationship between PB and CR surprisingly leads to the significant strengthening of the effect of service operational factors on CR. Notably, the path coefficients of PEO, PRM, ICT and CUS on CR increase by 28%, 45%, 60%, and 49%, respectively, while those on PB are constant. Therefore, the proposed path model after adding PO moderator is in the form as follows: Furthermore, recalling hypothesis 3b, which suggested that PO strengthens the positive relationship between PB and CR, the figures in Table 9 reveal a significant positive interaction between PB and CR. In high versus low PO presence, the relationship between PB and CR will be more robust (as shown in Figure 3). Therefore, hypothesis H3b is accepted.

Discussions
It is interesting to note that all hypotheses in this study are accepted. The research outcomes may affirm that service operational factors significantly contribute to PB and CR in supermarket chains, where PB also plays a critical mediating role between the relationships according to the interpretation of the commitment-trust theory developed by Morgan and Hunt (1994).
Remarkably, these research findings are consistent with that of Mazumdar (1993), who claims that consumers often purchase based on their careful consideration of what benefits they obtain". Moreover, these results reflect the concepts of Ahmad and Buttle (2002), Bojei et al. (2013), Huarng and Yu (2020), Ray and Chiagouris (2009), Kanwal andRajput (2014), andSharmeela-Banu et al. (2012), who argue that operational factors may influence CR in retailing. Although the research findings seem consistent with previous studies, which found that customers can gain benefits when shopping in physical stores, it is based primarily on an operations perspective to discuss the dimensions of PB and the relationship of PB and CR in an omnichannel retailing context. The possible sources of PB and CR are service operational factors, which have been proved through the survey results.
Indeep, the current study has demonstrated similarity with existing research that retail staff as "brand ambassadors" in-store can create the majority source of customer value (Berman et al., 2018;Buttle, 2009;Pal & Byrom, 2003;Zentes et al., 2017). The article also develops their concepts according to the view of customers that supermarket staff have high competence, for example, being well-educated, knowledgeable, professional, intelligent and skilled, which satisfy customers when shopping. Additionally, the results demonstrated that staff's positive interaction and working attitude are helpful for shoppers with respect and empathy to offer personal attention, meet their requirements, or exceed their expectations. Furthermore, respondents show that the staff's honesty instils confidence in the customers and shows great interest and motivation to resolve their difficulties or problems.
The results of this study support evidence from previous observations that the factors of the premises are critical aspects of supermarket service operations in supermarkets (e.g., Blut et al., 2018), in which accessibility was one of the prerequisites for convenient location of stores to attract customers. Additionally, the format and design of the store with safety and security conditions, a comfortable shopping environment with modern infrastructure, additional suitable facilities, and physical equipment are mandatory requirements and competitive advantages of retailers to improve customer convenience and trust. Practically, since the early century 2000, convenience and other entertainment services in the retail business have been combined to create an enjoyable shopping environment for customer experience as a common trend (Ayad & Rahim, 2013;De Nisco & Napolitano, 2006;Sit et al., 2003). Currently, retailers invest more in their stores as an entertainment ecosystem, integrating multi-services in one place for customer experience and enjoyment to attract customers and increase CR.
In addition, thanks to ICT systems, online shopping is a vivid experience generated to increase customer convenience (Alexander & Kent, 2020;Grewal et al., 2020). Consistent with the literature, this research discovers that shoppers feel more satisfied and convenient when shopping in any physical store or supermarket website. Practically, ICT enhances the speed of the shopping process, primarily generating effectiveness and helpfulness for customers in searching and ordering anytime, anywhere without having to go to the supermarkets, even during the COVID-19 (Source: The authors) outbreak, as respondents confirmed. The scholars claim that ICT allows customers to access retailers' information, including stores' locations and business time, products and services information, promotion programs, etc., via PC or mobile devices at any time. Therefore, customers can save shopping time and experience more convenience and benefits. Furthermore, one of the crucial findings is payment flexibility thanks to ICT in online and in-store shopping.
The most prominent finding to emerge from the analysis is that customer services of supermarkets can contribute to PB and CR. Practically, respondents confirm that they can benefit from additional after-sales services, whether they purchase online or in-store, and the effective handling of their complaints. In fact, experimental evidence has illustrated that supermarkets are more dominant than other retail channels because of customer care to maximize customer satisfaction and personalized services to motivate customers' positive emotions and repurchase. This result can be partially explained and supported by the concepts of Blut et al. (2018), Kumar et al. (2011), and M. Zhao and Wang (2021). Last but not least, this investigation of the effect of service operational factors on BP and CR was successful, as it identified store commitment as a dimension of customer service. These results may be supported by N. Slack et al. (2020), Bordoloi et al. (2019), andPatel et al. (2017), who also admit that services provided as commitment are indispensable to show consistent responsibilities of supermarkets to customers and ensure customer satisfaction. Therefore, with the perception of the benefits of shopping in supermarkets or on-line, customers believe that they can be instantly satisfied with their purchases, save time and effort to make a purchase, gain more convenience and enjoyment and reduce risks. It also suggests a strong link between PB and CR when customers continue to repurchase or increase the frequency of shopping in supermarkets or via their preferred websites as their first choice. Although the study is based on an operations perspective, its findings also support previous research from a marketing point of view. From the data analysis, it can be inferred that the feeling of loyalty towards supermarkets, the willingness to say positive things about the supermarkets, recommend others to do shopping in supermarkets or the websites, and give suggestions to improve the quality of products and services of supermarkets of customers are derived from services and benefits received (detailed in Table 2).
An interesting newly finding of this study is that PO significantly affects CR and positively moderates the effect of PB on CR, which is the first time discussed in the retail context. Undoubtedly, the hypothesis that a customer's sense of ownership for a store or its website, an unpaid product, or a service can motivate him or her to revisit the store for purchasing is partially supported. In this study, familiarity is one of the dimensions of PO, which is redefined as the strong sense of connection with the supermarket(s) or their websites and a good knowledge of the store (Source: The authors) and its products and services so well. Besides, with the sense of closeness with supermarkets, customers usually feel liking and want to spend time with them. From the data analyzed, the research has also shown that the sense of familiarity and closeness are indispensable components of PO, which strongly influences repurchase intention and increases the relationship between PB and CR in the retail environment.
Surprisingly, an unexpected finding is that while PO moderates PB's effect on CR, it also increases the effects of service operational factors on CR. It would be exciting to do further research in the future.

Theoretical Implications
This research is an excellent opportunity to examine the relationship-trust theory in the retail context from the operations and customer perspectives. Based on the review of existing concepts and studies, the research also expands and develops definitions of the components of service operations, namely People, Premises, ICT systems, and Customer services with relevant dimensions in the retail and service industry. The observational study suggests that service operational factors significantly contribute to PB and CR in supermarkets, the typical format of the omnichannel retailer. It also illustrates the mediating role of PB in the relationships between service operational factors and CR. Moreover, the positive effects of PO on CR and its moderating role in the relationship between PB and CR are first studied in an emerging country's retail industry. Statistically, the study has demonstrated that the relationship between PB and CR is more robust when PO is high. In other words, it can therefore be assumed that a customer with a high level of PO will pay more attention to the benefits and will be more likely to retain shopping in certain supermarkets.

Practical implications
The findings of this study have several important implications for business practice. First, these findings may help us understand the significant roles of store service operations, which primarily create customer benefits and enhance CR in addition to attractive products, reasonable prices offered, and marketing programs in supermarkets. Nowadays, the point of view that customers pay for trust and commitment (Morgan & Hunt, 1994) or that customers are not attracted to the best products or lowest price instead of what benefits they will get against their payment (Mazumdar, 1993), is still prominent. So, consistent with these views, the research critically implies that omnichannel retailers should focus on service operational factors to maximize customer benefits and increase CR. One issue emerging from the findings is that PO can predict positive consumer attitudes or behaviour. Therefore, this combination of results provides some support that retailers should understand the motives and routes of PO, concentrate on investment in highly skilful staff, effective and convenient process, comfortable shopping environment, and quality services for customer experience to gain customer trust, satisfaction, perceived benefit, and CR to maintain the long term relationship as the commitment-trust framework.

Conclusions
The study has discussed the effects of service operational factors, i.e., People, Premises, ICT systems, and Customer services, on PB and CR and the mediating role of PB in the relationships between service operational factors and CR based on commitment-trust theory in a retailing context. The present study was also conducted to investigate how PO affects CR and moderates the effect of PB on CR. One of the most prominent findings of this study is that People, Premises, ICT systems, and Customer services integrated directly and indirectly affect CR through PB. The second significant finding is that PO positively affects CR and significantly moderates the effect of PB on CR. This research provides insights into supermarket service operational factors and how they contribute to customer benefits and improve CR. It allows the suggestion that store service operations greatly support the success of loyalty programs in supermarkets. Taken together, this research implies that understanding the effects of service operational factors and PO on PB and CR and the role of PO in promoting the relationship between PB and CR allows supermarket managers to predict trends in CR through attitudinal and behavioural dimensions.
In general, this study was conducted in Vietnam, a country with a retail market and a shopping culture quite similar to those of ASEAN countries, so the results of this study may reflect the whole picture of the region. However, it is not convincing enough to be generalized to all emerging countries. Another limitation of this study is that the authors did not consider and examine how PO moderates the effects of each service operating factor on CR. Besides, the authors neglected to discuss the changes that affect the level of service operational factors on CR, although they were surprisingly discovered during the data analysis process.
Notwithstanding these limitations, this would be a fruitful area for establishing a set of service operational quality measurements which can be applied to all omnichannel retailers. In addition, further research should be undertaken to investigate how PO moderates the effects of each service operating factor on CR and explore the optimistic possibility that PO can strengthen the relationships between service operational factors and CR while it moderates the effect of PB and CR in any retail format.