# The Influence of Substituting Prices, Product Returns, and Service Quality on Repurchase Intention

Authors: Jian Xue; Zeeshan Rasool; Mohsin Ali Khan; Ahmad Imran Khan; Farooq Khan; Anum Afzal Khan; Rabail Shoukat; Hina Ali
Journal: Complexity
 (2021)

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## Abstract

The textile industry is production-intensive and incorporates diverse transactions made by multiple suppliers, corporate buyers, and supply chain members. In the business-to-business context, the cost for attracting a new customer is notably much higher than that needed to retain a present one. Customer loyalty in terms of customer repurchase intention has, therefore, been considered as a key determinant for textile companies to improve their efficiency and competitive advantage. This study aims to investigate the business-to-business repurchase intentions of Pakistan textile and clothing industry customers. The study framework specifically consolidates the mutual dynamics of appealing (service quality), facilitating (product returns), and averting (switching costs) factors altogether and the effect of these variables on customer satisfaction and thus on customer retention (repurchase intent) in the textile’s transactional scenario. A sample survey method is used for this study. The data collected through self-administered questionnaires (n = 325) from All Pakistan Textile Mills Association enlisted the employees of the companies. The structural equational modeling technique was applied to examine the study hypotheses. The findings contended that service quality and switching costs are essential determinants that shape the repurchase intentions. Therefore, product returns do not contribute toward customer satisfaction and also do not shape the intentions of business-to-business customers to repurchase from the same supplier after having even a good product return experience in past.


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## Body

## 1. Introduction

Pakistan is currently positioned as the fifth largest cotton producer in the world and has capitalized on its capability for promoting and developing its textile sector. The country’s established textile industry is steered by the availability of locally produced cotton [1]. Globally, Pakistan is ranked third in yarn production and it has a domestically well-expanded textile industry from ginning to made-ups (ginning, spinning, weaving, processing, dyeing, printing, and made-ups) [2]. The industry is considered the backbone of Pakistan’s economy and it is one of the major contributors, both in terms of exports and employment. The industry eventually contributes a big part in the country’s economy [3].Regardless of the mature and progressive textile value chain, the Pakistan textile industry’s performance is disappointing and it is not generating the returns according to its potential [4]. In a broader spectrum, the major issues of the textile industry have been the inadequate implementation of quality practices, inadequate customer services, delay or hesitation in product returns, poor complaint management system, failure in observing the production lead times, and cost of the value chain [5]. Despite Pakistan’s government textile industry bailout packages worth Rs. 29 billion, the year 2018 was also a disappointing year for the textile sector as it failed to deliver the required results despite a mass devaluation of the Pak rupee (29%) throughout the calendar year [4]. Frederick and Daly [1] grouped the Pakistan textile industry, the least coherent with successful quality practices in its supply chain in South Asia. The main hurdles in the expansion of the textile sector are lack of product diversification, imperfect competition, market diversification, financial crises, import duties, and some other factors like poor customer retention policy, whereas marketing literature frequently addressed the positive association of these factors, especially service quality with customer satisfaction and loyalty [6–9]. Kanat and colleagues blamed Pakistan textile and clothing industry that buyers are getting low service quality responses, thus reducing their confidence to do business with their existing suppliers [10]. They further argue that textile customers are conveniently switching from one supplier to another. This frequent customer switching rate signaled an alarming issue that instigated to initiate this study too. The model was developed to explain the relationships between switching costs, product returns, and service quality, arguing that customer satisfaction influences the corporate customers’ loyalty with their present suppliers. It hypothesizes that a dynamic returns management system, improved service quality, and overcoming switching costs could advantage the supplier for gaining customer satisfaction that eventually generates the customer repurchase intentions [11]. However, there is little evidence about how foreseen concepts relate to the textile sector. This study framework sought to evaluate and particularly integrate these interactive antecedents of customer satisfaction to assess their loyalty (repurchase intent) with the present supplier within the B2B textile industry context.In the textile value chain, customers, suppliers, and supply chain members develop relationships that mutually benefit all the stakeholders of the industry. This value chain comprises several activities that are essential to transform a product from conception to consumption through various other intermediary manufacturing stages (including physical transformation and other manufacturer services) [12]. In the textile industry, both buyers and sellers are, therefore, involved in value cocreation practices for economic exchange [13]. The textile firms want to keep this mutually beneficial process continue for the long term through superior customer services because the cost for appealing a new customer is multiple times higher than that required to sustain an existing one in the B2B transactional scenario [14]. So, customer loyalty with the current supplier firm has, therefore, been viewed as a significant factor for the firm’s performance and the competitive advantage [15].From the last few decades, the manufacturing industry has transformed itself by offering complete solutions or adding services to its actual product [16]. The provision of better service quality is one of the most essential factors for the satisfaction of the customers, and eventually, it increases their loyalty and strengthens repurchase intentions [17]. The findings of Chen also concluded that the provision of better services to the customers increased their satisfaction level and then it led toward the repurchase intentions [18]. The literature also recommended that strategically addressing product returns could also result in improved customer loyalty and increased customer satisfaction [19, 20]. A substantial returns management system could also lead to competitive advantages and superior performance [20, 21]. It creates an opportunity for the firms to manage positive long-term relationships with their clients, enhance customer satisfaction, and make a positive impact on their repurchase intention [22].Theoretically, Pakistan’s textile industry is increasing its production and export output because of its conventional goods, due to its intrinsic quality. However, major investment in machines and new technology is necessary for sustaining its situation, growing its portion, and expanding hooked on high-quality goods. Workforce preparation, performance improvement, research and development, product diversification, and branding are the immediate areas of focus.Along with an effective returns management system and superior service quality, B2B markets use a traditional switching cost or switching barrier strategy to discourage customers to switch to another supplier [23]. The supply chain management literature has consistently shown that switching cost is an influential mechanism that discourages customers to switch and forces them to repurchase from the existing supplier [23, 24]. Most organizations attempt to form switching barriers that eventually increase the switching costs for their customers. The literature has found the positive impact of switching costs on repurchase intentions and that the customers stay connected with the present supplier if the perceived switching cost is higher for them [23].Pakistan’s textile industry’s economic contribution is shown in Table1, which indicates that the textile business funded 54% to exports, 46% to built-up production, 38% to employment, 8.5% to GDP in total GDP, 7.0 billion USD to investment, and 5.0% to market capitalization (listed companies). During the 5th Five-Year Plan, the synthetic filament yarn industry has identified impulses as demand increases, and therefore, imports have been increased as well as investments by the private sector in improving the market conditions (Pakistan Economic Survey, 2018–19). The textile sector is the second largest segment to contribute in GDP (gross domestic product) after agriculture in Pakistan.Table 1
Sociodemographic characteristics of the participants (n = 325).
VariableCategoriesFrequencyPercentageCurrent position/designationGeneral manager278.3Manager8726.8Deputy/asst. manager17152.6Other staff4012.3DepartmentManagement5617.2Procurement/sourcing12739.1Commercial8827.1Others5416.6No. of years on current position1–5 years13340.96–10 years12739.111–15 years4614.2Above 15 years195.8No. of years in industry1–5 years6219.06–10 years11134.011–15 years10733.0Above 15 years4514.0Education levelIntermediate/diploma216.5Graduate12137.5Masters/post graduate15246.8Others319.5GenderMale29691.0Female299.0Marital statusSingle11535.4Married20864.0Widowed00.0Divorced020.6Age25–30 years6720.531–40 years14845.541–50 years9429.051 and above165.0Length of relationship with the main supplier1–5 years15648.06–10 years13340.910–15 years298.9Above 15 years072.2Does the main supplier has exclusive status (contract)Yes4012.3No28587.7The research questions are as follows:RQ1: Do the product returns influence the repurchase intention of B2B customers in the textile industry?RQ2: Do the switching costs create an exit barrier for the B2B textile industry customers to switch to the other supplier?RQ3: Does service quality enhance customer satisfaction and push B2B textile industry customers to repurchase from the present supplier?RQ4: Does customer satisfaction mediate the relationship between the repurchase intention of B2B textile industry customers and the antecedents?


## 2. Literature Review

### 2.1. Switching Costs

In business literature, switching costs are also known as switching barriers. These costs refer to a customer’s costs of time, money, and efforts associated with customer switching from one supplier to another, rather than continuing to have business with the current supplier [15]. It has also been shown in the literature that even when the customer is not satisfied with the present supplier, the customer retains and maintains a long-term relationship with the same company due to perceived switching costs, which is associated with breaching of the relationship with the present supplier [25]. High switching costs force customers to pursue even if the relationship with the current supplier is not satisfactory; it exists due to the customer assessment of additional costs to cease the current relationship [26, 27]. Bozzo revealed that corporate customers often repurchase from the present supplier despite not having any positive association and good links with the supplier due to perceived high switching costs [28]. Switching costs are not only considered as the loss in monetary terms, while these also have an impact on the psychological behaviour of the customer because it involves lots of effort looking for a new supplier for the same kind of products or services [29–31]. In a B2B setting, purchase decisions are not that much simple as these are in a B2C setting because the switching costs factor is highly complex and decisive in the B2B transactional scenario [32]. The switching process is quite painful, and the switching costs factor is substantial, even displeased customers want to keep going business relationships with the current product or service suppliers and retain from suspending the connection [27]. An enormous effect of switching costs on customer retention can be witnessed in commerce literature, where it is seen as useful for the firms for utilizing it as an instrument associated with marketing self-protecting strategy for maintaining and fortifying its connections with the present customer [33].Generally in the satisfaction-loyalty relationship, switching costs are assumed to be a good moderator [23, 34] but substantial industrial marketing literature has taken the switching costs as a criterion variable that independently influences customer satisfaction and loyalty (e.g., [9, 23]). So, the study took switching costs as an independent variable to define the hypothesized relationship that switching costs affect the B2B customer satisfaction and the repurchase intent.The study hypothesizes the following:Product returns: for the corporate sector, product returns management involves substantial operational challenges and enormous costs for the suppliers. Suppliers usually recall the products for inventory repositioning, or these recalls are initiated due to quality or security concerns that could be voluntary or instructed by government agencies [19]. Product returns management is more than just a cost [35], but generally, it is not so, as suppliers often recall their products to reposition the inventory. This repositioning of inventory and strategically managing returns could result in superior customer services, higher levels of customer satisfaction, and improved customer loyalty [19, 20]. The performance of product returns management associates with how effectively and efficiently an organization manages and handles customer issues and returns. In managing product returns, product vendors are provided with a chance to sought out customer objections and ensure the transition from unsatisfied to satisfied customers who were displeased due to initial supply experiences. Previous literature also emphasized that returns management could also lead to competitive advantages and superior performance [20, 21].There exists a considerable body of commerce literature that witnessed that an effective product returns management can improve customer satisfaction, reduce costs, enhance profitability [20], shape favourable customer reviews, and make positive referrals [36], and thus, it leads to continuing customer relationship [37]. There are several reasons for the customers that they may return the product to the supplier. The reasons include product discontinuation, product replacement, in-transit damage, product off-season, expired date code, product defects, and high inventory of supplier [38]. Poorly managed product returns result in a significant negative effect on the firm’s performance and customers’ repurchase intentions. A bad product return experience leads toward switching behaviour and has a negative effect on customers’ repurchase intentions [39].Autry argues that a dynamic product returns management process provides an opportunity to form a positive effect on customers’ repurchase intentions who earlier suffered service failures [40]. Although product returns management includes momentous operative and enormous costs, but simultaneously, it is an opening for managing and maintaining long-term relations with the customers and has a significant positive impact on their repurchase intentions [22]. Hence, product returns could also be viewed as customer satisfaction and service recovery opportunity. For a better understanding of the factors affecting repurchase intent, this study examined the role of product returns on B2B textile customer satisfaction and thus on their repurchase intent. Contrarily, Russo’s study on the Italian audiology industry found no effect of managing product returns in the B2B context. He revealed that a high or low level of customer satisfaction does not trigger a positive effect on the repurchase intention of B2B customers.


### 2.2. Service Quality

Service quality is the provision of service that meets or surpasses the expectations of customers [41]. One has to compare the acquired service of the customer with perceived service to examine customer satisfaction with service quality [7]. The service quality-satisfaction-loyalty chain has been widely discussed in past literature [7, 9, 42]. Studies on consumer behaviour have frequently addressed the service quality issue, and the service quality has been proven to positively associate with customer satisfaction and loyalty [43].Over the past decades, the manufacturing industry has experienced a shift by offering complete solutions or adding services to their actual product that is termed as “servitization” by [16]. The idea of providing products along with necessary services has created great value for the customers as compared to when these are acquired separately, and customer loyalty and satisfaction have also been increased [44]. This relationship between service quality and repurchasing intentions was frequently examined in the past (e.g., [45–47] and thus proven to positively associate with customer satisfaction and loyalty.Sattari and colleagues state that the firm that needs to maintain a competitive edge over its rivals should come up with superior services to its customers. To survive and compete in the long run, the firm is required to focus on investing a substantial budget and time for the provision of improved quality services [48]. The provision of quality services is one of the most essential factors for customer satisfaction, and thus, it leads to customer loyalty and repurchase intentions of B2B customers [17].The study hypothesizes the following:Customer satisfaction: customer satisfaction is “the degree to which a customer perceives that an individual, firm or organization has effectively provided a product or service that meets the customer needs in the context in which the customer is aware of and/or using the product or service” [49], p. 79). Satisfaction is a socially fabricated response to the customer-product-supplier relationship as it is not coherent to an individual or the product. Customer satisfaction is a growing managerial interest of business managers as a means of evaluating service quality. A higher customer satisfaction score is extensively believed to be a fair indicator of a firm’s future profit. Satisfaction can be widely characterized as a postpurchase assessment of product quality compared to the prepurchase expectation [50]. It is a cumulative assessment of all kinds of interactions of costumer while interacting with the firm. Therefore, customers’ experiences of all kinds of supplier offerings have a significant effect on customer satisfaction. Researchers call customer satisfaction an emotional response that a customer shows right for the first time when the transaction is done. Once a customer gets satisfied with the service of the supplier, he/she is more likely to be returned for another transaction for the same or different product [51]. In B2B markets, the major differences among end-consumers arise when the company policymakers evaluate the features of a product/service of their industrial client. They consider and assess the satisfaction of the various stakeholders of the buying-house who are currently interacting with the industrial supplier [52]. Although these stakeholders are extensively guided by the firm’s objectives, still they assess the product or service performance as per their self-reference standards due to their intrinsic motivations and objectives.Several past studies that discussed a behavioural course of action found B2B customer satisfaction to be significantly and positively associated with repurchase intentions [45, 46, 51, 53, 54]. These studies illustrated that customer satisfaction plays a mediating role between switching costs, service quality, and customer loyalty [39, 55–61] and also have a consensus that customer satisfaction can explain the relationship between repurchase intentions (criterion variable) and their antecedents (predictor variables).The study hypothesizes the following:Repurchase intention: the repurchase intention is defined as customers’ behavioural actions to purchase a similar product and available services more than one time from the same supplier [62]. In marketing, customer retention is the most important variable that commonly terms “repurchase” [43, 63–66]. In customer retention management, retaining an existing customer is quite cheaper than attracting a new one for the same product as it costs five times higher [14]. When a customer anticipates repurchasing from the present supplier and the intention to keep these relationships continues for the foreseeable future, it shows the loyalty of the customer to the supplier [67]. The loyalty with the present supplier or retailer could also be considered when a customer recommends the same product or service to the other corporate counterparts.


### 2.3. Theoretical Framework

For understanding the research hypotheses, Figure1 presents the proposed theoretical model and comprehended the idea of the study that elaborated in diagrammatical form.Figure 1
Diagrammatic representation of the relationships among study variables (theoretical model).


## 2.1. Switching Costs

In business literature, switching costs are also known as switching barriers. These costs refer to a customer’s costs of time, money, and efforts associated with customer switching from one supplier to another, rather than continuing to have business with the current supplier [15]. It has also been shown in the literature that even when the customer is not satisfied with the present supplier, the customer retains and maintains a long-term relationship with the same company due to perceived switching costs, which is associated with breaching of the relationship with the present supplier [25]. High switching costs force customers to pursue even if the relationship with the current supplier is not satisfactory; it exists due to the customer assessment of additional costs to cease the current relationship [26, 27]. Bozzo revealed that corporate customers often repurchase from the present supplier despite not having any positive association and good links with the supplier due to perceived high switching costs [28]. Switching costs are not only considered as the loss in monetary terms, while these also have an impact on the psychological behaviour of the customer because it involves lots of effort looking for a new supplier for the same kind of products or services [29–31]. In a B2B setting, purchase decisions are not that much simple as these are in a B2C setting because the switching costs factor is highly complex and decisive in the B2B transactional scenario [32]. The switching process is quite painful, and the switching costs factor is substantial, even displeased customers want to keep going business relationships with the current product or service suppliers and retain from suspending the connection [27]. An enormous effect of switching costs on customer retention can be witnessed in commerce literature, where it is seen as useful for the firms for utilizing it as an instrument associated with marketing self-protecting strategy for maintaining and fortifying its connections with the present customer [33].Generally in the satisfaction-loyalty relationship, switching costs are assumed to be a good moderator [23, 34] but substantial industrial marketing literature has taken the switching costs as a criterion variable that independently influences customer satisfaction and loyalty (e.g., [9, 23]). So, the study took switching costs as an independent variable to define the hypothesized relationship that switching costs affect the B2B customer satisfaction and the repurchase intent.The study hypothesizes the following:Product returns: for the corporate sector, product returns management involves substantial operational challenges and enormous costs for the suppliers. Suppliers usually recall the products for inventory repositioning, or these recalls are initiated due to quality or security concerns that could be voluntary or instructed by government agencies [19]. Product returns management is more than just a cost [35], but generally, it is not so, as suppliers often recall their products to reposition the inventory. This repositioning of inventory and strategically managing returns could result in superior customer services, higher levels of customer satisfaction, and improved customer loyalty [19, 20]. The performance of product returns management associates with how effectively and efficiently an organization manages and handles customer issues and returns. In managing product returns, product vendors are provided with a chance to sought out customer objections and ensure the transition from unsatisfied to satisfied customers who were displeased due to initial supply experiences. Previous literature also emphasized that returns management could also lead to competitive advantages and superior performance [20, 21].There exists a considerable body of commerce literature that witnessed that an effective product returns management can improve customer satisfaction, reduce costs, enhance profitability [20], shape favourable customer reviews, and make positive referrals [36], and thus, it leads to continuing customer relationship [37]. There are several reasons for the customers that they may return the product to the supplier. The reasons include product discontinuation, product replacement, in-transit damage, product off-season, expired date code, product defects, and high inventory of supplier [38]. Poorly managed product returns result in a significant negative effect on the firm’s performance and customers’ repurchase intentions. A bad product return experience leads toward switching behaviour and has a negative effect on customers’ repurchase intentions [39].Autry argues that a dynamic product returns management process provides an opportunity to form a positive effect on customers’ repurchase intentions who earlier suffered service failures [40]. Although product returns management includes momentous operative and enormous costs, but simultaneously, it is an opening for managing and maintaining long-term relations with the customers and has a significant positive impact on their repurchase intentions [22]. Hence, product returns could also be viewed as customer satisfaction and service recovery opportunity. For a better understanding of the factors affecting repurchase intent, this study examined the role of product returns on B2B textile customer satisfaction and thus on their repurchase intent. Contrarily, Russo’s study on the Italian audiology industry found no effect of managing product returns in the B2B context. He revealed that a high or low level of customer satisfaction does not trigger a positive effect on the repurchase intention of B2B customers.


## 2.2. Service Quality

Service quality is the provision of service that meets or surpasses the expectations of customers [41]. One has to compare the acquired service of the customer with perceived service to examine customer satisfaction with service quality [7]. The service quality-satisfaction-loyalty chain has been widely discussed in past literature [7, 9, 42]. Studies on consumer behaviour have frequently addressed the service quality issue, and the service quality has been proven to positively associate with customer satisfaction and loyalty [43].Over the past decades, the manufacturing industry has experienced a shift by offering complete solutions or adding services to their actual product that is termed as “servitization” by [16]. The idea of providing products along with necessary services has created great value for the customers as compared to when these are acquired separately, and customer loyalty and satisfaction have also been increased [44]. This relationship between service quality and repurchasing intentions was frequently examined in the past (e.g., [45–47] and thus proven to positively associate with customer satisfaction and loyalty.Sattari and colleagues state that the firm that needs to maintain a competitive edge over its rivals should come up with superior services to its customers. To survive and compete in the long run, the firm is required to focus on investing a substantial budget and time for the provision of improved quality services [48]. The provision of quality services is one of the most essential factors for customer satisfaction, and thus, it leads to customer loyalty and repurchase intentions of B2B customers [17].The study hypothesizes the following:Customer satisfaction: customer satisfaction is “the degree to which a customer perceives that an individual, firm or organization has effectively provided a product or service that meets the customer needs in the context in which the customer is aware of and/or using the product or service” [49], p. 79). Satisfaction is a socially fabricated response to the customer-product-supplier relationship as it is not coherent to an individual or the product. Customer satisfaction is a growing managerial interest of business managers as a means of evaluating service quality. A higher customer satisfaction score is extensively believed to be a fair indicator of a firm’s future profit. Satisfaction can be widely characterized as a postpurchase assessment of product quality compared to the prepurchase expectation [50]. It is a cumulative assessment of all kinds of interactions of costumer while interacting with the firm. Therefore, customers’ experiences of all kinds of supplier offerings have a significant effect on customer satisfaction. Researchers call customer satisfaction an emotional response that a customer shows right for the first time when the transaction is done. Once a customer gets satisfied with the service of the supplier, he/she is more likely to be returned for another transaction for the same or different product [51]. In B2B markets, the major differences among end-consumers arise when the company policymakers evaluate the features of a product/service of their industrial client. They consider and assess the satisfaction of the various stakeholders of the buying-house who are currently interacting with the industrial supplier [52]. Although these stakeholders are extensively guided by the firm’s objectives, still they assess the product or service performance as per their self-reference standards due to their intrinsic motivations and objectives.Several past studies that discussed a behavioural course of action found B2B customer satisfaction to be significantly and positively associated with repurchase intentions [45, 46, 51, 53, 54]. These studies illustrated that customer satisfaction plays a mediating role between switching costs, service quality, and customer loyalty [39, 55–61] and also have a consensus that customer satisfaction can explain the relationship between repurchase intentions (criterion variable) and their antecedents (predictor variables).The study hypothesizes the following:Repurchase intention: the repurchase intention is defined as customers’ behavioural actions to purchase a similar product and available services more than one time from the same supplier [62]. In marketing, customer retention is the most important variable that commonly terms “repurchase” [43, 63–66]. In customer retention management, retaining an existing customer is quite cheaper than attracting a new one for the same product as it costs five times higher [14]. When a customer anticipates repurchasing from the present supplier and the intention to keep these relationships continues for the foreseeable future, it shows the loyalty of the customer to the supplier [67]. The loyalty with the present supplier or retailer could also be considered when a customer recommends the same product or service to the other corporate counterparts.


## 2.3. Theoretical Framework

For understanding the research hypotheses, Figure1 presents the proposed theoretical model and comprehended the idea of the study that elaborated in diagrammatical form.Figure 1
Diagrammatic representation of the relationships among study variables (theoretical model).


## 3. Research Methodology

### 3.1. Research Design

The study has chosen the raw material procurement employees of textile firms as the study population that is located in the main textile manufacturing regions, i.e., Karachi, Lahore, Faisalabad, and Multan. The survey questionnaire draft comprises 33 questions and was used to collect data from the target population. A data screening procedure was applied to the collected data. Missing data, outliers, normality, and inter-item correlations were checked for every single item. Adequacy of covariances and Cronbach’s alpha was measured for all five constructs. Moreover, confirmatory factor analysis was conducted for each study construct.After the data screening process, the conceptual model was partially interpreted through the AMOS model, containing a measurement part (confirmatory factor analysis, CFA) and a structural equation part (structural equational modeling, SEM). SEM technique was applied to simultaneously observe a chain of unified dependent association among a set of constructs and for accounting the measurement error at the same time (refer to [68]). The technique suggested how the observed variables relate to the latent constructs and the unified dependent association among these sets of variables.


### 3.2. Measures

The literature drawn measuring scales were adopted from the past literature that has already been tested for measuring a similar concept. These applied multi-item instruments were found to be valid and reliable for employing and measuring the behavioural intention of industrial customers. Table2 illustrates the number of items adopted from existing scales in the five-point Likert scale.Table 2
Measurement of study variables.
VariableNo. of itemsMeasurementSwitching costs5[23]Product returns3[22]Service quality15[69]Customer satisfaction6[70]Repurchase intent4[71]


### 3.3. Procedure/Data Collection

This study was designed to populate statistics from a considerable sample size (n = 325) to gain statistically valid results. The target population was comprised of the procurement-related employees or intermediaries of the APTMA (All Pakistan Textile Mills Association) member firms to ascertain the valuable inputs of B2B respondents. The respondents (unit of analysis) were designated to purchase raw materials for their companies, which are listed in APTMA (i.e., spinning and weaving mills, dyeing and finishing mills, garment stitching, apparel factories, home textile units, trading companies, and buying houses). The specific population characteristics led the study to follow the specific criterion for sample selection. So, the purposive sampling technique was applied for extracting the sample since the target population has particular traits in terms of their industrial affiliation, responsibilities, and nature of the job.The questionnaire draft, consisting of closed-ended questions, was designed with the measures of relevant constructs. This questionnaire draft had two main sections. One of the sections comprised respondents’ demographics, duration of association with the supplier firms, and a screening question (i.e., Did you ever purchase or involve in the purchase process of textile raw material from your main supplier?), whereas the other section consists of construct measurements where agreement or disagreement was judged on a five-point Likert scales (1 =strongly disagree and 5 = strongly agree). The questionnaire draft, consisting of closed-ended questions, was made very neutral by keeping the researcher’s personal opinions aside to avoid response bias. Questions were easy to understand and clear as there was no item ambiguity in question statements to ensure nonresponse bias. There was no item similarity, or proximity vis-à-vis length of the questionnaire was also moderate. The respondents were conveyed to fill up questionnaires and rate their raw material suppliers in terms of their service quality, returns management system, and switching barriers. A sum of 450 questionnaires was couriered to the respondents of various textile firms, and 375 out of them were received back, producing 83.3% response rate. After data screening, the rest of (n) 325 valid responses were analyzed via statistical package for the social sciences (SPSS) (version 21). A total of 50 questionnaires were excluded from the data due to having some issues. These issues were related to either nonresponse bias or the presence of acquiescence bias where the respondents tended to agree with all questions or they concurred with a particular position. The rest questionnaires were adequate for further analysis.


### 3.4. Participants

The survey carries the responses of (n) 325 B2B textile materials procurement officials. The total number of respondents included 296 men and 29 women. All respondents were asked to choose from a list of answer choices whether they agree or disagree with a question statement.


## 3.1. Research Design

The study has chosen the raw material procurement employees of textile firms as the study population that is located in the main textile manufacturing regions, i.e., Karachi, Lahore, Faisalabad, and Multan. The survey questionnaire draft comprises 33 questions and was used to collect data from the target population. A data screening procedure was applied to the collected data. Missing data, outliers, normality, and inter-item correlations were checked for every single item. Adequacy of covariances and Cronbach’s alpha was measured for all five constructs. Moreover, confirmatory factor analysis was conducted for each study construct.After the data screening process, the conceptual model was partially interpreted through the AMOS model, containing a measurement part (confirmatory factor analysis, CFA) and a structural equation part (structural equational modeling, SEM). SEM technique was applied to simultaneously observe a chain of unified dependent association among a set of constructs and for accounting the measurement error at the same time (refer to [68]). The technique suggested how the observed variables relate to the latent constructs and the unified dependent association among these sets of variables.


## 3.2. Measures

The literature drawn measuring scales were adopted from the past literature that has already been tested for measuring a similar concept. These applied multi-item instruments were found to be valid and reliable for employing and measuring the behavioural intention of industrial customers. Table2 illustrates the number of items adopted from existing scales in the five-point Likert scale.Table 2
Measurement of study variables.
VariableNo. of itemsMeasurementSwitching costs5[23]Product returns3[22]Service quality15[69]Customer satisfaction6[70]Repurchase intent4[71]


## 3.3. Procedure/Data Collection

This study was designed to populate statistics from a considerable sample size (n = 325) to gain statistically valid results. The target population was comprised of the procurement-related employees or intermediaries of the APTMA (All Pakistan Textile Mills Association) member firms to ascertain the valuable inputs of B2B respondents. The respondents (unit of analysis) were designated to purchase raw materials for their companies, which are listed in APTMA (i.e., spinning and weaving mills, dyeing and finishing mills, garment stitching, apparel factories, home textile units, trading companies, and buying houses). The specific population characteristics led the study to follow the specific criterion for sample selection. So, the purposive sampling technique was applied for extracting the sample since the target population has particular traits in terms of their industrial affiliation, responsibilities, and nature of the job.The questionnaire draft, consisting of closed-ended questions, was designed with the measures of relevant constructs. This questionnaire draft had two main sections. One of the sections comprised respondents’ demographics, duration of association with the supplier firms, and a screening question (i.e., Did you ever purchase or involve in the purchase process of textile raw material from your main supplier?), whereas the other section consists of construct measurements where agreement or disagreement was judged on a five-point Likert scales (1 =strongly disagree and 5 = strongly agree). The questionnaire draft, consisting of closed-ended questions, was made very neutral by keeping the researcher’s personal opinions aside to avoid response bias. Questions were easy to understand and clear as there was no item ambiguity in question statements to ensure nonresponse bias. There was no item similarity, or proximity vis-à-vis length of the questionnaire was also moderate. The respondents were conveyed to fill up questionnaires and rate their raw material suppliers in terms of their service quality, returns management system, and switching barriers. A sum of 450 questionnaires was couriered to the respondents of various textile firms, and 375 out of them were received back, producing 83.3% response rate. After data screening, the rest of (n) 325 valid responses were analyzed via statistical package for the social sciences (SPSS) (version 21). A total of 50 questionnaires were excluded from the data due to having some issues. These issues were related to either nonresponse bias or the presence of acquiescence bias where the respondents tended to agree with all questions or they concurred with a particular position. The rest questionnaires were adequate for further analysis.


## 3.4. Participants

The survey carries the responses of (n) 325 B2B textile materials procurement officials. The total number of respondents included 296 men and 29 women. All respondents were asked to choose from a list of answer choices whether they agree or disagree with a question statement.


## 4. Results

### 4.1. Descriptive Statistics

In Table3, mean values (3.24–3.83) are clearly showing that the values are evidently spread around the central value and ideally describe the central tendency of data. Similarly, low standard deviation values (0.41–0.68) are also showing that the values are not much deviating from the central point and the data points tend to be close to the mean of the data set. It shows fairly close agreement among respondents and a little variance about the answers to the questions. The mean and the standard deviation values confirmed the significance of the data that there were very few chances of error.Table 3
Descriptive statistics of the study.
N StatisticsMinimum statisticsMaximum statisticsMean statisticsStd. deviation statisticsVariance statisticsService quality3252.404.933.8160.4110.169Switching costs3251.004.803.2440.6590.435Product returns3251.675.003.8050.6780.460Customer satisfaction3251.675.003.8290.5030.253Repurchase intention3252.005.003.7060.6270.393Valid N (listwise)325


### 4.2. Multivariate Normality

Skewness and Kurtosis values were observed within the acceptable range of normality, and the test established the multivariate normality of the data. The values (see Table4) showed that the distribution is negatively skewed and the extreme values or outliers are not present in the distribution. Simultaneously, Kurtosis values describe that the sample is slightly platykurtic. Its peak is just a bit shallower than the peak of a perfect distribution. So, it is confirmed that the existing data were normally distributed.Table 4
Frequencies of the study constructs.
Service qualitySwitching costsProduct returnsCustomer satisfactionRepurchase intentNValid325325325325325Missing00000Skewness−0.662−0.272−0.958−1.011−0.277Std. Error of skewness0.1350.1350.1350.1350.135Kurtosis1.393−0.1920.5682.3070.008Std. Error of kurtosis0.2330.2700.2700.2700.270P-value0.0010.0020.3190.001Note. Skewness value >+1 or  <-1 (balance distribution) – Kurtosis value <1 (flat distribution) [72].Kaiser–Meyer–Olkin (KMO) and Bartlett’s test confirmed the adequacy and suitability of the data. Taken together, these tests satisfied the minimum standards that are required to accomplish before conducting CFA on data. The KMO values of all five study constructs are greater than the recommended range (>0.6) (refer to [73]) and show the adequacy of the percentage of variance in data. Thus confirmed, sampling was adequate and the data were suitable for conducting confirmatory factor analysis (CFA) on it.


### 4.3. Validation of the Measurement Model

Convergent and discriminant validities of study instruments were assessed before undertaking the confirmatory factor analysis that how thoroughly these constructs measure the intended concepts. As per the criterion set by Fornell and Larcker [74], the items with poor factor loadings were removed from the measurement model to achieve convergent validity. The removal of the seven items (i.e., SC-money, SC-effort, SQ-accurate, SQ-damage, SQ-response, SQ-safe, and SQ-specific) had lower regression weights than the criterion (<.4) set by Avkiran and Ringle [75]. The remaining factor loadings significantly exceeded the threshold value (≥0.70). The removal of these items strengthened the values of different model fit indices, for instance, GFI, AGFI, CFI, TLI, and RMSEA for the achievement of their respective threshold values. These removals also enhanced the AVE up to the acceptable level (i.e., ≥0.50) with the values ranging between 0.50 and 0.52, as shown in Table 5. So, all the factors satisfied the discriminant validity, and these were precise in nature (truly measure the characteristics being represented by the variables).Table 5
KMO and Bartlett’s test.
KMO and Bartlett’s testSwitching costsProduct returnsService qualityCustomer satisfactionRepurchase intentOverallKaiser–Meyer–Olkin measure of sampling adequacy0.7100.6780.8310.8530.7360.877Bartlett's test of sphericity approx. chi-square346.0235.01533.4744.9364.24616.5Df103105156528Sig.0.0010.0010.0010.0010.0010.001Note. Value (KMO >0.5 or ideally >0.7) for adequacy of percentage of variance [73].To ensure the uniformity and stability of the measures, the internal and the composite reliabilities were measured. Cronbach’s alpha test confirmed the internal consistency and reliability of the concepts with the values (ranging 0.72–0.85) and satisfied the criterion set by Wollack et al. [76]. The composite reliability values (ranging 0.75–0.90) were also above the proposed level ≥0.70 (refer to [77]). Thus, convergent validity was also confirmed by the empirical data.Multicollinearity was assessed by measuring tolerance and variance inflation factor (VIF), where the VIF values exist around 1. It postulated that predictor variables are either not correlated or are moderately correlated with other variables. The VIF values of all study constructs are less than the recommended threshold value (<3) (refer to [78, 79]) and confirmed that there exists no multicollinearity issue. Also, the model is free of common method bias (CMB) and it is adequate for further statistical analysis. Common method bias (Table 6) is also checked through Harman’s single factor method as the total variance extracted by the first common factor that is an account for the 27.6% covariance that is far below the criterion percentage (50%) (criterion set by the developer), whereas CMB statistics (Table 7) also confirmed the absence of bias in data using the common latent factor method.Table 6
Common method bias (CMB) test through common latent factor method.
Standardized regression weights (with common latent factor)Standardized regression weights (without common latent factor)DifferenceItemsEstimateItemsEstimateSC_money<---SC0.456SC_money<---SC0.4650.009SC_effort<---SC0.585SC_effort<---SC0.555−0.03SC_time<---SC0.649SC_time<---SC0.6910.042SC_technical<---SC0.531SC_technical<---SC0.6320.101SC_uncertain<---SC0.516SC_uncertain<---SC0.5970.081PR_guarantee<---PR0.702PR_guarantee<---PR0.830.128PR_care<---PR0.618PR_care<---PR0.6790.061PR_convenient<---PR0.593PR_convenient<---PR0.6230.03CS_services<---CS0.445CS_services<---CS0.5960.151CS_pleased<---CS0.535CS_pleased<---CS0.7120.177CS_experience<---CS0.528CS_experience<---CS0.7050.177CS_overall<---CS0.442CS_overall<---CS0.6280.186CS_choose<---CS0.224CS_choose<---CS0.4250.201CS_easier<---CS0.466CS_easier<---CS0.6490.183RI_next<---RI0.634RI_next<---RI0.7930.159RI_relation<---RI0.391RI_relation<---RI0.5670.176RI_percent<---RI0.454RI_percent<---RI0.6210.167RI_pincrease<---RI0.583RI_pincrease<---RI0.6280.045SQ_accurate<---SQ0.148SQ_accurate<---SQ0.3440.196SQ_comply<---SQ0.106SQ_comply<---SQ0.2930.187SQ_ontime<---SQ0.24SQ_ontime<---SQ0.4220.182SQ_damage<---SQ0.002SQ_damage<---SQ0.2010.199SQ_prodstatus<---SQ−0.092SQ_prodstatus<---SQ0.410.502SQ_response<---SQ−0.144SQ_response<---SQ0.2680.412SQ_busy<---SQ−0.085SQ_busy<---SQ0.2640.349SQ_enthusi<---SQ0.055SQ_enthusi<---SQ0.1960.141SQ_expertise<---SQ0.046SQ_expertise<---SQ0.2110.165SQ_safe<---SQ0.27SQ_safe<---SQ0.4830.213SQ_person<---SQ0.546SQ_person<---SQ0.6690.123SQ_imp<---SQ0.844SQ_imp<---SQ0.63−0.214SQ_specific<---SQ0.257SQ_specific<---SQ0.3660.109SQ_needs<---SQ0.217SQ_needs<---SQ0.4350.218SQ_status<---SQ0.185SQ_status<---SQ0.3590.174Table 7
Common method bias (CMB) through Harman’s single factor method.
Total variance explainedComponentInitial eigenvaluesExtraction sums of squared loadingsTotal% of varianceCumulative %Total% of varianceCumulative %19.11227.61327.6139.11227.61327.61322.5697.78635.39932.4177.32642.72541.4424.37047.09551.3814.18451.27961.3524.09655.37671.2803.87959.25481.0823.27962.53390.9552.89465.427100.8762.65668.083110.8492.57470.657120.8252.50173.158130.7292.20975.367140.6972.11377.480150.6471.96179.442160.5861.77681.218170.5701.72782.945180.5381.63184.576190.5191.57386.149200.4801.45487.603210.4551.38088.982220.4201.27290.255230.3871.17291.427240.3701.12092.547250.3441.04393.590260.3120.94694.535270.3060.92795.462280.2920.88496.345290.2780.84397.188300.2540.77197.959310.2440.73898.697320.2320.70299.399330.1980.601100.000Extraction method: principal component analysis.The correlation matrix (Table8) was drawn to observe the interconstruct correlation. The matrix indicated that these variables are not mutually correlating with each other. Bivariate correlational values are below the recommended threshold value (<0.7) (refer to [80]). Service quality tends to have a strong positive correlation with customer satisfaction and thus moderate positive correlation with the repurchase intention (r = 0.66 and r = 0.52, respectively). Similarly, customer satisfaction also has a moderate correlation with repurchase intention (r = 0.54). Contrarily, product return has a weak correlation with customer satisfaction and repurchase intention (r = 0.22 and r = 0.25, respectively) as mentioned in Table 9. Similarly, service quality is also demonstrating a week correlation with switching costs (r = 0.27).Table 8
Measurement model result summary.
Factor loadingsReliability and validityCommon method bias (collinearity statistics)ConstructsItemMain loadingAVEComposite reliability (CR)Cronbach’s alphaToleranceVIFSwitching costsSC-time0.720.500.750.720.741.19SC-technical0.70SC-uncertain0.71Product returnsPR-guarantee0.690.510.760.750.701.25PR-care0.72PR-convenient0.74Service qualitySQ-needs0.710.510.900.850.511.96SQ-status0.70SQ-comply0.75SQ-ontime0.69SQ-prodstatus0.77SQ-enthusi0.69SQ-expertise0.72SQ-person0.72SQ-busy0.71SQ-imp0.74Customer satisfactionCS-services0.760.520.870.850.551.83CS-pleased0.69CS-experience0.69CS-overall0.71CS-choose0.76CS-easier0.74Repurchase intentRI-next0.750.510.810.76RI-relation0.72RI-percent0.79RI-pincrease0.69Note. Dependent variable: repurchase intention; AVE: average variance extracted; VIF: variance inflation factor.Table 9
Pearson’s correlation matrix.
ModelService qualitySwitching costsProduct returnsCustomer satisfactionRepurchase intentService quality1Switching costs0.273∗∗1Product returns0.382∗∗0.311∗∗1Customer satisfaction0.655∗∗0.313∗∗0.217∗∗1Repurchase intent0.515∗∗0.444∗∗0.254∗∗0.542∗∗1Note.∗∗Correlation is significant at the 0.01 level (2-tailed).


### 4.4. Model’s Measurement

The study models and their indicators were specified and labeled as the error terms. The model was also identified as it had enough pieces of information in an equation to produce unique estimates of unknown parameters. In the third stage, different model fit indices, for instance, GFI, AGFI, CFI, TLI, and RMSEA, were estimated. The degree of exactitude of the model was measured through chi-square (x2) value, as the value is sensitive to a big sample size, i.e., (n > 200) (refer to [81]). Finally, the model was respecified till acquiring the value of chi-square/df is < 3 by covarying error terms and by enforcing some path coefficient constraints. The respecified model achieved the acceptable values of fit indices that were observing poor fit otherwise. Therefore, some assumptions, for instance, no equality constraints, were enforced on the factor loadings and were also considered for these indicators.The distribution of latent constructs is significantly different from each other such as CMIN/df < 3. Likewise, the values of GFI, AGFI, CFI, and TLI are above 0.9, which shows the overall good fit of the model. For population, root mean square error of approximation (RMSEA) value (≤0.07) (refer to [82]) indicates a good fit of the model in relation to the degrees of freedom (df).The re-specified model fit indices confirmed that the hypothesized model explained a good fit to the data where (n = 325), p<0.001, GFI = 0.918, AGFI = 0.905, CFI = 0.917, TLI = 0.976, and RMSEA = 0.054, available in Table 10. With overall fit indices, the study model is confidently supposed to be a fairly good fit to the present data due to significant and meaningful path indications in the model. The items of the model are significantly related to their constructs, so all hypothesized relationships are supported between constructs and their indicators.Table 10
Model fit summary (original and revised model indices).
ModelItemsCMIN/dfdfGFIAGFICFITLIRMSEAOriginal model332.3244610.8360.8000.8570.8360.064Revised model262.1902550.9180.9050.9170.9760.054Note. GFI: goodness of fit index; AGFI: adjusted goodness of fit index; CFI: comparative fit index; TLI: Tucker–Lewis index; RMSEA: root mean square error of approximation.


### 4.5. Structural Equation Modeling (SEM)

#### 4.5.1. Structural model assessment

Standardized path coefficients with a bootstrap value of 2,000 resamples were measured to test the study hypotheses. A large number of replicates were required to achieve the same level of the estimate due to longer alignment. In this study, the mean statistics of switching costs were very close to the central (mean) value, so test statistics can fall into one of the critical regions either. So, two-tailed significance with a 95% confidence interval was determined due to both types of perceived positive or negative relationships. The data results confirmed the projected path structure (direct and indirect) authenticity and adequacy. Table11 summarizes the test results of study hypotheses and explains the direct, indirect, and total effects along with the path coefficients and p-values of the variables.Table 11
Hypothesis testing result summary.
HypothesisRelationshipsPath coefficientsp-valueConfidence intervalResultsH1aSWTCST⟶REPINT0.224∗∗0.0020.152–0.301SupportedH1bSWTCST⟶CUSTSATF0.167∗∗0.0030.075–0.259SupportedH1cSWTCST⟶CUSTSATF⟶REPINT0.111∗∗0.0020.051–0.176SupportedH2aPRODRTN⟶REPINT−0.0380.319−0.031–0.127Not supportedH2bPRODRTN⟶CUSTSATF−0.1000.059−0.218–0.004Not supportedH2cPRODRTN⟶CUSTSATF⟶REPINT−0.0660.055−0.150–0.001Not supportedH3aSERVQLTY⟶REPINT0.618∗∗0.0010.551–0.711SupportedH3bSERVQLTY⟶CUSTSATF0.646∗∗0.0020.543–0.724SupportedH3cSERVQLTY⟶CUSTSATF⟶REPINT0.426∗∗0.0020.337–0.521SupportedH4CUSTSATF⟶REPINT0.660∗∗0.0010.570–0.752SupportedNote.∗ mean significant at level p<0.05; ∗∗ mean significant at level p<0.01.SWTCST: switching costs; PRODRTN: product returns; SERVQLTY: service quality; CUSTSATF: customer satisfaction; REPINT: repurchase intention.The structural model assessment revealed that except for product returns, the other two constructs, switching costs and service quality, have significant direct positive effects on customer satisfaction and thus direct and indirect positive effects on repurchase intentions of the B2B textile industry customers. The results showed that customer satisfaction is a significant determinant of repurchase intention and it effectively explains the relationship between the predictors and the outcome variable of the study.Hypothesis H1b postulated that switching costs are directly positively associated with customer satisfaction and thus directly and indirectly positively associated (H1a and H1c) with repurchase intent mentioned in Table12. SEM results exhibited proper support for the hypotheses H1a (β = 0.224, p<0.05), H1b (β = 0.167, p<0.05), and H1c (β = 0.111, p<0.05) and proved that switching costs are the salient determinant of customer satisfaction and repurchase intention.Table 12
Standardized direct/indirect effects with two-tailed significance.
Standardized direct effects (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF−0.1000.6460.1670.000REPINT−0.0380.6180.2240.660Standardized direct effects: lower bounds (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF−0.2180.5430.0750.000REPINT−0.031−0.1030.1520.570Standardized direct effects: upper bounds (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0040.7240.2590.000REPINT0.1270.1110.3010.752Standardized direct effects: two-tailed significance (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0590.0020.0030.0.0.REPINT0.3190.0010.0020.001Standardized indirect effects (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0000.0000.0000.000REPINT−0.0660.4260.1110.000Standardized indirect effects: lower bounds (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0000.0000.0000.000REPINT−0.1500.3370.0510.000Standardized indirect effects: upper bounds (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0000.0000.0000.000REPINT0.0010.5210.1760.000Standardized indirect effects: two-tailed significance (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF............REPINT0.0550.0020.002...Hypotheses H2a, H2b, and H2c were assumed that product return has direct positive associations with customer satisfaction and repurchase intention and also has indirect positive relationships with repurchase intention. The analysis results found no support for these three hypotheses and exhibited no relationships of product returns with customer satisfaction and repurchase intention of B2B customers. SEM results for these hypotheses are H2a (β = -0.038, p<0.05), H2b (β = −0.100, p<0.05), and H2c (β = −0.066, p<0.05).Hypotheses H3a and H3c postulated that direct and indirect relationships exist between service quality and repurchase intent. Results endorsed these significant positive relationships with the values (β = 0.618, p<0.05, and β = 0.426, p<0.05), respectively, and thus established that service quality is an effective indicator of generating repurchase intention of B2B textile industry customers. Similarly, hypothesis H3b proposed a positive relationship between service quality and customer satisfaction. Results also endorsed this significantly positive relationship with the value (β = 0.646, p<0.05).Finally, the study hypothesized a relationship (H4) between the mediator (customer satisfaction) and the DV (repurchase intention) of the study. Results also endorsed this significant positive relationship as (β = 0.660, p<0.05). It indicated that customer satisfaction is a strong indicator of forming repurchase intention of the B2B customers and mediates between predictors (i.e., switching costs and service quality) and the outcome variable (repurchase intent). Therefore, it does not mediate between product returns and repurchase intention mentioned in Table 1.


## 4.1. Descriptive Statistics

In Table3, mean values (3.24–3.83) are clearly showing that the values are evidently spread around the central value and ideally describe the central tendency of data. Similarly, low standard deviation values (0.41–0.68) are also showing that the values are not much deviating from the central point and the data points tend to be close to the mean of the data set. It shows fairly close agreement among respondents and a little variance about the answers to the questions. The mean and the standard deviation values confirmed the significance of the data that there were very few chances of error.Table 3
Descriptive statistics of the study.
N StatisticsMinimum statisticsMaximum statisticsMean statisticsStd. deviation statisticsVariance statisticsService quality3252.404.933.8160.4110.169Switching costs3251.004.803.2440.6590.435Product returns3251.675.003.8050.6780.460Customer satisfaction3251.675.003.8290.5030.253Repurchase intention3252.005.003.7060.6270.393Valid N (listwise)325


## 4.2. Multivariate Normality

Skewness and Kurtosis values were observed within the acceptable range of normality, and the test established the multivariate normality of the data. The values (see Table4) showed that the distribution is negatively skewed and the extreme values or outliers are not present in the distribution. Simultaneously, Kurtosis values describe that the sample is slightly platykurtic. Its peak is just a bit shallower than the peak of a perfect distribution. So, it is confirmed that the existing data were normally distributed.Table 4
Frequencies of the study constructs.
Service qualitySwitching costsProduct returnsCustomer satisfactionRepurchase intentNValid325325325325325Missing00000Skewness−0.662−0.272−0.958−1.011−0.277Std. Error of skewness0.1350.1350.1350.1350.135Kurtosis1.393−0.1920.5682.3070.008Std. Error of kurtosis0.2330.2700.2700.2700.270P-value0.0010.0020.3190.001Note. Skewness value >+1 or  <-1 (balance distribution) – Kurtosis value <1 (flat distribution) [72].Kaiser–Meyer–Olkin (KMO) and Bartlett’s test confirmed the adequacy and suitability of the data. Taken together, these tests satisfied the minimum standards that are required to accomplish before conducting CFA on data. The KMO values of all five study constructs are greater than the recommended range (>0.6) (refer to [73]) and show the adequacy of the percentage of variance in data. Thus confirmed, sampling was adequate and the data were suitable for conducting confirmatory factor analysis (CFA) on it.


## 4.3. Validation of the Measurement Model

Convergent and discriminant validities of study instruments were assessed before undertaking the confirmatory factor analysis that how thoroughly these constructs measure the intended concepts. As per the criterion set by Fornell and Larcker [74], the items with poor factor loadings were removed from the measurement model to achieve convergent validity. The removal of the seven items (i.e., SC-money, SC-effort, SQ-accurate, SQ-damage, SQ-response, SQ-safe, and SQ-specific) had lower regression weights than the criterion (<.4) set by Avkiran and Ringle [75]. The remaining factor loadings significantly exceeded the threshold value (≥0.70). The removal of these items strengthened the values of different model fit indices, for instance, GFI, AGFI, CFI, TLI, and RMSEA for the achievement of their respective threshold values. These removals also enhanced the AVE up to the acceptable level (i.e., ≥0.50) with the values ranging between 0.50 and 0.52, as shown in Table 5. So, all the factors satisfied the discriminant validity, and these were precise in nature (truly measure the characteristics being represented by the variables).Table 5
KMO and Bartlett’s test.
KMO and Bartlett’s testSwitching costsProduct returnsService qualityCustomer satisfactionRepurchase intentOverallKaiser–Meyer–Olkin measure of sampling adequacy0.7100.6780.8310.8530.7360.877Bartlett's test of sphericity approx. chi-square346.0235.01533.4744.9364.24616.5Df103105156528Sig.0.0010.0010.0010.0010.0010.001Note. Value (KMO >0.5 or ideally >0.7) for adequacy of percentage of variance [73].To ensure the uniformity and stability of the measures, the internal and the composite reliabilities were measured. Cronbach’s alpha test confirmed the internal consistency and reliability of the concepts with the values (ranging 0.72–0.85) and satisfied the criterion set by Wollack et al. [76]. The composite reliability values (ranging 0.75–0.90) were also above the proposed level ≥0.70 (refer to [77]). Thus, convergent validity was also confirmed by the empirical data.Multicollinearity was assessed by measuring tolerance and variance inflation factor (VIF), where the VIF values exist around 1. It postulated that predictor variables are either not correlated or are moderately correlated with other variables. The VIF values of all study constructs are less than the recommended threshold value (<3) (refer to [78, 79]) and confirmed that there exists no multicollinearity issue. Also, the model is free of common method bias (CMB) and it is adequate for further statistical analysis. Common method bias (Table 6) is also checked through Harman’s single factor method as the total variance extracted by the first common factor that is an account for the 27.6% covariance that is far below the criterion percentage (50%) (criterion set by the developer), whereas CMB statistics (Table 7) also confirmed the absence of bias in data using the common latent factor method.Table 6
Common method bias (CMB) test through common latent factor method.
Standardized regression weights (with common latent factor)Standardized regression weights (without common latent factor)DifferenceItemsEstimateItemsEstimateSC_money<---SC0.456SC_money<---SC0.4650.009SC_effort<---SC0.585SC_effort<---SC0.555−0.03SC_time<---SC0.649SC_time<---SC0.6910.042SC_technical<---SC0.531SC_technical<---SC0.6320.101SC_uncertain<---SC0.516SC_uncertain<---SC0.5970.081PR_guarantee<---PR0.702PR_guarantee<---PR0.830.128PR_care<---PR0.618PR_care<---PR0.6790.061PR_convenient<---PR0.593PR_convenient<---PR0.6230.03CS_services<---CS0.445CS_services<---CS0.5960.151CS_pleased<---CS0.535CS_pleased<---CS0.7120.177CS_experience<---CS0.528CS_experience<---CS0.7050.177CS_overall<---CS0.442CS_overall<---CS0.6280.186CS_choose<---CS0.224CS_choose<---CS0.4250.201CS_easier<---CS0.466CS_easier<---CS0.6490.183RI_next<---RI0.634RI_next<---RI0.7930.159RI_relation<---RI0.391RI_relation<---RI0.5670.176RI_percent<---RI0.454RI_percent<---RI0.6210.167RI_pincrease<---RI0.583RI_pincrease<---RI0.6280.045SQ_accurate<---SQ0.148SQ_accurate<---SQ0.3440.196SQ_comply<---SQ0.106SQ_comply<---SQ0.2930.187SQ_ontime<---SQ0.24SQ_ontime<---SQ0.4220.182SQ_damage<---SQ0.002SQ_damage<---SQ0.2010.199SQ_prodstatus<---SQ−0.092SQ_prodstatus<---SQ0.410.502SQ_response<---SQ−0.144SQ_response<---SQ0.2680.412SQ_busy<---SQ−0.085SQ_busy<---SQ0.2640.349SQ_enthusi<---SQ0.055SQ_enthusi<---SQ0.1960.141SQ_expertise<---SQ0.046SQ_expertise<---SQ0.2110.165SQ_safe<---SQ0.27SQ_safe<---SQ0.4830.213SQ_person<---SQ0.546SQ_person<---SQ0.6690.123SQ_imp<---SQ0.844SQ_imp<---SQ0.63−0.214SQ_specific<---SQ0.257SQ_specific<---SQ0.3660.109SQ_needs<---SQ0.217SQ_needs<---SQ0.4350.218SQ_status<---SQ0.185SQ_status<---SQ0.3590.174Table 7
Common method bias (CMB) through Harman’s single factor method.
Total variance explainedComponentInitial eigenvaluesExtraction sums of squared loadingsTotal% of varianceCumulative %Total% of varianceCumulative %19.11227.61327.6139.11227.61327.61322.5697.78635.39932.4177.32642.72541.4424.37047.09551.3814.18451.27961.3524.09655.37671.2803.87959.25481.0823.27962.53390.9552.89465.427100.8762.65668.083110.8492.57470.657120.8252.50173.158130.7292.20975.367140.6972.11377.480150.6471.96179.442160.5861.77681.218170.5701.72782.945180.5381.63184.576190.5191.57386.149200.4801.45487.603210.4551.38088.982220.4201.27290.255230.3871.17291.427240.3701.12092.547250.3441.04393.590260.3120.94694.535270.3060.92795.462280.2920.88496.345290.2780.84397.188300.2540.77197.959310.2440.73898.697320.2320.70299.399330.1980.601100.000Extraction method: principal component analysis.The correlation matrix (Table8) was drawn to observe the interconstruct correlation. The matrix indicated that these variables are not mutually correlating with each other. Bivariate correlational values are below the recommended threshold value (<0.7) (refer to [80]). Service quality tends to have a strong positive correlation with customer satisfaction and thus moderate positive correlation with the repurchase intention (r = 0.66 and r = 0.52, respectively). Similarly, customer satisfaction also has a moderate correlation with repurchase intention (r = 0.54). Contrarily, product return has a weak correlation with customer satisfaction and repurchase intention (r = 0.22 and r = 0.25, respectively) as mentioned in Table 9. Similarly, service quality is also demonstrating a week correlation with switching costs (r = 0.27).Table 8
Measurement model result summary.
Factor loadingsReliability and validityCommon method bias (collinearity statistics)ConstructsItemMain loadingAVEComposite reliability (CR)Cronbach’s alphaToleranceVIFSwitching costsSC-time0.720.500.750.720.741.19SC-technical0.70SC-uncertain0.71Product returnsPR-guarantee0.690.510.760.750.701.25PR-care0.72PR-convenient0.74Service qualitySQ-needs0.710.510.900.850.511.96SQ-status0.70SQ-comply0.75SQ-ontime0.69SQ-prodstatus0.77SQ-enthusi0.69SQ-expertise0.72SQ-person0.72SQ-busy0.71SQ-imp0.74Customer satisfactionCS-services0.760.520.870.850.551.83CS-pleased0.69CS-experience0.69CS-overall0.71CS-choose0.76CS-easier0.74Repurchase intentRI-next0.750.510.810.76RI-relation0.72RI-percent0.79RI-pincrease0.69Note. Dependent variable: repurchase intention; AVE: average variance extracted; VIF: variance inflation factor.Table 9
Pearson’s correlation matrix.
ModelService qualitySwitching costsProduct returnsCustomer satisfactionRepurchase intentService quality1Switching costs0.273∗∗1Product returns0.382∗∗0.311∗∗1Customer satisfaction0.655∗∗0.313∗∗0.217∗∗1Repurchase intent0.515∗∗0.444∗∗0.254∗∗0.542∗∗1Note.∗∗Correlation is significant at the 0.01 level (2-tailed).


## 4.4. Model’s Measurement

The study models and their indicators were specified and labeled as the error terms. The model was also identified as it had enough pieces of information in an equation to produce unique estimates of unknown parameters. In the third stage, different model fit indices, for instance, GFI, AGFI, CFI, TLI, and RMSEA, were estimated. The degree of exactitude of the model was measured through chi-square (x2) value, as the value is sensitive to a big sample size, i.e., (n > 200) (refer to [81]). Finally, the model was respecified till acquiring the value of chi-square/df is < 3 by covarying error terms and by enforcing some path coefficient constraints. The respecified model achieved the acceptable values of fit indices that were observing poor fit otherwise. Therefore, some assumptions, for instance, no equality constraints, were enforced on the factor loadings and were also considered for these indicators.The distribution of latent constructs is significantly different from each other such as CMIN/df < 3. Likewise, the values of GFI, AGFI, CFI, and TLI are above 0.9, which shows the overall good fit of the model. For population, root mean square error of approximation (RMSEA) value (≤0.07) (refer to [82]) indicates a good fit of the model in relation to the degrees of freedom (df).The re-specified model fit indices confirmed that the hypothesized model explained a good fit to the data where (n = 325), p<0.001, GFI = 0.918, AGFI = 0.905, CFI = 0.917, TLI = 0.976, and RMSEA = 0.054, available in Table 10. With overall fit indices, the study model is confidently supposed to be a fairly good fit to the present data due to significant and meaningful path indications in the model. The items of the model are significantly related to their constructs, so all hypothesized relationships are supported between constructs and their indicators.Table 10
Model fit summary (original and revised model indices).
ModelItemsCMIN/dfdfGFIAGFICFITLIRMSEAOriginal model332.3244610.8360.8000.8570.8360.064Revised model262.1902550.9180.9050.9170.9760.054Note. GFI: goodness of fit index; AGFI: adjusted goodness of fit index; CFI: comparative fit index; TLI: Tucker–Lewis index; RMSEA: root mean square error of approximation.


## 4.5. Structural Equation Modeling (SEM)

### 4.5.1. Structural model assessment

Standardized path coefficients with a bootstrap value of 2,000 resamples were measured to test the study hypotheses. A large number of replicates were required to achieve the same level of the estimate due to longer alignment. In this study, the mean statistics of switching costs were very close to the central (mean) value, so test statistics can fall into one of the critical regions either. So, two-tailed significance with a 95% confidence interval was determined due to both types of perceived positive or negative relationships. The data results confirmed the projected path structure (direct and indirect) authenticity and adequacy. Table11 summarizes the test results of study hypotheses and explains the direct, indirect, and total effects along with the path coefficients and p-values of the variables.Table 11
Hypothesis testing result summary.
HypothesisRelationshipsPath coefficientsp-valueConfidence intervalResultsH1aSWTCST⟶REPINT0.224∗∗0.0020.152–0.301SupportedH1bSWTCST⟶CUSTSATF0.167∗∗0.0030.075–0.259SupportedH1cSWTCST⟶CUSTSATF⟶REPINT0.111∗∗0.0020.051–0.176SupportedH2aPRODRTN⟶REPINT−0.0380.319−0.031–0.127Not supportedH2bPRODRTN⟶CUSTSATF−0.1000.059−0.218–0.004Not supportedH2cPRODRTN⟶CUSTSATF⟶REPINT−0.0660.055−0.150–0.001Not supportedH3aSERVQLTY⟶REPINT0.618∗∗0.0010.551–0.711SupportedH3bSERVQLTY⟶CUSTSATF0.646∗∗0.0020.543–0.724SupportedH3cSERVQLTY⟶CUSTSATF⟶REPINT0.426∗∗0.0020.337–0.521SupportedH4CUSTSATF⟶REPINT0.660∗∗0.0010.570–0.752SupportedNote.∗ mean significant at level p<0.05; ∗∗ mean significant at level p<0.01.SWTCST: switching costs; PRODRTN: product returns; SERVQLTY: service quality; CUSTSATF: customer satisfaction; REPINT: repurchase intention.The structural model assessment revealed that except for product returns, the other two constructs, switching costs and service quality, have significant direct positive effects on customer satisfaction and thus direct and indirect positive effects on repurchase intentions of the B2B textile industry customers. The results showed that customer satisfaction is a significant determinant of repurchase intention and it effectively explains the relationship between the predictors and the outcome variable of the study.Hypothesis H1b postulated that switching costs are directly positively associated with customer satisfaction and thus directly and indirectly positively associated (H1a and H1c) with repurchase intent mentioned in Table12. SEM results exhibited proper support for the hypotheses H1a (β = 0.224, p<0.05), H1b (β = 0.167, p<0.05), and H1c (β = 0.111, p<0.05) and proved that switching costs are the salient determinant of customer satisfaction and repurchase intention.Table 12
Standardized direct/indirect effects with two-tailed significance.
Standardized direct effects (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF−0.1000.6460.1670.000REPINT−0.0380.6180.2240.660Standardized direct effects: lower bounds (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF−0.2180.5430.0750.000REPINT−0.031−0.1030.1520.570Standardized direct effects: upper bounds (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0040.7240.2590.000REPINT0.1270.1110.3010.752Standardized direct effects: two-tailed significance (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0590.0020.0030.0.0.REPINT0.3190.0010.0020.001Standardized indirect effects (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0000.0000.0000.000REPINT−0.0660.4260.1110.000Standardized indirect effects: lower bounds (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0000.0000.0000.000REPINT−0.1500.3370.0510.000Standardized indirect effects: upper bounds (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0000.0000.0000.000REPINT0.0010.5210.1760.000Standardized indirect effects: two-tailed significance (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF............REPINT0.0550.0020.002...Hypotheses H2a, H2b, and H2c were assumed that product return has direct positive associations with customer satisfaction and repurchase intention and also has indirect positive relationships with repurchase intention. The analysis results found no support for these three hypotheses and exhibited no relationships of product returns with customer satisfaction and repurchase intention of B2B customers. SEM results for these hypotheses are H2a (β = -0.038, p<0.05), H2b (β = −0.100, p<0.05), and H2c (β = −0.066, p<0.05).Hypotheses H3a and H3c postulated that direct and indirect relationships exist between service quality and repurchase intent. Results endorsed these significant positive relationships with the values (β = 0.618, p<0.05, and β = 0.426, p<0.05), respectively, and thus established that service quality is an effective indicator of generating repurchase intention of B2B textile industry customers. Similarly, hypothesis H3b proposed a positive relationship between service quality and customer satisfaction. Results also endorsed this significantly positive relationship with the value (β = 0.646, p<0.05).Finally, the study hypothesized a relationship (H4) between the mediator (customer satisfaction) and the DV (repurchase intention) of the study. Results also endorsed this significant positive relationship as (β = 0.660, p<0.05). It indicated that customer satisfaction is a strong indicator of forming repurchase intention of the B2B customers and mediates between predictors (i.e., switching costs and service quality) and the outcome variable (repurchase intent). Therefore, it does not mediate between product returns and repurchase intention mentioned in Table 1.


## 4.5.1. Structural model assessment

Standardized path coefficients with a bootstrap value of 2,000 resamples were measured to test the study hypotheses. A large number of replicates were required to achieve the same level of the estimate due to longer alignment. In this study, the mean statistics of switching costs were very close to the central (mean) value, so test statistics can fall into one of the critical regions either. So, two-tailed significance with a 95% confidence interval was determined due to both types of perceived positive or negative relationships. The data results confirmed the projected path structure (direct and indirect) authenticity and adequacy. Table11 summarizes the test results of study hypotheses and explains the direct, indirect, and total effects along with the path coefficients and p-values of the variables.Table 11
Hypothesis testing result summary.
HypothesisRelationshipsPath coefficientsp-valueConfidence intervalResultsH1aSWTCST⟶REPINT0.224∗∗0.0020.152–0.301SupportedH1bSWTCST⟶CUSTSATF0.167∗∗0.0030.075–0.259SupportedH1cSWTCST⟶CUSTSATF⟶REPINT0.111∗∗0.0020.051–0.176SupportedH2aPRODRTN⟶REPINT−0.0380.319−0.031–0.127Not supportedH2bPRODRTN⟶CUSTSATF−0.1000.059−0.218–0.004Not supportedH2cPRODRTN⟶CUSTSATF⟶REPINT−0.0660.055−0.150–0.001Not supportedH3aSERVQLTY⟶REPINT0.618∗∗0.0010.551–0.711SupportedH3bSERVQLTY⟶CUSTSATF0.646∗∗0.0020.543–0.724SupportedH3cSERVQLTY⟶CUSTSATF⟶REPINT0.426∗∗0.0020.337–0.521SupportedH4CUSTSATF⟶REPINT0.660∗∗0.0010.570–0.752SupportedNote.∗ mean significant at level p<0.05; ∗∗ mean significant at level p<0.01.SWTCST: switching costs; PRODRTN: product returns; SERVQLTY: service quality; CUSTSATF: customer satisfaction; REPINT: repurchase intention.The structural model assessment revealed that except for product returns, the other two constructs, switching costs and service quality, have significant direct positive effects on customer satisfaction and thus direct and indirect positive effects on repurchase intentions of the B2B textile industry customers. The results showed that customer satisfaction is a significant determinant of repurchase intention and it effectively explains the relationship between the predictors and the outcome variable of the study.Hypothesis H1b postulated that switching costs are directly positively associated with customer satisfaction and thus directly and indirectly positively associated (H1a and H1c) with repurchase intent mentioned in Table12. SEM results exhibited proper support for the hypotheses H1a (β = 0.224, p<0.05), H1b (β = 0.167, p<0.05), and H1c (β = 0.111, p<0.05) and proved that switching costs are the salient determinant of customer satisfaction and repurchase intention.Table 12
Standardized direct/indirect effects with two-tailed significance.
Standardized direct effects (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF−0.1000.6460.1670.000REPINT−0.0380.6180.2240.660Standardized direct effects: lower bounds (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF−0.2180.5430.0750.000REPINT−0.031−0.1030.1520.570Standardized direct effects: upper bounds (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0040.7240.2590.000REPINT0.1270.1110.3010.752Standardized direct effects: two-tailed significance (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0590.0020.0030.0.0.REPINT0.3190.0010.0020.001Standardized indirect effects (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0000.0000.0000.000REPINT−0.0660.4260.1110.000Standardized indirect effects: lower bounds (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0000.0000.0000.000REPINT−0.1500.3370.0510.000Standardized indirect effects: upper bounds (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF0.0000.0000.0000.000REPINT0.0010.5210.1760.000Standardized indirect effects: two-tailed significance (BC) (group number 1: default model)PRODRTNSERQLTYSWTCSTCUSTSATFCUSTSATF............REPINT0.0550.0020.002...Hypotheses H2a, H2b, and H2c were assumed that product return has direct positive associations with customer satisfaction and repurchase intention and also has indirect positive relationships with repurchase intention. The analysis results found no support for these three hypotheses and exhibited no relationships of product returns with customer satisfaction and repurchase intention of B2B customers. SEM results for these hypotheses are H2a (β = -0.038, p<0.05), H2b (β = −0.100, p<0.05), and H2c (β = −0.066, p<0.05).Hypotheses H3a and H3c postulated that direct and indirect relationships exist between service quality and repurchase intent. Results endorsed these significant positive relationships with the values (β = 0.618, p<0.05, and β = 0.426, p<0.05), respectively, and thus established that service quality is an effective indicator of generating repurchase intention of B2B textile industry customers. Similarly, hypothesis H3b proposed a positive relationship between service quality and customer satisfaction. Results also endorsed this significantly positive relationship with the value (β = 0.646, p<0.05).Finally, the study hypothesized a relationship (H4) between the mediator (customer satisfaction) and the DV (repurchase intention) of the study. Results also endorsed this significant positive relationship as (β = 0.660, p<0.05). It indicated that customer satisfaction is a strong indicator of forming repurchase intention of the B2B customers and mediates between predictors (i.e., switching costs and service quality) and the outcome variable (repurchase intent). Therefore, it does not mediate between product returns and repurchase intention mentioned in Table 1.


## 5. Discussion

The statistical results verified the strong influence of customer satisfaction on the repurchase intention of industrial customers. Customer satisfaction is a strong apparatus for shaping the repurchase intention of the B2B textile industry customers. These findings are consistent as these are in tandem with the various past studies with similar context (e.g., [9, 23, 34, 83, 84]). Results confirmed that customer satisfaction effectively explains the relationship and function as a mediator between B2B repurchase intention and its antecedents (i.e., switching costs and service quality). The results confidently confirmed that satisfied customers are more likely to repurchase. Therefore, customer satisfaction did not explain any relationship between product returns and repurchase intent, since no significant direct or indirect relationship was found between these two constructs, specifically in the case of B2B behavioural course of action. So, there exists no partial mediation or indirect relationship between product returns and repurchase intention as it was initially assumed in hypothesis H2c. The results somehow supported all perceived study hypotheses except H2a, H2b, and H2c. The product returns did not establish any significant positive or negative relationship with customer satisfaction and thus with the DV (repurchase intent) through the mediator. It pays neither a positive nor a negative effect on B2B customer satisfaction and thus on their repurchase intentions.The data analysis verified that switching costs and service quality are significantly important determinants that positively influence customer satisfaction and repurchase intention that eventually force the B2B textile industry customer to repurchase from the present supplier. Hypotheses H1a, H1b, and H1c were effectively supported by the study results, since switching cost has a significant direct and indirect positive association with DV (repurchase intent) and direct association with the mediator (customer satisfaction). Results endorsed that switching cost is an extended and ideally important construct to be considered in the B2B transactional scenario of the textile industry. It is an essential determinant that builds positive repurchase intentions and influences B2B customers to be with their present suppliers. Customers become more comfortable with the present supplier as their switching costs to replace the present supplier increase.The switching cost has a pivotal role in describing B2B customer repurchase intent, as it resulted in enhancing customer satisfaction and transforming positive repurchase intentions for having a long-term relationship with the present supplier. Mostly, past studies affirmed switching costs, a basic requirement, as a barrier to stop customers from switching (e.g., [85–89]). Simultaneously, the past literature also describes that the absence of switching barriers is the main reason for the convenient switching of the customer from one supplier to another, as the state of vulnerability of the supplier is quite high in the dynamic disposition of market space [90]. In the B2B transactional scenario, empirical evidence revealed that investing to raise switching costs resists customers to switch and these are beneficial for the firms in retaining their current clientele. This could be achieved through making changes in procedures, investing in new technology, revising payment terms, and training personnel to utilize advanced technology techniques to retain the customers if other suppliers try to step in.Therefore, in the case of product returns, the findings are quite different from the other two antecedents of customer satisfaction and loyalty (i.e., switching costs and service quality). Product return does not increase customer satisfaction and also does not shape the intentions of B2B customers to repurchase from the same supplier even after having a good product return experience in past. More importantly, the most critical and commonly discussed weak point that is associated with the product return policy of the firm is specifically the weak management of customer complaints and product returns. This weak point is also noticed by Lambert and Enz. Their study on supply chain management revealed that organizations should utilize customer knowledge to manage returns [91]. Organizations must handle customer information or knowledge strategically to develop value for the return processes.The customer repurchase intentions are extensively shaped by their satisfaction with the current service supplier, but a good product return experience does not form any added effect to enhance B2B customer satisfaction. A field experiment study by Petersen and Kumar also revealed that product return has a significant effect as it determines the future attitudes and behavior of customers—their willingness to refer the company to friends [92]. Therefore, other markets, for instance, B2C, might be effectively encouraged or influenced by the firm’s effective product returns management system. It ascertains that once the B2B customers have experienced good or bad product returns from the supplier side, it does not have any added influence on their satisfaction and loyalty. Although this may be surprising since there is a mass discussion about the important role of product returns in supply chain literature, the past literature witnessed that effective product returns management can improve customer satisfaction, reduce costs, enhance profitability [20], shape favourable customer reviews, and make positive referrals [36], and thus, it leads to continuing customer relationship [37]. Askariazad and Babakhani [39] found that the bad product return experience of customers contributes toward dissatisfaction and has a significant negative impact on the repurchase intention of B2B customers, but this study found that neither good nor bad product return experience contributes toward customer satisfaction. There is inconsistency with the findings, given by the past literature on product returns, but the findings are consistent with Russo’s study where he conducted research on the Italian audiology industry [84]. So, the hypotheses H2a, H2b, and H2c are not supported by the study results.The delivery of superior quality services and high switching costs are both equally effective ways to enhance industrial customer loyalty [15, 32]. The relationship of service quality with customer satisfaction (mediator) and then with repurchase intention (DV) was found significant. Service quality has a significant positive direct effect (H3b) on the mediator and also significant positive direct and indirect effects (H3a and H3c) on DV. The link between service quality and customer satisfaction has shown the strong relationship that eventually forces B2B customers to be with and repurchase from the present supplier. The results showed that service quality is an indicator of building or transforming B2B customer loyalty for considering the current supplier for repurchasing the textile raw materials. Service quality is a salient determinant of customer satisfaction and repurchase intention, and the respondents had overall a favourable evaluation of supplier quality services and they generally acknowledge it. The results showed that service quality is quite useful in upholding customer satisfaction that stimulates the repurchase intentions in the B2B transactional context. Thus, the textile firms should provide superior quality services to their customers to increase customer satisfaction and to stop other suppliers to step in. The organization, which needs to form and sustain a competitive edge over competitors, should deliver greater services to their organizational customers. These firms are required to focus on investing substantial efforts and time to provide superior quality services to their customers to be in the market and to survive in the long run. Otaigbe Otaigbe [93] also revealed in his study that business managers who consistently review their supply chain management system and processes to ensure service quality experience more satisfied and loyal customers and increased profitability [94]. Hence, the study results provided empirical evidence that service quality tends to be a fair indicator of customer satisfaction and the repurchase intention of B2B textile industry customers. A better service quality increases customer satisfaction and thus assures the retention of the current organizational clientele. The finding of this study is also consistent with that of previous studies that showed similar relationships between these variables [42, 95–97].Finally, marketing literature widely focuses on customer satisfaction and verifies that firms extensively focus on their customers and their success entirely depends upon customer satisfaction. Baidya and Ghosh [98] assessed customer repeat purchase as the output of a firm’s successful customer satisfaction strategy. The study results also showed a significant relationship of customer satisfaction with the B2B repurchase intent, as satisfied industrial customers showed higher behavioural loyalty toward supplier firms. These findings are consistent with past research studies on customer satisfaction [99–104].Firms are required to be more focused on customer satisfaction than sales since the customer is the key entity for the organizational business, and customer satisfaction is a substantial measuring tool for corporate sustainability. The logic behind the relationship between customer satisfaction and its positive effect on customer retention is that an increase in customer satisfaction will boost customer retention and generate future revenues for the business [99, 105]. Customer satisfaction programs, greater customer switching barriers, and service quality practices can upgrade business processes.So, a firm needs to develop firm and long-term relationships with its customers by constantly targeting and trying to retain them with distinctive attention. A satisfied customer becomes a loyal customer, provides positive word-of-mouth, and repeats future purchases that lead to increased revenue and that improve the stock prices of the business. For a firm, there is a need to pay more focus on customer satisfaction by all means, [98] as customer satisfaction boosts customer retention [99, 105]. The study results, therefore, confirmed, if an organization elevates customer satisfaction by taking necessary measures, for instance, it increases the service quality and enhances the switching costs to create an exit barrier, it succeeds to generate repurchase intentions of their existing customers.


### 5.1. Study Contributions

From a theoretical perspective, the study contributes to the literature by combining switching costs, product returns, and service quality on the satisfaction-retention framework. The study depicts a reviewed framework of B2B-specific variable combination in the context of the Pakistan textile industry. The study framework is unique, and it does add up a distinct concept by exploring the behavioural intention of a textile industry-specific population, which is comparatively a new population so far. The outcome of the study regarding product returns contradicts the broader consensus in supply chain literature. It proved insignificant as product return has not any added influence on B2B customer satisfaction and repurchase intent, no matter the customers experienced a vigorous product return even in the past. However, the results of this study are consistent with Russo’s findings on the Italian audiology industry [84].The study particularly contributes to commerce literature and work practices differently. Contrary to the past studies, which have taken either service quality or switching costs to predict customer satisfaction and loyalty, this study explicitly and simultaneously integrated both switching costs (repulsive force) and service quality (attractive force) in the study framework. It examines the outcome of these repulsive and attractive forces in the context of the Pakistan textile industry. The study framework consolidates the mutual dynamics of appealing (service quality), facilitating (product returns), and averting (switching costs) factors altogether and their effect on customer satisfaction and thus on customer retention (repurchase intent) in B2B transactional scenario.


### 5.2. Managerial Implications

In a practical sense, these study inferences will be valuable for the planning managers to assess the behavioural intention of B2B customers in developing or reviewing their marketing strategies for the Pakistan textile markets. The study recommended that the textile seller firms should design such a viable system that supports and appeals customers toward obtaining their satisfaction by forming exit barriers and improving the service quality. It is essential because mostly customers question the integrity and after-sale services of the supplier firms while performing a transaction. So, the firms must, therefore, convince their industrial customers to have trust in their service quality since it is found that service quality is a significant determinant of B2B customer satisfaction and their repurchase intent. The findings suggested that firms need to strengthen the exit barriers for their clientele to attract and motivate them more to repurchase form the present supplier.The firms that are still targeting to project their businesses outside of their current portfolio may utilize this study as a guide for the transition from conventional to progressive firms. These firms can focus even better on upholding the current clientele by exerting a stronger influence on customer satisfaction and their repurchase intention, which they could have missed otherwise.


## 5.1. Study Contributions

From a theoretical perspective, the study contributes to the literature by combining switching costs, product returns, and service quality on the satisfaction-retention framework. The study depicts a reviewed framework of B2B-specific variable combination in the context of the Pakistan textile industry. The study framework is unique, and it does add up a distinct concept by exploring the behavioural intention of a textile industry-specific population, which is comparatively a new population so far. The outcome of the study regarding product returns contradicts the broader consensus in supply chain literature. It proved insignificant as product return has not any added influence on B2B customer satisfaction and repurchase intent, no matter the customers experienced a vigorous product return even in the past. However, the results of this study are consistent with Russo’s findings on the Italian audiology industry [84].The study particularly contributes to commerce literature and work practices differently. Contrary to the past studies, which have taken either service quality or switching costs to predict customer satisfaction and loyalty, this study explicitly and simultaneously integrated both switching costs (repulsive force) and service quality (attractive force) in the study framework. It examines the outcome of these repulsive and attractive forces in the context of the Pakistan textile industry. The study framework consolidates the mutual dynamics of appealing (service quality), facilitating (product returns), and averting (switching costs) factors altogether and their effect on customer satisfaction and thus on customer retention (repurchase intent) in B2B transactional scenario.


## 5.2. Managerial Implications

In a practical sense, these study inferences will be valuable for the planning managers to assess the behavioural intention of B2B customers in developing or reviewing their marketing strategies for the Pakistan textile markets. The study recommended that the textile seller firms should design such a viable system that supports and appeals customers toward obtaining their satisfaction by forming exit barriers and improving the service quality. It is essential because mostly customers question the integrity and after-sale services of the supplier firms while performing a transaction. So, the firms must, therefore, convince their industrial customers to have trust in their service quality since it is found that service quality is a significant determinant of B2B customer satisfaction and their repurchase intent. The findings suggested that firms need to strengthen the exit barriers for their clientele to attract and motivate them more to repurchase form the present supplier.The firms that are still targeting to project their businesses outside of their current portfolio may utilize this study as a guide for the transition from conventional to progressive firms. These firms can focus even better on upholding the current clientele by exerting a stronger influence on customer satisfaction and their repurchase intention, which they could have missed otherwise.


## 6. Limitations and Future Research Directions

There are some limitations such as the study sample was limited to an explicit segment of industrial society (B2B textile material procurement officials) and of a specific geographical area (firms located in the main textile manufacturing regions, i.e., Karachi, Lahore, Faisalabad, and Multan) So, extending the findings to some other geographical area or service sectors may lead some different results.Second, the study only addresses the behavioural intention of corporate buyers who are technically sound and well familiar with the quality and features of the product. So, the statistics may not appeal to B2C or virtual markets having different populations. Even, within the textile industry, a longitudinal study may demonstrate some different results due to changing market dynamics at two different points of time.A future research pattern could be carried out by testing the study framework within the B2B service industry or virtual markets to enlarge the research agenda. Culture exerts an interdependent and complementary influence on the behaviours that drive demographic change [106]. So, cross-cultural impact or the specific demographical pattern that might base on a specific gender, designation, or job role can be taken to design the future pattern of the industrial population.



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