“I am delighted!”: the effect of perceived customer value on repurchase and advocacy intention in B2B express delivery services

sustainability Article “I am Delighted!”: The Effect of Perceived Customer Value on Repurchase and Advocacy Intention in B2B Express Delivery Services Carlos Correa 1, David Alarcón1,* and Ignacio Cepeda 2   Citation: Correa, C.; Alarcón, D.; Cepeda, I. “I am Delighted!”: The Effect of Perceived Customer Value on Repurchase and Advocacy Intention in B2B Express Delivery Services. Sustainability 2021,13, 6013. https://doi.org/10.3390/su13116013 Academic Editors: Lester Johnson and Evangelos Grigoroudis Received: 9 April 2021 Accepted: 25 May 2021 Published: 26 May 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1Department of Social Anthropology, Psychology and Public Health, Pablo de Olavide University, 41013 Sevilla, Spain; [email protected] 2Department of Business Administration and Marketing, University of Seville, 41004 Sevilla, Spain; [email protected] *Correspondence: dalar[email protected]; Tel.: +34-9-5497-7406 Abstract: Express parcel delivery has increased significantly in recent years because of changes in technology and consumer habits, as has the number of express parcel delivery companies and the competitiveness among them. For an express parcel business to be sustainable, it must succeed in increasing customer perceived value (CPV). This study aims to investigate the business-to-business (B2B) performance between companies and their main express parcel service provider. The main objective was to analyze the impact of CPV on both the intention to repurchase services and the intention to recommend such services in the business-to-business (B2B) express parcel delivery sector. This study develops a research model that is analyzed in the express parcel sector in Spain using the variance-based structural equation technique, partial least squares (PLS-SEM), for data analysis. The findings reveal that with express parcel services in the B2B sector, perceived value was positively associated with the customer’s perceived satisfaction and trust, which in turn enhances the intention to repurchase and stimulates advocacy. In a market as competitive as the express parcel market in the B2B environment, customer perceived value is a critical factor in ensuring company sustainability. Keywords: customer perceived value; trust; repurchase intention; advocacy intention; express parcel 1. Introduction The express delivery services (EDS) sector, which includes the activities of collecting, transporting, and delivering documents, packages, and other goods from door to door in a limited time frame [ 1 ], is experiencing very significant growth worldwide due to changes in traditional consumption habits in the online consumption of both end customers (B2C) and businesses (B2B). In addition, this growth has been accentuated by the recent global pandemic caused by COVID-19 (acronym for coronavirus disease 2019), which has driven and entrenched this new type of consumption [ 2 ]. These behavioral changes have been aided by information and communication technologies and the democratization of mobile devices, tablets, social networks, and B2B and B2C digital channels [ 3 ]. Due to this increased demand for express parcel delivery services, in recent years, the number of express parcel delivery companies and the competitiveness among them has increased significantly. This can lead to an increase in unsustainable consumption, both by production systems and distribution systems. For this reason, parcel delivery companies are increasingly taking into consideration the objective of enhancing efficiency and reducing their ecological footprint. However, to promote the transition to sustainable consumption, express parcel delivery companies must also meet the needs of their customers, while offering value to their consumers in such a way that the customer favors the continuity of the relationship over profits [ 4 ]. Thus, for an express parcel business to be sustainable in the long term, it must succeed in satisfying customer needs, increasing sales, and stimulating advocacy [4,5]. Sustainability 2021,13, 6013. https://doi.org/10.3390/su13116013 https://www.mdpi.com/journal/sustainability Sustainability 2021,13, 6013 2 of 19 According to the DBK Sector Observatory [ 6 ], a leader in the provision of commercial, financial, sectoral, and marketing information in Spain and Portugal, the increase in ecommerce has a notable impact on the increase in the turnover of parcel and courier companies. This has led to significant growth in the use of express parcels, reaching a turnover of EUR 7.45 billion in 2019 (4.9% more than in the 2018 financial year) in B2B parcels. The explanation for this performance of the courier and parcel market can be partly supported by the increase in online consumption and in the outsourcing of services [ 7 , 8 ], as well as the improvement of supply chain management that has generated greater efficiency in the commercial distribution sector [ 9 ]. In 2018, revenues from postal shipments reached EUR 4872.2 million (+19.4% compared to 2017). Of this, 27.7% corresponded to the traditional postal segment, i.e., approximately EUR 1350.9 million, down 1.1% compared to 2017. Revenues from the express courier and parcel segment reached EUR 3521.3 million, 72.3% of the total, with a growth of 29.8% compared to 2017 [10]. Companies require express parcel services for both receiving materials from their suppliers and shipping products or services to their end customers and businesses. However, studies show that most companies tend to use a limited number of express parcel service providers, eventually establishing stable business relationships over time with their main express parcel provider [ 11 ]. Although the B2B relationship between the customer company and the express parcel service company is usually continuous over time [ 12 ], as in any competitive industry, the continuity of the business relationship depends on companies being satisfied with their express parcel providers [ 13 – 16 ]. In turn, customer satisfaction is directly dependent on the customer’s perceived value of the purchased service or product [ 13 , 17 , 18 ]. While, in the last decade, there has been a growing number of studies on customer perceived value in B2C, there is still not much evidence on the influence of perceived value on the business relationship in B2B in the express parcel industry. B2B relationships between express parcel companies and their customers tend to be long term and ongoing. This fact makes the study of factors influencing repurchase and recommendation to other companies a topic of particular relevance and interest. Empirical studies on these antecedent variables in B2C contexts are numerous, but in B2B contexts they are very scarce, and, specifically in the express parcel sector, they are almost nonexistent. This study aims to shed light on and fill the gap in the literature on empirical studies of express parcel companies in the B2B context by analyzing the main triggers that have an impact on the intention to repurchase from and to recommend these types of companies. The main objective of the study was to evaluate, in the context of B2B express parcel services, the antecedents of repurchase and recommendation intention of express parcel services. It examines what roles satisfaction, switching cost, and trust play in B2B express parcel services. Further, the study explores the indirect relationships between customer perceived value and repurchase and recommendation intention in B2B. To achieve these purposes, we reviewed previous studies of B2B customer perceived value (CPV) and the relationship CPV with mediating factors such as customer satisfaction, switching cost, and trust, with the final aim of examining their impact on repurchase intention and recommendation. Based on this review, the hypotheses proposed are tested on a structural model, and the results are discussed with the conclusions reached. 2. Literature Review and Hypotheses 2.1. B2B Customer Perceived Value Companies are increasingly realizing that their customers are choosing to purchase value-added products and services capable of providing a highly satisfying experience [ 19 ]. The concept of customer value is the basis of a large number of recent studies within the relationship marketing literature, and it has been positioned as one of the main approaches for understanding customers [ 20 – 22 ]. Several authors have attempted to define customer perceived value (CPV). Customer perceived value (CPV) represents the consumer’s overall evaluation of the utility of a product based on perceptions of what was paid and what was received [ 18 ]. Day and Huang [ 23 ] propose that customer perceived value (CPV) repre- Sustainability 2021,13, 6013 3 of 19 sents the difference between the benefits and costs perceived by the customer. Customer perceived value is the customer’s subjective evaluation of the characteristics of the product or service, its performance, and the consequences of using that product or service [24]. These definitions share the subjective and multidimensional nature of customer perceived value (CPV) with regard to received products or services [ 25 ]. Although product quality is a primary requirement for achieving customer perceived value [ 26 – 28 ], CPV is a multidimensional and subjective notion that ranges from product quality to the utility that the customer receives through the services given by the supplier [ 25 ]. Some studies assert that product quality alone is not sufficient for customer perceived value to exist [ 29 ]. Several studies argue that customer experience has an increasingly closer relationship with customer perceived value [3,30,31]. Other authors point out that value resides in the experience and not exclusively in the quality of the product or service consumed [ 32 – 36 ]. Several authors use these constructs of customer experience and customer perceived value as synonyms [29,37–40]. According to Pine and Gilmore’s theory [ 29 ], product quality, service quality, and contextual experience are the three main determinants of customer perceived value, which in turn will influence customers’ preferences and purchase intentions [ 41 ]. Previous studies show that there is an association between customer perceived value (CPV) and repurchase and recommendation intention [ 42 , 43 ]; however, to date, few studies have been conducted on express parcel services in the B2B service environment [44,45]. 2.2. Hypotheses Development 2.2.1. Customer Perceived Value and B2B Customer Satisfaction According to the disconfirmation paradigm [ 18 ], customer satisfaction increases when perceived value exceeds expectations. Satisfaction in the financial services sector is defined from the perspective of the attitude, evaluation, and response expressed by a customer in the purchase process, and some studies find that the customer’s overall attitude towards their financial service provider is satisfactory when it exceeds their expectations [ 46 , 47 ]. Similarly, in the luxury sector, it has been observed that customer satisfaction with products or services depends on whether such products or services meet or exceed customer expectations [ 48 , 49 ]. In Internet banking services, e-satisfaction is defined as meeting expectations regarding purchased services. It has been observed that if the performance of an online service exceeds expectations, the customer will be satisfied; otherwise, the customer will not be satisfied [50]. As overall customer satisfaction is defined by the differences between the value the customer expects and the value actually received [ 51 , 52 ], satisfaction will depend on CPV [ 28 , 44 , 45 , 53 ]. In the logistics industry in general, and specifically with B2B express parcels, customer satisfaction will depend on the perceived value of the service, which will directly and indirectly impact their trust and business continuity with the express parcel provider [54–60]. Our hypothesis is as follows: Hypothesis 1 (H1). The customer perceived value (CPV) of a B2B express parcel company positively influences customer satisfaction. 2.2.2. Customer Perceived Value and B2B Customer Trust Trust is defined as the belief that consumers must have that everything will go correctly in a customer–supplier relationship [ 61 , 62 ]. Additionally, trust is defined as the illusion that a particular firm will carry out actions that will yield positive results [ 63 ]. Moorman et al. [ 64 ] describe trust between two parties as the conviction that both parties are involved in a beneficial relationship. This type of conviction prolongs the duration of the relationship [ 65 ] by enhancing commitment to the relationship [ 66 ]. To that end, trust can be considered a valuable component of any successful relationship [ 66 ], fostering the development of long-term relationships and increasing commitment to the relationships [65,67]. Sustainability 2021,13, 6013 4 of 19 Especially in various B2B industries, trust in suppliers is a critical variable in business [ 68 ]. Trust increases customers’ predisposition for long-term business relationships [ 69 ]. In B2B service companies, trust has an important weight in the retention of suppliers with good reputations [ 70 ]. Trust in a supplier in the B2B environment is linked to emotions that may imply calmness, security, and hope. In contrast, distrust is associated with fear, uncertainty, stress, and worry. Thus, an untrusted supplier is perceived as a threat that requires a higher level of control to ensure that it delivers as expected [71]. Trust depends on satisfaction and customer perceived value (CPV); when a customer perceives value, they tend to experience satisfaction and trust the supplier more [ 28 ]. In the B2B services sector, trust plays a key role in personal relationships between the sellers and buyers of services; the more customer perceived value a service has, the more trust the customer will have in the supplier [72]. Our hypotheses are as follows: Hypothesis 2 (H2). The customer perceived value (CPV) of a B2B express parcel company positively influences customer trust. Hypothesis 3 (H3). The degree of customer satisfaction with a B2B express parcel company is positively related to customer trust. 2.2.3. Customer Perceived Value and B2B Switching Costs Switching costs refer to the perceived cost that customers discover once they change their service provider [ 73 ]. Switching costs significantly influence the intention to continue with the provider [ 74 ], explaining even more variance than customer satisfaction [ 75 ]. Burnham et al. [ 74 ] identified three types of switching costs: procedural, which involves the loss of time and effort invested; financial, which results from quantifiable economic loss; and relational, which is due to the rupture of ties and identity. Furthermore, the loss of a commercial relationship is the factor that most increases switching costs, and a good customer–supplier relationship makes it less likely that the buyer will consider the possibility of changing suppliers [73]. From the perspective of the customer–supplier relationship in B2B, higher customer perceived value and customer satisfaction will imply higher switching costs, improving the relationship and minimizing the possibility of supplier switching [ 76 , 77 ]. Our hypotheses are as follows: Hypothesis 4 (H4). The customer perceived value (CPV) of a B2B express parcel company positively influences switching costs. Hypothesis 5 (H5). Customer satisfaction with a B2B express parcel company positively influences the cost of switching suppliers. 2.2.4. Customer Perceived Value and B2B Repurchase Intent In a competitive business environment, maintaining customer repurchase intention and avoiding switching behavior are very important [ 78 ]. The cost of attracting new customers is higher than that of keeping current customers [ 79 ]; thus, much of the efforts in B2B marketing should be directed at increasing repurchase intention. Repurchase intention is defined as an individual’s judgment about buying a designated service from the same company again, taking into account the individual’s current situation and likely future circumstances [ 80 ]. From a behavioral point of view, repurchase intention refers to the likelihood that an individual will continue to purchase products from the seller in the future [ 81 , 82 ]. Repurchase intention can be the result of customer satisfaction with and trust in a particular company’s product or service [ 83 ]. Repurchase intention is strongly dependent on service quality [ 84 , 85 ]. According to the customer value paradigm [ 86 ], service quality is an additional component of customer perceived value, and overall CPV is positively associated with repurchase intention [44,87–89]. Sustainability 2021,13, 6013 5 of 19 In the B2B service sector, interpersonal aspects, such as switching costs, trust, and the customer’s perceived value of the supplier, have been shown to have a high impact on the repurchase intention of service buyers [ 90 , 91 ]. Specifically, in express parcel services, it has been found that situational factors, such as company location and delivery times, are less important in ensuring customers’ repurchase intention than individual and social service factors, such as innovativeness, optimism, and human interaction [ 92 ]. Particularly in the transportation services industry, for companies such as express parcel services, trust has been found to be a key factor in choosing suppliers as they transport customer goods that can be very valuable [ 93 ]. In the B2B relationship, customer perceived value (CPV) increases trust in the customer’s express parcel provider and, in turn, increases repurchase and recommendation intention [28,75]. Our hypotheses are as follows: Hypothesis 6 (H6). B2B express parcel company customer switching costs are positively associated with repurchase intention. Hypothesis 7 (H7). B2B express parcel company customer trust positively influences repurchase intention. Hypothesis 8 (H8). B2B express parcel company customer trust mediates the relationship between customer perceived value (CPV) and repurchase intention. 2.2.5. Customer Perceived Value and B2B Recommendation Intention In this study, the intention to promote or recommend is defined as the probability that a customer will recommend a service provider to others in the future [ 94 ]. A concept that is widely used in previous studies and related to recommendation intention is socalled WOM (word of mouth), which is understood as a form of informal recommendation in relation to a certain product or service [ 95 – 98 ]. Specifically, WOM could be defined as the comments made by customers about a given service or product [ 99 ]. Recently, other studies have used a new concept that has evolved in different digital environments: electronic word of mouth (e-WOM), which analyzes purchase decisions based on customer recommendations on the web [ 100 ]. In this study, which is focused on B2B, we employ the concept of customer recommendation intention broadly referring to both formal and informal modes of communicating the benefits of using a supplier to others [101]. In general, recommendation intention can be considered to be an expressive response of trust motivated by emotional factors of satisfaction or perceived benefits versus costs [ 102 ]. VPC exerts influence through relationship satisfaction, trust in repurchase, and recommendation intention [ 25 ]. Chai et al. [ 101 ] found that customer repurchase intention is positively associated with advocacy intention, and, in turn, customer perceived value directly and indirectly influences advocacy intention. Customers who perceive value in the purchase of a product or service tend to repeat the purchase and recommend their supplier to new customers [ 101 ]. In the context of B2B services, it has been found that customer perceived value has a positive impact on loyalty and subsequent advocacy intention for such services [ 103 ]. Customer perceived value (CPV) influences both trust and customer recommendation intention [28]. Our hypotheses are as follows: Hypothesis 9 (H9). Repurchase intention positively influences express parcel provider recommendation intention. Hypothesis 10 (H10). B2B express parcel company customer trust and repurchase intention mediate the relationship between customer perceived value (CPV) and recommendation intention. Sustainability 2021,13, 6013 6 of 19 2.3. Conceptual Model Customer perceived value (CPV) is a critical factor in ensuring company sustainability in the B2B environment. This study aims to investigate the business-to-business (B2B) performance between companies and their main express parcel service provider. The main objective was to analyze the impact of CPV on both the intention to repurchase services and the intention to recommend such services in the business-to-business (B2B) express parcel delivery sector. This study develops a research model (see Figure 1) that analyzes the effect of customer perceived value on the satisfaction, trust, and switching costs of express parcel services and how these variables predict repurchase intention and recommendation intention in the B2B sector. Figure 1. Research proposed model of customer perceived value, satisfaction, trust, switching costs, repurchase intention, and advocacy intention. 3. Materials and Methods 3.1. Sample A total of 186 multisector companies participated in this study. The inclusion criterion was that all of these companies had express parcel providers as strategic partners for their business activity. Online surveys were sent to the participants by email requesting participation and explaining the objectives of the research along with a link to the online survey. The survey was sent to 325 companies, of which 185 (57%) responded fully. The survey was conducted online using the Typeform platform. The surveys were collected between 3 March 3 and 8 April 2020. The sample was drawn from mailing lists of the national chambers of commerce and entrepreneurs’ associations, using corporate Facebook and LinkedIn contacts, and submitted to the managers of B2B ecommerce companies who use express parcel providers. The participants were asked questions through online surveys using the Typeform digital platform for sample management. The survey was sent by email to companies that use express delivery services for their business. The email was distributed to the membership lists of business associations, national chambers of commerce, and the Spanish business trade confederation. Participation in the questionnaire was encouraged through professional contacts on social networks such as LinkedIn and Facebook. Contact with the companies was accomplished by email. The questionnaire was completed by Sustainability 2021,13, 6013 7 of 19 329 companies , of which 185 were completed, i.e., 56.20%, with an average response time of 14 min and 36 s. At the beginning of the survey, the participants were informed of the objectives of the present investigation. The text informed the participants of the purely experimental purposes of the study and that the information would not be stored longer than necessary to obtain the conclusions of the investigation. Permission to participate was requested, with exclusive use of the data for conferences or future scientific publications. The participants were informed that they could withdraw from the study at any time if they so wished. Similarly, the participants were informed that their identity would be kept anonymous. 3.2. Measurement Scales A 7-point Likert-type scale was used to measure each of the statements on each item. Specifically, the response options ranged from 1 (strongly disagree) to 7 (strongly agree) (see Table 1). To measure the value perceived by the customer, we used a scale adapted from Gounaris [ 26 ]. The scale is composed of 4 items that assess the value that the customer gives to the expectations fulfilled by the product or service [ 25 , 26 ]. Adapted versions of the scale have been employed to measure customer perceived value of the banking, food, and motor sector services associated with customer satisfaction, trust, and loyalty [ 28 , 104 , 105 ]. Customer satisfaction was measured using a scale adapted from Ruiz et al. [ 44 ] and based on Taylor and Baker’s [ 106 ] and Oliver’s [ 107 ] work. The scale is composed of 6 items . This scale has been used in previous studies to compare customer satisfaction of service providers in the U.S. and Spain [44]. To measure the cost of change in the service sector, the Fandos et al. scale [ 75 ], adapted from Burnham et al. [ 74 ], was used. The scale is composed of 5 items. The switching cost scale has been used to analyze the relationship between switching costs and loyalty in customers of financial institutions [ 75 ] and to measure the switching costs of telephone company customers [108]. The Morgan and Hunt scale [ 66 ] was used to measure the level of trust in the express delivery provider. The scale is composed of 3 items. The scale has been employed in previous studies to analyze the relationship of customer trust to purchase intention in the automotive, retail, and ecommerce sectors [66,109–111]. The Ruiz et al. [ 44 ] scale, adapted from Zenithal et al. [ 85 ], was used to measure the intention to repurchase express parcel logistics services. The scale is composed of 3 items. This scale has been used to analyze the relationship of service quality, customer perceived value, satisfaction, and repurchase intention in sectors such as the restaurant and online shopping industries [42,112]. The Chai et al. scale [ 101 ] was used to measure the intention to recommend. The scale is composed of 3 items. The scale has been used to study the relationship between customer perceived value and recommendation intention in the insurance, online shopping, and private education sectors [113–115]. 3.3. Data Analysis To analyze the data derived from the questionnaires, the partial least squares (PLSSEM) technique was used. This SEM technique is based on variance, and it is part of the multivariate analysis family used to analyze the relationships between indicated variables. In our model, all measures are operationalized as composites [ 116 , 117 ]; thus, we decided to use PLS-SEM to test our model and hypotheses. This decision was made because we use composites estimated in Mode A [ 118 , 119 ], and we adopt an explanatory approach following Henseler [120]. Sustainability 2021,13, 6013 8 of 19 Table 1. Indicator loadings and items of used scales. Item Scales 1REP ADV SAT SC CPV TRU I will probably use my express delivery provider again. 0.899 I have the intention to purchase the services of my express delivery provider in the future. 0.912 I may use my express delivery provider’s services very frequently in the future. 0.912 I will say positive things about my express delivery provider to other people. 0.939 I will recommend my express delivery provider to other people who ask me. 0.933 I will encourage friends and family to do business with my express delivery supplier. 0.874 I am satisfied with the services of my express delivery provider. 0.873 Overall, I am satisfied when using the services of my express delivery provider. 0.921 Using the services of this express delivery provider is a satisfying experience. 0.910 My choice to use my express delivery provider was the right one. 0.942 Overall, I am satisfied with my express delivery supplier. 0.917 I believe I made the right decision in choosing to rely on my express delivery provider for my service needs. 0.896 Certainly if I switch to another express delivery provider it probably would not be as good as the current one. 0.770 I am not sure if switching to another express delivery provider would be good for me. 0.706 I already know the staff; changing express delivery providers would be like starting from scratch. 0.720 My express delivery supplier has a strong social reputation. 0.712 My express delivery provider’s employees are available to help my company achieve my objectives. 0.874 My express delivery supplier’s employees are available to add value to my business. 0.898 My express delivery supplier’s employees are available to help improve my company’s sales and image. 0.915 I am satisfied with my express delivery supplier’s level of innovation and leadership position in their field. 0.789 In our relationship, I can always rely on my express delivery supplier. 0.905 In our relationship, my express delivery supplier always gets it right. 0.911 In our relationship, my express delivery supplier has high reliability and integrity. 0.911 1Notes: REP: repurchase; ADV: advocacy; SAT: customer satisfaction; SC: switching cost; CPV: customer perceived value; TRU: trust. Sustainability 2021,13, 6013 9 of 19 Composites are estimated in Mode A when the indicators that compose the latent variable are correlated. Similarly, Mode A composites are a good choice with small or medium sample sizes [ 121 ]. A two-step process has been indicated to consistently evaluate models using PLS-SEM [ 119 ] as follows: (1) evaluation of the measurement model and (2) evaluation of the structural model. We use a bootstrap procedure [ 122 ] to find the significance of the indices. With bootstrapping, which is a resampling procedure, we can determine the significance of the path coefficients, weights, and indicator loadings for each composite (i.e., latent variable). For data analysis, we used the SmartPLS 3.2.6. software package (Bönningstedt, Germany) [ 123 ]. To test for mediation effects, we followed the procedure described by Nitzl et al. [124] and Cepeda-Carrion et al. [125]. 4. Results Using the indicated PLS-SEM technique, the following results were obtained by analyzing the measurement model and the structural model [ 119 ]. This section presents the results obtained based on the study’s general objective. The factor loadings of all items in the measurement model are above 0.70, which is an appropriate level at which 50% of the variance of the indicators can be explained [126]. 4.1. Analysis of the Measurement Model A good measurement model should demonstrate sufficient reliability and validity. The results show that the measurement model meets all of the commonly stipulated requirements . The most appropriate measures of internal consistency reliability are composite reliability, Dijkstran and Henseler’s rhoA, and Cronbach’s alpha [ 127 ]. The model satisfies the prerequisite of construct reliability, and all measurements are greater than 0.8 (see Table 2). Table 2. Descriptive statistics, reliability, and AVEs of the constructs. Scales 1Mean SD CA Rho A CR AVE REP 5.145 1.311 0.893 0.893 0.934 0.824 ADV 5.036 1.312 0.905 0.936 0.940 0.838 SAT 5.198 1.106 0.958 0.960 0.967 0.828 SC 4.696 1.414 0.707 0.712 0.818 0.529 CPV 4.626 1.393 0.892 0.892 0.926 0.758 TRU 4.735 1.251 0.895 0.896 0.934 0.826 1 Notes: Mean = the average score for all the items included in this measure; SD = standard deviation; CA = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted; REP: repurchase; ADV: advocacy; SAT: customer satisfaction; SC: switching cost; CPV: customer perceived value; TRU: trust. The average variance extracted (AVE) serves as a measure of unidimensionality [ 128 ]. The average variance extracted value (AVE) for all constructs is greater than 0.5 (Table 2), showing appropriate convergence and convergent validity of the measures. The square root of AVE for each individual construct exceeds the correlation value between itself and the other constructs, fulfilling the discriminant validity requirement and showing that the variance captured by the latent variable exceeds 50% [ 128 – 130 ]. Finally, the heterotraitmonotrait ratio (HTMT) provides evidence of discriminant validity [ 131 ]. Some authors suggest a threshold of 0.9 [ 119 ], others propose a strictest value of 0.85 [ 126 ]. Most of the variables attain discriminant validity, as indicated by values below 0.85 in the HTMT (Table 3). However, discriminant validity between switching cost and customer perceived value is reached below a threshold of 0.9. Sustainability 2021,13, 6013 16 of 19 43. Wu, L.Y.; Chen, K.Y.; Chen, P.Y.; Cheng, S.L. Perceived value, transaction cost, and repurchase-intention in online shopping: A relational exchange perspective. J. Bus. Res. 2014,67, 2768–2776. [CrossRef] 44. Ruiz, D.M.; Gremler, D.D.; Washburn, J.H.; Carrión, G.C. Service value revisited: Specifying a higher-order, formative measure. J. Bus. Res. 2008,61, 1278–1291. [CrossRef] 45. Wang, Y.; Po Lo, H.; Chi, R.; Yang, Y. An integrated framework for customer value and customer-relationshipmanagement performance: A customer-based perspective from China. Manag. Serv. Qual. Int. J. 2004,14, 169–182. [CrossRef] 46. Minarti, S.N.; Segoro, W. The influence of customer satisfaction, switching cost and trusts in a brand on customer loyalty—The survey on student as IM3 users in Depok, Indonesia. Procedia Soc. Behav. Sci. 2014,143, 1015–1019. [CrossRef] 47. Leninkumar, V. The relationship between customer satisfaction and customer trust on customer loyalty. IJARBSS 2017 ,7, 450–465. [CrossRef] 48. Chiou, J.; Droge, C. Service quality, trust, specific asset investment, and expertise: Direct andindirect effects in a satisfaction-loyalty framework. J. Acad. Mark. Sci. 2006,34, 613–627. [CrossRef] 49. Santini, F.D.O.; Ladeira, W.J.; Sampaio, C.H. The role of satisfaction in fashion marketing: A meta-analysis. J. Glob. Fash. Mark. 2018,9, 305–321. [CrossRef] 50. Amin, M. Internet banking service quality and its implication on e-customer satisfaction and e-customer loyalty. Int. J. Bank Mark. 2016,34, 280–306. [CrossRef] 51. Oliver, R.L. Measurement and evaluation of satisfaction processes in retail settings. J. Retail. 1981,57, 25–48. 52. Anderson, E.W.; Fornell, C.; Lehmann, D.R. Customer satisfaction, market share, and profitability: Findings from Sweden. J. Mark. 1994,58, 53–66. [CrossRef] 53. Sweeney, J.C.; Soutar, G.N. Consumer perceived value: The development of a multiple item scale. J. Retail. 2001 ,77, 203–220. [CrossRef] 54. Otsetova, A.; Enimanev, K. A Study on Customer Satisfaction of Courier Services in Bulgaria. Int. J. Entrep. Innov. 2014 ,6, 70–82. 55. Otsetova, A. Relationship between logistics service quality, customer satisfaction and loyalty in courier services industry. Manag. Educ. 2017,13, 51–64. 56. Kesari, B.; Atulkar, S. Satisfaction of mall shoppers: A study on perceived utilitarian and hedonic shopping values. J. Retail. Consum. Serv. 2016,31, 22–31. [CrossRef] 57. El-Adly, M.I. Modelling the relationship between hotel perceived value, customer satisfaction, and customer loyalty. J. Retail. Consum. Serv. 2019,50, 322–332. [CrossRef] 58. Chand, M. The impact of HRM practices on service quality, customer satisfaction and performance in the Indian hotel industry. Int. J. Hum. Resour. Manag. 2010,21, 551–566. [CrossRef] 59. Dhar, R.L. Service quality and the training of employees: The mediating role of organizational commitment. Tour. Manag. 2015 , 46, 419–430. [CrossRef] 60. Wikhamn, W. Innovation, sustainable HRM and customer satisfaction. Int. J. Hosp. Manag. 2019,76, 102–110. [CrossRef] 61. Lu, B.; Fan, W.; Zhou, M. Social presence, trust, and social commerce purchase intention: An empirical research. Comput. Hum. Behav. 2016,56, 225–237. [CrossRef] 62. Wang, Y.; Min, Q.; Han, S. Understanding the effects of trust and risk on individual behavior toward social media platforms: A meta-analysis of the empirical evidence. Comput. Hum. Behav. 2016,56, 34–44. [CrossRef] 63. Anderson, J.C.; Narus, J.A. A model of distributor firm and manufacturer firm working partnerships. J. Mark. 1990 ,54, 42–58. [CrossRef] 64. Moorman, C.; Deshpande, R.; Zaltman, G. Factors affecting trust in market research relationships. J. Mark. 1993 ,57, 81–101. [CrossRef] 65. Ganesan, S. Determinants of long-term orientation in buyer-seller relationships. J. Mark. 1994,58, 1–19. [CrossRef] 66. Morgan, R.; Hunt, S. The commitment-trust theory of relationships marketing. J. Mark. 1994,58, 20–38. [CrossRef] 67. Anderson, E.; Weitz, B. The use of pledges to build and sustain commitment in distribution channels. J. Mark. Res. 1992 ,29, 18–34. [CrossRef] 68. Askariazad, M.H.; Babakhani, N. An application of European Customer Satisfaction Index (ECSI) in Business to Business (B2B) Context. J. Bus. Ind. Mark. 2015,30, 17–31. [CrossRef] 69. Chai, L.; Li, J.; Tangpong, C.; Clauss, T. The interplays of coopetition, conflicts, trust, and efficiency process innovation in vertical B2B relationships. Ind. Mark. Manag. 2020,85, 269–280. [CrossRef] 70. Milan, G.S.; Eberle, L.; Bebber, S. Perceived value, reputation, trust, and switching costs as determinants of customer retention. J. Relatsh. Mark. 2015,14, 109–123. [CrossRef] 71. McKnight, D.H.; Lankton, N.K.; Nicolaou, A.; Price, J. Distinguishing the effects of B2B information quality, system quality, and service outcome quality on trust and distrust. J. Strat. Inf. Syst. 2017,26, 118–141. [CrossRef] 72. Haghkhah, A.; Rasoolimanesh, S.M.; Asgari, A.A. Effects of Customer Value And Service Quality on Customer Loyalty: Mediation Role of Trust and Commitment in Business-To-Business Context. Manag. Res. Pr. 2020,12, 27–47. 73. Blut, M.; Evanschitzky, H.; Backhaus, C.; Rudd, J.; Marck, M. Securing business-to-business relationships: The impact of switching costs. Ind. Mark. Manag. 2016,52, 82–90. [CrossRef] 74. Burnham, T.A.; Frels, J.K.; Mahajan, V. Consumer switching costs: A typology, antecedents, and consequences. J. Acad. Mark. Sci. 2003,31, 109–126. [CrossRef] Sustainability 2021,13, 6013 17 of 19 75. Fandos Herrera, C.; Puyuelo Arilla, J.M. La generación de lealtad a un destino de turismo gastronómico como factor clave en el desarrollo rural. Cuad. Aragon. Econ. 2013,23, 47–73. 76. Lesmono, S.U.; Santoso, T.; Wijaya, S.; Jie, F. The Effect of Switching Cost and Product Return Management on Repurchase Intent: A Case Study in the B2B Distribution Channel Context in Indonesia. Int. J. Supply Chain Manag. 2020,9, 44–53. 77. Padgett, D.; Hopkins, C.D.; Williams, Z. Buyer dependence in B2B relationships: The role of supplier investments, commitment form, and trust. J. Bus. Res. 2020,119, 13–24. [CrossRef] 78. Kuo, Y.; Hu, T.; Yang, S. Effects of inertia and satisfaction in female online shoppers on repeat-purchase intention: The moderating roles of word-of-mouth and alternative attraction. Manag. Serv. Qual. Int. J. 2013,23, 168–187. [CrossRef] 79. Emami, J.; Lajevardi, M.; Fakharmanesh, S. An Integrated Model in Customer Loyalty Context: Relationship Quality and Relationship Marketing View. Aust. J. Basic Appl. Sci. 2013,7, 399–407. 80. Hellier, P.K.; Geursen, G.M.; Carr, R.A.; Rickard, J.A. Customer repurchase intention: A general structural equation model. Eur. J. Mark. 2003,37, 1762–1800. [CrossRef] 81. Chiu, C.M.; Lin, H.Y.; Sun, S.Y.; Hsu, M.H. Understanding customers’ loyalty intentions towards online shopping: An integration of technology acceptance model and fairness theory. Behav. Inf. Technol. 2009,28, 347–360. [CrossRef] 82. Seiders, K.; Voss, G.B.; Grewal, D.; Godfrey, A.L. Do satisfied customers buy more? Examining moderating influences in a retailing context. J. Mark. 2005,69, 26–43. [CrossRef] 83. Samad, A. Examining the impact of perceived service quality dimensions on repurchase intentions and word of mouth: A case from software industry of Pakistan. IOSR J. Bus. Manag. 2014,16, 37–41. [CrossRef] 84. Zeithaml, V.A.; Berry, L.L.; Parasuraman, A. The nature and determinants of customer expectations of service. J. Acad. Mark. Sci. 1993,21, 1–12. [CrossRef] 85. Zeithaml, V.A.; Berry, L.L.; Parasuraman, A. The behavioral consequences of service quality. J. Mark. 1996 ,60, 31–46. [CrossRef] 86. Gale, B.; Gale, B.T.; Wood, R.C. Managing Customer Value: Creating Quality and Service that Customers can See; Simon and Schuster: New York, NY, USA, 1994. 87. Lin, L.; Chen, Y. A study on the influence of purchase intentions on repurchase decisions: The moderating effects of reference groups and perceived risks. Tour. Rev. 2009,64, 28–48. [CrossRef] 88. Irshad, S. Mediating role of customer value between Innovative Self-Service Technology (ISST) factors and online repurchase intention. J. Internet Bank. Commer. 2016,21, 1–18. 89. Suryadi, N.; Suryana, Y.; Komaladewi, R.; Sari, D. Consumer, customer and perceived value: Past and present. Acad. Strat. Manag. J. 2018,17, 1–9. 90. Chou, S.; Chen, C.W. The influences of relational benefits on repurchase intention in service contexts: The roles of gratitude, trust and commitment. J. Bus. Ind. Mark. 2018,33, 680–692. [CrossRef] 91. Doan, M.T.M. The factors affect organization customer perceived value and repurchase intention-A empirical study of professional B2B general insurance service in Vietnam. Ph.D. Thesis, International University-HCMC, Ho Chi Minh City, Vietnam, 2017. 92. Chen, Y.; Yu, J.; Yang, S.; Wei, J. Consumer’s intention to use self-service parcel delivery service in online retailing: An empirical study. Internet Res. 2018,28, 500–519. [CrossRef] 93. Garver, M.S.; Williams, Z.; Goffnett, S.P.; Gibson, B.J. Parcel shipping: Understanding the needs of business shippers. J. Transp. Manag. 2016,26, 29–46. [CrossRef] 94. Wang, C.Y. Investigating antecedents of consumers’ recommend intentions and the moderating effect of switching barriers. Serv. Ind. J. 2009,29, 1231–1241. [CrossRef] 95. Brown, S.W.; Cowles, D.L.; Tuten, T.L. Service recovery: Its value and limitations as a retail strategy. Int. J. Serv. Ind. Manag. 1996 , 32–46. [CrossRef] 96. Lang, B. How word of mouth communication varies across service encounters. Manag. Serv. Qual. 2011 ,21, 583–598. [CrossRef] 97. Chawdhary, R.; Dall’Olmo Riley, F. Investigating the consequences of word of mouth from a WOM sender’s perspective in the services context. J. Mark. Manag. 2015,31, 1018–1039. [CrossRef] 98. Simpson, P.M.; Siguaw, J.A. Destination Word of Mouth: The Role of Traveler Type, Residents, and Identity Salience. J. Travel Res. 2008,47, 167–182. [CrossRef] 99. Belvisi, M.; Pianeti, R.; Urga, G. Modelling Financial Markets Comovements during Crises: A Dynamic Multi-Factor Approach. Adv. Econ. 2016,35, 317–360. [CrossRef] 100. Reyes-Menendez, A.; Saura, J.R.; Martinez-Navalon, J.G. The impact of e-WOM on hotels management reputation: Exploring tripadvisor review credibility with the ELM model. IEEE Access 2019,7, 68868–68877. [CrossRef] 101. Chai, J.C.Y.; Malhotra, N.K.; Alpert, F. A two-dimensional model of trust-value-loyalty in service relationships. J. Retail. Consum. Serv. 2015,26, 23–31. [CrossRef] 102. Jones, K.; Leonard, L.N. Factors Influencing Buyer’s Trust in Consumer-to-Consumer E Commmerce. J. Comput. Inf. Syst. 2014 , 54, 71–79. [CrossRef] 103. Roy, S.K. Consequences of customer advocacy. J. Strat. Mark. 2013,21, 260–276. [CrossRef] 104. Javed, F.; Cheema, S. Customer satisfaction and customer perceived value and its impact on customer loyalty: The mediational role of customer relationship management. J. Internet Bank. Commer. 2017,22, 1–14. 105. Nyadzayo, M.W.; Khajehzadeh, S. The antecedents of customer loyalty: A moderated mediation model of customer relationship management quality and brand image. J. Retail. Consum. Serv. 2016,30, 262–270. [CrossRef] Sustainability 2021,13, 6013 18 of 19 106. Taylor, S.A.; Baker, T.L. An assessment of the relationship between service quality and customer satisfaction in the formation of consumers’ purchase intentions. J. Retail. 1994,70, 163–178. [CrossRef] 107. Oliver, R.L. A cognitive model of the antecedents and consequences of satisfaction decision. J. Mark. Res. 1980 ,17, 460–469. [CrossRef] 108. Awan, S.; Cheng, Y. Improve Communication Quality by Understanding Customer Switching Behavior in China’s Telecom Sector. Ibusiness 2016,8, 70–83. [CrossRef] 109. Inman, J.J.; Nikolova, H. Shopper-facing retail technology: A retailer adoption decision framework incorporating shopper attitudes and privacy concerns. J. Retail. 2017,93, 7–28. [CrossRef] 110. Sullivan, Y.W.; Kim, D.J. Assessing the effects of consumers’ product evaluations and trust on repurchase intention in e-commerce environments. Int. J. Inf. Manag. 2018,39, 199–219. [CrossRef] 111. Song, H.; Wang, J.; Han, H. Effect of image, satisfaction, trust, love, and respect on loyalty formation for name-brand coffee shops. Int. J. Hosp. Manag. 2019,79, 50–59. [CrossRef] 112. Mensah, I.; Mensah, R.D. Effects of Service Quality and Customer Satisfaction on Repurchase Intention in Restaurants on University of Cape Coast Campus. JTHSM 2018,4, 27–36. [CrossRef] 113. Nghia, H.T.; Olsen, S.O.; Trang, N.T.M. Shopping value, trust, and online shopping well-being: A duality approach. Mark. Intell. Plan. 2020,38, 545–558. [CrossRef] 114. Moradi, B.; Maroofi, F.; Ahmadizad, A. The investigating of cognitive and affective two-dimensional pattern between consumer trust, perceived value and behavioral loyalty in Iran governmental and non-governmental insurance areas. Bankacılık Ve Sigortacılık Ara¸stırmaları Derg. 2017,2, 21–35. 115. Bustamam, N.M. Does Re-Enroll Intention Mediate Relationship Between Perceived Value, Student Satisfaction and Advocacy Behavior? A Perspective of Private Higher Education. IJARES 2020,2, 130–141. 116. Rigdon, E.E. Choosing PLS path modeling as analytical method in European management research: A realist perspective. Eur. Manag. J. 2016,34, 598–605. [CrossRef] 117. Henseler, J. Bridging design and behavioral research with variance-based structural equation modeling. J. Advert. 2017 ,46, 178–192. [CrossRef] 118. Rigdon, E.E.; Sarstedt, M.; Ringle, C.M. On comparing results from CB-SEM and PLS-SEM: Five perspectives and five recommendations. Mark. ZFP 2017,39, 4–16. [CrossRef] 119. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019 ,31, 2–24. [CrossRef] 120. Henseler, J. Partial least squares path modeling: Quo vadis? Qual. Quant. 2018,52, 1–8. [CrossRef] 121. Becker, A.; Deckers, T.; Dohmen, T.; Falk, A.; Kosse, F. The relationship between economic preferences and psychological personality measures. Annu. Rev. Econ. 2012,4, 453–478. [CrossRef] 122. Chin, W.W. Bootstrap cross-validation indices for PLS path model assessment. In Handbook of Partial Least Squares; Springer: Berlin/Heidelberg, Germany, 2010; pp. 83–97. 123. Ringle, C.M.; Wende, S.; Becker, J.M. SmartPLS 3 [Software]; SmartPLS: Bönningstedt, Germany, 2015. 124. Nitzl, C.; Roldán, J.L.; Cepeda, G. Mediation analyses in partial least squares structural equation modeling: Helping researchers discuss more sophisticated models. Ind. Manag. Data Syst. 2016,116, 1849–1864. [CrossRef] 125. Cepeda-Carrión, G.; Nitzl, C.; Roldán, J.L. Mediation analyses in partial least squares structural equation modeling: Guidelines and empirical examples. In Partial Least Squares Path Modeling; Springer: Cham, Switzerland, 2017; pp. 173–195. 126. Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications: Thousand Oaks, CA, USA, 2016. 127. Henseler, J.; Hubona, G.; Ray, P.A. Using PLS path modeling in new technology research: Updated guidelines. Ind. Manag. Data Syst. 2016,116, 2–20. [CrossRef] 128. Fornell, C.; Larcker, D.F. Structural equation models with unobservable variables and measurement error: Algebra and statistics. J. Mark. Res. 1981,18, 382–388. [CrossRef] 129. Barclay, D.; Thompson, R.; Higgins, C. The Partial Least Squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Technol. Stud. 1995,2, 285–309. 130. Hair, J.F.; Ringle, C.M.; Sarstedt, M. PLS-SEM: Indeed a silver bullet. J. Mark. Pract. 2011,19, 139–151. [CrossRef] 131. Hair, J.F., Jr.; Sarstedt, M.; Hopkins, L.; Kuppelwieser, G.V. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. Eur. Bus. Rev. 2014,26, 106–121. [CrossRef] 132. Yang, Z.; Peterson, R.T. Customer perceived value, satisfaction, and loyalty: The role of switching costs. Psychol. Mark. 2004 ,21, 799–822. [CrossRef] 133. Fornell, C. A national customer satisfaction barometer: The Swedish experience. J. Mark. 1992,56, 6–21. [CrossRef] 134. Lee, J.; Lee, J.; Feick, L. The impact of switching costs on the customer satisfaction-loyalty link: Mobile phone service in France. J. Serv. Mark. 2001,15, 35–48. [CrossRef] 135. Oliver, R.L. Whence Consumer Loyalty? J. Mark. 1999,63, 33–44. [CrossRef] 136. Neal, W.D. Satisfaction is nice, but value drives loyalty. Mark. Res. 1999,11, 21–23. 137. Sharma, N.; Patterson, P.G. Switching costs, alternative attractiveness and experience as moderators of relationship commitment in professional, consumer services. Int. J. Serv. Ind. Manag. 2000,11, 470–490. [CrossRef] Sustainability 2021,13, 6013 19 of 19 138. Bolton, R.N.; Lemon, K.N. A dynamic model of customers’ usage of services: Usage as an antecedent and consequence of satisfaction. J. Mark. Res. 1999,36, 171–186. [CrossRef] 139. Ram, S.; Jung, H.S. How product usage influences consumer satisfaction. Mark. Lett. 1991,2, 403–411. [CrossRef] 140. Hadi, N.U.; Aslam, N.; Gulzar, A. Sustainable service quality and customer loyalty: The role of customer satisfaction and switching costs in the Pakistan cellphone industry. Sustainability 2019,11, 2408. [CrossRef] 141. Nenadál, J.; Vykydal, D.; Tyleˇcková, E. Complex Customer Loyalty Measurement at Closed-Loop Quality Management in B2B Area—Czech Example. Sustainability 2021,13, 2957. [CrossRef] 142. Strenitzerová, M.; Gaˇna, J. Customer satisfaction and loyalty as a part of customer-based corporate sustainability in the sector of mobile communications services. Sustainability 2018,10, 1657. [CrossRef] 143. Heinonen, K.; Strandvik, T. Reframing service innovation: COVID-19 as a catalyst for imposed service innovation. J. Serv. Manag. 2020,32, 101–112. [CrossRef] 144. Shin, Y.J.; Lee, J.Y. South Korea’s proactive approach to the COVID-19 global crisis. Psychol. Trauma. 2020 ,12, 475–477. [CrossRef] [PubMed] 145. Vargo, S.L.; Lusch, R.F. Evolving to a new dominant logic for marketing. J. Mark. 2004,68, 1–17. [CrossRef] 146. Eggert, A.; Ulaga, W. Customer perceived value: A substitute for satisfaction in business markets? J. Bus. Ind. Mark. 2002 ,17, 107–118. [CrossRef] 147. Agnihotri, R.; Dingus, R.; Hu, M.Y.; Krush, M.T. Social media: Influencing customer satisfaction in B2B sales. Ind. Mark. Manag. 2016,53, 172–180. [CrossRef] 148. Roy, S.; Sreejesh, S.; Bhatia, S. Service quality versus service experience: An empirical examination of the consequential effects in B2B services. Ind. Mark. Manag. 2019,82, 52–69. [CrossRef] 149. Chung, M.; Ko, E.; Joung, H.; Kim, S.J. Chatbot e-service and customer satisfaction regarding luxury brands. J. Bus. Res. 2020 ,117, 587–595. [CrossRef]