Traditional Journal of Law and Social Sciences (TJLSS) Volume 01, issue 02, 2022, Pages 123 – 147 Journal Home Page http://traditionaljournaloflaw.com/journal Impact of E- Service Quality, E-Satisfaction, and E-Loyalty in the Online Fashion Industry, Pakistan M. Imtiyaz Ahmed Khan1 1 Iqra University, Karachi, Pakistan ARTICLE INFO ABSTRACT Article History: This research is primarily attentive on the development of E-commerce platforms which has allowed businesses to harness the power of the Received: August 01, 2022 internet and reach a larger audience with their products and services. Revised: September 07, 2022 Ecommerce platforms provide businesses with a number of advantages Accepted: September 15, 2022 that were not possible with brick-and-mortar stores. They offer Available Online: October 15, 2022 businesses a wider reach as they are able to ship their products to customers anywhere in the world. E-commerce platforms allow Keywords: businesses to offer a more personalized shopping experience to their customers as they can recommend products based on the customer's Security, Fulfillment, Customer Service, previous purchase history. Companies have found e-business marketing Online Satisfaction, efficiency, System to be an easy and popular way to motivate their customers to buy a Availability, Online Satisfaction, Online Trust product while carrying out marketing activities. Data were collected and E-Loyalty using a structured questionnaire based on the Website Quality Instrument (WQI) and Customers Satisfaction Mediating Effect (SSME). JEL Classification Codes: To inspect the reliability of the scale, Cronbach Alpha is used in this research. Results showed that website quality has a significant impact O15, O47, R13 on purchase intention. Customer satisfaction mediates the relationship between website quality and purchase intention. This research found that online satisfaction has a significant impact on e-loyalty. In other words, the more satisfied customers are with their online shopping experience, the more likely they are to be loyal to that particular online store. This loyalty, in turn, leads to increased satisfaction and further loyalty, creating a virtuous circle for the online retailer. This Study will benefit online business owners, and it will also help to grow their business. © 2022 The Authors, Published by (TJLSS). This is an Open Access Article under the Creative Common Attribution Non-Commercial 4.0 Corresponding Author: [email protected] INTRODUCTION Globally, in this battle of digital marketing, online companies competing on the edge to consider a new way to attract consumers through various social media marketing activities. The technical 123 Traditional Journal of Law and Social Sciences (TJLSS) Volume 1, Number 2, 2022 development period gives new fields of customer engagement activity to online retail organizations and focuses on optimizing engagement, enabling extension and transformation of user and company relationships. Online businesses actively concerned inside the advertising and marketing activities but the visitors is scrolling the pages that make the anxiety in thoughts that response to hold on a particular marketing advancement (Grace T, 2017). Therefore, E-commerce has exploded in popularity around the world in recent years, but the vast majority of these sales were done by local purchasers buying from domestic merchants. However, buying from international merchants has recently gained popularity, with purchasers increasing from 15 to 21 percent. Around 25% of global purchasing is now done online, with China accounting for 440 million internet customers. The majority of retailers profit from their mobile web store/e- commerce platforms. Although, E-commerce emerged because of social reforms aimed at meeting the needs of the people in the most efficient and effective ways. A web-based commercial center is a rapidly growing type of web-based trading business. Customer e service quality is the main attraction that establishes a good customer-brand relation. The consumer notices a difference in the commodity they see on-line compared to the one they got (Ming. O et al., 2016). Thus, the expansion of business lines has given motivation to businesses to focus on their online presence and develop strategies for e-commerce website development that can create a sustainable competitive edge (Shiau, Dwivedi & Lai, 2018). Many different types of businesses are using e-commerce platforms to drive sales and grow their businesses (Lawden & Travor, 2012). Moreover, e-brand Satisfaction is effected by e service quality and it further effect on brand loyalty (Bowden, 2009). Brand satisfaction is described as a measuring index of post-activity that examines the internal state of the customer's perception regarding retail and shopping experiences. Investigating the degree of customer satisfaction is an important because happiness with the service of distribution impacts the choice of the customer to continue to use the product. Consumer satisfaction relies on fulfilling their initial assumption of a product / service output, according to the expectation-disconfirming principle (Oliver, 1980). Therefore, Business are facing a number of the most important demanding situations that consist of buyer’s engagement (Lin.Z et al., 2016) and consumer trust (Long.L et al., 2016) brand loyalty can be increased with the increase in customer engagement with the product or services over the internet. In Pakistan, several studies have been conducted the customer e loyalty in online retailing sector (Abbas et al., 2018; Shahid and Ayaz, 2018; Haider et al., 2014; Irfan et al., 2019). But, rapidly increasing in the online shopping trends, retail brands are still stuck and not, yet studies find any most considerable tool for influencing e loyalty through online shopping. Therefore, this study is different from the previous literature owing to its failure to grasp the meaning of customer e loyalty through e quality services and e satisfaction. Thus, this study fills the gap as this study is to measure the impact of E- Service Quality, E-Satisfaction, and E-Loyalty in the Online Fashion Industry in Pakistan. Therefore, this study model performs on service quality factors of with the contribution of mean- end chain theory and builds e satisfaction and e trust role among customer e loyalty 124 Traditional Journal of Law and Social Sciences (TJLSS) Volume 1, Number 2, 2022 LITERATURE REVIEW Pakistan Online Retail Fashion industry Pakistan is the 37th-largest Ecommerce market with a market value of $6 billion in 2021, coming in behind Israel and ahead of Iran. A 29 percent global growth rate was aided by a 45 percent increase in the Pakistani e-commerce business in 2021. Sales of online goods are increasing. Market dynamics provide the possibility for even further expansion as new markets are developing. The world will continue to increase during the coming years. This trend will be driven by East and Southeast Asia because of their expanding middle classes and lagging offline retail capabilities. The Statistic Digital Market Outlook predicts that market growth in Pakistan would persist in the upcoming years. The anticipated compound annual growth rate (CAGR 21–25) for the ensuing four years is 7%. This decrease, when contrasted with the year-over-year rise of 45 percent, shows a relatively competitive market. Pakistan's 19% internet penetration, which indicates that by 2021, 19% of the country's population would have made at least one online purchase, is another indicator of a saturated market. Therefore, Pakistan has been on a quest to shake up its economy and develop a cost-effective strategy for achieving broad commercialization. To a certain extent. The growing IT sector, population expansion, and the number of individuals utilizing the internet have all aided this aim. Hence, E-Commerce in Pakistan would provide a sector that would assist citizens in achieving a national perception of wealth, and with its untapped potential, it would be beneficial to the people. It can give Pakistan with the optimal technique to travel a large area in a short amount of time by combining social and economic development with technology progress. According to one forecast, Pakistan's information technology exports might expand by 25% by 2022. Theoretical Background This study model applied mean-end chain theory and performs on service quality dimensions with the contribution of mean-end chain model theory and builds role among satisfaction, trust and loyalty of brand in e-retail stores. According to (Gutman, 1982), the theory of mean-end-chain stated that the service or product’s attributes mean is to achieve values. However, the consumption of a brand iisinotilimited only toithe attributes’ purchases or usage of the brand. The brand’s consumption goes beyond the purchaseiandiextends toithe direct psychological benefits such as sign, pleasure, etc., related with the brand’s experience. However, the customer always keeps in memory about the product and services and its attributes (Claeys et ial., 1995). Thus, brand’s choice from the perceived desirable outcomes and reduces theiundesirableioutcomes associated with the services and products consumption (Gutman, 1982). Furthermore, The theory of means- end chain suggested that the value propositions of the product and services should be achieve after use the product and services, The means-end chain theory also suggested that, consumption of the product and services is not only the limitations, but psychological benefits are also mandatory to repurchase of the product. However, from the category of product and services play a vital role through loyalty in making customers satisfied when the customer is in search of pleasure, interest & sign from the services or products. 125 Traditional Journal of Law and Social Sciences (TJLSS) Volume 1, Number 2, 2022 E-Service Quality E-service quality can be defined as the overall satisfaction that a customer experiences when using an online service. This includes factors such as the ease of use, functionality, and overall user experience (Colby and Parasuraman, 2003). The E-Service quality scale was created by (Parasuraman et. al.2005), utilizing the means-end system to survey electronic help. First, the E- Service quality scale centers on genuine associations between the customer and the assistance sponsor rather than the nature of the Web website, which is steady with the essential objective of the current review, which is to examine the effect of e-administration quality on clients' eagerness to participate in web-based co-creation encounters. Second, E-Service quality incorporates the whole shopping experience of the customer(for example the connection point insight and the post- communication contribution), giving a complete image of the customer experience while reviewing e-Service quality as something other than how a customer mixes with a Web webpage. Third, the responsiveness, pay and contact aspects are enough location the nature of the e- administration recuperation process. Online Satisfaction Online satisfaction has been defined as the satisfaction of a customer with regard to previous purchases made through a specific retail-focused website. While, Oliver (1997) described customer online satisfaction as the psychological overview condition that arises when the emotion that follows unconfirmed expectations is combined with the previous customer experience feeling. The marketing literature agrees that satisfaction is positively connected to loyalty, the earlier interpretation tends to define consumers ' buying habits, including all their predictable buying patterns (Bennett et al., 2005). However, Szymanski and Hise (2000) found positive impact of brand satisfaction on loyalty and argued that, in evaluating e-satisfaction, customer expectations of web accessibility, merchandising (product offerings and product information), site design and financial security play a significant role in affecting brand loyalty. However, Royo Vela and Casamassima, (2011) concluded that satisfaction with the preferred brand is one of the determinants of brand loyalty and found a positive connection between brand satisfaction and brand loyalty in the context of CE. Furthermore, in recent study, Dawra, & Sahay, (2019), exposed that CE involvement factors highly positively associated with the brand satisfaction which tends to increase the brand loyalty in an online shopping brand. Further reveals that highly satisfied buyers are more likely to remain loyal to the brand. Online Trust Online trust is "the average consumer's propensity to depend on the brand's ability to fulfill its claimed purpose" defined by (Chaudhuri & Holbrook, 2001). Trust is typically more of a concern when there is asymmetry of facts and opportunism. Online trust is a feeling of confidence you have when using the internet, knowing that your personal information is safe and secure. When you trust a website, you feel confident that it will protect your information and keep it private. You also believe that the site is legitimate and that the content is accurate. When you don't have trust online, you might be worried about giving your personal information to a website (Sitkin and Roth, 1993). Furthermore, Online trust is the conviction that permits buyers readily become helpless 126 Traditional Journal of Law and Social Sciences (TJLSS) Volume 1, Number 2, 2022 against site traders, because of the assumption that the Internet dealer will act wellbeing and act in specific legit and reliable ways is guaranteed, no matter what the purchaser's capacity to screen or control the web-based buy process (Hou, 2005). Moreover, Reichheld and Schefter, (2000) contended that trust is the absolute most significant element for clients when choosing a web-based provider. On the grounds, that without up close and personal contact, individuals require a lot of consolation in the web-based business. Then before online organizations can fabricate a relationship with their clients, they should initially exhibit that client’s trust in them. E-Loyalty E-loyalty can be shown by the customers in the online shopping context by repurchasing it, promoting, and using positive word of mouth on community pages (Kim et al., 2009). Moreover, Kotler (2008) characterizes consumer loyalty as the effect of customers' assumptions on organization execution. Consumer loyalty is characterized by (Hellier et. al.2003) as the general sensation of delight and fulfillment felt by purchasers because of the capacity to satisfy the needs, assumptions, and requirements of customers regarding the organization's administration. With the development of internet business, consumer loyalty in the web-based climate has been named e- fulfillment. During this phase of faithfulness, clients are ordinarily dedicated to a brand or item (Oliver, 1999). Consumer loyalty is an aftereffect of administration given to clients and is an antecedent to re-dedication (Rachjaibun, 2007). Consumer loyalty is habitually characterized as the client's post-buy correlation of pre-buy assumptions versus execution got (Oliver, 1980; Zeithaml et al., 1993). Dishonestly faithful clients show rehash support without enthusiastic connection, as found in current lodging or carrier devotion programs (Shoemaker and Lewis, 1999). Attitudinal limit shows customers' enthusiastic or psychosomatic connection to an item/brand yet rarely brings about social ends like regular buys (Riley et al., 2001). Mediating role of Online Satisfaction The marketing literature agrees that satisfaction is positively connected to loyalty, the earlier interpretation tends to define consumer’s buying habits, including all their predictable buying patterns (Bennett et al., 2005). A customer's positive attitude toward a certain web portal, which is the outcome of their total pleasure with online service interactions, determines loyalty toward any service provider. The relationship between customers' pleasure with a certain site and purchase intentions was later supported by research. It is therefore noticed that web pleasure increases the likelihood of making a purchase when purchase intent is taken into account as a loyalty factor. In recent study, Dawra, & Sahay, (2019), exposed that service quality involvement factors highly positively associated with the brand satisfaction which tends to increase the brand loyalty in an online shopping brand. Further reveals that highly satisfied buyers are more likely to remain loyal to the brand. Another study found a positive correlation between overall satisfaction with online retailers and the propensity to shop at the same e-store. However, Royo Vela and Casamassima, (2011) concluded that satisfaction with the preferred brand is one of the determinants of brand loyalty and found a positive connection between brand satisfaction and brand loyalty. 127 Traditional Journal of Law and Social Sciences (TJLSS) Volume 1, Number 2, 2022 Moderating Role of Online Trust Customer trust is normally characterized as the readiness to depend on a trade accomplice in whom one has confidence in a weak circumstance (Moorman et al., 1992). Though trust in a web-based store is characterized by a purchaser's ability to depend on the merchant and act in conditions that make the customer defenseless against the vender (Mayer et al., 1995). One more meaning of online trust is the conviction that permits buyers readily become helpless against site traders, because of the assumption that the Internet dealer will act wellbeing and act in specific legit and reliable ways is guaranteed, no matter what the purchaser's capacity to screen or control the web- based buy process (Hou, 2005). Therefore, E-trust will characterize as trust that clients have in web-based exchanges or the web-based trade channel. In such a manner, the significance of confidence in electronic settings has been reliably contended (Stewart, 2003). Solid proof has arisen, specifically, that customers are especially worried about installment security and expected extortion (e.g., Hoffman et al., 1999; Ratnasingham, 1998). However, Stewart (2003) finds a solid connection between trusts and buy expectations, while Lynch et al. (2001) find that trust is reliable, connected with online unwavering ness in an assortment of differentiating public settings. But Reichheld et al. (2000) and Reichheld and Schefter (2000) have been the most compelling in stressing the significance of confidence in building up and keeping up with faithfulness. Conceptual Model METHODOLOGY In this study, quantitative research is used to test theories and conduct deductive research (Bryman, 2012). Moreover, Correlation design is used to examine the relationship between variables. According to (Sekaran & Bougie, 2016), correlation is conducted in the natural environment to determine whether a relationship exists between two variables. While, the target population was the consumers who have done online shopping from different fashion websites apps etc. And sample data have been collected from the audience of Karachi Pakistan. 128 Traditional Journal of Law and Social Sciences (TJLSS) Volume 1, Number 2, 2022 Nevertheless, Convenience sampling is applied. Convenience testing is a type of non-probability testing in which the example is drawn from a subset of the population nearby. This investigation is generally beneficial for pilot testing. It is the most appropriate fit for the study. A self-based survey questionnaire were distributed and data has been analyzed by using Smart PLS 3. (SEM). This accumulation followed the literature's rule of thumb (Hair et al., 2011) However, the sample size in a multivariate analysis should be 10 times or greater than the number of predictor variables. There were five predictors in this study, and a sample size of 103 or greater was required. Based on the 10 times rules of thumb, the current study used the G*Power software version to ensure the sample size was adequate. G*Power's power analysis was based on a variety of statistical factors to determine the required sample sizes (Erdfelder, Faul, Lang, & Buchner, 2007). The study used seven predictors to arrive at a medium effect size of 0.15 and a 5% significance level. Based on these parameters, a sample size of 100 was calculated with a statistical power of 0.80, as in Figure DATA ANALYSIS Descriptive Analysis Descriptive analysis was run to obtain the descriptive scores where the maximum and minimum scores, standard deviation, and the mean of all variables were assessed. Earlier in chapter three, a five-point Likert scale was used that ranged from “1 = strongly disagree to 5= strongly agree” Table 4.3 exhibits the mean scores of the variables ranging from 4.229 to 4.195 and the standard deviation scores are ranging from 0.59384 to 0.62630 in Table 4.2 below. 129 Traditional Journal of Law and Social Sciences (TJLSS) Volume 1, Number 2, 2022 Table 4.2 Descriptive Statistics Variable N Minimum Maximum Mean Std. Deviation S 318 2.25 5.00 4.229 .59384 F 318 2.75 5.00 4.252 .49694 S.A 318 2.50 5.00 4.176 .61241 E 318 3.00 5.00 4.218 .48610 O.S 318 2.25 5.00 4.248 .55501 EL 318 2.75 5.00 4.199 .55504 OT 318 2.78 5.00 4.254 .50069 C.S 318 1.75 5.00 4.195 .62630 Note: S= Security, F= Fulfilment, S.A= System Availability, E= Efficiency, O.S= Online Satisfaction, E.L= E-Loyalty, O.T= Online Trust, C.S= Customer Service. Measurement Model Convergent validity The convergent validity is tested through four different categories by utilizing PLS-SEM to measure the model internal and external validity, according to (Fornell and Larcker 1981). The four categories used to determine convergent validity are; Individual-item reliability, Cronbach’s alpha, Composite reliability and Average variance extracted (AVE). Table 4.4, result illustrates the analyzed data to assess the convergent validity. To measure the competency of internal model, the Validity and Construct Reliability is used, Black (1999). Further suggest that by using analysis of Cronbach Alpha, the internal model reliability is measured. Reliability and validity data analysis is very beneficial for measurement of internal model consistency (Nunnally 1978). However, to examine the internal consistency, individual-items reliability is performed in loading section and result based on the criteria set by Fidell (2007) which suggest that individual-item loading should be greater than 0.5 which indicates the good reliability of individual-item. In loading column, each item loading figure is greater than 0.5 which meet the reliable criteria set by Fidell (2007). However, the Cronbach’s*alpha and composite reliability*of all the variables are greater than 0.7, which meet the bench mark criteria of (Straub 1989). According to Cronbach L, J (1951), to show data reliability, values must be equal to or greater than 0.7. Moreover, Table 2 demonstrates that each Cronbach Alpha value is greater than 0.7, indicating a high level of reliability and validity. In addition, average variance extracting (AVE) was used to determine convergent validity. The value of each latent variable extracted in average variance (AVE) must be greater than or equal to 0.5 in order to certify the data recommended by (Fornell and Larcker, 1981), so all values of the Average variance extracted (AVE) are based on the rule. Furthermore, in the composite reliability 130 Traditional Journal of Law and Social Sciences (TJLSS) Volume 1, Number 2, 2022 column, the values should be greater than or equal to 0.7, so as shown in Table.2, all of the variables have values greater than or equal to 0.7 that support the rule state (Gefen et.al. 2000). This indicates that the data is trustworthy for further analysis and that its internal consistency is acceptable. Table 4.4: Construct Reliability, Cronbach’s Alpha, Composite Reliability, and AVE of Latent Variables Construct Items Loading Cronbach’s Alpha CR AVE Customer Service CS1 0.798 0.754 0.844 0.576 CS2 0.727 CS3 0.768 CS4 0.740 E-Loyalty EL1 0.859 0.876 0.915 0.728 EL2 0.828 EL3 0.863 EL4 0.863 Efficiency E1 0.801 0.898 0.924 0.710 E2 0.854 E3 0.837 E4 0.858 E5 0.861 Fulfilment F1 0.845 0.873 0.913 0.724 F2 0.839 F3 0.855 F4 0.864 Online OS1 0.863 0.882 0.919 0.738 Satisfaction OS2 0.845 OS3 0.856 OS4 0.872 131 Traditional Journal of Law and Social Sciences (TJLSS) Volume 1, Number 2, 2022 Online Trust OT1 0.717 0.892 0.913 0.538 OT2 0.731 OT3 0.742 OT4 0.779 OT5 0.761 OT6 0.723 OT7 0.713 OT8 0.711 OT9 0.720 Security S1 0.792 0.751 0.843 0.573 S2 0.748 S3 0.756 S4 0.730 System SA1 0.852 0.869 0.911 0.718 Availability SA2 0.843 SA3 0.838 SA4 0.856 Discriminant Validity Discriminant validity is defined as the percentage of construct variables whose percentages differ from one another (Carmines and Zeller, 1979). When comparing an indicator's outer loading on other related constructs, discriminant validity can be determined, and it should be greater than all of its loading on other constructs (Rahi, 2017). The discriminant validity of the constructs is confirmed by the fact that all items measuring a single construct loaded higher on that construct and lower on the others. When variables have an AVE stacking larger than 0.5, discriminant validity is appropriate, and it should not be less than half of the estimation fluctuation was wedged by the develop (Jaw, 1998). Furthermore, discriminating validity test that values between each row and its previous values are higher than the value shown in between one variable-construct and the other variable-construct. 132