*Corresponding Author P-ISSN: 1412-1212 E-ISSN: 2541-2388 81 The Winners, 23(1), March 2022, 81-93 DOI: 10.21512/tw.v23i1.7516 The Determinant Factors of E-Loyalty in Customer-to-Customer E-Commerce Moderated by Gender Fernanda Losaura1*; Putra Iyang Bodronoyo2; Dimas Tri Wibowo3 1,2,3Creative Marketing Program, Management Department, BINUS Business School Master Program, Bina Nusantara University Jl. Hang Lekir I No. 6, Gelora, Jakarta 10270, Indonesia 1losauranandaa@gmail.com; 2putraiyangb@binus.ac.id; 3dimas.wibowo001@binus.ac.id Received: 29th April 2021/ Revised: 04th July 2022/ Accepted: 04th July 2022 How to Cite: Losaura, F., Bodronoyo, P. I., & Wibowo, D. T. (2022). The determinant factors of e-loyalty in customer-to-customer e-commerce moderated by gender. The Winners, 23(1), 81-93. https://doi.org/10.21512/tw.v23i1.7516 Abstract - E-commerce businesses dealt with one of the most important challenges, which was to create personalized e-loyalty for their customers to stay loyal to their e-commerce platform. The research used the 8C's framework developed to determine the effect of customization, contact interactivity, care, community, convenience, cultivation, choice, and character variables on e-loyalty by using gender as a moderating variable. A quantitative method was implemented by having 247 respondents belonging to the millennial category who have made online transactions on the C2C e-commerce platform at least once in the past month. Using the PLS-SEM method, the findings show that customization, contact interactivity, cultivation, community, and convenience can affect e-loyalty on C2C e-commerce platforms. In addition, the research finds that gender can have a moderating effect on customization and community. Keywords: e-commerce, e-loyalty, customer gender I. INTRODUCTION The development of the retail industry is expected to continue growing, especially for the e-commerce industry. Lipsman (2019) estimates that the global retail market will increase by 4,5% over the previous year, with e-commerce developments taking place, Asia-Pacific will remain to lead the growth of global e-commerce where the expected growth is 25%, representing 64,3% of global e-commerce growth (Lipsman, 2019). It is also predicted that e-commerce sales in Asia Pacific will nearly double in the next five years, growing at a compound annual growth rate of 14,0% and accounting for 28% of total retail sales. It is estimated that e-commerce, mainly through smartphone devices, will grow with a compound annual growth rate of 16,9% and account for 81% of online retail sales by 2023 (“Forrester: Online retail sales”, 2019). Six of the ten fastest growing e-commerce countries in 2019 come from the Asia-Pacific region. Indonesia is included in the 10 countries in Asia Pacific that have the highest e-commerce sales by occupying the 7th position at 20,6% (Lipsman, 2019). McKinsey predicts there will be significant growth for e-commerce in Indonesia, where sales will be able to reach US $65 billion per year by 2022, which is eight times what was recorded in 2017 (“Indonesia’s e-commerce sales”, 2018; Nurfadilah, 2018). Millennials are a large and potential market share for the e-commerce market in Indonesia for now and in the future. Nestled within individual countries, millennials are tech-savvy working professionals who seek value and modernity in their purchases. They are also a powerful shopping cohort and the largest customer segment in most countries (Chuah et al., 2017). The results of Lokadata.id processing of the 2019 National Socio-Economic Survey explain that the number of internet users in Indonesia reaches 116 million, of which 15 million are millennials who claim to like shopping using the internet (Lokadata.id, 2020). However, as a large and very potential market share, millennials are known to have a low level of loyalty (Purani, Kumar, & Sahadev, 2019). Compared to the previous generation, millennials have different characteristics, they are easily influenced by the surrounding environment, so 82 The Winners, Vol. 23 No. 1 March 2022, 81-93 they are not to be easily loyal (Purani et al., 2019). Every individual among millennials has at least two e-commerce applications downloaded and most of them claim to have made transactions in both e-commerce applications (Alvara Strategic, 2019). According to Ferrer (2018), about 80% of millennials find information on products and services online and 60% of them make online purchases. Customers who are accustomed to using e-commerce in buying products, especially millennials, state that transaction through e-commerce is easier. It shows that in addition to having great growth opportunities, e-commerce in Indonesia also has a competitive competition. E-commerce businesses in Indonesia are aware that more services provided by e-commerce players make customers easily move from one e-commerce to another, so loyalty will be a challenge for them (Ulya, 2019). In other research, it has been proven that e-loyalty greatly affects customer service and satisfaction in online shopping and can be accepted and used well by customers (Khan, Zubair, & Malik, 2019). Getting a personalized layout makes customers feel cared for, making them comfortable to be in the platform (Robinson, 2019). In addition, customers consider choosing an e-commerce for the convenience, easy access to information, wider range of products and services, and lower prices (Agyapong, 2018). The company image also becomes a consideration for customers to continue using or switching to other e-commerce platforms that are considered more suitable for them (Robinson, 2019). By being able to provide good e-commerce services to customers, it will affect customer loyalty to e-commerce organically or organic loyalty. It helps e-commerce businesses to reduce additional expenses in creating loyalty programs and helps to create loyal customers of e-commerce with or without the rewards provided (Hänninen et al., 2019). Loyalty is described as a positive attitude towards future purchases, also reflected in customer commitment to a brand, as well as showing customer loyalty to certain objects, such as brands, products, services, or stores (Wibowo et al., 2020). Experience and convenience in using an e-commerce can be a competitive advantage compared to giving rewards. One e-commerce company should avoid providing opportunities for customers to compare their rewards to the ones offered by competitors. The implementation of an e-loyalty will be more effective with the personalization carried out. McKinsey states that personalization can provide greater opportunities for e-loyalty (Lindecrantz, Gi, & Zerbi, 2020). The first and easiest personalization to implement is based on demographics, namely gender, where businesses only need to provide two different types of categories but can provide significant results (Saasquatch, 2019). Personalization can be done starting from different website layout designs, where it is studied that men prefer bright colors compared to women who prefer soft colors (Rancea, 2021). Men and women also have different top list product categories, where men prefer to buy products in the computer hardware and computer software categories compared to women who prefer to buy food, groceries, and fashion products (Angelovska, 2018). Research on e-loyalty is also growing and becoming a concern, especially in the era of e-commerce development. Srinivasan et al. (2002) propose the 8C's Framework model which is then widely used in research to find out what factors to be considered in creating good e-loyalty in e-commerce. The advantage of 8C's Framework compared to other frameworks or research models lies in its very strong relevance to the e-commerce media used such as applications or websites today. In its formulation, 8C's Framework is also adapted to the customer journey in e-commerce. Several studies on e-loyalty also show men and women have different perceptions of what is important to be provided by an online store to make a positive shopping experience on e-commerce platforms, with different behaviors, perspectives, and purchasing decisions in online shopping between men and women, it affects their level of e-loyalty to a brand (Szymkowiak & Garczarek-Bąk, 2018). One observer of the e-commerce business in Asia, Director of Consulting Deloitte Southeast Asia, states that the rapid growth of e-commerce in Indonesia is a challenge for its players to retain their customers (Ulya, 2019). Thus, the faster the development of this e-commerce business, the higher the urgency level for e-loyalty. E-loyalty is significantly an important thing to achieve since e-commerce cannot continue to compete in retaining customers only by providing continuous promotions. The research aims to examine the application of e-loyalty to e-commerce using the 8C's framework using gender as a moderating variable in the case study of e-commerce in Jakarta. The research discusses e-loyalty using the 8C's framework developed by Srinivasan et al. (2002) with the variables studied including customization, contact interactivity, cultivation, care, community, choice, convenience, and character. The difference from previous research lies in the location and focus analyzing the role of gender variables in influencing e-loyalty. Customization makes it possible to fulfil the needs and expectations of the customer and helps in maintaining loyal customers (Rodrigues & Ferreira (2016). Customization in online shopping is a strategy that may help in persuading customers to select a product or service leading to a purchase (Pappas et al., 2017) and lead to a unique and memorable brand experience (Wetzlinger et al., 2017). Customization is widely implemented in e-commerce and besides maintaining loyal customers, it has been proven to increase customer loyalty (Kaminskas et al., 2017). Customization provides the possibility that customers will find something they want to buy according to their preferences. With the satisfaction generated from this customization, they are expected to repurchase (Srinivasan et al., 2002). Therefore, the proposed research hypothesis is: 83The Determinant Factors of E-Loyalty.... (Fernanda Losaura, et al.) H1: Customization has a positive effect on e-loyalty in e-commerce In online commerce, interaction represents the high level of engagement and communication between the buyers and the sellers, and emphasizes immediate and mutual communication (Bao et al., 2016). The level of customers’ overall satisfaction is partially determined by user perceived interactivity during communication (Lee, 2017). Roy (2018) defines interaction as part of the customer experience, which is the affective and cognitive aspects resulting from services that can lead to behavioral results, for instance, customer loyalty. Previous research has shown that contact interactivity can affect e-loyalty in e-commerce (Srinivasan et al., 2002), so that the proposed research hypothesis is: H2: Contact interactivity has a positive effect on e-loyalty in e-commerce Increased competition and the availability of sample alternative options for customers to select their superior service or product provider is a great challenge to most of the organizations in retaining existing customers for an extended period (Feliz & Maggi, 2019). Interaction allows a search process to quickly locate a desired product or service, reducing the customers’ dependence on having a detailed memory (Padmavathy et al., 2019). Frequent interaction between the company and customers are necessary due to influence from internal and external factors that impact customers’ expectation, and behaviors differ over the time to retain the existing customer (Ali & Ali, 2018). Cultivation is the extent to which e-commerce provides relevant information and incentives to customers that are used to broaden and deepen customer purchases over time (Srinivasan et al., 2002). Preceding research have shown that cultivation can affect e-loyalty in e-commerce (Rao & Kothari, 2017), so that the proposed research hypothesis is: H3: Cultivation has a positive effect on e-loyalty in e-commerce Care is shown when the company continues to remind customers about the status of orders, upcoming products, and others. Customers will feel that the company pay attention to maintaining good relationships with them (Hänninen, 2019). The quality of care has a direct positive effect on customer behavior, they can feel satisfied and become loyal if one’s service meets their expectations (Asnawi et al., 2019). It is also believed that care has positive direct effects on customer loyalty which is explained by repurchase (Lai et al., 2018). It has been shown that care can affect e-loyalty in e-commerce (Srinivasan et al., 2002), so the research proposes hypothesis as: H4: Care has a positive effect on e-loyalty in e-commerce It is found that the ability of customers to exchange information and compare product experiences can increase the loyalty of these customers as well as other customers (Srinivasan et al., 2002). It shows an honest statement from fellow customers about a product sold in an e-commerce. E-loyalty can be enhanced by the participation and commitment of the community or customers online. In online brand community, e-loyalty is expected not only to be influenced by voluntary customer participation in the community, but also by autonomous management of websites (Hsieh & Wei, 2017). The e-loyalty research should come from a community’s perspective, in which the customer is socially integrated, with relationships which infer and direct his or her choices on online media (Handarkho, 2020). It has been previously found that community can affect e-loyalty in e-commerce (Kristyassari et al., 2018), so that the proposed research hypothesis is: H5: Community has a positive influence on e-loyalty in e-commerce Choice is critical because it lays the foundation for an intentional purchase (Kotler et al., 2019). Choice in e-commerce plays an important role because increasing the number of alternatives available in one e-commerce can greatly reduce the opportunity cost and inconvenience cost of finding and buying products online. By having many and varied product choices in one e-commerce, it can make e-commerce dominant and top-of-mind, as it is the only go-to e-commerce if customers want to buy a product, so it ultimately creates e-loyalty (Srinivasan et al., 2002). E-loyalty on an e-commerce site makes the customer prioritize searching for products, choosing the site for their online shopping needs, and giving the website’s name when being asked about the shop (Srivastava & Rai, 2018). Several previous research have shown that choice can affect e-loyalty in e-commerce (Pasumarthy et al., 2016), so that the proposed research hypothesis is: H6: Choice has a positive effect on e-loyalty in e-commerce Scholars have examined the concept of convenience and its effects on customer behavior, and previous research has shown that service convenience plays a significant role in customers’ evaluations of their overall experience (Berry, 2016). Convenience plays a role when customers have plans to purchase products online. It is perceived as saving time and effort related to searching for and selecting a product or brand (Duarte, Costa e Silva, & Ferreira, 2018). The discomfort they feel when they are in the process of finding and buying products on an e-commerce will cause a decline or even the loss of customer e-loyalty (Srinivasan et al., 2002). Convenience has a positive correlation with repurchase and impact loyalty (Kumar et al., 2020). It is shown that convenience can affect e-loyalty in e-commerce (Pasumarthy et al., 2016), so 84 The Winners, Vol. 23 No. 1 March 2022, 81-93 that the proposed research hypothesis is: H7: Convenience has a positive effect on e-loyalty in e-commerce Character is a creative website design that can help e-commerce in building a positive reputation and characterization for itself in the minds of customers. Therefore, through the perspective and reputation, it can influence the level of customer e-loyalty to e-commerce (Srinivasan et al., 2002). In e-commerce context, website has a significant role in mediating the relationship between the seller and the buyer. Interactions between both parties are mediated by a website, in which through the website, both parties could establish and maintain a good interaction and communication with each other (Wilson et al., 2019). Higher customer perception of an e-commerce website increases the customer intention to repurchase on the website, which will create loyal customers (Wilson et al., 2018). Character can affect e-loyalty in e-commerce (Hendrian, 2012), so that the proposed research hypothesis is: H8: Character has a positive effect on e-loyalty in e-commerce Gender is one of the most frequently used segments to understand customer behavior in online shopping (Pereira & Salgueiro, & Rita, 2016). Khan & Rahman (2016) show that gender can moderate the effect of e-tail brand experience on e-loyalty. The e-tail brand experience includes easy access and website navigation in finding product or service information (convenience), different offers for each customer or custom (customization), and maintaining healthy customer relationships and positive online customer reviews (community) (Khan & Rahman, 2016). Gender is also found to be able to moderate the effect of visual engagement on e-loyalty (Pandey & Chawla, 2018). Where this visual engagement is a different layout design (customization) aimed at male and female respondents, information (contact interactivity) is the availability of relevant information needed by customers. E-brand trust which includes customer confidence in providing recommendations provided by e-commerce is significantly relevant and useful for them (cultivation) and customer trust in e-commerce to always provide the best service and will not take unilateral advantage (care) is also found to be possibly moderated by gender variable in its effect on e-loyalty (Khan & Rahman, 2016). Therefore, the proposed hypothesis are formulated, and the research model can be described as seen in Figure 1. H9: Gender moderates the effect of customization on e-loyalty in e-commerce H10: Gender moderates the effect of contact interactivity on e-loyalty in e-commerce H11: Gender moderates the effect of cultivation on e-loyalty in e-commerce H12: Gender moderates care's influence on e-loyalty in e-commerce H13: Gender moderates the influence of community on e-loyalty in e-commerce H14: Gender moderates the effect of choice on e-loyalty in e-commerce H15: Gender moderates the effect of convenience on e-loyalty in e-commerce H16: Gender moderates the effect of character on e-loyalty in e-commerce Research on e-loyalty using the 8C's framework in Indonesia is still very limited. Lajar et al. (2019) has conducted research using 8C's framework but did not use gender as a moderating variable. The gender differences will also create different behaviors in online shopping, men are more affected by the interactivity of a website than women are. In contrast, women are more affected by vividness, diagnosticity of the information, and perceived risk (Lin et al., 2018). Moreover, men are studied to have different Figure 1 Research Model 85The Determinant Factors of E-Loyalty.... (Fernanda Losaura, et al.) levels of sensitivity than women regarding e-loyalty (Sugianto, 2017). Suggestions from previous studies suggest conducting further research by analyzing the role of gender variables in influencing e-loyalty (Chen & Lee, 2015). Therefore, the research is the only one studying the application of e-loyalty using the 8C's framework and gender as a moderating variable in a case study of e-commerce in Indonesia. II. METHODS Associative research is used to determine the effect of the independent variables, namely customization, contact interactivity, cultivation, care, community, choice, convenience, and character on the dependent variable, namely e-loyalty. This is reinforced by a moderating variable in the form of gender. The research subject is an individual belonging to the millennials who have made purchases at least one time in the last one month on the customer-to-customer (C2C) e-commerce platform in Indonesia. Millennials were born in 1977-2000 (Kotler & Armstrong, 2014), so that in 2020 millennials are those who are in the 20 – 43 years age group. The research uses non-probability sampling with judgmental sampling method. Determination of the minimum sample size in the research refers to Heir et al. (1995) with the number of question indicators used using the assumption of n x 5 observed variables (indicators) up to n x 10 observed variables (indicators). Therefore, to measure nine variables with 49 statement items, the number of respondents required is 245 respondents. The research conducts a pre-test by distributing questionnaires to 30 respondents who have shopped online on the C2C e-commerce platform in the past month using a questionnaire link on Google Form. Based on the results of the reliability tests, all items are considered reliable because they have a Composite Reliability value above 0,7 (Ghozali & Latan, 2015). Meanwhile, the results of the validity test that have been carried out are 38 valid questionnaire items with factor loading > 0,7, namely 5 customization items, 3 contact interactivity items, 4 cultivation items, 4 care items, 4 community items, 3 choice items, 3 convenience items, 4 character items, and 8 e-loyalty items. The remaining 11 items are invalid because they have a factor loading value lower than 0,7, therefore, the research discards 11 items (CUS3, CI2, CI5, CUL5, CAR5, COM1, CHO3, CON4, CON5, EL4, and EL5) which are invalid in the questionnaire. The analytical methods is SEM or Structural Equation Modeling which is processed using SmartPLS version 3.0 software. The research uses PLS-SEM since it is the most appropriate method for research in marketing science or information system, where this research is based on the influence test or regression (Hair et al., 2016). In addition, the PLS- SEM model can explain the variance of the dependent variable better and evaluate the quality of the data based on the characteristics of the measurement model (Hair et al., 2016). PLS-SEM provides approximate parameters that maximize the explained variance (R² value) of the dependent construct. Therefore, this method supports goal-oriented in predicting the target construction in the structural model. In addition, this research is also complex research since it has many constructs and indicators, so the PLS-SEM method is more appropriate for the research (Hair et al., 2016). Evaluation of the measurement model or outer model is carried out to assess the validity and reliability of the model. The outer model with reflexive indicators is evaluated through convergent validity and discriminant validity of the latent construct forming indicators and composite reliability and Cronbach alpha for the indicator block. Validity testing can be seen from the factor loading value for each construct indicator. The rule of thumb used is the factor loading value must be greater than 0,7 to be valid. The next process is discriminant validity testing, namely by comparing the Average Variance Extracted (AVE) value for each variable with 0,5 as the rule of thumb to be declared valid (Ghozali & Latan, 2015). After testing the validity, it is continued with reliability testing using the results on composite reliability. The rule of thumb used is that the composite reliability value must be greater than 0,7 to be said to be reliable. It can be concluded that testing at the outer model stage is by testing the validity based on the value of factor loading, and Average Variance Extracted (AVE). Then proceed with reliability testing based on the value of composite reliability. Evaluation of the structural model or inner model aims to predict the effect between latent variables. The inner model is evaluated by looking at the value of R-squared to see the magnitude of the influence. In addition, determining the occurrence of influence between these variables is determined based on the t-statistics value compared to the t-value. The t-value of 1,96 is used for an error rate of 5%. If the resulting t-statistics value is not greater than the t-value, it can be concluded that there is no effect between these variables. The results of the t-statistics value can be seen in the path coefficient section. The results of the t-statistics are also in line with the p-value generated at the processing output (Ghozali & Latan, 2015). The significance level used in the research is 95% with a margin of error of 5%. This indicates that the significance value (p-value) should not be greater than 0,05 to state that the hypothesis can be accepted. Furthermore, the t-value that must be achieved must be greater than 1,96 so that the hypothesis can be considered to have a significant effect. III. RESULTS AND DISCUSSIONS The questionnaire was successfully distributed to 311 respondents online using the google form, but only 247 respondents have the criteria that match the target respondents in the research. 86 The Winners, Vol. 23 No. 1 March 2022, 81-93 The convergent validity test in Table 1, all items have met the results of the convergent validity test because the AVE value is > 0,5 and the factor loading value is > 0,7 so that all items are declared valid. Based on the results of the reliability test in Table 2, all items have composite reliability > 0,7 so all items are declared reliable. The results of the discriminant test can be seen in Table 2. Based on the results of the discriminant validity test, the value of the square root of the AVE in each variable is greater than the correlation construct. Based on the data on the characteristics of the respondents, it is known that the respondents are dominated by women on 59% and the age range of 25 - 28 years living in Greater Jakarta. As many as 38% of respondents work as private employees with an average monthly income ranging from IDR 5.000.000 to IDR 10.000.000 with an average monthly expenditure for transactions on the C2C e-commerce platform ranging from IDR 500.000 up to IDR 1.000.000. In the past month, 72% of respondents admitted that they made transactions on the C2C e-commerce platform at least one to three times, of which 58% preferred Shopee to other C2C e-commerce platforms. The results of data processing on the value of the coefficient of determination (R2) on the e-loyalty variable is 0,563. The result in Table 3 shows that the e-loyalty variable can be explained through the Table 1 Outer Model Variabel Item Factor Loading Composite Reliability AVE Customization (CUS) CUS1 0,791 0,888 0,614 CUS2 0,803 CUS4 0,791 CUS5 0,759 CUS6 0,771 Contact Interactivity (CI) CI1 0,832 0,869 0,689 CI3 0,857 CI4 0,801 Cultivation (CUL) CUL1 0,794 0,860 0,605 CUL2 0,830 CUL3 0,810 CUL4 0,825 Care (CAR) CAR1 0,732 0,888 0,664 CAR2 0,769 CAR3 0,798 CAR4 0,811 Community (COM) COM2 0,780 0,855 0,596 COM3 0,805 COM4 0,803 COM5 0,745 Choice (CHO) CHO1 0,774 0,829 0,617 CHO2 0,800 CHO4 0,783 Convenience (CON) CON1 0,790 0,835 0,628 CON2 0,783 CON3 0,804 Character (CHA) CHA1 0,778 0,864 0,614 CHA2 0,782 CHA3 0,714 CHA4 0,810 E-Loyalty (EL) EL1 0.752 0,923 0,601 EL2 0,783 EL3 0,805 EL6 0,799 EL7 0,790 EL8 0,796 EL9 0,718 EL10 0,752 87The Determinant Factors of E-Loyalty.... (Fernanda Losaura, et al.) variables of customization, contact interactivity, cultivation, care, community, choice, convenience, and character by 56,3%, while the remaining 43,7% is explained by other variables not examined in the research. The significance level in the research is 95% with a margin of error of 5%. The results in Table 3 show that there are seven accepted hypotheses and nine rejected hypotheses. In line with Sukmongkol et al. (2019), Rao and Kothari (2017), and Khan and Rahman (2016) explaining that customization, contact interactivity, cultivation, community, and convenience affect e-loyalty on e-commerce platforms. Moreover, gender is able to have a moderating effect on each of the effects of customization and community on e-loyalty. Furthermore, the results of testing the moderating effect of gender variables using the Partial Least Square Multi-Group Analysis (PLSMGA) methods in Table 4, show a significant difference in the customization variable to e-loyalty (p-value <0,05) and the community variable to e-loyalty (p-value < 0,05). Hypothesis 1 states that customization has a positive effect on e-loyalty because the t-value or t-statics is > 1,96, which is 2,334. Meanwhile, p-value < 0,05, which is 0,020 indicates a significant positive effect between customization and e-loyalty. The result is in line with Sukmongkol et al. (2019), Hendrian (2012), Srinivasan (2002), Hendrian (2012) showing that customization has positive influence on e-loyalty. Sukmongkol et al. (2019) also explain that customization is an important variable in measuring the success of e-loyalty of an electronic platform. Customization does not have a positive Table 2 Discriminant Validity CA CHA CHO COM CI CON CUL CUS EL CA 0,778 CHA -0,368 0,722 CHO 0,568 -0,527 0,786 COM -0,524 0,669 -0,482 0,784 CI -0,351 0,06 -0,432 0,081 0,830 CON 0,637 -0,511 0,601 -0,552 -0,362 0,793 CUL -0,539 0,594 -0,407 0,763 0,093 -0,577 0,815 CUS -0,633 0,411 -0,414 0,545 0,241 -0,503 0,738 0,783 EL -0,604 0,444 -0,562 0,454 0,439 -0,692 0,464 0,388 0,775 Table 3 Hypothesis Testing Result Hypothesis Path Path Coefficient T Statistics P Value Summary H1 CUS -> E-Loyalty 0,181 2,334 0,020 Accept H1 H2 CI -> E-Loyalty 0,215 3,777 0,000 Accept H2 H3 CUL -> E-Loyalty 0,242 3,233 0,001 Accept H3 H4 CA -> E-Loyalty 0,260 1,694 0,091 Reject H4 H5 COM -> E-Loyalty 0,359 4,591 0,000 Accept H5 H6 CHO -> E-Loyalty 0,026 0,717 0,205 Reject H6 H7 CON -> E-Loyalty 0,120 2,017 0,044 Accept H7 H8 CHA -> E-Loyalty 0,007 1,224 0,221 Reject H8 Table 4 Hypothesis Testing Result for Moderating Effect Hypothesis Path Male Female Diff P-Value Summary Path Coeff. T Stat P-Value Path Coeff. T Stat P-Value H9 CUS -> E-Loyalty -0,355 1,866 0,06 0,219 2,180 0,030 0,580 0,020 Accept H9 H10 CI -> E-Loyalty 0,184 0,144 0,151 0,336 3,744 0,000 0,152 0,328 Reject H10 H11 CUL -> E-Loyalty -0,494 2,483 0,013 -0,187 2,311 0,021 0,307 0,164 Reject H11 H12 CA -> E-Loyalty 0,066 0,338 0,736 -0,120 1,008 0,314 -0,185 0,420 Reject H12 H13 COM -> E-Loyalty 0,289 2,008 0,045 -0,115 1,086 0,278 -0,404 0,028 Accept H13 H14 CHO -> E-Loyalty 0,009 0,067 -0,230 3,114 3,114 0,002 -0,240 0,109 Accept H14 H15 CON -> E-Loyalty -0,021 0,154 -0,220 2,679 2,679 0,008 -0,203 0,216 Accept H15 H16 CHA -> E-Loyalty 0,024 0,186 0,115 1,037 1,037 0,300 0,091 0,555 Accept H16 88 The Winners, Vol. 23 No. 1 March 2022, 81-93 effect on e-loyalty for men because the resulting p-value is greater than 0,05, which is 0,063, but on the contrary for women where customization has a positive effect on e-loyalty since the resulting p-value is 0,03, which turns out smaller than 0,05. The p-value generated in the PLS-MGA test for gender is smaller than 0,05, namely 0,018 indicating a significant difference between the male and female groups. From the results of the PLS-MGA test, it is concluded that gender can have a moderating effect on the effect of customization on e-loyalty. These results indicate that the gender variable can moderate the effect of different experiences or personalization perceived by customers on e-loyalty on C2C e-commerce platforms. Gender is one of the most frequently used segments to understand customer behavior in online shopping (Pereira et al., 2016). Khan and Rahman (2016) show that gender can moderate the effect of e-tail brand experience on e-loyalty, where the experience includes different offers for each customer or custom (customization). Gender is also found to be able to moderate the effect of personalization experience on e-loyalty (Pandey & Chawla, 2018). Hypothesis 2 states that contact interactivity has a positive effect on e-loyalty because the t-value or t-statics is > 1,96, which is 3,777. Meanwhile, p-value < 0,05, which is 0,000 indicates a significant positive effect between contact interactivity on e-loyalty. The results of this hypothesis test are in line with several previous studies from Jiang, Jun, and Yang (2015) and Srinivasan et al (2002) which show that contact interactivity has a positive effect on e-loyalty. Contact interactivity does not have a positive effect on e-loyalty for men because the resulting p-value is greater than 0,05, which is 0,151, but on the contrary for women where contact interactivity has a positive effect on e-loyalty because the resulting p-value is 0,00, smaller than 0,05. The p-value generated in the PLS-MGA test for gender is greater than 0,05, namely 0,328 indicating that there is no significant difference between the male and female groups. From the results of the PLS-MGA test, gender cannot provide a moderating effect on the effect contact interactivity on e-loyalty. The results of the hypothesis test are supported by the research of Sugianto (2017), which explains that both male and female customers are equally concerned with interactions with e-commerce platforms, especially when they encounter problems or difficulties. Hypothesis 3 states that cultivation has a positive effect on e-loyalty because the t-value or t-statics is > 1,96, which is 3,233. Meanwhile, p-value < 0,05, which turns out to be 0,001 indicates a significant positive influence between cultivation and e-loyalty. In line with what is explained by Teigland et al. (2018) that cultivation shows the extent to which companies can provide relevant information and incentives to persuade customers to make more purchases. The results of this hypothesis test are in line with previous studies from Srinivasan et al. (2002) and Rao and Kothari (2017), who show that cultivation has a positive influence on e-loyalty. Cultivation has a positive effect on e-loyalty for men as the resulting p-value is smaller than 0.05, which is 0,013. Similarly for women, contact interactivity has a positive effect on e-loyalty because the resulting p-value is 0,021. The p-value generated in the PLS-MGA test for gender is greater than 0,05, namely 0,164 which indicates there is no significant difference between the male and female groups. Seeing results of the PLS-MGA test, gender cannot provide a moderating effect on the effect cultivation of e-loyalty. The results contradict the research by Khan & Rahman (2016) and Pandey & Chawla (2018) but supported by Goel (2018) and Pitchayadejanant & Nakpathom (2016). Goel (2018) explains that the absence of a moderating effect of gender can be caused by the development of technology and customer behavior that is increasingly fast and digital savvy. Hypothesis 4 states that care does not have a positive effect on e-loyalty because the t-value or t-statistics is < 1,96, which is 1,694. Meanwhile, p-value turn out to be 0,091, which is greater than 0,05, indicating that there is no significant positive effect between care and e-loyalty. This is contrary to Srinivasan et al. (2002) and Jiang et al. (2015) but is in line with Li et al. (2015), who find and point out that care does not affect e-loyalty due to other variables that considered more important for customers such as affordable prices. In addition, Saini et al. (2019) also supports the results that errors and unresponsive attitudes of e-commerce (care) do not affect customer repurchase of the e-commerce. The research explains that customers tend not to have high expectations from e-commerce so that errors and deficiencies that occur do not affect their intention to repurchase as long as the required product is available in the e-commerce. Care does not have a positive effect on e-loyalty for men because the resulting p-value is greater than 0,05, which is 0,736, as well as for women where care does not have a positive effect on e-loyalty since the resulting p-value is greater than 0,05, which is 0,314. The p-value generated in the PLS-MGA test for gender is 0,420, which is found greater than 0,05. It indicates that there is no significant difference between the male and female groups. The PLS-MGA test shows that gender cannot have a moderating effect on the effect of care on e-loyalty. According to Ratnasari et al. (2020), the same perception between male and female customers impacts in absence of gender moderation on the effect of care on e-loyalty. Sensitivity regarding payment and delivery is usually moderated by the variable age, where customers at an older age and less accustomed to transacting online have higher concerns than customers at a younger age (Acheampong et al, 2017). In addition, respondents are dominated by those whose last education is Bachelor's degree (77%) and Master's degree (16%). It can be concluded that the respondents have higher education and good knowledge to probably look for information and make payments on the e-commerce platform or overcome the problems on the e-commerce platform. Based on the results of the interview, it is found that the seller 89The Determinant Factors of E-Loyalty.... (Fernanda Losaura, et al.) is a more important factor to be more responsive than the e-commerce platform. Both male and female respondents also have the same opinion, so this can also be a factor in the absence of the effect of the care variable on e-loyalty, and the absence of a moderating effect of the gender variable from this influence. Hypothesis 5 states that community has a positive influence on e-loyalty because the t-value or t-statics is > 1,96, which is 4,591. Meanwhile, the p-value < 0,05, which is 0,000 indicates a significant positive influence between the community on e-loyalty. Yoo et al. (2013) explain that the community for an e-commerce is a supporter of interaction between fellow customers, where they are the most trusted source of information. As fellow customers, they exchange information that is relevant to the product or the problem at hand. The results of this hypothesis test are in line with Srinivasan et al. (2002) and Perera, Nayak, and Long (2019) which show that community has a positive influence on e-loyalty. Community has a positive influence on e-loyalty for men because the resulting p-value is smaller than 0,05, which is 0,045. On the contrary for women, community does not have a positive influence on e-loyalty because the resulting p-value is 0,278, which obviously known to be greater than 0,05. The p-value generated in the PLS-MGA test for gender is smaller than 0,05, namely 0,028 which indicates a significant difference between the male and female groups. It is concluded that gender can have a moderating effect on the influence of community on e-loyalty. The result are in line with Khan & Rahman (2016), Pandey & Chawla (2018) and Sahli (2018), who find that there is a moderating effect of gender variables on the influence of community on e-loyalty on e-commerce platforms. Sahli (2018) explains that there are different responses from male and female customers in responding to product reviews available on e-commerce. Female customers are considered more social-connected, so the information obtained through the community greatly influences the purchasing decisions, and will have an impact on their e-loyalty on certain e-commerce platforms. Hypothesis 6 states that there is no positive effect of choice towards e-loyalty because the t-value or t-statistics is < 1,96, which is 0,363. Meanwhile, p-value > 0,05, namely 0,717 indicates that there is no significant positive effect of choice on e-loyalty. The result is supported by Sardinha (2015) and Morales- Solana, Cotas, and Esteban-Millat (2019), who point out that choice cannot affect e-loyalty on e-commerce platforms. It is explained that customers who want to buy a certain item usually will not depend on only one C2C e-commerce platform, so it is very possible for them to look for the product on several C2C e-commerce platforms. Furthermore, customers are more likely to see the affordability of the price offered for this item. Choice does not have a positive effect on e-loyalty for men because the resulting p-value is 0,946, greater than 0,05. On the contrary, for women, choice has a positive effect on e-loyalty because the resulting p-value is smaller than 0,05, namely 0,002. The p-value generated in the PLS-MGA test for gender is greater than 0,05, namely 0,109. It indicated there is no significant difference between the male and female groups. Hence, it is concluded that gender cannot have a moderating effect on the choice of e-loyalty. The results are supported by Jung & Shin (2019) and Sardinha (2015). The advanced stage, after choosing a product, has a more vital role to create customer e-loyalty (Solana et al., 2019). This can be a factor in the absence of influence of the choice variable on e-loyalty on the C2C e-commerce platform. The interview with respondents shows that there are other factors causing the absence of the influence of the choice on e-loyalty and the absence of a moderating effect of the gender variable. Both male and female respondents tend to already have e-commerce platforms and subscription sellers to visit for specific product categories. In addition, respondents also tend to prioritize the ease of finding information and using the platform. Hypothesis 7 states that convenience has a positive effect on e-loyalty because the t-value or t-statics is > 1,96, which is 2,017. Meanwhile, p-value < 0,05, which is to 0,044. There is a significant positive influence between convenience and e-loyalty. The finding is supported by Srinivasan et al. (2002), who point out that a convenient e-commerce platform will also minimize the possibility of customers making mistakes and will provide a more satisfying experience, where the results of this satisfaction can have an influence on e-loyalty from customers. Convenience does not have a positive effect on e-loyalty for men because the resulting p-value is greater than 0,05, which is 0,878. In contrast, for women convenience has a positive effect on e-loyalty because the resulting p-value is smaller than 0,05, namely 0,008. The p-value generated in the PLS-MGA test for gender is greater than 0,05, namely 0,216 indicating that there is no significant difference between the male and female groups. It is concluded that gender cannot have a moderating effect on the effect of gender convenience to e-loyalty. Hypothesis 8 states that the character does not have a positive effect on e-loyalty because the t-value or t-statics is < 1,96, which is 1,224. Meanwhile, p-value of 0,221, greater than 0,05, indicates that there is no significant positive effect between characters on e-loyalty. Nugroho et al. (2015) explain that the character variable cannot affect e-loyalty on the e-commerce platform. E-commerce customers are divided into two segment groups, namely goal-oriented shoppers and experiential shoppers. The character variable has no effect on e-loyalty for experiential shoppers since they are a group of customers who consistently browse on e-commerce platforms and really enjoy time looking for products. Character does not have a positive effect on e-loyalty for men because the resulting p-value is greater than 0,05, which is 0,853. For women, the resulting p-value is greater than 0,05, which is 0,300 indicating that the character does not have a positive effect on e-loyalty. The p-value 90 The Winners, Vol. 23 No. 1 March 2022, 81-93 generated in the PLS-MGA test for gender is greater than 0,05, namely 0,555, so there is no significant difference between the male and female groups. It can be concluded that gender cannot have a moderating effect on the effect character towards e-loyalty. IV. CONCLUSIONS There is an effect of each variable of customization, contact interactivity, cultivation, community, and convenience on e-loyalty on the C2C e-commerce platform. Community is the variable that has the greatest influence on e-loyalty compared to other 8C's framework variables. However, there is no effect of each care, choice, and character variable on e-loyalty on the C2C e-commerce platform. There is a moderating effect on customization and community variables in influencing e-loyalty on the C2C e-commerce platform. To be loyal, male and female customers have preferences regarding customization and community. Information is important for male customers before purchasing products online. Meanwhile, female customers are more emotional where they want to feel the understanding of the e-commerce platform to themselves personally about who they are and what they need (Szymkowiak & Garczarek-Bąk, 2018). Female customers enjoy shopping activities more as an impressive experience and entertainment, so they spend more time on e-commerce platforms, in contrast to male customers who tend to want fast, instant and practical things (Chiu et al., 2019). Female customers enjoy the community as a form of concern between customers related to their experience when shopping for a product, in contrast to male customers who like community because they can obtain accurate and most honest information related to the product to be purchased (Rebelo, 2017). The research argues that female customers can be stimulated by an emotional experience to create e-loyalty, while male customers can be stimulated by a quick and practical experience. Personalization or customization can be done by giving different treatment between male and female customers. This can be in the form of designs, product category recommendations, different product offerings and recommendations. For female customers, customization can be done through website design that can provide an emotional experience through appropriate colors. For male customers, e-commerce should provide relevant information so that they can obtain information quickly and easily. The ease of using and obtaining information is an element that can be provided by e-commerce businesses. E-commerce can create an easy-to-navigate platform design, clear and easily accessible features, contact support to help customers when experiencing difficulties, and frequently asked questions (FAQ) feature. Appropriate offers and recommendations are also things that can be optimized by e-commerce businesses since they may help customers to get products. In addition, it can also help e-commerce businesses to increase sales. Product offerings can be done in the form of complementary or substitute goods in accordance with previous customer purchases or searches. Community is a variable with the highest path coefficient value, so it is important to be considered properly by businesses to be able to create a community in a form of interaction and exchange of trusted information between fellow customers. By conducting online transactions, the consideration made by customers is more than offline shopping in which they can directly see and get the product. For female customers, a community can be created by providing a discussion forum for customers related to their experience of buying certain products. Meanwhile, male customers can be provided a column to write a review and rating of a product. In addition, sellers can provide clear details and descriptions of the products. The research limitation lies on using gender as a moderating variable. Hence, it is suggested that further research add other demographic variables such as age, income, and so forth. There are differences in product categories that are often purchased by male and female customers. Different product categories can influence various decision making in searching for information related to products and purchases, so that further research can consider adding product category as moderating variables. 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