Determinants of consumer’s continuance intention to contribute online reviews in ecommerce applications

Nội dung text: Determinants of consumer’s continuance intention to contribute online reviews in ecommerce applications

1. Trường Đại học Kinh tế - Đại học Đà Nẵng DETERMINANTS OF CONSUMER’S CONTINUANCE INTENTION TO CONTRIBUTE ONLINE REVIEWS IN E- COMMERCE APPLICATIONS GVHD: ThS. Tran Danh Nhan SVTH: Le Thi Thanh Van, Nguyen Thi Ngoc Anh, Tran Thi Phuong Man, Nguyen Thi Nhu Thuy, Tran Thi Yen University of Economics – The University of Danang thanhvan250298@gmail.com ABSTRACT As the fast growth of E-Commerce in this modern world, more and more online platforms and other online applications grow dramatically. The increasing number of online platforms and applications that give a great chance for customers to interact more with retailer and supplier through Internet. However, there are plentiful reviews appearing on the online application day by day. That can be good review and recommendations for good product, services or even bad reviews and warnings from customers for specific kinds of products and services. However, after considering this topic in current situation, we do not see any articles about this topic in Vietnam. While E-commerce has grown significantly recently along with a number of reviews from customers. The research model adopted previous studies and conduct empirical survey with 258 valid respondents. The survey is also conducted in major cities of Vietnam such as Da Nang, Ho Chi Minh, Ha Noi, etc. This research utilizes the structural equation modeling (SEM) approach to clarify the relationships between all factors in the research model. The research results show that Commitment and Reciprocity have a great effect on Consumer’s Continuance Intention on review on online applications. Exploration on other factors that could have indirect impacts on Continuance Intention are also conducted. Keywords: Continuance Intention, Online Reviews, E-Commerce Applications, Commitment, Reciprocity 1. Introduction Nowadays, E-commerce websites and online applications are increasingly popular for human life. In particular, unlike traditional purchases, people can now purchase goods on online websites or order through applications and can respond to their reviews to distributors and sellers via the Internet. However, what affects the writing and reviewing intentions often or repeating this behavior is less of a concern. In the world today there is very little research on this issue. Therefore, we did research on this topic in order to complete the model that represents the continuing intent of consumers for online applications. What’s more, there are no any Vietnamese articles on the intention to continue reviewing on E-commerce sites, partly because E- commerce has just exploded and developed strongly in the last two years. Previously in an article by author Vo Thi Ngoc Thuy was written in 2016 on the issue “The impact of brand values on the loyalty intention: The intermediate role of trust, cohesion and sense of harmony” used sample of research “Customers of 3 restaurants Omega Clevie (fish oil and vitamin supplements), Angela (aging prevention), Filiform Berry (healthy weight loss)”. And article by two authors Nguyen Thi Yen Oanh and Pham Thi Bich Uyen were also written in 2016 on the issue “Factors affecting the intention of using mobile commerce services of An Giang consumers” and used sample of research “Consumers are living and working in An Giang province with the condition that consumers use mobile devices”. These are the two most recent Vietnamese articles 94
2. Hội nghị Sinh viên nghiên cứu khoa học năm học 2018-2019 with the most closely written research topic, both of articles written about the impact factors affecting consumer behavior. While E-commerce has grown significantly recently along with a number of reviews from customers, we expect that we can complete the study of this topic in Vietnam and contribute to E- commerce in general. In addition, our research also add-in and bring abundant knowledge to the available theory from original articles with the aim of clarifying topic in diverse perspectives. 2. Literature review and Research methods 2.1. Literature review 2.1.1. eWOM Word-of-mouth (WOM), which was considered as an oral form of interpersonal noncommercial among acquaintances (Arndt, 1967), has been more and more well-known and become another form with the support of the Internet named electronic word of mouth (eWOM). eWOM communication is a form of communication that allow consumer to share their opinions, comments and product experiences on the Internet such as discussion forum, Facebook, or some social networking sites (Lakhani & Von Hippel, 2004). In comparison with WOM, eWOM owns unprecedented scalability and speed of diffusion, (Hung & Li, 2007). Adding that eWOM communication are more measurable than traditional WOM like format, quantity because the appearance of the eWOM is realisable and countable somehow. A theory of eWOM communication motives by Hennig-Thurau, Gwinner, Walsh, and Gremler (2004). They found that innovativeness, internet access and social connection may be significant factors affecting on eWOM behavior as well as consumer’s continuous intention in writing reviews. These studies will bring a basic background to explore and find out more about motives behind eWOM to continue writing reviews of the customers. 2.1.2. IS continuance, Satisfaction, and perceived benefits To analyze the factors which affects the continued review of a customer's online review to a product or a vendor; we conducted this study by basing on two different perspectives: the logical view from literature about the Continuous Information System (IS) and perspective from committed research. It can be said that the continuation of IS has received a lot of positive attention from scientific researchers over the years because of its importance for the long-term survival of IS (Bhattacherjee, 2001; Chen, 2007; Karahanna, Straub, & Chervany, 1999). For example, Bhattacherjee (2001), Hsu and Chiu (2004), Tiwana and Bush (2005) studied that it have strongly sketched the intention to continue IS and the relationships between user satisfaction. According to Danaher and Haddrell (1996), users were satisfied very much with their experience when using IS and developing the intention of maintaining, continuing to reuse the system. There are a lot of recent studies that have studied the levels of consumer satisfaction online. Satisfaction acts as a great intermediary between economic interests and the intention to continue to review online reviews in E-commerce applications, according to Cheung and Lee (2009). It is also considered an important factor in whether a person intends to continue to participate in an online community (Jin, Lee, & Cheung, 2010). When someone feels satisfied, feeling excited with their reviews of eWOM contribution on an OOP, they are likely to continue to do so (Xiang, Zheng, Zhang, & Lee, 2018). Some studies have investigated that economic incentives have a profound impact on online review writing in E-commerce applications. Hennig-Thurau et al. (2004) found a link between the online connection. From the social needs of consumers and their identity to the community, research has shown that the interests of society are rooted in or related to those needs and identities. Therefore, the technical support platform will help some consumers in using the product evaluation. In addition, reviewing is also viewed as a way of removing negative emotions, which refers to the unfavorable experience of consumers in the process of using their services. This view points out that individuals consumers are really happy when the benefits 95
3. Trường Đại học Kinh tế - Đại học Đà Nẵng are greater than what they have to do to bring for them happiness (Oliver, 1980; Pritchard, 1969). Researchers have also come up with factors that are believed to affect satisfaction (Bailey & Pearson, 1983). This research can help us identify, maintain and improve the factors that affect strongly satisfaction. Satisfaction is influenced by not only economic incentives but also the subjective standard. It is the standard created by the belief that consumers will do what the people associated with them think they should do and the beliefs that motivate them to do so (Karahanna et al., 1999). The impact of subjective standards on satisfaction has rarely been studied in the past on satisfaction, but the literature of other fields has somewhat demonstrated that strong relationship (Hsu & Chiu, 2004). Therefore, it is important not to underestimate the role of subjective standards in research related to e-commerce satisfaction. According to Bhattacherjee (2000), subjective norm includes two forms: interpersonal influence and external influence. Interpersonal influence includes: “influence of friends, family members, colleagues, superiors, and experienced individuals known to the potential adopter’’ and external influence includes: “influence of mass media reports, expert opinions, and other non-personal information considered by individuals in performing a behavior” (Roca, Chiu, & Martínez, 2006). Base on the discussion about the influence of satisfaction on continuance intention and the factors which impact on satisfaction, the hypotheses are proposed: H1a: Economic incentives influence on satisfaction. H1b: Concern for other consumers positively affects the satisfaction of eWOM contributors with the experience of contributing eWOM on OOPs H1c: Social benefits positively affect the satisfaction of eWOM contributors with the experience of contributing eWOM on OOPs H1d: Platform assistance positively affects user satisfaction with the experience of contributing eWOM on OOPs. H1e: Venting negative feelings about unfavorable consumption experiences on an OOP positively affects user satisfaction with the experience of contributing eWOM on that platform. H1f: Interpersonal influence has a positive effect on satisfaction. H1g: External influence has a positive effect on satisfaction. H2: The satisfaction of eWOM contributors with their experience of contributing eWOM on OOPs positively affects their continuance intention to contribute eWOM on those platforms. 2.1.3. Commitment and investment model Commitment is trust and assurance in a relationship. According to Meyer and Allen (1991), there are three types of commitments: emotional commitment, continual commitment and standard commitment. This research only studies two types of commitment that impact on satisfaction and continuance intention. Firstly, affective commitment – emotional commitment is the feeling of attachment and familiarity with another. It to influence is evolving over time, emotions that consumers experience in the past will have a strong impact on future engagement. Consumers will tend to continue to stick in the future if their experience is satisfaction about it. Thus, H3: The satisfaction of eWOM contributors with the experience of contributing eWOM on an OOP positively affects their affective commitment to that platform. Affective commitment also assumes that a person will intend to continue in the long run if they have a relationship involving the other party (Casalo, Flavián, & Guinalíu, 2007). According Jin et al. (2010), affective commitment is one of the important factors for the continued intention of participating in the online community. Affective commitment is nurtured over time, consumers will feel safer in a relationship if they feel familiar with that emotion (Casalo et al., 2007). A person will be able to stay in the online community if they have an affective commitment to the community (Xiang et al., 2018). Thus, 96
4. Hội nghị Sinh viên nghiên cứu khoa học năm học 2018-2019 H4: The affective commitment of eWOM contributors to an OOP positively affects their continuance intention to contribute eWOM on that platform. Secondly, continuance commitment is associated with the perceived lack of attractive alternatives and with the costs and investments in a relationship – (Meyer and Allen (1984). Besides, continuance commitment affects users’ patronage of a website because they recognize the costs and rewards of a relationship with the website, (Gustafsson, Johnson, & Roos, 2005). Researchers further posited that consumers who perceive high costs have strong continuance commitment. Moreover, continuance commitment focuses on the perceived costs of leaving the relationship (Allen & Meyer, 1990). Therefore, Kelley and Thibaut (1978) gave us the investment model provides important insights into the determinants of relationship commitment. This investment model stems from interdependence theory (Le & Agnew, 2003) and assumes that individuals are generally motivated to maximize rewards while minimizing costs (Adams, 1965). Continuance commitment highlights the cognitive cost–benefit analysis of maintaining a relationship, which could not just simply be influenced by the use experience without any comparisons of benefits and costs. Continuance commitment reflects the rewards associated with continuing and the costs associated with discontinuing recognized by users. More specifically, continuance commitment could be affected when users recognized the rewards of continuing a relationship with the website and the switching costs of alternative websites (Meyer & Allen, 1991). H5: Continuance commitment has a positive effect on Continuance Intention Besides, the investment model indicates that relationship commitment is determined by satisfaction, quality of alternatives, and investment size (Rusbult, Martz, & Agnew, 1998). In there, quality of alternatives is defined as “the perceived desirability of the best available alternative to a relationship” (Rusbult et al., 1998). By this way, users depend on a relationship when they are satisfied with the benefits or rewards they obtain from the relationship (Fournier, 1998). We suppose it as the desirability of available alternative OOPs that provide technical features and services similar to those of the platform currently used. (Dey, Abowd, & Wood, 1998). The influence of attractive alternatives is also important because satisfaction may not prevent users from switching to alternatives when users are strongly attracted to these alternatives (Astin, 1998). Furthermore, attractive alternatives are not necessarily other people or other relationships. It is possible that having no relationship is seen as preferable to any given available relationship. For example, the model has been used to predict relationship continuance and termination (Le & Agnew, 2003), friendships (Lin & Rusbult, 1995), and abusive relationships (Choice & Lamke, 1999). H6a: Quality of alternatives has a positive influence on continuance commitment. The social status that the relationship brings, and material possessions also serve as investments that con- tribute to commitment (Le and Agnew (2003). Investment size refers to the magnitude of resources placed by consumers on an OOP (Bernstein (1997). By Rusbult and Farrell (1983), Investment size also exerted greater impact on job commitment with the passage of time. In Commitment and its theorized determinants: A meta–analysis of the Investment Model, Benjamin and Christopher (2003) proposed that investment size also contributes to the stability of a partnership. Investments are those concrete or intangible resources attached to the partnership that would be lost or seriously diminished upon relationship dissolution (Le & Agnew, 2003). Investments include intrinsic resources that are put into the partnership, such as time and effort, experienced emotions, disclosure of personal information, and the importance the relationship holds for one’s identity. Therefore, satisfaction level, quality of alternatives, and investment size are posited to be, individually and collectively, the antecedents of commitment (Johnston, Hausman, & Marketing, 2006). H6b: Investment size has a positive influence on continuance commitment 97
5. Trường Đại học Kinh tế - Đại học Đà Nẵng 2.1.4. The public good Sharing information is a manifestation for the benefit of the community, having a positive effect. A good technology platform will create an environment in which resources are shared with all team members. And everyone can benefit from that very useful sharing of information, whether or not they have a personal contribution to the supply, and the availability of resources does not reduce when they use it (Cabrera & Cabrera, 2002). In the online environment, anyone can access and receive knowledge without directly contributing to it, which is a fundamental issue of public benefits. Individuals have the right to choose the site or source of information to access, track. Molly McLure Wasko and Faraj (2005) argue that although public goods are very difficult to control, however, they are created and maintained through the action of the group (Molly McLure Wasko & Faraj, 2005). In sum, public goods continue to be shared and volunteered through the cooperation of individuals. In terms of social psychology, there are four perspectives that explain why consumers want to continue to spread eWOM in the online consumer platform: individualism, collectivism, altruism and neo-liberalism. One of the perspectives chosen to study is egoism. It refers to serving the community as well for the benefit of oneself. Individuals are believed as egoistic in case they will look for other tangible or intangible values after sharing knowledge, experience with someone. Social exchange theory has been adopted to explain the action for the public good in terms of egoism in recent years (Bock, Zmud, Kim, & Lee, 2005; Kankanhalli, Tan, & Wei, 2005). As a human nature, people seem to look forward for return such as prize, reputation, reward and recognition by maximizing their benefit and cut down on minimum all expenses during information exchange (Lakhani & Von Hippel, 2004) . In some examples, reputation is considered as an crucial determinant of information sharing behavior (Constant, Kiesler, & Sproull, 1994; Constant, Sproull, & Kiesler, 1996). People share their knowledge by virtue of gaining reputation and admiration from others as a expert and brushing up on their knowledge as well (Molly McLure Wasko & Faraj, 2005). Similarly, we assume that as long as a consumer has intention of gaining reputation in an online consumer-opinion platform, they will spread eWOM more regularly. This leads to the following hypothesis: H7: The perception of the opportunity to enhance one's own reputations is positively related to one's eWOM intention. Another egoistic motivator of the act for the public good is reciprocity, which is also conceived as a benefit for individuals to engage in social exchange. When information providers do not know each other, the kind of reciprocity that is relevant is called “generalized” exchange (Ekeh, 1974), and the person who offers help to others is expecting returns in the future (Lakhani & Von Hippel, 2004). Prior research found that people who share knowledge in online communities value reciprocity (M McLure Wasko & Faraj, 2000), and it is this belief that drives them to participate and share. Thus, this leads to the following hypothesis: H8: The perception of the opportunity for reciprocity is positively related to one's eWOM intention. All in all, based on the aforementioned discussion, the conceptual model has been developed and shown in Figure 1. 98
6. Hội nghị Sinh viên nghiên cứu khoa học năm học 2018-2019 Economic Incentives H Concern for Other Consumers 1a H Social Benefits 1b H 1c Platform Assistance H Satisfaction 1d H Venting Negative Feeling 1e H H 1f H 3 H Interpersonal Influence 1g Affective 2 Commitment External Influence H 4 Continuance Investment Size H H 6a Continuance Intention H Commitment 5 Qualities of Alternatives 6b H 7 H Reputation 8 Reciprocity Figure 1. Proposed Research Model. 2.2. Research methods 2.2.1. Measurement instruments A questionnaire-based survey was developed in order to test the theoretical constructs. Constructs and measurement items were adapted with slight modifications from the literature review to build the questionnaire. Measurement items for constructs are adapted from studies of Xiang et al. (2018) and Cheung and Lee (2012). All main measurement items were measured on a seven-point Likert scale, ranging from totally disagree (1) to totally agree (7). Four demographic variables related to gender, age, occupation, income level were also included in the questionnaire. The questionnaire was primarily developed in English, based on the literature with reviewing for content validity experts from a university. Because the data collection procedure was operated in Vietnamese context, then later all English instruments was translated into Vietnamese language by a professional translator. The questionnaire was built online with Google Form service and distributed via Facebook and Youtube social networks from January to May 2019. There are 343 respondents took part in the survey and data of 258 respondents was appropriate. After the data has been collected from official surveys, coding process and data processing techniques to carry out research objectives with the main support of SPSS 22.0 and AMOS 18. Beside descriptive analysis, compare mean techniques with t-Test and One-way ANOVA, the authors also conduct exploratory factor analysis (EFA), validating scale reliability with Cronbach's Alpha, confirmatory factor analysis (CFA) as well as conduct path analysis (SEM) for testing the research model and hypotheses. 99
7. Trường Đại học Kinh tế - Đại học Đà Nẵng 3. Results and discussion 3.1. Results 3.1.1. Descriptive analysis Based on the descriptive analysis results shown in the Table 1, variables that could affect on customer satisfaction of contributing reviews on e-commerce sites are responded ranged from neutral to very agree (4.03~5.07). It is indicated that customers consider these aspects ranged form neutral to be very positive. The highest positive aspects of contributing online reviews in e-commerce sites are variables named COC1 and COC2 that refer to their concern for other consumers (mean = 5.5 and 5.67, respectively). Interestingly, in observations that refer to interpersonal influence effects, consumers have responded that their family members do not have much encouragement (mean = 4.03) while other influenceable people such as colleagues or friends have quite more encouragement (mean = 4.45 and 4.62, respectively). Besides, variables that could affect on their continuance commitment of contributing reviews on e-commerce sites are responded ranged from quite disagree to quite agree (3.27~4.48). In the aspects of time and effort spend, customers tend to respond that do not spend a lot of time or effort to utilize the applications to contribute reviews while they tend to respond in neutral level toward aspects of quality of alternatives. Descriptive statistical results for observed variables of factors affecting customers’ continuance intention to contribute online reviews in e-commerce applications also show that most of customers quite agree with the thinking that contributing online reviews will result in positive reciprocity (mean = 4.52~5.02). They also have quite high satisfaction when utilize the e-commerce site to share their reviews to others (mean = 4.62~4.72). For remaining aspects, they mostly consider them in neutral level of agree (mean = 3.70~4.50). Furthermore, about aspects of continuing intention to contribute online reviews in e-commerce applications, customers quite agree that they will continue their contributions in the future (mean = 4.95~5.10). 3.1.2. Compare mean with t-Test and One-way ANOVA In order to examine whether there is any difference in observed aspects between different genders, ages, occupations and income levels, we conduct tests using t-Test and Oneway ANOVA techniques. The testing results show that there are 3 aspects that are responded differently between male and female customers and one aspect that is responded differently between income levels. There is no difference between customers have different ages or occupations. 3.1.3. Measurement model In order to evaluate the constructs’ reliability, Cronbach’s Alpha reliability test was utilized. As seen from Table 1, Cronbach’s Alpha of all constructs are above the expected threshold of 0.6 and all their indicators of item-total correlation are above the expected threshold of 0.3, showing evidence of internal consistency and would be utilized in further analysis. In the next step, in order to identify the dimensionality of measurement scales, principle components factor analysis with varimax rotation could be adopted to examine the underlying patterns for the vast numbers of variables. The results of the seventh run of exploratory factor analysis (EFA) with with orthogonal rotation (varimax) showed that 41 initial measurement variables were reduced to 34 variables and divided into 8 factors (KMO = 0.927; χ² (561) = 6231.847, p <0.001). Besides, the results of scale reliability test with Cronbach's Alpha also showed that these 8 factors meet the requirements of reliability coefficient of 0.6 or higher and all item-total correlation of each measurement variables are greater than 0.3. Results of these steps are shown in the Table 2. In the fourth step, the result of confirmatory factor analysis (CFA) for the eight factors confirmed that these factors are suitable (in which, only two observed variable is excluded from the CFA model are AC1 and REP2). The analytical results show that all standardized factor loadings of observed variables are greater than 0.5, all latent variables have construct reliability (CR) indicators are approximately equal or greater than 0.7, and the average extract variance (AVE) indicators are greater than 0.5 and the squared root of the average extract 100
8. Hội nghị Sinh viên nghiên cứu khoa học năm học 2018-2019 variance are greater than the inner-construct correlations. In addition, the model fit indicators also confirm that the model is appropriate (χ² / df = 1.870 0.90; RMSEA = 0.058 < 0.07). The CFA results are presented in Figure 2 and Table 3. Based on the testing results, the modified research model and hypotheses are presented in Figure 3. Table 1. Means, standard deviations, and constructs’ reliability for the measurement model. Corrected Item-Total Constructs and Items Mean SD Cronbach's Alpha Correlation Economic Incentives EcIn 4.35 1.86 Concern for Other Consumers 0.801 COC1 5.50 1.47 0.669 COC2 5.67 1.39 0.669 Social Benefits 0.888 SB1 4.78 1.74 0.782 SB2 4.45 1.69 0.854 SB3 4.16 1.72 0.712 Platform Assistance 0.820 PA1 4.55 1.78 0.688 PA2 4.48 1.77 0.727 PA3 4.74 1.68 0.610 Venting Negative Feeling 0.672 VNF1 5.25 1.61 0.515 VNF2 4.42 1.93 0.515 Interpersonal Influence 0.893 II1 4.03 1.71 0.719 II2 4.45 1.63 0.843 II3 4.62 1.61 0.813 External Influence 0.851 EI1 4.47 1.69 0.712 EI2 4.38 1.59 0.777 EI3 4.21 1.77 0.680 Satisfaction 0.910 SAT1 4.66 1.60 0.777 SAT2 4.77 1.52 0.835 SAT3 4.72 1.54 0.819 SAT4 4.62 1.56 0.756 Investment Size 0.886 IS1 4.00 1.66 0.703 IS2 3.27 1.81 0.789 IS3 3.58 1.75 0.774 IS4 3.50 1.77 0.737 Quality of Alternatives 0.850 QA1 4.13 1.52 0.690 QA2 3.79 1.52 0.733 QA3 4.27 1.56 0.719 QA4 4.48 1.56 0.615 101
9. Trường Đại học Kinh tế - Đại học Đà Nẵng Affective Commitment 0.838 AC1 4.50 1.70 0.632 AC2 3.78 1.79 0.750 AC3 4.05 1.78 0.726 Continuance Commitment 0.860 CC1 4.38 1.55 0.678 CC2 4.43 1.51 0.778 CC3 4.46 1.51 0.753 Reputation 0.770 REP1 4.34 1.67 0.631 REP2 3.70 1.89 0.631 Reciprocity 0.863 REC1 4.52 1.70 0.613 REC2 4.97 1.53 0.771 REC3 4.78 1.54 0.777 REC4 5.02 1.47 0.697 Continuance Intention 0.879 CI1 5.10 1.46 0.790 CI2 4.77 1.50 0.735 CI3 4.95 1.47 0.774 Table 2. Results of EFA and Cronbach’s Alpha (after EFA) for factors that could have direct or indirect effects on continuance intention to contribute online reviews in e-commerce applications. Rotated Factor Loading (EFA – 7th run) Measurement Factor Factor Factor Factor Factor Factor Factor Factor Variables 1 2 (IEI) 3 4 5 (ISI) 6 (ESI) 7 (PAS) 8 (COM) (QAL) (REC) (VNF) AC1 0.594 AC2 0.742 AC3 0.765 CC1 0.701 CC2 0.761 CC3 0.736 REP1 0.638 REP2 0.561 EI1 0.747 EI2 0.651 EI3 0.566 II1 0.763 II2 0.778 II3 0.781 QA1 0.813 QA2 0.798 QA3 0.772 QA4 0.689 REC2 0.826 REC3 0.761 102
10. Hội nghị Sinh viên nghiên cứu khoa học năm học 2018-2019 REC4 0.787 IS1 0.709 IS2 0.725 IS3 0.731 IS4 0.644 EcIn 0.713 SB1 0.741 SB2 0.770 SB3 0.581 PA1 0.792 PA2 0.722 PA3 0.629 VNF1 0.746 VNF2 0.848 Eigenvalues 15.384 2.645 2.420 1.897 1.232 1.222 1.083 1.014 Percentage of 41.6% 7.2% 6.5% 5.1% 3.3% 3.3% 2.9% 2.7% variance explained (%) Coefficient 0.920 0.912 0.850 0.869 0.886 0.863 0.820 0.789 alpha 103
11. Trường Đại học Kinh tế - Đại học Đà Nẵng Figure 2. Result of confirmatory factor analysis (2nd-run) for factors that could have direct or indirect effects on continuance intention to contribute online reviews in e-commerce applications. Table 3. Reliability and validity measures (CR, CA, and AVE) of factors that could have direct or indirect effects on continuance intention to contribute online reviews in e-commerce applications. CR AVE ESI PAS VNF IEI ISI QAL COM REC ESI 0.905 0.613 0.783 a PAS 0.877 0.646 0.522 b 0.803 a VNF 0.825 0.612 0.572 b 0.627 b 0.782 a IEI 0.690 0.531 0.479 b 0.479 b 0.497 b 0.729 a ISI 0.906 0.617 0.660 b 0.758 b 0.708 b 0.526 b 0.786 a QAL 0.886 0.662 0.783 b 0.510 b 0.522 b 0.438 b 0.625 b 0.813 a COM 0.852 0.591 0.555 b 0.427 b 0.462 b 0.216 b 0.516 b 0.540 b 0.769 a REC 0.870 0.692 0.614 b 0.506 b 0.498 b 0.408 b 0.568 b 0.458 b 0.387 b 0.832 a a Square root of the average variance extracted (AVE) of each latent variable. b Correlation between latent variables Figure 2. Result of confirmatory factor analysis (2nd-run) for factors that could have direct or indirect effects on continuance intention to contribute online reviews in e-commerce applications. Table 3. Reliability and validity measures (CR, CA, and AVE) of factors that could have direct or indirect effects on continuance intention to contribute online reviews in e-commerce applications. 104
12. Hội nghị Sinh viên nghiên cứu khoa học năm học 2018-2019 CR AVE ESI PAS VNF IEI ISI QAL COM REC ESI 0.905 0.613 0.783 a PAS 0.877 0.646 0.522 b 0.803 a VNF 0.825 0.612 0.572 b 0.627 b 0.782 a IEI 0.690 0.531 0.479 b 0.479 b 0.497 b 0.729 a ISI 0.906 0.617 0.660 b 0.758 b 0.708 b 0.526 b 0.786 a QAL 0.886 0.662 0.783 b 0.510 b 0.522 b 0.438 b 0.625 b 0.813 a COM 0.852 0.591 0.555 b 0.427 b 0.462 b 0.216 b 0.516 b 0.540 b 0.769 a REC 0.870 0.692 0.614 b 0.506 b 0.498 b 0.408 b 0.568 b 0.458 b 0.387 b 0.832 a a Square root of the average variance extracted (AVE) of each latent variable. b Correlation between latent variables Figure 3. Modified Research Model. 3.1.4. Hypotheses testing To test the relationships between latent variables in the modified research model, the structural equation modeling (SEM) approach was utilized and illustrated in Figure 4. Model fit indicators confirm that the model is appropriate for SEM (χ² / df = 2.056 0.90; RMSEA = 0.064 < 0.07). Figure 4. Structural model with path analysis Based on the path analysis (SEM) results, the hypotheses H’1e, H’1f, H’2, and H’3 are confirmed while H’1a, H’1b, H’1c, and H’1d are rejected. The hypotheses testing results are shown in Table 4. Table 4. Hypotheses testing results based on path analysis (SEM) 105
13. Trường Đại học Kinh tế - Đại học Đà Nẵng Unstandardized Standardized p-value Structural Standard Confirmed / Parameter t-value Parameter Relationship Error Rejected Estimate Estimate H’1a: ESI COM -0.046 0.098 -0.468 -0.037 0.640 Rejected H’1b: PAS COM 0.089 0.080 1.118 0.089 0.264 Rejected H’1c: VNF COM 0.106 0.075 1.406 0.100 0.160 Rejected H’1d: IEI COM 0.188 0.108 1.737 0.178 0.082 Rejected H’1e: ISI COM 0.572 0.086 6.622 0.526 < 0.001 Confirmed H’1f: QAL COM 0.151 0.075 2.010 0.128 0.044 Confirmed H’2: COM CI 0.340 0.041 8.242 0.420 < 0.001 Confirmed H’3: REC CI 0.686 0.051 13.402 0.803 < 0.001 Confirmed 3.2. Discussion From the results, Investment Size (ISI) has the most influential role to Commitment (β = 0.572, p < 0.001). This is explained by the time and effort that users spend when using this application has a great impact on their Commitment. Therefore, an application that is easy to use and makes interesting for users, it is easy to get their Commitment. Besides, the Reciprocity factor (REC) have a positive impact on Continuance Intention (CI). This is consistent with the research results of Cheung and Lee (2012). Moreover, the factor Commitment (COM) has a positive effect on the user's Intention Continuance. Therefore, suppliers should gradually improve and improve the quality of services, online platform, maintain a good operation system, increase incentive programs for customers to feel for consumers. Supplier reputation is enhanced when they use the service of the business, which will positively impact Commitment and reputation of customers and from that affect the Continuance Intention. 4. Conclusion With the tremendous development of E-commerce and Online application, the customer review is indispensable on each platform. The research was survey in major cities of Vietnam such as Da Nang, Ho Chi Minh, Ha Noi, etc. Most people in big cities will have many opportunities to contact websites E- commerce and they can easily be experienced with these apps. The research results show that Commitment and Reciprocity have a great effect on Consumer’s Continuance Intention on review on online applications. It is also found that the time and effort that customers spend and the quality of alternatives have significant impact on customer commitment. However, beside of our study’s main contribution that adds into the existing body of knowledge, we also recognize its limitations, mostly regarding the sampling with typically young, highly educated people as responders. The respondents’ behavioral patterns might diverge to some extent in comparison with the population average. With the behaviors that are mostly more pioneering and rapider to adopt new technologies, this sampling may have biased the effects. It is likely that seniors and less educated consumers or those who hold low computing or Internet-related capability would recognize more difficulty in adopting mobile payment and greater intrinsic mobile payment usage risks. Future research can be constructed based on this study by examining the proposed model in different age groups or applying this model to other countries and also other contexts. REFERENCES [1] Adams, J. S. (1965). Inequity in social exchange Advances in experimental social psychology (Vol. 2, pp. 267-299): Elsevier. 106
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