Predicting consumer intentions to shop online: An empirical test of competing theories

The proliferation of commercial Web sites providing consumers with a new medium to purchase products and services has increased the importance of understanding the determinants of consumer intentions to shop online. This study compared the technology acceptance model and two variations of the theory of planned behavior to examine which model best helps to predict consumer intentions to shop online. Data were gathered from 297 Taiwanese customers of online bookstores, and structural equation modeling was used to compare the three models in terms of overall model fit, explanatory power and path significance. Decomposing the belief structures in the theory of planned behavior moderately increased explanatory power for behavioral intention. The results also indicate that the decomposed theory of planned behavior provides an improved method of predicting consumer intentions to shop online. Finally, the implications of this study are discussed.     

Predicting continuance in online communities: model development and empirical test

Popular interest in online communities has grown rapidly in recent years as a result of the widespread diffusion of Web 2.0 applications. However, the full values and potential of online communities cannot be realised without users' ongoing participation. Thus, this study aims at developing and empirically testing a research model to examine users' continuance intention to participate in an online community based on an extended information systems (IS) continuance model. Specifically, entertainment value and affective commitment are included in the IS continuance model and empirically examined in the context of online communities. A total of 240 returns collected from an online survey, which was conducted among users of a website bulletin board-based community in China, were analysed using partial least squares. The results reveal that users' continuance intention to participate in an online community is determined by both satisfaction and affective commitment. Satisfaction and affective commitment are, in turn, influenced by positive disconfirmations of purposive and entertainment values. The findings of this study contribute not only to theory building in online community continuance but also inform online community moderators in their effort to develop strategies for retaining their users.     

How to select search keywords for online advertising depending on consumer involvement: An empirical investigation

In paid search advertising on Internet, advertisers bid for specific keywords. Consumer involvement affects a consumer’s purchase intention. However, there has been no research on selecting keywords in paid search advertising depending on consumer involvement from the advertisers’ perspective. Our paper investigates the effects of several factors including impressions, click-through rate, conversion rate, the number of competitors and the quantity of online customer reviews on the performance of individual keywords. Our analysis shows that regardless of consumer involvement, the conversion rate and the number of reviews significantly affect to the performance of individual keywords but the click-through rate does not. In addition, the effects of impressions and the number of competitors partially exist depending on consumer involvement. This study provides managerial insights into paid search advertising and can give some assumptions in the theoretical modeling literature for allocating advertising budgets.     

Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance model

Derived from the theory of reasoned action, the technology acceptance model (TAM) focuses on two specific salient beliefs—ease of use and usefulness. It has been applied in the study of user adoption of different technologies, and has emerged as a reliable and robust model. However, this has not discouraged researchers from incorporating additional constructs to the original model in their quest for increased predictive power. Here, an attempt is made in the context of explaining consumer intention to use on-line shopping. Besides ease of use and usefulness, compatibility, privacy, security, normative beliefs, and self- efficacy are included in an augmented TAM. A test of this model, with data collected from 281 consumers, show support for seven of nine research hypotheses. Specifically, compatibility, usefulness, ease of use, and security were found to be significant predictors of attitude towards on-line shopping, but privacy was not. Further, intention to use on-line shopping was strongly influenced by attitude toward on-line shopping, normative beliefs, and self-efficacy.     

An empirical test to measure the effectiveness of online advertising in online marketplaces using a hierarchical Bayes model

Online marketplace, taken the form of “open market” where a very large number of buyers and sellers participate, has occupied a rapid increasing position in e-commerce, which resulting in sellers’ increasing investment on online advertising. Hence, there is a growing need to identify the effectiveness of online advertising in the online marketplaces such as eBay.com. However, it is problematic to directly apply the existing online advertising effect models for click-through data of online marketplaces. Therefore, there is a need for developing a model to estimate the effectiveness of online advertising in online marketplace considering its characteristics. In this paper, we develop an analytical Bayesian approach to modeling click-though data by employing the Poisson-gamma distribution. Our results have implications for online advertising effect measurement, and may help guide advertisers in decision-making.     

An empirical study on consumer first purchase intention in online shopping: integrating initial trust and TAM

To understand the first purchase mechanism in online shopping, this study established an integrated model of initial trust and TAM. According to the analysis results, initial trust beliefs were significantly influenced by initial trust bases such as company reputation, structural assurance, and trusting stance, and initial trust beliefs indirectly influenced the first purchase intention through consumer attitude. In TAM constructs, only perceived usefulness directly influenced the usage attitude of online shopping systems, then indirectly influenced the first purchase intention. These results imply that online consumers consider perceived ease of use as a basic requirement for system design. It was also found that there is a time cushion between the time of first purchase and the time of belief formation. On the whole, these results reinforced the theory that a relationship between belief (trust and perceived usefulness) and intention was better explained when it was mediated by attitude.     

The influence of online store beliefs on consumer online impulse buying: A model and empirical application

Our study provides insight into the relationships between online store beliefs and consumer online impulse buying behavior. Drawing upon cognitive emotion theory, we developed a model and showed how beliefs about functional convenience (online store merchandise attractiveness and ease of use) and about representational delight (enjoyment and website communication style) related to online impulse buying. The model was tested using survey data from 532 customers of a Dutch online store. Our results showed significant effects of merchandise attractiveness, enjoyment, and online store communication style, mediated by consumers’ emotions. The study should enhance our understanding of online impulse buying and, by assessing the impact of the online store, of beliefs in non-rational decision-making settings.     

Predicting knowledge sharing practices through intention: A test of competing models

In knowledge management (KM)-related research, effective knowledge sharing is considered to be one of the most critical components of KM success. For the present research, the authors conducted a longitudinal, two-phased study to evaluate if the Theory of Reasoned Action (TRA) and three variations of the Theory of Planned Behavior—namely, TPB, decomposed TPB (DTPB), and revised TPB (RTPB)—can adequately predict knowledge sharing behaviors. The first TRA-based study shows a severe limitation in the ability of the intention to predict actual knowledge sharing behaviors collected from a knowledge management platform. In a subsequent study, three variations of TPB-based models were employed to show that, although the independent variables (i.e., attitude, subjective norm, and perceived behavior control that is decomposed into controllability and self-efficacy) give satisfactory explanations of variance in intention (R² > 42%), the intention–behavior gap still exists in each of the three models. Only the perceived self-efficacy in the revised TPB can directly predict knowledge sharing behaviors. This gap highlights the importance of knowledge sharing as a fundamentally social activity for which the actualization of intention into actions may be interrupted due to barriers such as a mistake-free culture or others’ deliberate misinterpretations that may in turn cause unanticipated negative consequences to the person. The theoretical implication of this study is that in applying TPB to study knowledge sharing practices, researchers must focus on control beliefs that reflect people’s capacity to overcome possible environmental challenges encountered in carrying out their knowledge sharing intentions.     

Securing online privacy: An empirical test on Internet scam victimization,online privacy concerns,and privacy protection behaviors

The current study identified the antecedents of being an Internet scam victim and how it impacts online privacy concerns and privacy protection behaviors. Structural equation modeling on data from a survey of 11,534 Internet users revealed that one indicator of weak self-control (i.e., willingness of risky investments) and two indicators of routine Internet activities (i.e., online shopping and opening emails from unknown sources) positively predicted being an Internet scam victim. Subsequently, being an Internet scam victim predicted increased online privacy concerns, which, in turn, predicted elevated privacy protection behaviors. Moreover, we found that being an Internet scam victim mediated the effects of routine Internet activities on privacy protection behaviors and that online privacy concerns mediated the effect of being an Internet scam on privacy protection behaviors. Unlike most Internet privacy studies using protection motivation theory only, the current study contributes to the understanding of the Internet scam victimization by incorporating three new theories—extended parallel process model, self-control theory, and routine activity theory. The research findings provided valuable implications for theory and practice related to Internet scam processes and prevention.     

An intelligent simulation model of online consumer behavior

This paper describes the design of an Intelligent Simulation Model of Online Consumer Behavior (ISMOCB) that incorporates a knowledge base using some form of the Artificial Intelligence methods such as Na?ve Bayes Classifier and Artificial Neural Networks. This study investigates modeling online consumer behavior by using demographic characteristics such as age, gender, marital status, educational status, monthly income and number of people in the family. This will provide producing more synthetic data and creating an “Artificial Database” which includes the demographics of online consumers and their purchase transactions. The model is built for online shopping based on empirical data gathered in Turkey via an online survey. Two different inference systems are used for which product group is chosen by whom has which demographic characteristics. The quality of the data, gathered exclusively for this project, allows a fine validation of the simulation results.     

Communication goals and online persuasion: An empirical examination

Computer-mediated communication (CMC) is frequently applied as a tool for organizational marketing and consumer research. This paper explores the underlying structure of message receivers’ communication goals and their impact on persuasiveness in the context of CMC. Extending prior research on the structure of primary and secondary goals, we identified five specific communication goals that are important to receivers. We conducted an online exercise in which subjects respond to a message requesting them to volunteer their time. The results demonstrate all five communication goals are important to one or more indicators of persuasiveness, including attitude toward the issue, source credibility, perceived information quality, and behavioral intention to comply with the request.     

Understanding consumer acceptance of mobile payment services: An empirical analysis

Mobile technology has become increasingly common in today’s everyday life. However, mobile payment is surprisingly not among the frequently used mobile services, although technologically advanced solutions exist. Apparently, there is still a lack of acceptance of mobile payment services among consumers. The conceptual model developed and tested in this research thus focuses on factors determining consumers’ acceptance of mobile payment services. The empirical results show particularly strong support for the effects of compatibility, individual mobility, and subjective norm. Our study offers several implications for managers in regards to marketing mobile payment solutions to increase consumers’ intention to use these services.     

How virtual community participation influences consumer loyalty intentions in online shopping contexts: an investigation of mediating factors

Extant studies generally recognise that virtual community building is an effective marketing programme for forging deep and enduring affective bonds with consumers. This study extends previous research by proposing and testing a model that investigates key mediating processes (via trust, satisfaction and identification) that underlie the relationship between virtual community participation and consumer loyalty intentions. The authors test the hypotheses using data obtained from three large online retailing stores. Virtual community participation significantly enhances loyalty intentions, through both social mechanisms (via community identification) and psychological mechanisms (via trust and satisfaction). Moreover, the findings provide insights into the complex relationship between the two mechanisms in online shopping settings. Community identification is a pivotal factor for enhancing customer loyalty intentions. The results advance understanding of the process by which virtual community building facilitates the development of business-to-consumer relationships in the computer-mediated environment. The authors discuss the managerial implications of the findings, as well as avenues for further research.     

How to improve consumer attitudes toward using credit cards online: An experimental study

Most companies involved in e-commerce use trust-promoting messages to persuade consumers that the websites they visit are safe. Although these messages are common, many consumers still hesitate to use a credit card online. To understand why, we applied social judgment theory and Cialdini’s model of persuasion in an experiment exploring the extent to which trust-promoting messages can be used to change consumers’ attitudes toward online credit card use. By manipulating message proximity and message source, we found that authority-plus-contrast-plus-scarcity was the most persuasive combination, and there was no significant difference between international and domestic trustmarks. Consumers with positive credit card attitudes were included as a control condition. The results show that the authority-plus-contrast-plus-scarcity combination was essential for consumers holding the same positive attitude, because there was no significant attitude change in this condition. Our findings help shopping websites analyze their existing trust-promoting messages and improve them by adding persuasive elements.     

Predicting product adoption intentions: An integrated behavioral model-inspired multiview learning approach

Mining product adoption intentions from social media could provide insights for many business practices, such as social media marketing. Existing methods mainly focus on text information but overlook other types of data. In light of the Integrated Behavioral Model (IBM), in this study, we argue that it is valuable to consider users’ social connections in addition to postings for identifying product adoption intentions. Based on this rationale, we propose a novel multiview deep learning framework to identify product adoption intentions. Extensive experiments show our proposed approach is effective, and demonstrate the benefit of incorporating social network information for intention identification.     

An empirical study of information contribution to online feedback systems: A motivation perspective

Online feedback systems (OFSs) are increasingly available on online shopping websites; they allow consumers to post their ratings and consumption reviews for products. We employed motivation theory and a goal attainment perspective to model a set of motivating and inhibiting factors that could influence a consumer's intention to contribute to an OFS. Our experiment, which involved 168 university students, showed that a consumer's intention to contribute product reviews is influenced by perceived satisfaction gained in helping other consumers, perceived satisfaction gained in influencing the merchant, perceived probability of enhancing self-image, and perceived executional costs. In addition, the presence of an economic rewarding mechanism was found to promote a contribution when a consumer's perceived probability of enhancing self-image was relatively high or when perceived cognitive cost was relatively low. Implications of our findings are discussed.     

Predicting consumer sentiments using online sequential extreme learning machine and intuitionistic fuzzy sets

Predicting consumer sentiments revealed in online reviews is crucial to suppliers and potential consumers. We combine online sequential extreme learning machines (OS-ELMs) and intuitionistic fuzzy sets to predict consumer sentiments and propose a generalized ensemble learning scheme. The outputs of OS-ELMs are equivalently transformed into an intuitionistic fuzzy matrix. Then, predictions are made by fusing the degree of membership and non-membership concurrently. Moreover, we implement ELM, OS-ELM, and the proposed fusion scheme for Chinese reviews sentiment prediction. The experimental results have clearly shown the effectiveness of the proposed scheme and the strategy of weighting and order inducing.     

Community engagement and online word of mouth: An empirical investigation

How customer engagement in a firm’s social media marketing platform ? online brand community ? might affect word-of-mouth behavior is an open research issue as well as a vital business problem in e-commerce. To this end, we collected a set of longitudinal data that include community engagement behavior, e-commerce transactions, and customers’ post-purchase reviews. Using a control function approach, we find that consumer engagement in a brand community not only increases the likelihood of generating post-purchase reviews but also increases the likelihood of posting positive online reviews. Furthermore, we find that customer tenure has a positive moderating effect.     

An empirical comparison of consumer usability preferences in online shopping using stationary and mobile devices: results from a college student population

To determine the differences in user preferences between electronic and mobile commerce in consumer shopping, a 25-item survey concerning usability aspects of the two media was implemented on one hundred and eighteen college e-shoppers with experience in mobile devices. The Mobile Commerce Comparison Survey (MCCS) had four validated comparison factors: human factors-related, interface features, product-, and service-related factor. The comparison concludes that usability issues are perceived to be behind in m-commerce, and that m-commerce should be a shopping medium complementary to classic e-commerce rather than a direct alternative to it. Future studies may include empirical usability experiments for m-commerce tasks.     

Predicting consumer intention to use mobile service

Abstract. Advances in wireless technology have increased the number of people using mobile devices and accelerated the rapid development of mobile service (m-service) conducted with these devices. However, although many companies are today making considerable investments to take advantage of the new business possibilities offered by wireless technology, research on mobile commerce suggests potential consumers may not adopt these m-services in spite of their availability. Thus, there is a need for research to identify the factors that affect consumer intention to use m-services. Based on the technology acceptance model (TAM), theory of planned behaviour (TPB) and Luarn & Lin's 2005 mobile banking acceptance model, the current research respecifies and validates an integrated model for predicting consumer intention to use m-service by adding one trust-related construct ('perceived credibility') and two resource-related constructs ('self-efficacy' and 'perceived financial resources') to the TAM's nomological structure and re-examining the relationships between the proposed constructs. Data collected from 258 users in Taiwan were tested against the research model using the structural equation modelling approach. The results strongly support the proposed model in predicting consumer intention to use m-service. Several implications for information technology/information system acceptance research and m-service management practices are discussed.