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.     

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.     

Effects of consumer visit to online community and product channel on local sales of large consumer goods: Evidence from real estate industry

Online intermediaries have become important information sources for consumers’ purchase decision making. However, the economic values of consumer visits to two online intermediaries, namely, online community and product channel, have not been well studied. By collecting a dataset on consumer visits from a large real estate website to match with local offline housing sales data, we empirically explore the respective, relative, and interaction effects of consumer visits to online communities and product channels on sales of large consumer goods. We control for relevant factors, account for potential endogeneity issues, and perform various robustness checks to validate the consistency of our findings. Our results show that consumer visits to online communities have a more significant effect than those to product channels in driving sales. However, the interaction effect of consumer visits to these two online intermediaries on sales is negative. We also find that consumer website-related experience has a significant moderating effect on the relationship between consumer visits to product channels and sales. Our findings provide important theoretical contributions and managerial implications.     

Becoming Friends in Online Brand Communities: Evidence From China

With the aid of information technology, consumers have increasingly engaged in social interaction in online brand communities. How can these strangers make friends online? Drawing on embeddedness theory and media richness theory, we examine the antecedents and intermediate mechanisms of online friendship. We theorize that online brand community interactivity aided by instant messaging technology is the main driving force of online friendship, whereas social presence and a sense of yuan (a Chinese concept describing predetermined relations) mediate online friendship development. Online friendship in turn enhances consumer online brand community commitment. We test our conceptual model with a sample of consumers from Chinese online sporting goods forums. The results support our hypotheses and inform online brand community research and practice.     

Product recommendation in online social networking communities: An empirical study of antecedents and a mediator

Intention to facilitate individual voluntary product recommendation in online social networking communities has attracted increased attention. However, it is not obvious why individuals recommend products in a community composed of non-transactional users. What are the critical factors influencing such a behavior? To attempt to provide insights into the product recommendation behavior in online social networking communities, we develop a model to investigate the effects of community atmospheric cues on affective social distance and subsequent product recommendations based on the stimulus–organism–response paradigm. By analyzing the results of a survey using a questionnaire, we found that community reciprocity and community receptivity have a direct influence on product recommendation, while community atmospheric cues have an indirect influence through affective social distance. Theoretical and managerial implications are discussed.     

A dynamic bibliometric model for identifying online communities

Predictive modelling of online dynamic user-interaction recordings and community identification from such data becomes more and more important with the widespread use of online communication technologies. Despite of the time-dependent nature of the problem, existing approaches of community identification are based on static or fully observed network connections. Here we present a new, dynamic generative model for the inference of communities from a sequence of temporal events produced through online computer- mediated interactions. The distinctive feature of our approach is that it tries to model the process in a more realistic manner, including an account for possible random temporal delays between the intended connections. The inference of these delays from the data then forms an integral part of our state-clustering methodology, so that the most likely communities are found on the basis of the likely intended connections rather than just the observed ones. We derive a maximum likelihood estimation algorithm for the identification of our model, which turns out to be computationally efficient for the analysis of historical data and it scales linearly with the number of non-zero observed (L + 1)-grams, where L is the Markov memory length. In addition, we also derive an incremental version of the algorithm, which could be used for real-time analysis. Results obtained on both synthetic and real-world data sets demonstrate the approach is flexible and able to reveal novel and insightful structural aspects of online interactions. In particular, the analysis of a full day worth synchronous Internet relay chat participation sequence, reveals the formation of an extremely clear community structure.     

Industry classification with online resume big data: A design science approach

Industry classification is a vital step of industry analysis and competitive intelligence. However, existing schemes and methods are limited by the lagged information of firms’ business and the lack of consideration of the human resource aspects. In this paper, we adopt a design science approach to develop and evaluate a novel industry classification method by constructing a labor mobility network using online resume big data collected from the professional social network. We also propose a hierarchical extension of the community detection algorithm to better discover scalable firm clusters on the constructed network. The evaluation conducted on real-world datasets shows that our method outperforms the existing industry classification schemes and the state-of-the-art methods by improving their explanatory power and enlarging the cross-industry variation. Moreover, two application cases confirm the validity of our method in earlier revealing firms’ action of entering new industries.     

Understanding the intention to follow the advice obtained in an online travel community

Online travel communities are an increasing phenomenon that is motivating great changes in consumer behavior in the travel sector. Travelers prefer to rely on peers’ recommendations and thus visit these communities to look for unbiased information. This work analyzes some of the precursors of the consumer intention to follow the advice obtained in an online travel community. Data show the relevant role of attitude toward the advice, trust in the online community that provides the advice and perceived usefulness of this information in order to determine the consumer intention to follow the advice obtained in the community. As well, trust and usefulness have been found to influence consumer attitude, and usefulness is also directly affected by trust in the community that provides the advice. Finally, a specific personal attribute - namely, consumer susceptibility to interpersonal influence -, moderates the effects of the antecedents of the intention to follow the advice obtained in a travel community. Based on these results, some implications for practice are widely discussed.     

What drives consumer knowledge sharing in online travel communities?: Personal attributes or e-service factors?

Consumer voluntary sharing of e-service knowledge in an online community is of great value to both business and consumers. This study develops and tests a research model integrating two personal attributes (consumer innovativeness and subjective knowledge) and two e-service factors (perceived ease of use and usefulness) in predicting consumer voluntary knowledge sharing in an online community. Data were collected using a web-based survey of 364 airline travelers recruited through an online travel community in China. Partial Least Squares Structural Equation Modeling (PLS-SEM) technique was used to analyze the data. The results reveal that personal factors are stronger drivers of knowledge sharing than e-service factors. This study advances consumer knowledge sharing in online communities, and has implications for enhancing a firm’s e-service strategies.     

Selecting the most helpful answers in online health question answering communities

The online question answering (QA) community has been popular in recent years. In this paper, we focus on the online health question answering (HQA) community. The HQA community provides a platform for health consumers to inquire about health information. There are two ways to use this platform. One is to post a question and wait for answers to be provided by authenticated doctors. The other is to search for relevant questions with answers. For the latter, health consumers may prefer an accepted answer marked by the previous health consumer. However, there is a large proportion of questions without an accepted answer and it is inconvenient for people who want to search for relevant questions. To address this issue, we aim to select high-quality answers from the answers without marked accepted answers. We propose a deep learning approach to achieve this goal. To train the model for the prediction of answer quality, we first view the accepted answer as the positive answer and propose a method to label the negative answer. Next, we capture the semantic information on the question and the answer by the deep learning structure. We then combine the information to predict the quality score of the answer. We collect data from one of the biggest Chinese HQA community and divide them into groups by the medical departments for detailed analysis. Finally, we conduct experiments to show the effectiveness of categorization and the labeling method. The results show that our approach outperforms other studies and we further research into the differences among the results of different categories.

     

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 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.     

Consumer's decision to shop online: The moderating role of positive informational social influence

While much of the current literature tends to focus on the direct effect of social influence on consumer online shopping behavior, our study drew heavily on social influence theory and argued for an alternative theory focusing on the moderating role of social influence. In particular, we explored how positive messages in online discussion forums may affect consumers’ decisions to shop online. We used a laboratory experiment in which the treatment group was required to read positive messages about online shopping experience in an online discussion forum. Positive social influence was found to reinforce the relationship between beliefs about and attitude toward online shopping, as well as the relationship between attitude and intention to shop. We believe our alternative theory provides new insight into the complex processes through which social influence is brought to bear on consumers’ online shopping decisions. Implications of our work are discussed.     

Conflict and identity shape shifting in an online financial community

Abstract.  This paper challenges traditional explorations of online communities that have relied upon assumptions of trust and social cohesion. In the analysis presented here, conflict becomes more than just dysfunctional communication and provides an alternative set of unifying principles and rationales for understanding social interaction and identity shape shifting within an online community. A model is advanced that describes the systematic techniques of hostility and aggression in technologically enabled communities that take the form of contemporary tribalism. It is argued that this tribe-like conflict embodies important rituals essential for maintaining and defining the contradictory social roles sometimes found in online environments. This research offers a critical interpretive perspective that focuses on the link between identity shape shifting behaviours and the power relations within an online financial community. The analysis reveals how conflict between positions of power can help to align the values and ideals of an online community. With this study we seek to motivate a re-examination of the design and governance of online communities.     

Study on the influencing factors of unplanned consumption in a large online promotion activity

Large online promotion activities are different from traditional online marketing activities. Issues about consumer behavior in large online promotion activities deserve further discussion. Via consumer surveys before and after the “Double 11” promotion on Taobao.com, we studied the unplanned consumption and its influencing factors (promotion, time pressure, social environment (SE), in-store slack) based on the SOR model and theories of self-regulation. The results show that consumers allocate in-store slack in their shopping budget as a strategy of controlling unplanned consumption. Promotion range, preparation time, SE and in-store slack have effects on unplanned consumption. Actual shopping time and preparation time moderate the relationship between in-store slack and unplanned consumption. Actual shopping time attenuates the relationship between in-store slack and unplanned consumption, while preparation time strengthens the relationship between in-store slack and unplanned consumption. Managerial implications are also discussed based on the research.     

Consumer behavior in online game communities: A motivational factor perspective

The concept of online communities has been used to improve customers’ loyalty in recent years. While studies on transaction community such as online auction have received more attention in the literature, entertainment community such as online game has seldom been addressed. This study applies the theory of reasoned action (TRA) and modifies the technology acceptance model (TAM) to propose a research model. An empirical study involving 356 subjects was conducted to test this model. The results indicate that customer loyalty is influenced by perceived enjoyment, social norms and preference. Perceived cohesion has an indirect impact on loyalty. In addition, the finding’s practical implication suggests that community managers must overcome the problems users encounter, including suffering from an unstable system, malicious players and grief players.     

The Identification and Influence of Social Roles in a Social Media Product Community

This research focuses on the identification of social roles and an investigation of their influence in online context. Relying on a systemic approach for role conceptualization, we investigate member's activity, shared content and position in the network within a consumer to consumer social media‐based community (SMC) around a product. This investigation led to the identification of ten core roles, based on three key elements: object of interest (product, practice, and community), main contribution type (sharing information and seeking information), individual orientation (factual, emotional). We propose an explanation about how these roles, through their positioning, participate in the community dynamics and how they contribute to the creation and diffusion of cookery as a social practice, shaping the periphery around this practice.     

Factors affecting online group buying intention and satisfaction: A social exchange theory perspective

This study investigates factors that affect consumer continuous use intention toward online group buying and the degree that reciprocity and reputation of social exchange, trust, and vendor creativity affect consumer satisfaction and intention toward online purchasing. Data from 215 valid samples was obtained using an online survey. The research model is assessed using partial least squares (PLS) analysis. The results show that the intention to engage in online group buying is predicted collectively by consumer satisfaction, trust, and seller creativity. Consumer satisfaction with online group buying is predicted primarily by trust, followed by consumer reciprocity. The proposed research model explains 67.7% of variance for satisfaction and 39.7% of variance for intention to engage in online group buying. The results suggest that reciprocity, trust, satisfaction, and seller creativity provide considerable explanatory power for intention to engage in online group buying behavior.     

How online self-customization creates identification: Antecedents and consequences of consumer-customized product identification and the role of product involvement

Online self-customization (OSC) enables customers to design a product tailored to their preferences and needs via the online platform. This study mainly argues that a successful OSC experience goes beyond simply increasing a consumer’s preference fit; it provides an opportunity to develop a meaningful relationship with customers by allowing them to embed their sense of self into the customized products and thus identify themselves with the products. Consumer-customized product identification (C-C identification) was proposed as central to our understanding of why and under what conditions OSC processes enable consumers articulate their identities. This study is theoretically based on internal motivations from social identity theory and identification literature to develop a model. The model was tested using a scenario-based survey with respondents collected from Amazon’s Mechanical Turk. Structural equation modeling analyses showed that value congruence and distinctiveness of the customized products positively influenced C-C identification, which in turn positively influenced attitudinal responses. The results further showed that the relative impact of two antecedents on C-C identification varies with a consumer’s product involvement. A similar pattern of results was obtained in two product categories. Theoretical and managerial implications for OSC marketers are also discussed.