How perceived personalities of earlier contributors influence the content generation on online knowledge-sharing platforms?

Recent years have witnessed the prevalence of online knowledge-sharing platforms, in which user management and content creation are the central issues to the governance. Interactions take place through users’ linguistics, which helps shape users’ perceived personalities in the eyes of others. To understand the impact of perceived personality on online discussions, a novel method that combines natural language processing method and unsupervised learning is developed to extract contributors’ perceived personalities based on the contents they generated on the platform, which is further validated by a lab experiment. An empirical analysis is then carried out to unpack the role of different personality dimensions on online discussions. Our results reveal that the first contributor’s perceived conscientiousness and openness exhibit a significant but contrary role on content generation. Our method and empirical analysis can provide insights into the governance of online knowledge-sharing platforms

When Do Likes Create Bias?

The rise of online communities has ushered in a new era of content sharing with platforms that serve many functions and overcome the geographic and synchronous limitations of traditional word-of-mouth communications. Community-based question answering sites (CQA) have emerged as convenient platforms for users to exchange knowledge and opinions with others. Research on CQA has primarily focused on engaging members to voluntarily contribute to these communities. Helpfulness ratings and “likes” are one mechanism platforms can use to engage members, but these subjective evaluations can also create bias. In this ERF paper, the elaboration likelihood model is applied to better understand when bias can occur with these platforms. An experimental design and a planned data collection are reported

Impacts of Live Chat on Refund Intention: Evidence from an Online Labor Market

Live chat plays a significant role in online labor markets, which mitigates the information asymmetry caused by the highly customized nature of service products. This study examines the impacts of live chat on refund intention in online labor markets and how these impacts are moderated by business familiarity. We collect unique archived data from a leading online labor market in Asia and hypothesize that reply speed has a negative effect on refund intention while both politeness intensity and sentiment intensity have a U-shaped effect on refund intention. In addition, these effects are proposed to be weakened by business familiarity formed by previous transaction experience. The study not only offers theoretical contributions to the online labor market literature by providing empirical insights on the impact of live chat on refund intention but also yields managerial implications for service providers and platform operators

Get a Word in Edgewise: Post Character Limit and Social Media-Based Customer Service

In this paper, we study the role of extending character limits on firm responses on social media. By leveraging a natural experiment setting: the unexpected increase in post character limit on Twitter, we empirically investigate the impact on the linguistic styles of social media-based customer service responses. Using a Regression Discontinuity in Time Design and leveraging a panel dataset, our results suggest that extending character limits influences firm to change the linguistic styles in their responses which could influence consumers' perceptions. Our results show that extending post-character limits significantly reduces the readability ease of firm responses, on average, while increasing the concreteness and personal closeness scores of these responses, on average. We show that these changes were effective in influencing customer satisfaction

Home Bias in Knowledge Adoption: Evidence From Location Disclosure in An Online Q&A Community

This study investigates whether and how answerers’ location information can bias the askers’ knowledge adoption decisions in online Q&A communities. Drawing on the theories underlying in-group favoritism, we propose that home bias can exist due to categorization and the expectation of better reciprocity from in-group members. We leverage the location disclosure in an online Q&A community in China as a natural experiment setting to identify home bias in knowledge adoption. We find that askers are more likely to adopt answers provided by answerers in the same location after the location disclosure. Moreover, the moderation/heterogeneity analysis suggests: (1) location information serves as a cue related to credibility, and askers rely less on it when other factors signal the answerers\u27 credibility, and (2) askers are more favorable toward answerers in the same location when adopting an answer is associated with an expectation of better reciprocation

Examining Users’ Information Disclosure and Audience Support Response Dynamics in Online Health Communities: An Empirical Study

Online healthcare communities (OHCs) facilitate two-way interaction. Examining users’ information disclosure-audience support response dynamics can reveal insights for fostering a supportive environment, community engagement, bond formation, knowledge sharing, and sustained participation in OHCs. We propose a structural vector autoregression (SVAR) model of user disclosure and response dynamics in OHCs. Based on the health disclosure decision-making model and daily time series data, we examine the two-way interaction of two dimensions of disclosure efficacy with audience support response acceptance. Findings of the impulse response functions reveal that user information density leads to positive support response acceptance, whereas support response acceptance reduces the information density of a user post over time. Further, higher information efficacy leads to more support response acceptance with long run improved information efficacy. Theoretically, findings extend the disclosure decision-making model in OHCs. Practically, the results provide insights for OHC management to facilitate two-way dynamic users’ interactions

Examining Users’ Information Disclosure and Audience Support Response Dynamics in Online Health Communities: An Empirical Study

Online healthcare communities (OHCs) facilitate two-way interaction. Examining users’ information disclosure-audience support response dynamics can reveal insights for fostering a supportive environment, community engagement, bond formation, knowledge sharing, and sustained participation in OHCs. We propose a structural vector autoregression (SVAR) model of user disclosure and response dynamics in OHCs. Based on the health disclosure decision-making model and daily time series data, we examine the two-way interaction of two dimensions of disclosure efficacy with audience support response acceptance. Findings of the impulse response functions reveal that user information density leads to positive support response acceptance, whereas support response acceptance reduces the information density of a user post over time. Further, higher information efficacy leads to more support response acceptance with long run improved information efficacy. Theoretically, findings extend the disclosure decision-making model in OHCs. Practically, the results provide insights for OHC management to facilitate two-way dynamic users’ interactions

Answer Viewing Design on Paid Q&A Platforms

The low-participation problem has long been a challenge facing many paid Q&A platforms. Recently, a new business model wherein users, in addition to raising a question and receiving a personalized answer, can pay a small amount of fees to view a non-personalized answer to a question asked by others, has drawn considerable public attention and is considered an effective means to tackling such a challenge. In this paper, we build a theoretical model to explore whether this new business model benefits the key stakeholders (Q&A platform, answerers, and users). We find that the platform is not always better off when the answer-viewing feature is introduced. Another interesting finding is that while answerers may engage in direct competition with the platform, they can sometimes be better off. Additionally, we find that although having a new way to participate in the platform, users may sometimes be worse off under the answer-viewing feature

Is Best Answer Really the Best Answer? The Politeness Bias

Popular knowledge management platforms such as community-based question answering sites (CQAs) and electronic networks of practice (ENPs) rely on accurate quality assessment of user-contributed content to ensure effective knowledge creation and exchange. However, quality assessment is subjective by nature. Based on the politeness theory, we hypothesize that answers written more politely are more likely to be perceived as high quality answers by the question asker due to the low face threat. We first test our hypotheses through a random coefficient logit model with data obtained from Stack Exchange, a popular CQA platform. We then conduct a randomized experiment where we exogenously manipulate the politeness level of otherwise similar answers. Our analyses, based on both the Stack Exchange dataset and the randomized experiment, lend strong support to the existence of a politeness bias, which affects question askers’ subjective evaluation of answer quality. This study contributes to the literature in knowledge management, cognitive bias, and behavioral issues in information systems