“Ask Everyone?” Understanding How Social Q&A Feedback Quality Influences Consumers\u27 Purchase Intentions

Social question & answer (Q&A) feedback is a novel form of electronic word-of-mouth that allows customers to ask questions and share opinions with peer customers. Based on the stimulus-organism-response framework, this paper proposes a model to describe how social Q&A feedback quality affects consumers\u27 willingness to purchase by influencing their perceived risk, perceived usefulness, and use intention. We focused on the social Q&A feature named Ask Everyone in Taobao and collected 153 valid responses through an online survey. Canonical correlation analysis was used to identify the association between feedback characteristics and feedback quality. Then, PLS-SEM was conducted to test the proposed research model. Results show that feedback quality negatively associated with perceived risk, but had a positive impact on perceived usefulness, use intention, and purchase intention. Findings of this research has both theoretical and practical implications for facilitating social Q&A design in e-commerce platforms

Using a Question Answering System to Enhance Knowledge and Improve the Exchange of Information among Physicians

Due to limited time, physicians often find it challenging to find the exact answers to their questions among search engine results; however, question and answer (Q&A) systems can facilitate more rapidly identify accurate solutions. This study aims to develop and evaluate a Q&A system for physicians at Tabriz University of Medical Sciences. Four clinical and informatics experts and the two health information managers agreed on 19 features and themes throughout two focus group meetings. Subsequently, a system was developed on a MySQL database using the PHP web development language and then uploaded to the web. Finally, the system was opened up to 40 users and, over three months, evaluated using a community evaluation questionnaire and the six-dimension Users’ Experience Questionnaire. The focus group results in determining the features of the Q&A system consisted of 19 requirements. The average attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty were equal to 1.76, 1.625, 1.9, 1.425, 1.475, and 1.375, respectively. The Q&A system improved the tasks such as share of knowledge, transfer of information, social partnership, and cooperation among users. The physicians were able to obtain the information they required through contact with their co-practitioners over the system.https://dorl.net/dor/20.1001.1.20088302.2021.19.2.14.

Answer quality characteristics and prediction on an academic QandA site: A case study on researchgate

Despite various studies on examining and predicting answer quality on generic social QandA sites such as Yahoo! Answers, little is known about why answers on academic QandA sites are voted on by scholars who follow the discussion threads to be high quality answers. Using 1021 answers obtained from the QandA part of an academic social network site ResearchGate (RG), we firstly explored whether various web-captured features and human-coded features can be the critical factors that influence the peer-judged answer quality. Then using the identified critical features, we constructed three classification models to predict the peer-judged rating. Our results identify four main findings. Firstly, responders' authority, shorter response time and greater answer length are the critical features that positively associate with the peer-judged answer quality. Secondly, answers containing social elements are very likely to harm the peer-judged answer quality. Thirdly, an optimized SVM algorithm has an overwhelming advantage over other models in terms of accuracy. Finally, the prediction based on web-captured features had better performance when comparing to prediction on human-coded features. We hope that these interesting insights on ResearchGate's answer quality can help the further design of academic QandA sites

Reinventing the Wheel: Explaining Question Duplication in Question Answering Communities

Duplicate questions are common occurrences in Question Answering Communities (QACs) and impede the development of efficacious problem-solving communities. Yet, there is a dearth of research that has sought to shed light on the mechanisms underlying question duplication. Building on the information adoption model, we advance a research model that posits information quality and source credibility as factors deterring users from asking redundant questions within QACs. Furthermore, considering the question-answer dichotomy intrinsic to QACs, we distinguish the quality and credibility of questions from those of answers as distinctive inhibitors of question duplication. We empirically validate our hypotheses on a leading QAC platform by harnessing a deep learning algorithm to detect duplications on over 9,380,000 question pairs. Results revealed that while the credibility of both questions and answers could alleviate question duplication, visual and actionable elements are more effective in preventing question duplication by boosting the quality of questions and answers respectively

Knowledge fixation and accretion: Longitudinal analysis of a social question-answering site

© 2014, Emerald Group Publishing Limited. Purpose – The purpose of this paper is to investigate longitudinal features of an established social question-answering (Q&A) site to study how question-answer resources and other community features change over time. Design/methodology/approach – Statistical analysis and visualisation was performed on the full data dump from the Stack Overflow social Q&A site for programmers. Findings – The timing of answers is as strong a predictor of acceptance – a proxy for user satisfaction – as the structural features of provided answers sometimes associated with quality. While many questions and answer exchanges are short-lived, there is a small yet interesting subset of questions where new answers receive community approval and which may end up being ranked more highly than early answers. Research limitations/implications – As a large-scale data oriented research study, this work says little about user motivations to find and contribute new knowledge to old questions or about the impact of the resource on the consumer. This will require complementary studies using qualitative and evaluative methods. Practical implications –While content contribution to social question-asking is largely undertaken within a very short time frame, content consumption is usually over far longer periods. Methods and incentives by which content can be updated and maintained need to be considered. This work should be of interest to knowledge exchange community designers and managers. Originality/value – Few studies have looked at temporal patterns in social Q&A and how time and the moderation and voting systems employed may shape resource quality

Sociable knowledge sharing online: Philosophy, patterns and intervention

Purpose – This paper seeks to outline a social epistemological and ethical warrant for engaging in knowledge exchange on the social web, and to emphasise sociocognitive and emotional factors behind motivation and credibility in communities supported by social software. An attempt is made to identify positive and negative patterns of interaction from this perspective and to argue for more positive intervention on the part of the information profession. Design/methodology/approach – The paper outlines social epistemological and related theory, cognitive and social drivers of behaviour and then draws together evidence to justify the definition of patterns that will be important to the project. Research limitations/implications – A programme of evaluating online knowledge exchange behaviour using a social epistemological framework is needed. In order to do this, methodological development coupling formal epistemological with interpretive techniques for examining belief formation are also necessary. Practical implications – Considerations for the design and deployment of knowledge platforms and for engagement with existing communities are outlined. Social implications – The ideas presented attempt to define an important role for the information profession within a new paradigm of participation and social interaction online. Originality/value – The connection between social epistemology theory and LIS has long been appreciated, but social epistemology is rarely applied to practice or to online social platforms and communities. © 2010, Emerald Group Publishing Limite

Advertisement Placement in Online Knowledge: The Effect of Advertisement Placement on Perceived Information Quality and Reuse of Online Q&A Services.

This study aimed to investigate the importance of managing perceived information quality in open knowledge sharing Q&A services. Especially, advertisement placement inside information is examined whether it has the effect on changing users’ perceived information quality, satisfaction, and behavior intention. One of the Q&A samples was randomly shown to the respondents, and their perceptions were surveyed. With a structured equation model and ANOVA test the data were analyzed. The results explain the effect that advertising information may bring about and also the results show that the individual perception on the additional advertisement placement may vary depending on the characteristics of information topics. In addition, this study implies the particular needs on controlling the advertisement placement inside the information of everyday topics and on managing. These results extend the understanding of the advertisement placement inside information of open knowledge sharing services and guide online information service managers to manage their information with more care placed on advertisement placement so as to increase user satisfaction and behavior intention

Can Social News Websites Pay for Content and Curation? The SteemIt Cryptocurrency Model

This is an accepted manuscript of an article published by SAGE Publishing in Journal of Information Science on 15/12/2017, available online: https://doi.org/10.1177/0165551517748290 The accepted version of the publication may differ from the final published version.SteemIt is a Reddit-like social news site that pays members for posting and curating content. It uses micropayments backed by a tradeable currency, exploiting the Bitcoin cryptocurrency generation model to finance content provision in conjunction with advertising. If successful, this paradigm might change the way in which volunteer-based sites operate. This paper investigates 925,092 new members’ first posts for insights into what drives financial success in the site. Initial blog posts on average received $0.01, although the maximum accrued was$20,680.83. Longer, more sentiment-rich or more positive comments with personal information received the greatest financial reward in contrast to more informational or topical content. Thus, there is a clear financial value in starting with a friendly introduction rather than immediately attempting to provide useful content, despite the latter being the ultimate site goal. Follow-up posts also tended to be more successful when more personal, suggesting that interpersonal communication rather than quality content provision has driven the site so far. It remains to be seen whether the model of small typical rewards and the possibility that a post might generate substantially more are enough to incentivise long term participation or a greater focus on informational posts in the long term

“I can't express my thanks enough”: The “gratitude cycle” in online communities

Gratitude is a fundamental aspect of social interaction that positively influences emotional and social well-being. It is also crucial for promoting online community health by motivating participation. However, how gratitude occurs and can be encouraged in online communities is not yet well understood. This exploratory study investigated how online community users experience gratitude, focusing on how gratitude expression and acknowledgment occurs, can break down or can be reinforced. Semistructured Critical Incident interviews were conducted with 8 users of various online communities, including discussion and support groups, social Q&A sites, and review sites, eliciting 17 memorable examples of giving and receiving thanks online. The findings gave rise to a process model of gratitude in online communities—the “gratitude cycle,” which provides a detailed, holistic understanding of the experience of gratitude online that can inform the design of online community platforms that aim to motivate users to perpetuate the cycle. An enriched understanding of gratitude in online communities can help ensure future platforms better support the expression and acknowledgment of thanks, encouraging participation