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Although the numbers of online community members continue to increase every day, most of the user-generated content in online communities comes from only a small percentage of members who are motivated to participate and contribute. While studies have been conducted on other professional online communities (e.g. teachers, mathematics practitioners, software developers, etc.), the online community for ServiceNow practitioners is not fully understood. Studies of this group could be useful to organizations that seek to understand ServiceNow practitioners’ motives to participate in these communities, fulfill their specific needs, and build and maintain a thriving community of users.

A qualitative case study method was used to learn what motivates ServiceNow practitioners to contribute and participate in ServiceNow communities. Data were collected from interviews with the participants, their stories and testimonials and physical artifacts created and used by them. Data analysis was carried out using data triangulation and continuous coding process.

Three primary motives were identified: find answers to questions or issues, learn about ServiceNow products and services, and share knowledge and expertise with others.

As a single-case research method was used, findings of this study may not be generalized to a larger population.

Results should encourage and increase participation by ServiceNow Community's members, create a repository of knowledge and relationships that can improve their value and effectiveness, and help their organizations maintain competitive advantage.

The purpose of the paper is to investigate how cross-cultural elements such as cultural difference and stereotype are integrated into collaborative modes and actions and to explore their corresponding effectiveness.

The sample of the quantitative content analysis is drawn from the posts with the topic of China on Quora. A collaborative case, where two users have a question-and-answer interaction, is taken as the unit of analysis. The effectiveness of collaboration is operationalized as the extent to which a collaboratively produced answer is visited and favorably reviewed, using the feedback index (the number of upvotes*1,000/views). One of the sampled collaborative cases is further analyzed qualitatively to see how cultural differences, stereotypes and other factors are incorporated into users' interaction.

This content analysis reveals nine modes of collaborative production of knowledge on Quora: initial questioning, pointed answering, raising doubts, responding to others, agreeing with others, correcting mistakes, enriching content, further questioning and extending issues. Diversity of the cross-cultural acts of collaborative production, particularly two of often-used collaborative actions, correcting stereotypes and supplementing cultural differences, helps to enhance overall collaborative effectiveness.

This paper offers new perspectives and ideas for strategies to change socially problematic stereotypes, e.g. to correct stereotypes where necessary and use more convincing resources such as reliable images as collaborative actions to bridge cultural differences. It also calls on social Q&A website developers to create more international users-friendly design by providing various channels for users with diverse cultural backgrounds to interact with each other.

This study is one of the first to investigate online collaborative knowledge production within a broader cross-cultural context. Specifically, cultural factors and cross-cultural collaborative actions have been innovatively integrated into this research, enriching the dimensions that can be used for collaboration classification. It is helpful for users from different countries to actively adopting different strategies to overcome cultural differences, preconceptions and other negative factors that are not conducive to communication and knowledge acquisition.

In community question and answer (CQA) services, because of user subjectivity and the limits of knowledge, the distribution of answer quality can vary drastically – from highly related to irrelevant or even spam answers. Previous studies of CQA portals have faced two important issues: answer quality analysis and spam answer filtering. Therefore, the purposes of this study are to filter spam answers in advance using two-phase identification methods and then automatically classify the different types of question and answer (QA) pairs by deep learning. Finally, this study proposes a comprehensive study of answer quality prediction for different types of QA pairs.

This study proposes an integrated model with a two-phase identification method that filters spam answers in advance and uses a deep learning method [recurrent convolutional neural network (R-CNN)] to automatically classify various types of questions. Logistic regression (LR) is further applied to examine which answer quality features significantly indicate high-quality answers to different types of questions.

There are four prominent findings. (1) This study confirms that conducting spam filtering before an answer quality analysis can reduce the proportion of high-quality answers that are misjudged as spam answers. (2) The experimental results show that answer quality is better when question types are included. (3) The analysis results for different classifiers show that the R-CNN achieves the best macro-F1 scores (74.8%) in the question type classification module. (4) Finally, the experimental results by LR show that author ranking, answer length and common words could significantly impact answer quality for different types of questions.

The proposed system is simultaneously able to detect spam answers and provide users with quick and efficient retrieval mechanisms for high-quality answers to different types of questions in CQA. Moreover, this study further validates that crucial features exist among the different types of questions that can impact answer quality. Overall, an identification system automatically summarises high-quality answers for each different type of questions from the pool of messy answers in CQA, which can be very useful in helping users make decisions.

Paying to view others' answers is a new mode for question and answer (Q&A) platforms. The purpose is to build a model to explore the determinants of the number of listeners and further explore certain meaningful characteristics of the model in the context of different types of questions and answerers.

The authors develop an empirical model and use real panel data to test the hypothesis. Specifically, cues from the answerer and from the question elicit the listener's trust in the answerer (including direct and indirect trust) and perceived value in the question (including intrinsic and extrinsic attributes), respectively.

The authors find that cues from answerers (experience for paid Q&As and popularity for free Q&As) and questions (length, sentence structure, value and number of likes) all have positive effects on the number of listeners. The impact of answerer authentication is more significant than the popularity of free Q&As. Moreover, the length of the question matters only for subjective questions, while sentence structure matters only for objective questions. In addition, the answerer's own attributes and the behavior and feedback of others have greater impacts when the answerer is below average in popularity.

The authors summarize the unique features of the mode of paying to view others' answers in contrast with the traditional mode of paid Q&As. In addition, the authors focus on the characteristics of the question (including the subjectivity and the sentence structure of the question), a topic which has not been studied previously. Our research provides a reference for exploring user behavior patterns. The practical implications for knowledge platforms are also concretely described.

The purpose of this paper is to examine some drivers of users’ participation in online social question-and-answer (Q&A) communities based on social cognitive theory and then identify the underlying mechanism of this process.

This study developed a research model to test the proposed hypotheses, and an online survey was employed to collected data. Totally, 313 valid responses were collected, and partial least squares structural equation modeling was adopted to analyze these data.

This study empirically finds that the outcome expectations (personal outcome expectations and knowledge self-management outcome expectations) are positively related to participation in online social Q&A communities. At the same time, users’ self-efficacy positively influences their participation behaviors. It can not only directly motivate users’ participation, but also indirectly promote participation behaviors through the two dimensions of outcome expectations. Besides, perceived expertise and perceived similarity are two positive and significant environmental elements affecting users’ participation.

This study extends the understanding about how participation behaviors will be motivated in the context of online social Q&A communities. Drawing on the social cognitive theory, constructs were established based on the features of these communities. Meanwhile, some mediating effects in the motivating process were also discussed.

In the era of mobile internet, the social Q&A community has built a large-scale and complex knowledge label network through its internal knowledge units, and the scale and structure of the network have changed over time. By analysing the structural characteristics and evolution rules of knowledge label networks, the main purpose of this study is to understand the internal mechanisms of the replacement of old and new knowledge and the expansion of knowledge element boundaries, so as to explore the realization path of knowledge management in the new era from the perspective of complex networks.

This paper uses distributed crawlers to capture 419,349 samples from the Zhihu platform. Each sample contains 33 characteristic dimensions, and the natural year is used as the sliding window to divide the whole. In this study, the global knowledge label network and 11 local knowledge label networks are first constructed. Then, the degree distribution analysis and central node exploration of the knowledge label network are carried out using the complex network method. Finally, the average shortest path and average clustering coefficient of the network are analysed by the time series method, and the ARIMA model is used to predict the evolution of the correlation coefficient.

The research results show that the dissimilation degree of the degree distribution of the knowledge label network has gradually decreased from 2011 to 2021, and the attention of users in the knowledge community has shown a trend of distraction and diversification over time. With the expansion of the scale of the knowledge label network and the transformation to an information network, the network sparsity is becoming more and more obvious, and the knowledge granularity of the Q&A community is being refined and diversified. The prediction of the correlation coefficient of the knowledge label network by the ARIMA model shows that the connection between the labels is lacking diversity and the opinion strengthening phenomenon tends to strengthen, which is more likely to form the “echo chamber effect”, resulting in mutual isolation and even opposition between different circles. The Q&A community is about to enter a mature stage, and the corresponding status of each label has been finalized. The future development trend of label networks will be reflected in the substitution between labels, and the specific structure will not change significantly.

The Q&A community model is the trend in Web 2.0 community development. This study proves the effectiveness of complex networks and time series prediction methods in knowledge label network mining in the Q&A community.

As academic social Q&A networking websites become more popular, scholars are increasingly using them to meet their information needs by asking academic questions. However, compared with other types of social media, scholars are less active on these sites, resulting in a lower response quantity for some questions. This paper explores the factors that help explain how to ask questions that generate more responses and examines the impact of different disciplines on response quantity.

The study examines 1,968 questions in five disciplines on the academic social Q&A platform ResearchGate Q&A and explores how the linguistic characteristics of these questions affect the number of responses. It uses a range of methods to statistically analyze the relationship between these linguistic characteristics and the number of responses, and conducts comparisons between disciplines.

The findings indicate that some linguistic characteristics, such as sadness, positive emotion and second-person pronouns, have a positive effect on response quantity; conversely, a high level of function words and first-person pronouns has a negative effect. However, the impacts of these linguistic characteristics vary across disciplines.

This study provides support for academic social Q&A platforms to assist scholars in asking richer questions that are likely to generate more answers across disciplines, thereby promoting improved academic communication among scholars.

The purpose of this paper is to examine the quality of answers on the Wikipedia Reference Desk, and to compare it with library reference services. It aims to examine whether Wikipedia volunteers outperform expert reference librarians and exemplify the paradox of expertise.

The study applied content analysis to a sample of 434 messages (77 questions and 357 responses) from the Wikipedia Reference Desk and focused on three SERVQUAL quality variables: reliability (accuracy, completeness, verifiability), responsiveness, and assurance.

The study reports that on all three SERVQUAL measures quality of answers produced by the Wikipedia Reference Desk is comparable with that of library reference services.

The collaborative social reference model matched or outperformed the dyadic reference interview and should be further examined theoretically and empirically. The generalizability of the findings to other similar sites is questionable.

Librarians and library science educators should examine the implications of the social reference on the future role of reference services.

The study is the first to: examine the quality of the Wikipedia Reference Desk; extend research on Wikipedia quality; use SERVQUAL measures in evaluating Q&A sites; and compare Q&A sites with traditional reference services.

With the rapid development of paid online social question and answer (Q&A) communities, monetary social functions have been introduced and have potential benefits for both platforms and users. However, these functions' impact on knowledge contribution remains uncertain. This study proposes a conceptual model based on the stimulus–organism–response framework, according to which monetary and non-monetary social functions can help nurture short-term and long-term relationships among community users, and thereafter improves social identity and knowledge-sharing intentions.

This study selects Zhihu, a famous online social Q&A community in China, and conducts an online survey to collect data from its frequent users. A sample of 286 valid questionnaires was collected to test our research model by using a structural equation modeling method. In addition, a bootstrapping approach is used to test the mediation effect.

Results indicate that monetary social functions help nurture short-term and long-term relationships among community users. However, non-monetary social functions only affect short-term relationships directly. Short-term and long-term relationships both have a positive relationship with social identity and thereafter improve users' knowledge-sharing intentions.

This study focuses on users' knowledge-sharing intentions in Q&A communities from the perspective of social. Specifically, we separated social functions in Q&A platforms into monetary and non-monetary ones and explored their impact on the development of short-term and long-term relationships. Results demonstrate the importance of monetary social functions and explain how monetary and non-monetary social functions affect users' knowledge-sharing intentions in different approaches.

Knowledge adoption is the key to effective knowledge exchange in virtual question-and-answer (Q&A) communities. Although previous studies have examined the effects of knowledge content, knowledge source credibility and the personal characteristics of knowledge seekers on knowledge adoption in virtual Q&A communities from a static perspective, the impact of answer deviation on knowledge adoption has rarely been explored from a context-based perspective. The purpose of this study is to explore the impact of two-way deviation on knowledge adoption in virtual Q&A communities, with the aim of expanding the understanding of knowledge exchange and community management.

The same question and the same answerer often yield multiple answers. Knowledge seekers usually read multiple answers to make adoption decisions. The impact of deviations among answers on knowledge seekers' knowledge adoption is critical. From a context-based perspective, a research model of the impact of the deviation of horizontal and vertical answers on knowledge adoption is established based on the heuristic-systematic model (HSM) and empirically examined with 88,287 Q&A data points and answerer data collected from Zhihu. Additionally, the moderation effects of static factors such as answerer reputation and answer length are examined.

The negative binomial regression results show that the content and emotion deviation of horizontal answers negatively affect knowledge seekers' knowledge adoption. The content deviation of vertical answers is negatively associated with knowledge adoption, while the emotion deviation of vertical answers is positively related to knowledge adoption. Moreover, answerer reputation positively moderates the negative effect of the emotion deviation of horizontal answers on knowledge adoption. Answer length weakens the negative correlation between the content deviation of horizontal and vertical answers and knowledge adoption.

This study extends previous research on knowledge adoption from a static perspective to a context-based perspective. Moreover, information deviation is expanded from a one-way variable to a two-way variable. The combined effects of static and contextual factors on knowledge adoption are further uncovered. This study can not only help knowledge seekers identify the best answers but also help virtual Q&A community managers optimize community design and operation to reduce the cost of knowledge search and improve the efficiency of knowledge exchange.