Search results

The purpose of this paper is to identify the triggers, strategies and outcomes of collaborative information-seeking behaviours of researchers on the ResearchGate social networking site.

Data were collected from the population of researchers who use ResearchGate. The sample was limited to the Ph.D. students and assistant professors in the library and information science domain. Qualitative interviews were used for data collection.

Based on the findings of the study, informal communications and complex information needs lead to a decision to use collaborative information-seeking behaviour. Also, easy access to sources of information and finding relevant information were the major positive factors contributing to collaborative information-seeking behaviour of the ResearchGate users. Users moved from collaborative Q&A strategies to sharing information, synthesising information and networking strategies based on their needs. Analysis of information-seeking behaviour showed that ResearchGate users bridged the information gap by internalizing new knowledge, making collaborative decisions and increasing their work's visibility.

As one of the initial studies on the collaborative information-seeking behaviour of ResearchGate users, this study provides a holistic picture of different triggers that affect researchers' information-seeking on ResearchGate.

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.

Through a two-stage survey, this paper examines how researchers judge the quality of answers on ResearchGate Q&A, an academic social networking site.

In the first-stage survey, 15 researchers from Library and Information Science (LIS) judged the quality of 157 answers to 15 questions and reported the criteria that they had used. The content of their reports was analyzed, and the results were merged with relevant criteria from the literature to form the second-stage survey questionnaire. This questionnaire was then completed by researchers recognized as accomplished at identifying high-quality LIS answers on ResearchGate Q&A.

Most of the identified quality criteria for academic answers—such as relevance, completeness, and verifiability—have previously been found applicable to generic answers. The authors also found other criteria, such as comprehensiveness, the answerer's scholarship, and value-added. Providing opinions was found to be the most important criterion, followed by completeness and value-added.

The findings here show the importance of studying the quality of answers on academic social Q&A platforms and reveal unique considerations for the design of such systems.

Academic social (question and answer) Q&A sites are now utilised by millions of scholars and researchers for seeking and sharing discipline-specific information. However, little is known about the factors that can affect their votes on the quality of an answer, nor how the discipline might influence these factors. The paper aims to discuss this issue.

Using 1,021 answers collected over three disciplines (library and information services, history of art, and astrophysics) in ResearchGate, statistical analysis is performed to identify the characteristics of high-quality academic answers, and comparisons were made across the three disciplines. In particular, two major categories of characteristics of the answer provider and answer content were extracted and examined.

The results reveal that high-quality answers on academic social Q&A sites tend to possess two characteristics: first, they are provided by scholars with higher academic reputations (e.g. more followers, etc.); and second, they provide objective information (e.g. longer answer with fewer subjective opinions). However, the impact of these factors varies across disciplines, e.g., objectivity is more favourable in physics than in other disciplines.

The study is envisioned to help academic Q&A sites to select and recommend high-quality answers across different disciplines, especially in a cold-start scenario where the answer has not received enough judgements from peers.

With the growth in popularity of academic social networking sites, evaluating the quality of the academic information they contain has become increasingly important. Users' evaluations of this are based on predefined criteria, with external factors affecting how important these are seen to be. As few studies on these influences exist, this research explores the factors affecting the importance of criteria used for judging high-quality answers on academic social Q&A sites.

Scholars who had recommended answers on ResearchGate Q&A were asked to complete a questionnaire survey to rate the importance of various criteria for evaluating the quality of these answers. Statistical analysis methods were used to analyze the data from 215 questionnaires to establish the influence of scholars' demographic characteristics, the question types, the discipline and the combination of these factors on the importance of each evaluation criterion.

Particular disciplines and academic positions had a significant impact on the importance ratings of the criteria of relevance, completeness and credibility. Also, some combinations of factors had a significant impact: for example, older scholars tended to view verifiability as more important to the quality of answers to information-seeking questions than to discussion-seeking questions within the LIS and Art disciplines.

This research can help academic social Q&A platforms recommend high-quality answers based on different influencing factors, in order to meet the needs of scholars more effectively.

This paper aims to explore question and answer (Q&A) participation and behavioral patterns on academic social networking sites (ASNSs) from the perspective of multiple subjects such as academic, corporate and government institutions.

Focused on the Q&A service of ASNSs, this study chooses ResearchGate (RG) as the target ASNS and collects a large-scale data set from it, involving a sample of users and a Q&A sample about academic, corporate and government institutions. First, it studies the law of Q&A participation and the distribution of the type of user according to the sample of users. Second, it compares question-asking behavior and question-answering behavior stimulated by questions among the three types of institutions based on the Q&A sample. Finally, it discusses the Q&A participation and behavioral patterns of the three types of institutions in academic Q&A exchanges with full consideration of institutional attributes, and provides some suggestions for institutions and ASNSs.

The results show that these three types of institutions generally have a low level of participation in the Q&A service of RG, and the numbers of questions and answers proposed by institutional users conform to the power-law distribution. There are differences in Q&A participation and Q&A behavioral patterns among academic, corporate and government institutions. Government and academic institutions have more users participating in the Q&A service and their users are more willing to ask questions, while corporate institutions have fewer users who participate in the Q&A service and their users are inclined to provide answers. Questions from corporate institutions attract much more attention than those from the other two types of institutions.

This study reveals and compares the Q&A participation and the behavioral patterns of the three types of institutions in academic Q&A, thus deepening the understanding of the attributes of institutions in the academic information exchange context. In practice, the results can help guide different institutions to use the Q&A service of ASNSs more effectively and help ASNSs to better optimize their Q&A service.

Scholarly communications, particularly, questions and answers (Q&A) present on digital scholarly platforms provide a new avenue to gain knowledge. However, several studies have raised a concern about the content anomalies in these Q&A and suggested a proper validation before utilizing them in scholarly applications such as influence analysis and content-based recommendation systems. The content anomalies are referred as disinformation in this research. The purpose of this research is firstly, to assess scholarly communications in order to identify disinformation and secondly, to help scholarly platforms determine the scholars who probably disseminate such disinformation. These scholars are referred as the probable sources of disinformation.

To identify disinformation, the proposed model deduces (1) content redundancy and contextual redundancy in questions (2) contextual nonrelevance in answers with respect to the questions and (3) quality of answers with respect to the expertise of the answering scholars. Then, the model determines the probable sources of disinformation using the statistical analysis.

The model is evaluated on ResearchGate (RG) data. Results suggest that the model efficiently identifies disinformation from scholarly communications and accurately detects the probable sources of disinformation.

Different platforms with communication portals can use this model as a regulatory mechanism to restrict the prorogation of disinformation. Scholarly platforms can use this model to generate an accurate influence assessment mechanism and also relevant recommendations for their scholars.

The existing studies majorly deal with validating the answers using statistical measures. The proposed model focuses on questions as well as answers and performs a contextual analysis using an advanced word embedding technique.

This study aims to investigate motivators, mediator and moderator of users' sustained information seeking on academic social networking sites (ASNSs).

Drawing upon the expectancy–value theory and related information-seeking literature, the study developed a theoretical model to explain why and how users intend to continue seeking information on ASNSs. Thereafter, a field survey with 385 participants was conducted to test the model. Finally, a content analysis of participants' post-survey feedback was performed to complement the model test results by showing more fine-grained findings.

Results suggest that information usefulness and information adoption (IA) are significant to users' sustained information seeking on ASNSs, while users' satisfaction with ASNSs may play a mediating role in the relationship between information usefulness and sustained information seeking. Additionally, self-efficacy for critical thinking (SCT) weakens the impact of IA on users' satisfaction with ASNSs. The post-survey feedback analysis indicates that information usefulness is more critical to sustained information seeking for users with high SCT, whereas IA becomes more crucial to users' satisfaction with ASNSs and sustained information seeking for users with low SCT.

Although the extant literature has distinguished between information seeking and sustained information seeking, empirical research into users' sustained information seeking on ASNSs is limited. The study fills this gap by proposing and validating relevant factors and the boundary condition of users' sustained information seeking.

Examines how perceived knowledge quality influences researchers' satisfaction with academic social media (ASM) site use, perceived learning from use, and loyalty toward the site.

Built upon the theoretical grounding of the information system success framework, it was hypothesized that satisfaction, perceived learning, and loyal behavior toward an ASM site are all functions of the perceived quality of knowledge obtained. Data were collected by online survey from 348 researchers registered on ResearchGate and subjected to SmartPLS structural equation modeling, bootstrapping, and blindfolding.

The hypothesized relationships were supported. Perceived knowledge quality significantly influences researchers' satisfaction with ASM site use, and satisfaction affects perceived learning and researchers' loyalty with the ASM site.

Identification of the relationship between perceived knowledge quality and ASM site success extends the study of ASM sites from description of usage patterns to understanding the effect of content quality on important outcomes of use.

ASM sites rely on the quality of knowledge contributed by their members for satisfaction, loyalty, and perceptions of value. The ongoing success of an ASM requires directed attention to quality knowledge provision.

This paper contributes a simplified DeLone & McLean information system success framework for studies of content quality. It also provides fresh insights into ASM site usage through a focus on the role of perceived knowledge quality in forming satisfaction, learning, and loyalty.

The use of recommender systems is increasing on academic social media (ASM). However, distinguishing the elements that may be influenced and/or exert influence over content that is read and disseminated by researchers is difficult due to the opacity of the algorithms that filter information on ASM. In this article, the purpose of this paper is to investigate how algorithmic mediation through recommender systems in ResearchGate may uphold biases in scholarly communication.

The authors used a multi-method walkthrough approach including a patent analysis, an interface analysis and an inspection of the web page code.

The findings reveal how audience influences on the recommendations and demonstrate in practice the mutual shaping of the different elements interplaying within the platform (artefact, practices and arrangements). The authors show evidence of the mechanisms of selection, prioritization, datafication and profiling. The authors also substantiate how the algorithm reinforces the reputation of eminent researchers (a phenomenon called the Matthew effect). As part of defining a future agenda, we discuss the need for serendipity and algorithmic transparency.

Algorithms change constantly and are protected by commercial secrecy. Hence, this study was limited to the information that was accessible within a particular period. At the time of publication, the platform, its logic and its effects on the interface may have changed. Future studies might investigate other ASM using the same approach to distinguish potential patterns among platforms.

Contributes to reflect on algorithmic mediation and biases in scholarly communication potentially afforded by recommender algorithms. To the best of our knowledge, this is the first empirical study on automated mediation and biases in ASM.