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The present study aims to investigate how textual features, depth of citation treatment, reasons for citation, and relationships between citers and citees predict author‐rated citation importance.

A total of 49 biology and 50 psychology authors assessed the importance, reason for citation, and relationship to the cited author for each cited reference in his or her own recently published empirical article. Participants performed their evaluations on individualized web‐based surveys.

The paper finds that certain textual features, such as citation frequency, citation length, and citation location, as well as author‐stated reasons for citation predicted ratings of importance, but the strength of the relationship often depended on citation features in the article as a whole. The relationship between objective citation features and author‐rated importance also tended to be weaker for self‐citations.

The study sample included authors of relatively long empirical articles with a minimum of 35 cited references. There were relatively few disciplinary differences, which suggests that citation behavior in psychology may be similar to that in natural science disciplines. Future studies should involve authors from other disciplines employing diverse referencing patterns in articles of varying lengths and types.

Findings of the study have enabled a comprehensive, profound level of understanding of citation behaviors of biology and psychology authors. It uncovered a number of unique characteristics in authors' citation evaluations, such as article‐level context effects and rule‐ versus affective‐based judgments. The paper suggests possible implications for developing retrieval algorithms based on automatically predicted importance of cited references.

Chatbot technology is growing swiftly across the banking and finance industry. The study aims to explore the influence of Task-Technology Fit (TTF), system suitability, perceived usefulness, social influence and satisfaction on respondents’ continuance intention among the chatbot-users. A total of 250 responses were collected using a structured questionnaire via social media across India. The measurement model was proposed based on the system usability (SUS) and TTF model with other constructs, and the hypothesis was tested using a structural equation model. The findings of the study highlighted and confirmed the association among five constructs: TTF, SUS, perceived usefulness, social influence, on satisfaction and continuance intention in the context of chatbots usage in banking.

This study demonstrates how individual paradigms implicate the questions asked, methods used and results drawn in association with a common object of study in human information behavior (HIB) research – the relationship between uncertainty and decision-making.

The author uses textual case studies to examine uncertainty and decision-making through the framework of four paradigms used in HIB research: positivism, cognitivism, collectivism and constructionism and suggests deconstructionism as a paradigm which raises new questions around this topic.

Positivistic approaches to uncertainty are often systems oriented; cognitive approaches are often user-oriented; collectivist approaches are intersubjective; and constructionist approaches blend a subjective and intersubjective research orientation. Deconstructionism raises new questions around ethics and responsibility in relation to decision-making, and the author therefore situates it as a new paradigmatic approach for this topic in HIB research.

Despite the presence of research aimed at recognizing and defining paradigms in HIB research, a comparative micro-examination of how individual paradigms implicate a specific research topic has yet to be conducted. Each paradigm uniquely shapes the ways in which uncertainty and decision-making are characterized, but the four central ones examined here have thus far left out questions of ethics and responsibility as being core elements of decision-making as tied to uncertainty. Therefore, this paper introduces deconstructionism as a paradigm new to HIB uncertainty research, arguing that it provides an important and novel complication of existent research questions and approaches.

This study aims to describe the current conceptualisation of self-management of dementia by family carers in the literature and from the views of dementia professionals and family carers, and to establish a more comprehensive concept of self-management of dementia by family carers.

A hybrid concept analysis included three phases: the theoretical phase reviewed the literature on self-management of dementia by family carers; the fieldwork phase interviewed professionals and family carers; and the analytical phase synthesised and discussed the findings from the previous two phases.

The findings revealed that self-management of dementia by family carers encompasses four domains: supporting care recipients, self-care, sustaining a positive relationship with care recipients, and personal characteristics and skills.

The findings highlighted the essential elements of the construct of self-management of dementia by family carers. The findings can be used as a conceptual framework of self-management and are useful in designing and evaluating self-management support interventions for family carers.

The purpose of this paper is to explore the longitudinal influence of gender, age, education level, organizational tenure and emotional intelligence on three dimensions of knowledge hiding over time.

A longitudinal study using two-wave data sets of 390 employees in Chinese enterprises was conducted to build fixed, continuous and interacting models for investigating the effects of individual differences on the processes of knowledge hiding over time.

This research uncovered the changing relationships of individual differences on knowledge-hiding behaviors over time, such that age correlates with rationalized hiding in the interacting model, indicating younger employees are less likely to choose rationalized hiding when facing situation changes; and education level, organizational tenure and emotional intelligence moderate knowledge hiding over time, implying individuals with better education, longer tenure and higher emotional intelligence tend to exhibit more rationalized hiding behaviors rather than evasive hiding and playing dumb behaviors at Time 2.

One of the novel contributions of this study is that it tests the longitudinal effect of individual differences on knowledge hiding, providing a vertical perspective, and thereby contributing to the body of knowledge in knowledge management. The study also constructs fixed, continuous and interacting models to measure the covering longitudinal influences, thus making the research original.

This study aims to examine whether Chinese firms increase their concerns about analysts’ earnings forecasts following the split-share structure reform (SSR) in 2005, which removed trading restrictions on approximately 70% of the shares of listed firms.

Using data from 2002 to 2019, the authors empirically test the association between meeting or beating analysts’ earnings expectations and the implementation of SSR.

The authors find that firms are more inclined to meet analysts’ earnings expectations after the introduction of SSR. Further analysis shows that firms guide analysts to walk their forecasts down by manipulating third-quarter earnings, suggesting enhanced value relevance between analysts’ forecasts and third-quarter earnings management in the postreform period.

The findings reveal an undesirable side effect of SSR and suggest that policymakers and regulators should consider and carefully manage the complex relationships between firms and analysts.

In contrast to prior studies that predominantly focus on the positive effects of the reform, this study reveals the side effects of SSR and provides new evidence on the mechanisms of meeting or beating analysts’ earnings expectations.

The purpose of this study is to determine the proportion of the population that will be susceptible to the COVID-19 pandemic, as well as the proportions of infections, recoveries and fatalities from the COVID-19 pandemic.

The design was a longitudinal survey of COVID-19 infections, recoveries and fatalities in Nigeria using the data on the daily updates of the Nigeria Centre for Disease Control for the period 1 May to 23 August 2020. Markov chain analysis was performed on the data.

The results showed that in the long run, 8.4% of the population will be susceptible to COVID-19 infections, 26.4% of them will be infected, 61.2% of the infected will recover and 4% will become fatal. Thus, if this pattern of infections and recoveries continue, the majority of the infected people in Nigeria will recover whilst a very small proportion of the infected people will die.

A dearth of the extant literature on the problem, especially from the management science perspective.

Results of the study will facilitate policymakers’ response to the curtailment of the pandemic in Nigeria.

Curtailing the pandemic through the results of this study will assist in easing the social consequences of the pandemic.

The proposed adjustment to the susceptibilities, infections and recoveries model through the introduction of a fourth state (fatality) to get the susceptibilities, infections, recoveries and fatalities model, signalling a point of departure from previous studies.

This paper aims to apply and integrate the existing literature of inclusion to develop a multi-level theory of organizational inclusion for the more and more diverse workforce.

This paper first analyzes the issue of workforce diversity in China, and then reviews the concept of organizational inclusion. After that, this paper develops a multi-level model of organizational inclusion catering to Chinese diversity issue.

This paper outlines a series of propositions on how organizational, group, interpersonal and individual factors affect inclusion at both organizational and individual levels, and the consequences of inclusion in the workplace.

This paper is the first research to discuss the inclusion management in Chinese context. This paper proposes a multi-level theoretical model of organizational inclusion to guide empirical studies on the integration of the diversity in workplace in China.

Academic conferences play a crucial role in fostering scholarly exchanges and discussions among core members, thereby shaping the core performance of these events. Understanding the contributions of scholars in academic conferences is essential for advancing the goals and development of such events. This paper aims to discuss the aforementioned idea.

This paper proposes a comprehensive measurement method for measuring scholars' academic activeness in academic conferences. Based on the essence of academic activeness, a measurement model is constructed, comprising the breadth of academic activeness using the RFM model and the depth of academic activeness using the ATM model. Additionally, effective categories of scholars are derived from the model, facilitating the identification of valuable scholars in academic conferences.

Empirical evidence from ASIS&T conference records over the past 20 years demonstrates that the proposed measurement indicators have better coverage compared to traditional evaluation indicators, thus complementing existing scholar evaluation indictators. Furthermore, the academic activeness measurement model presented in this paper exhibits good efficacy in identifying important value and active scholars.

This paper pays attention to the activeness of scholars in academic conferences. It broadens the evaluation of academic conferences and scholars and supplements the evaluation indicators of academic influence.

For decades, artificial intelligence (AI) has been utilized within the field of mental healthcare. This paper aims to examine AI chatbots, specifically as offered through mobile applications for mental healthcare (MHapps), with attention to the social implications of these technologies. For example, AI chatbots in MHapps are programmed with therapeutic techniques to assist people with anxiety and depression, but the promise of this technology is tempered by concerns about the apps' efficacy, privacy, safety and security.

Utilizing a social informatics perspective, a literature review covering MHapps, with a focus on AI chatbots was conducted from the period of January–April 2019. A borrowed theory approach pairing information science and social work was applied to analyze the literature.

Rising needs for mental healthcare, combined with expanding technological developments, indicate continued growth of MHapps and chatbots. While an AI chatbot may provide a person with a place to access tools and a forum to discuss issues, as well as a way to track moods and increase mental health literacy, AI is not a replacement for a therapist or other mental health clinician. Ultimately, if AI chatbots and other MHapps are to have a positive impact, they must be regulated, and society must avoid techno-fundamentalism in relation to AI for mental health.

This study adds to a small but growing body of information science research into the role of AI in the support of mental health.