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This paper aims to examine the impact of the COVID-19 infodemic on the public’s online information behaviour, offering insights critical for shaping effective informational responses in future public health emergencies.

This paper uses a structured online survey with 27 targeted questions using a five-point Likert scale to measure eight variables. Data analysis is conducted through structural equation modelling on 307 valid responses to rigorously test the research hypotheses.

This paper indicates that information quality significantly impacts the public’s capacity to select, share and use online information. Additionally, the comprehensibility of information plays a crucial role in shaping the public’s behaviours in terms of online information exchange and usage. The credibility of information sources emerges as a key determinant influencing the public’s online information selection, exchange and utilization behaviour. Moreover, social influence exerts a substantial effect on the public’s online information selection, acquisition, exchange and utilization behaviour. These findings highlight the presence of universality and sociality, mediation and guidance, as well as the purposefulness and selectivity performed by the public’s online information behaviour during an infodemic.

This paper introduces a novel research model for assessing the influence and identifies the patterns of the public’s online information behaviour during the COVID-19 infodemic. The findings have significant implications for developing strategies to tackle information dissemination challenges in future major public health emergencies.

Feature selection is an essential step in data mining. The core of it is to analyze and quantize the relevancy and redundancy between the features and the classes. In CFR feature selection method, they rarely consider which feature to choose if two or more features have the same value using evaluation criterion. In order to address this problem, the standard deviation is employed to adjust the importance between relevancy and redundancy. Based on this idea, a novel feature selection method named as Feature Selection Based on Weighted Conditional Mutual Information (WCFR) is introduced. Experimental results on ten datasets show that our proposed method has higher classification accuracy.

This systematic literature review aims to elaborate on the research progress and features of information source preferences to help other researchers attain a more comprehensive understanding of the field.

Following a systematic review protocol, 139 research articles from 11 academic databases were analyzed.

Overall, five separate results were obtained: first, information source horizon theory is the main theoretical foundation of information source preferences research, while other theories have been applied less. Second, information source preference research has strong context sensitivity and involves health, work, consumption, learning, survival and development and emergencies. Third, preference criteria can be summarized into three categories: information characteristics, user characteristics, needs characteristics and corresponding specific criteria. Fourth, information source preferences are influenced by both internal and external factors, including five specific aspects, namely demographics, the user's cognition, the user's affection, capital and contextual factors. Fifth, this field is dominated by quantitative methods and an information horizon mapping method could be applied more.

This study is the first to reveal the general picture of information source preferences. It also elaborates on the characteristics of this field and presents potential development directions.

The purpose of this study was to determine the extent to which teaching staff cooperated with librarians in collection development, specifically in relation to electronic resources, and to identify barriers they encountered while performing collection development activities.

A mixed methods approach was adopted for the study. Quantitative and qualitative techniques of data collection and analysis were used to obtain a comprehensive understanding of the research topic. Data were gathered through a self-administered questionnaire and interviews. A total of 149 faculty members completed the questionnaire, yielding a response rate of 51.2%, while 16 library staff members were interviewed to obtain qualitative data.

The majority of the teaching staff who participated in the study affirmed that they had cooperated with subject librarians in collection development. A high percentage (62.4%) of the faculty members had collaborated with subject librarians in collection development activities. Only 37.6% of the faculty members had not participated in collection development activities with subject librarians to acquire library electronic resources. According to faculty members, some of the main challenges affecting collection development at the University of Namibia were a lack of catalogues for electronic resources and a lack of lists of titles from vendors. Moreover, librarians were not always available to assist faculty members. It is recommended that faculty members be part of the process of selecting materials and that a good relationship be fostered between librarians and faculty members to bring value to collection development activities.

Collection development in respect of electronic resources is a complex process to be undertaken by a single entity and, therefore, requires the collaboration of all stakeholders involved. In the case of institutions of higher learning, these stakeholders include faculty, librarians and vendors. The emergence of a variety of e-resources demands a meticulous strategy on the part of libraries to ensure they can offer a wide range of up-to-date and accurate resources that meet the evolving needs of their users. To the best of the authors’ knowledge, studies that are similar to this one have not been conducted in Namibia before. This case study presents useful findings and lessons on faculty–librarian cooperation for effective collection development, not only at the University of Namibia library but also at other academic libraries in economies with similar characteristics.

Our purpose is to reconnect memetics to information, a persistent and unclear association. Information can contribute across a span of memetic research. Its obscurity restricts conversations about “information flow,” the connections between “form” and “content,” as well as many other topics. As information is involved in cultural activity, its clarification could focus memetic theories and applications.

Our design captures theoretical nuance in memetics by considering a long standing conceptual issue in memetics: information. A systematic review of memetics is provided by making use of the term information across literature. We additionally provide a citation analysis and close readings of what “information” means within the corpus.

Our initial corpus is narrowed to 128 pivotal memetic publications. From these publications, we provide a citation analysis of memetic studies. Theoretical directions of memetics in the informational context are outlined and developed. We outline two main discussion spaces, survey theoretical interests and describe where and when information is important to memetic discussion. We also find that there are continuities in goals which connect Dawkins’s meme with internet meme studies.

To our knowledge, this is the broadest, most inclusive review of memetics conducted, making use of a unique approach to studying information-oriented discourse across a corpus. In doing so, we provide information researchers areas in which they might contribute theoretical clarity in diverse memetic approaches. Additionally, we borrow the notion of “conceptual troublemakers” to contribute a corpus collection strategy which might be valuable for future literature reviews with conceptual difficulties arising from interdisciplinary study.

This paper aims to consolidate the state of research on information behaviour. Regardless of the scholarly surge on information behaviour using numerous models and extended ones, gaps still exist on the development of more pragmatic frameworks that explain and/or predict the information behaviour across different fragmented environmental situations. Therefore, this paper extends technology-organization-environment (T-O-E) framework and synthesizes different research positions into a multi-dimensional framework that assists small and medium enterprises (SMEs) to understand their information behaviour and to make informed decisions on the adoption of emerging information communication technology (EICT).

This study deployed an inductive approach; data were generated from unstructured and semi-structured interviews with 20 participants drawn purposively from Luton directories.

This study developed a more integrated framework underpinned by T-O-E framework, which helped to identify how EICT adoption is critically shaped by perceived information need, information sources selection and the factors determining information source selection.

Qualitative research is normally subjective, inductive, interpretive and limited on the sample used. However, because of the limited number of interviews used, as well as industry-specific nature of the study, the generalization of the framework and the findings will be difficult, hence, the need to test the framework on a broader population and cross-context testing.

The T-O-E framework is extended to Expectancy (T-O-E-E) to provide a more robust insight into information behaviour of SMEs from a multi-dimensional perspective. It also offers a more analytical framework for exploring critical issues identified and demonstrates the capacity to provide a reliable explanation to the findings. Further, it serves as a tool for assessing the benefits or challenges of SMEs information behaviour especially on the EICT adoption decision.

This study aims to investigate the configurational effects of five rules – artificial intelligence (AI)-based hiring decision transparency, consistency, voice, explainability and human involvement – on applicants' procedural justice perception (APJP) and applicants' interactional justice perception (AIJP). In addition, this study examines whether the identified configurations could further enhance applicants' organisational commitment (OC).

Drawing on the justice model of applicants' reactions, the authors conducted a longitudinal survey of 254 newly recruited employees from 36 Chinese companies that utilise AI in their hiring. The authors employed fuzzy-set qualitative comparative analysis (fsQCA) to determine which configurations could improve APJP and AIJP, and the authors used propensity score matching (PSM) to analyse the effects of these configurations on OC.

The fsQCA generates three patterns involving five configurations that could improve APJP and AIJP. For pattern 1, when AI-based recruitment with high interpersonal rule (AI human involvement) aims for applicants' justice perception (AJP) through the combination of high informational rule (AI explainability) and high procedural rule (AI voice), there must be high levels of AI consistency and AI voice to complement AI explainability, and only this pattern of configurations can further enhance OC. In pattern 2, for the combination of high informational rule (AI explainability) and low procedural rule (absent AI voice), AI recruitment with high interpersonal rule (AI human involvement) should focus on AI transparency and AI explainability rather than the implementation of AI voice. In pattern 3, a mere combination of procedural rules could sufficiently improve AIJP.

This study, which involved real applicants, is one of the few empirical studies to explore the mechanisms behind the impact of AI hiring decisions on AJP and OC, and the findings may inform researchers and managers on how to best utilise AI to make hiring decisions.

Guided by the Comprehensive Model of Information Seeking (CMIS), this article identifies significant predictors that impact individuals seeking COVID-19 information. People with different political ideologies read contradictory information about the COVID-19 pandemic. However, how political ideology may affect COVID-19 information seeking remains unclear. This study explores the major information channels for individuals with different political ideologies to seek COVID-19 information. It further examines how political ideologies influence CMIS's effectiveness in predicting online health information-seeking.

This study collected 394 completed survey responses from adults living in the United States after the 2020 lockdown. ANOVA analyses revealed the differences in salience, beliefs, information carrier characteristics, utilities and information-seeking actions between Liberals and Conservatives. Regression analyses discovered variables that predict Liberals' and Conservatives' online health information seeking.

Results suggest that the internet is the top channel for COVID-19 information seeking. Compared to Conservatives, Liberals report more COVID-19 information-seeking actions. Liberals also express stronger salience, perceive higher trustworthiness of online COVID-19 information, are more likely to think of seeking online COVID-19 information as useful and helpful and report more substantial efficacy to mitigate the risk. Most CMIS variables predict Liberals' information seeking; however, only salience significantly predicts Conservatives' information seeking.

This article indicates that CMIS should include political ideology to refine its prediction of information seeking. These findings offer practical implications for designing health messages, enhancing information distribution and reducing the public's uncertainty.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2022-0436.

This paper evaluates the precision of four metasearch engines (MSEs) – DuckDuckGo, Dogpile, Metacrawler and Startpage, to determine which metasearch engine exhibits the highest level of precision and to identify the metasearch engine that is most likely to return the most relevant search results.

The research is divided into two parts: the first phase involves four queries categorized into two segments (4-Q-2-S), while the second phase includes six queries divided into three segments (6-Q-3-S). These queries vary in complexity, falling into three types: simple, phrase and complex. The precision, average precision and the presence of duplicates across all the evaluated metasearch engines are determined.

The study clearly demonstrated that Startpage returned the most relevant results and achieved the highest precision (0.98) among the four MSEs. Conversely, DuckDuckGo exhibited consistent performance across both phases of the study.

The study only evaluated four metasearch engines, which may not be representative of all available metasearch engines. Additionally, a limited number of queries were used, which may not be sufficient to generalize the findings to all types of queries.

The findings of this study can be valuable for accreditation agencies in managing duplicates, improving their search capabilities and obtaining more relevant and precise results. These findings can also assist users in selecting the best metasearch engine based on precision rather than interface.

The study is the first of its kind which evaluates the four metasearch engines. No similar study has been conducted in the past to measure the performance of metasearch engines.

Managers must make numerous strategic decisions in order to initiate and implement a business model innovation (BMI). This paper examines how managers perceive the management team interacts when making BMI decisions. The paper also investigates how group biases and board members’ risk willingness affect this process.

Empirical data were collected through 26 in-depth interviews with German managing directors from 13 companies in four industries (mobility, manufacturing, healthcare and energy) to explore three research questions: (1) What group effects are prevalent in BMI group decision-making? (2) What are the key characteristics of BMI group decisions? And (3) what are the potential relationships between BMI group decision-making and managers' risk willingness? A thematic analysis based on Gioia's guidelines was conducted to identify themes in the comprehensive dataset.

First, the results show four typical group biases in BMI group decisions: Groupthink, social influence, hidden profile and group polarization. Findings show that the hidden profile paradigm and groupthink theory are essential in the context of BMI decisions. Second, we developed a BMI decision matrix, including the following key characteristics of BMI group decision-making managerial cohesion, conflict readiness and information- and emotion-based decision behavior. Third, in contrast to previous literature, we found that individual risk aversion can improve the quality of BMI decisions.

This paper provides managers with an opportunity to become aware of group biases that may impede their strategic BMI decisions. Specifically, it points out that managers should consider the key cognitive constraints due to their interactions when making BMI decisions. This work also highlights the importance of risk-averse decision-makers on boards.

This qualitative study contributes to the literature on decision-making by revealing key cognitive group biases in strategic decision-making. This study also enriches the behavioral science research stream of the BMI literature by attributing a critical influence on the quality of BMI decisions to managers' group interactions. In addition, this article provides new perspectives on managers' risk aversion in strategic decision-making.