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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.

Light emitting diode (LED) has been the best resource for commercial and industrial lighting applications. However, thermal management in high power LEDs is a major challenge in which the thermal resistance (R_th) and rise in junction temperature (T_J) are critical parameters. The purpose of this work is to evaluate the R_th and T_j of the LED attached with the modified heat transfer area of the heatsink to improve thermal management.

This paper deals with the design of metal substrate for heatsink applications where the surface area of the heatsink is modified. Numerical simulation on heat distribution proved the influence of the design aspects and surface area of heatsink.

T_J was low for outward step design when compared to flat heatsink design (ΔT ∼ 38°C) because of increase in surface area from 1,550 mm² (flat) to 3,076 mm² (outward step). On comparison with inward step geometry, the T_J value was low for outward step configuration (ΔT_J ∼ 6.6°C), which is because of efficient heat transfer mechanism with outward step design. The observed results showed that outward step design performs well for LED testing by reducing both R_th and T_J for different driving currents.

This work is authors’ own design and also has the originality for the targeted application. To the best of the authors’ knowledge, the proposed design has not been tried before in the electronic or LED applications.

This study aims to investigate the effect of deepfake video advertisements on hotel booking intention by applying the media richness theory (MRT) and information manipulation theory (IMT).

A quantitative survey was conducted using a structured questionnaire to understand the effect of deepfake hotel video advertisements on booking intention. A large cross-section of 1,240 tourists was surveyed and data were analyzed with partial least squares structural equation modeling (PLS-SEM).

The outcome of this research provides the factors affecting the booking intention due to deepfake hotel video advertisements. These factors are media richness (MR), information manipulation (IM) tactics, perceived value (PV) and perceived trust (PT). Cognitive load and perceived deception (DC) negatively influence the hotel booking intention.

The distinctive model that emerged is insightful for senior executives and managers in the hospitality sector to understand the influence of deepfake video advertisements. This research provides the factors of hotel booking intention due to deepfake video advertisements, which are helpful for designers, developers, marketing managers and other stakeholders in the hotel industry.

MR and IMT are integrated with variables such as PT and PV to explore the tourists' hotel booking intention after watching deepfake video advertisements. It is the first step toward deepfake video advertisements and hotel booking intentions for tourists. It provides an empirically tested and validated robust theoretical model to understand the effect of deepfake video advertisements on hotel booking intention.

This study aims to examine the tourists' visit intention by watching deepfake destination videos, using Information Manipulation and Media Richness Theory.

This study conducted a primary survey utilizing a structured questionnaire. In total, 1,360 tourists were surveyed, and quantitative data analysis was done using PLS-SEM.

The results indicate that the factors that affect the tourists' visit intention after watching deepfake videos include information manipulation tactics, trust and media richness. This study also found that perceived deception and cognitive load do not influence the tourists' visit intention.

The originality/salience of this study lies in the fact that this is possibly among the first to combine the Media Richness Theory and Information Manipulation for understanding tourists' visit intention and post-viewing deepfake destination videos.

The purpose of this paper is to propose a fractal model of thermal contact conductance (TCC) of rough surfaces based on cone asperity.

A detailed numerical study is conducted to examine the effects of contact load, fractal dimensional, fractal roughness and material properties on the TCC of rough surfaces.

The results indicate that when the fractal dimension D is less than 2.5, the TCC of rough surfaces increases nonlinearly with the increase of the contact load. However, when the fractal dimension D is greater than or equal to 2.5, the TCC of rough surfaces increases linearly with the increase of the contact load; the TCC of the rough surfaces increases with the increase of the fractal dimension D and the decrease of the fractal roughness G; the material parameters also have an influence on the TCC of the rough surfaces, and the extent of the effect on the TCC is related to the fractal dimension D.

A fractal model of TCC of rough surfaces based on cone asperity is established in this paper. Some new results and conclusions are obtained from this work, which provides important theoretical guidance for further study of TCC of rough surfaces.

The study aims to investigate the utilisation of open research data repositories (RDRs) for storing and sharing research data in higher learning institutions (HLIs) in Tanzania.

A survey research design was employed to collect data from postgraduate students at the Nelson Mandela African Institution of Science and Technology (NM-AIST) in Arusha, Tanzania. The data were collected and analysed quantitatively and qualitatively. A census sampling technique was employed to select the sample size for this study. The quantitative data were analysed using the Statistical Package for the Social Sciences (SPSS), whilst the qualitative data were analysed thematically.

Less than half of the respondents were aware of and were using open RDRs, including Zenodo, DataVerse, Dryad, OMERO, GitHub and Mendeley data repositories. More than half of the respondents were not willing to share research data and cited a lack of ownership after storing their research data in most of the open RDRs and data security. HILs need to conduct training on using trusted repositories and motivate postgraduate students to utilise open repositories (ORs). The challenges for underutilisation of open RDRs were a lack of policies governing the storage and sharing of research data and grant constraints.

Research data storage and sharing are of great interest to researchers in HILs to inform them to implement open RDRs to support these researchers. Open RDRs increase visibility within HILs and reduce research data loss, and research works will be cited and used publicly. This paper identifies the potential for additional studies focussed on this area.

This study examines the impact of perceived human resource management practices on talent turnover intention, with work engagement mediating and self-efficacy moderating the relationship. It examines how employees' perceptions of pay satisfaction, empowerment, participation and communication are related to their turnover intentions.

A total of 283 valid questionnaires from UAE government employees were used for data analysis. Partial least squares structural equation modeling (PLS-SEM) was used to examine the proposed hypothesis.

The results reveal that employees' perceptions of pay satisfaction, empowerment, participation and communication are significant contributors to work engagement. The findings further demonstrate that work engagement significantly negatively affects talent turnover intention and acts as a mediator between employees' perceptions of individual human resource management practices and talent turnover intention. However, the results contradict the hypothesis that self-efficacy moderates the association between work engagement and talent turnover intention.

This study focuses on the impact of perceived human resource management practices on talent turnover intention, an area that has received limited attention in literature. By focusing on perceived human resource management practices, this study illuminates employees' subjective experiences and how they perceive human resource management practices intended to reduce talent turnover intention. The inclusion of the mediating effect of work engagement offers a more profound understanding of how employees' perceptions of human resource management practices influence their turnover intentions. This comprehensive approach to understanding the interplay between these variables provides valuable insights for organizations seeking to improve their human resource management practices and talent turnover intention.

It is important to understand the factors that are significant in supply chain (SC) collaboration decision making and whether supply chain collaborative factors that are considered in the literature are still valid. To date, SC collaboration has not been extensively studied in the literature with supply chain finance (SCF) factors to evaluate SCF performance. Therefore, in this paper, the authors investigate the interrelationships between SCF and supply chain collaborative (SCC) factors for achieving SCF performance. The authors identified the most important factors from the literature on SCF and SCC and with inputs from experts in the textile industry in Pakistan.

The authors employed the Gray-Decision Making Trial and Evaluation Laboratory approach to help examine the cause-and-effect relationship between the factors and identify the influence of each factor on the others.

The findings showed that the most prominent factors of the study are “level of digitalization”, “information sharing”, and “collaborative communication”, and “most effect factors of this study are incentive alignment” and “information quality”. Furthermore, the “Level of digitalization” was identified as the factor with the central role and most significant correlation with other factors.

The major implication of the study is that textile industries should effectively develop their supply chain decisions after analyzing their internal and external factors, which will help in developing strategies that will facilitate better management of SCF relationships. The limitations of the study are that only 15 SCF and supply chain collaborative factors were considered, and time and scope are also limited. This study is only applied in the textile industry, so generalization may be limited.

To date, this study is the only one that has taken into consideration SCC with SCF factors to evaluate supply chain performance. This paper therefore makes this initial attempt and original contribution to this discussion, which can be helpful for those working to enhance supply chain performance, such as practitioners and policymakers.

The growing popularity of gig and sharing economy changes not only consumption models but also employment patterns. This study aims to analyze the potential entrepreneurial nature of gig and sharing economy initiatives. As such, the authors compare the entrepreneurial intentions of gig and sharing economy workers to the general population. Further, the authors consider commonalities and differences in terms of the driving forces of both intentions to start-up and participation in gig and sharing economy, treating them as connected phenomena. Finally, the authors look into gig and sharing economy experience as a direct antecedent to entrepreneurial intentions formation.

The empirical settings for this study are derived from the sample of 1,257 individuals who participated in the Global Entrepreneurship Monitor Adult Population Survey 2018 in Russia. Methodologically, the authors rely on analysis of variance-test and binary logistic regression analysis to test the study hypotheses.

The results indicate that entrepreneurial intentions of gig and sharing economy workers are significantly higher when compared to the general population. In terms of antecedents to gig and sharing economy participation and startup intentions, similar effects of age, entrepreneurial social capital, prior entrepreneurial exit and intrapreneurial experience were revealed, while perceived self-efficacy was associated only with engagement into digital platforms. Finally, gig and sharing economy experience showed significant and positive effect on entrepreneurial intentions formation.

This study represents a first substantive effort to systematize antecedents to gig and sharing economy participation through an entrepreneurship perspective. Beyond that, this research adds to the contextualization of entrepreneurship literature stream, further defining the mechanism of entrepreneurial intentions formation in empirical settings of an emerging market with a relatively low propensity of population to develop intentions to start-up.

Collaboration methods are unique strategies that can help organizations hedge against external and internal supply chain risks without stressing their relationships with supply chain partners. However, selecting the most appropriate collaboration method from a given set of strategies is a multifaceted challenge. This paper aims to address this issue.

The decision maker's dilemma of fighting data uncertainty in input parameters to check the efficacy of a given collaboration or mitigation approach is tackled by the integration of Grey theory with the technique for order of preference by similarity to ideal solution (TOPSIS) method. The proposed technique is applied and tested for an Indian diesel generator-set manufacturer to identify the most apposite set of sustainable collaboration strategies.

The results showed that when a firm is bidding for different horizontal collaboration strategies across its supply chain system technology and resource-sharing-centered collaboration strategies are the prominent option. In the case of the company's vertical collaboration deployment, the focus should be kept on information sharing to achieve impactful collaboration. The outcome of the analysis helped the Indian manufacturer to adopt transparent order and production information sharing with its regional distributors and core suppliers within its supply chain.

This study demonstrates from a methodological perspective the successful application of the Grey-TOPSIS approach that effectively captures data uncertainty. It also integrates sustainability parameters in collaboration strategy criteria selections.