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Using data from 36,981 respondents to the Global Drug Survey (GDS) COVID-19 Special Edition, this study aims to compare changes, following the first “lockdown,” in alcohol consumption between lesbian, gay, bisexual and other sexual minority (LGB+) and heterosexual respondents with and without lifetime mental health and neurodevelopmental (MHND) conditions.

Characteristics and drinking behavior of respondents to GDS who disclosed their sexual orientation and past 30-day alcohol use were described and compared. LGB+ participants with and without MHND conditions were compared, and logistic regression models identified correlates of increased drinking among LGB+ people. The impact of changed drinking on the lives of LGB+ participants with and without MHND conditions was assessed.

LGB+ participants who reported that they were “not coping well at all” with the pandemic had twofold greater odds of reporting increased binge drinking. LGB+ participants with MHND conditions were significantly more likely than those without to report increased drinking frequency (18.7% vs 12.4%), quantity (13.8% vs 8.8%) and that changed drinking had impacted their lives.

This study, which has a uniquely large and international sample, explores aspects of alcohol use not considered in other COVID-19 alcohol use research with LGB+ people; and to the best of the authors’ knowledge, this is the first study to explore alcohol use among LGB+ people with MHND conditions.

The present study aims to examine and explain cultural heritage destination by applying multiple dimension image model (cognitive–affective–conative aspects).

A total of 233 international heritage-based tourists were surveyed on-site at some of the most prominent historical attractions in Malacca, one of the first cities in Malaysia declared as a UNESCO World Heritage Site. The covariance-based structural equation modelling was applied to examine the hypotheses.

The structural equation modelling validated that cognitive image aspects such as living culture, intangible and tangible are affected positively. The effects are statistically significant for conative and affective images. On the other hand, cognitive tangible image is an aspect that impacted affective image to a lesser degree than conative image. Whereas affective image attributes were found to have significant and positive influence on conative image.

This study enriches the limited empirical research study on heritage image conceptualisation by expanding into tri-component model. The destination image has garnered a great deal of attention, particularly due to its significant and impactful influence on the decision-making and the sustainable behaviour of tourists, and it has since become the subject of many studies in the tourism and hospitality literatures. But most research concerning heritage image for destinations has considered the construct uni-dimensionally.

To remain sustainable, open source software (OSS) projects must attract new members—or newcomers—who make contributions. In this paper, the authors develop a set of hypotheses based on the knowledge barriers framework that examines how OSS communities can encourage contributions from newcomers.

Employing longitudinal data from the source code repositories of 232 OSS projects over a two-year period, the authors employ a Poisson-based mixed model to test how community characteristics, such as the main drivers of knowledge-based costs, relate to newcomers' contributions.

The results indicate that community characteristics, such as programming language choice, documentation effort and code structure instability, are the main drivers of knowledge-based contribution costs. The findings also suggest that managing these costs can result in more inclusive OSS communities, as evidenced by the number of contributing newcomers; the authors highlight the importance of maintaining documentation efforts for OSS communities.

This paper assumes that motivational factors are a necessary but insufficient condition for newcomer participation in OSS projects and that the cost to participation should be considered. Using the knowledge barriers framework, this paper identifies the main knowledge-based costs that hinder newcomer participation. To the best of the authors' knowledge, this is the first empirical study that does not limit data collection to a single hosting platform (e.g., SourceForge), which improves the generalizability of the findings.

Nowadays, star athletes are global brand personalities. The increased popularity of the professional sport has contributed to elevating exceptional athletes to international star status. This empirical study aims to assess the impact of athlete performance and brand social value on product involvement with the mediation effect of celebrity athlete endorsement.

A quantitative survey was conducted with 399 Chinese participants. PLS-SEM is adopted to examine the associated paths and the mediating effect of celebrity endorsement.

The results demonstrate the significant impact of athlete performance and brand social value on product involvement. Celebrity endorsement partially mediates the effects of athlete performance and brand social value on product involvement.

This study extends understanding of celebrity athlete endorsement and provides insight into the strategic implications for Chinese social media-based marketing initiatives in the context of the recent Olympic Game in Tokyo 2021.

The integration of industry 4.0 has become a priority for many organizations. However, not all organizations are suitable and capable of implementing industry 4.0 because it requires a dynamic and flexible implementation strategy. The implementation of industry 4.0 often involves overcoming several tensions between internal and external stakeholders. This paper aims to explore the paradoxical tensions that arise for health-care organizations when integrating industry 4.0. Moreover, it discusses how a paradox lens can support the conceptualization and proposes techniques for handling tensions during the integration of industry 4.0.

This qualitative and in-depth study draws upon 32 semi-structured interviews. The empirical case concerns how two health-care organizations handle paradoxical tensions during the integration of industry 4.0.

The exploration resulted in six recurring technology tensions: technology invention (modularized design vs. flexible design), technology collaboration (automation vs. human augmentation), technology-driven patient experience (control vs. autonomy), technology uncertainty (short-term experimentation vs. long-term planning), technology invention and diffusion through collaborative efforts among stakeholders (selective vs. intensive collaboration) and technological innovation (market maintenance vs. disruption).

A paradox theory-informed conceptual model is proposed for how to handle tensions during the integration of industry 4.0. To the best of the authors’ knowledge, this is the first paper to introduce paradox theory for quality management, including lean and Six Sigma.

This study aims to investigate the integration of blockchain technology into the food supply chain within the restaurant industry. It focuses on how blockchain can be applied to enhance transparency and trust in tracking food sources, ultimately impacting customer satisfaction.

A service design workshop (Study 1) and three between-subjects experiments (Studies 2–4) were conducted.

Results indicate that blockchain adoption significantly improves traceability and trust in the food supply chain. This improvement in turn enhances customer satisfaction through perceived improvements in food safety, quality and naturalness. This study also notes that the effects of blockchain technology vary depending on the type of restaurant (casual or fine dining) and its location (tourist destinations or residential areas).

The findings offer practical insights for restaurant owners, technology developers and policymakers. Emphasizing the benefits of blockchain adoption, this study guides decision-making regarding technology investments for enhancing customer service and satisfaction in the hospitality sector.

This research contributes novel insights to the field of technology innovation in the hospitality industry. It extends the understanding of signaling theory by exploring how blockchain technology can serve as a tool for signal transmission in restaurant food supply chains.

The COVID-19 impact across major sectors did not exempt the low-cost housing (LCH) sub-sector. This may have increased the existing LCH demand-supply gap, especially in developing countries such as Malaysia. Studies showed that government policy (GP) aids in mitigating COVID-19 impact on goods and services, including housing-related issues. However, there is an academic literature scarcity regarding GP on LCH demand-supply gap during the COVID-19 crisis in Malaysia. Hence, this study aims to investigate the moderating effect of GP on the relationship between LCH demand-supply gap and COVID-19 impact in Malaysia.

The research utilised a quantitative method in collating the data from four major cities in Malaysia. SmartPLS was utilised to analyse the usable 305 questionnaires retrieved from respondents. Structuralist Theory supported the developed framework.

Findings show that GP moderates the relationships between the LCH demand-supply gap and COVID-19 impact on Malaysia's low-income groups' (LIGs) homeownership delivery. It implies that the study's findings provide more understanding of issues influencing LCH demand-supply gap in the COVID-19 era via applying GP to mitigate the gap and improve homeownership for the disadvantaged.

The study intends to stir policymakers toward formulating policies and programmes that will mitigate LCH demand-supply gap during the present and future pandemics.

Besides the theoretical value of the developed model, policymakers can use the study's recommendations to mitigate future LCH demand-supply gaps during pandemics in developing countries using Malaysia as a case study.

Guided by the affective events theory (AET), the purpose of this paper was to explore the impact of interpersonal trust in peers, as an affective work event, on two affect-driven behaviors (i.e. job performance and organizational citizenship behavior toward individuals [OCBI]) via positive affect during the Covid-19 pandemic, particularly in the Asia–Pacific region.

This study is quantitative in approach, and longitudinal survey study in design. The authors collected data from lecturers in 2020 at the beginning, at the end and two months after the first Covid-19 lockdown in Malaysia. Then, the authors utilized the efficient partial least squares (PLSe2) estimator to investigate the relationships between the variables, while also considering gender as a control variable.

The findings show that positive affect fully mediates the relationship between interpersonal trust in peers and job performance and partially mediates the relationship between interpersonal trust in peers and OCBI. Given that gender did not demonstrate any significant relationships with interpersonal trust in peers, positive affect, job performance and OCBI, the recommended policies can be universally developed and applied, irrespective of the gender of academics.

This research contributes originality by integrating the widely recognized theoretical framework of AET and investigating a less explored context, specifically the Malaysian higher education sector during the challenging initial phase of the Covid-19 pandemic. Furthermore, the authors adopt a novel and robust methodological approach, utilizing the efficient partial least squares (PLSe2) estimator, to thoroughly examine and validate the longitudinal theoretical model from both explanatory and predictive perspectives.

This research aims to reveal the working principles of the decision mechanism that affects the use of neural implant acceptance and to discuss the leading role of digital literacy in this mechanism. In addition, it aimed to examine the theoretical connections of the research model with the conservation of resources (COR) and technology acceptance model (TAM) theories in the discussion.

The authors collected data from 300 individuals in an organization operating in the health sector and analyzed the data in the Smart Partial Least Squares (PLS) 3.3.3. This way, the authors determined the relationships between the variables, the path coefficients and the significance levels.

The study has found that strong digital literacy skills are linked to positive emotions and attitudes. Additionally, maintaining a positive mindset can improve one's understanding of ethics. Ethical attitudes and positive emotions can also increase the likelihood of adopting neural implants. Therefore, it is crucial to consider both technical and ethical concerns and emotions when deciding whether to use neural implants.

The research results determined the links between the cognitive, emotional and ethical factors in the cyborgization process of the employees and gave original insights to the managers and employees.

1. Determination of antecedents that affect individuals' acceptance of neural implant use.

2. Application to 300 individuals working in a health organization.

3. Path analysis using the least squares method via Smart PLS 3.3.3

4. Significant path coefficients among digital literacy, positive emotions, attitude, ethical understanding and acceptance of neural implant use.

Determination of antecedents that affect individuals' acceptance of neural implant use.

Application to 300 individuals working in a health organization.

Path analysis using the least squares method via Smart PLS 3.3.3

Significant path coefficients among digital literacy, positive emotions, attitude, ethical understanding and acceptance of neural implant use.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.