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This article explores the heterogenous social mechanisms that drive responsible environmental behaviours by investigating differences in the mean effect of the psychosocial determinants of the intention to buy organic foods.

Using data for a representative sample of the Spanish population, we estimated the mean effect of the constructs represented in the responsible environmental behaviour (REB) theory that affect sustainable food consumption, and examined the social mechanisms that may explain heterogeneity in the mean effect of those constructs. Confirmatory factor analysis, linear regression, and latent class regression were used in the analysis.

We found that the effect of REB’s psychosocial constructs varied significantly, demonstrating social heterogeneity in the estimated average effect. We identified different social mechanisms that explain variations in organic food purchase intentions: environmental attitudes and social norms shape these intentions among socioeconomically privileged consumers, whereas personal norms shape these intentions among less socially advantaged consumers.

Our research contributes to the literature by highlighting the existence of differing social mechanisms explaining organic food purchase intentions. The uncovering of three social mechanisms explaining differences in the mean effect of factors driving those intentions provides valuable insights with regard to both further developing a holistic framework for responsible environmental behaviours and developing new public policies and marketing strategies aimed at improving sustainable food consumption.

This study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.

The study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.

Generative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.

While each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.

This study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

Gamification has been constantly demonstrated as an effective mechanism for employee engagement. However, little is known about how gamification reduces cyberloafing and the mechanism by which it affects cyberloafing in the workplace. This study draws inspiration from self-determination and social bonding theories to explain how game dynamics, namely, personalised challenges, social interactivity and progression status, enhance tacit knowledge sharing behaviour, which, in turn, reduces cyberloafing. In addition, the study also examines the negative moderating effect of fear of failure on the positive relationship between game dynamics and tacit knowledge sharing.

Using a sample of 250 employees from information technology organisations, the study employed a 3-wave study to examine the conditional indirect effects.

The results ascertain that tacit knowledge sharing plays a central role in the relationship between gamification and cyberloafing. Further, game dynamics positively influenced tacit knowledge sharing, which in turn reduced cyberloafing. Especially, social interactivity and progression status greatly reduced cyberloafing behaviour when the fear of failure was low.

This study is one of the initial studies that suggest gamification as a progressive tool to reduce workplace cyberloafing behaviours. It utilises a problematisation approach to analyse and criticise the in-house assumptions regarding cyberloafing prevention measures. Further, the study proposes a conceptual model explaining the link between gamification and cyberloafing through alternate assumptions.

Global social restrictions driven by COVID-19 exposed the vast potential of digital technologies for remote mental health and wellbeing provision. To combat the youth mental health crisis, practitioners, researchers, and policy makers must seek innovative solutions. The digital adaptation of evidence-based, in-person programmes offers one such promising approach. Co-authored by a young person, this chapter discusses democratising the research agenda and presents an exploratory study with co-production at its heart. The aim of the project was to inform the development of digitalised resources based on the robustly evidenced Super Skills for Life psychosocial intervention for adolescents. ‘Key ingredients’ for effective online content are presented by the young person developer, alongside their personal reflections on co-production research. This case study illustrates how collaboration with intervention recipients can augment programme development and expand access to evidence-based support. Not only extending reach but offering a choice of support pathways to a digital generation.

Companies should enhance their market position and competitiveness by improving staff effectiveness, skills, resource commitment, and applying relevant managerial methods. This study aims to examine the impact of knowledge management (KM) and total quality management (TQM) on employee effectiveness (EE) and supply chain performance (SCP) in emerging economies.

The used methodology consists on conducting a survey within Tunisian companies, where the authors gathered 206 responses. Collected data was analyzed using statistical package for the social sciences (SPSS) software, enabling the authors to establish a conceptual model. This model was further examined through structural equation modeling, using analysis of moment structures (AMOS) software for hypothesis validation. Additionally, the authors’ research aimed to enhance SCP and boost EE while minimizing costs through a nonlinear mathematical model and the quality function deployment method.

The results indicate that TQM and KM positively impact EE, and KM and EE positively impact SCP. However, the significance of employee performance on SCP varies depending on company location and industry sector studied.

This work emphasized the involvement of small- and medium-sized enterprise managers from emerging economies in the studied concepts and confirmed the effects of KM and TQM practices on EE and SCP.

This paper aims to comprehensively analyze renewable energy alternatives in Brazil, focusing on identifying the most suitable option for investment in the country’s sustainable development.

The study adopts the step-wise weight assessment ratio analysis-multiobjective optimization by ratio analysis −3NAG (a combination of three normalization methods) methodology, a multicriteria decision-making approach, to evaluate and rank renewable energy sources based on key criteria such as resource availability, cost-effectiveness, job creation potential and environmental impact.

The analysis reveals that solar energy emerges as the preferred choice for Brazil, offering significant advantages over other alternatives such as hydroelectric, wind and biomass energy. Solar energy’s distributed generation capability, cost reduction trends and positive environmental impact contribute to its favorable position in meeting Brazil’s energy needs.

While the study provides valuable insights into renewable energy selection, there are limitations regarding the criteria’ scope and the exclusion of specific renewable energy options. Future research could explore sensitivity analyses and incorporate additional criteria to enhance the study’s comprehensiveness.

This research contributes to the existing literature by thoroughly analyzing renewable energy alternatives in Brazil using a robust multicriteria decision-making methodology. The study’s findings provide actionable guidance for policymakers, businesses and stakeholders seeking to promote sustainable energy development in the country.

This paper aims to develop and validate a scale to measure knowledge-sharing motives at work. It is aimed to construct a scale which is explicitly different from knowledge-sharing behavior and to develop a comprehensive and domain-specific scale for this special kind of work motivation.

The constructed scale was tested in two studies. Survey data (n = 355) were used to perform an exploratory factor analysis. Results were further tested on survey data from the core public sector (n = 314) and the health sector (n = 315). A confirmatory factor analysis confirms the results in both samples. The developed scale was further validated internally and externally.

The analysis underlines that knowledge-sharing motivation and knowledge-sharing behavior are different constructs. The data suggest three dimensions of knowledge-sharing motives: appreciation, growth and altruism and tangible rewards. While it is suggested that the developed scale works in the public as well as the private sector context, it is found that knowledge sharing of public employees is merely driven by “growth and altruism” and “appreciation of coworkers.”

No comprehensive and reproducible scale to measure knowledge-sharing motives, which is different from behavior and domain-specific as well, was available in the literature. Therefore, such a scale has been constructed in this study. Furthermore, this study uses samples from different organizational sectors to deepen the understanding of knowledge sharing in context.

Climate change poses effect on banking sector’s risks and profitability through adaptation of green technology. This study aims to incorporates green technology adaptation in three sectors: green banking, green entrepreneurial innovation (EI) and green human resource (HR), in a model of bank’s performance. And determines the impact of climate change on bank risk and profitability.

An assessment of profitability and risk profile of commercial banks is done for 27 European countries for 2013–2022, employing a two-step difference system-generalized method of moments estimation technique with a moderate effect of climate change by including interaction between climate change and green technology adaptation.

The results indicate that green banking increases profitability, reduces credit risk and increases liquidity risk. The results also show that green human resource increases profitability and becomes a source of credit and liquidity risks for the banks. Green EI increases credit risk and liquidity risk, while the effects of green EI on profitability vary with the use of two proxies: Green patents increase profitability and environment, social and corporate governance (ESG) scores decrease profitability.

Supportive government initiatives, including subsidies and tax rebates to green borrowers, may take the burden of green transition off the banking sector.

This paper observes the impact of green technology adaptation in three sectors: banks, EI and HR, moderated by climate change, adding substantially to the existing literature in conceptual framework and methodology.

Large language models such as ChatGPT are a challenge to academic principles, calling into question well-established practices, teaching and exam formats. This study aims to explore the adaptation process regarding text-generative artificial intelligence (AI) of students and teachers in higher education and to identify needs for change.

The issue is explored in a mixed-methods approach based on Domestication Theory (Silverstone et al., 1992; Silverstone, 1994), incorporating views of both teaching staff and students. Both statistical and content analyses were carried out.

The results show that both students and teachers are conflicted about generative AI and its usage. Trepidation and fear stand against a general feeling that AI is an integral part of the future and needs to be embraced. Both groups show marked needs for training and rules and offer a variety of ideas for new exam formats.

This study provides a unique insight by exploring the attitudes and usage intentions regarding generative AI of two stakeholder groups: students and teachers. Its results can be of significant use to institutions deciding on their strategy regarding AI. It illustrates attitudes and usage intentions as well as needs of both groups. In addition, ideas for new assessment and teaching formats were generated.