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This study examines online hate speech targeting gender-normative defiance during the 2023 Sanremo Music Festival in Italy, focusing on the performance of the rapper Rosa Chemical, who faced backlash after kissing a male singer on stage. Using machine learning analysis techniques, we compare user comments across three social media platforms: Facebook, Instagram and TikTok. Results reveal significant differences in the prevalence and nature of hate speech across these platforms, with Facebook exhibiting the highest levels of hate speech (35.9%), predominantly driven by anger and disgust, while TikTok had the lowest (1.9%). Hate speech was strongly correlated with negative emotions like anger and disgust, particularly on Facebook. Moreover, while on Facebook comments characterised by negative emotions produced more reactions, on TikTok comment negativity was not correlated with the number of responses. These findings are consistent with the interpretation that older audiences on platforms like Facebook feel more threatened by gender-normative challenges and resort to online hate speech as a form of cultural backlash. Moreover, platform-specific moderation policies, content distribution mechanisms and social norms about the perceived appropriateness of negative content may influence the amount of hate speech and the degree to which users decide to engage with it. This research study contributes to the understanding of the ‘supply side’ of online hate speech by highlighting how platform architecture and user demographics influence the production and reaction to hate speech.

The construction industry in Ghana faces significant challenges in managing health and safety risks, leading to high rates of accidents and fatalities. Despite the potential of artificial intelligence (AI) technologies to improve health and safety management, their adoption in the Ghanaian construction industry remains limited. This paper aims to identify and evaluate key factors influencing the uptake of AI technologies in construction health and safety management within the Ghanaian industry.

The study adopts a rigorous two-step qualitative approach to identify a set of 17 variables. First, an extensive analysis of scholarly publications was conducted to compile an initial variable list. Secondly, a pilot survey involving both academic and industry professionals assisted in refining the identified variables. Subsequently, a questionnaire survey involving 219 Ghanaian construction professionals then collects quantitative assessments of each variable using the purposive sampling technique. Statistical modelling using factor analysis and fuzzy synthetic evaluation (FSE) was applied to process the survey data and determine the criticality of the factor categories.

The factor analysis yielded a three-factor solution underlying the 17 adoption variables: Extensive technological requirements and costs, resistance to change and AI adoption and uncertainty about AI outcomes and value. Subsequently, FSE confirmation showed the Extensive Technological Requirements category as the most critical, with specialized algorithmic demands, infrastructure limitations and expert support needs presenting major obstacles Ghanaian firms face in AI adoption.

This research contributes robust empirical evidence and novel factor-based statistical analysis to augment the theoretical discourse surrounding construction safety technology integration and change dynamics. The developed fuzzy quantitative methodology offers a model for assessing complex innovation adoption decisions in the face of uncertainty. The research addresses a gap in existing literature by providing a comprehensive assessment of the technological, organizational and environmental factors shaping AI adoption decisions and offering practical strategies for overcoming adoption barriers.

Though there is literature on green collaboration to supplier selections, there are hardly any empirical studies that analyze collaborative networks toward green supplier selection (GSS) from the perspective of green relational capital (GRC). Therefore, this study aims to fill this research gap by analyzing the development of collaboration toward GSS through the lenses of GRC. Also, this study explores how collaboration between institutions and their relevant green stakeholders, framed through the lens of GRC influences the selection of green suppliers.

This study uses an exploratory case study approach involving public universities in Ghana. This study is based on interviews conducted with 27 key respondents across seven universities. A purposive sampling technique was used in selecting respondents who were interviewed face-to-face with a semi-structured interview guide. Atlas ti software was used to generate themes for discussion.

This study’s findings suggest that the reason green criteria are not integrated into supplier selection is due to an insufficient collaboration among relevant green stakeholders. Through green training workshops, conferences, continuous professional development and affiliation with professional bodies, procurement practitioners can develop a collaborative network among themselves to promote the integration of green sustainability into supplier selection. Constructs that help to establish strong collaborative network identified in this study include trust and consistency, mutual benefits, obvious intentions and effective communication.

This study identified constructs promoting effective green collaboration toward the adoption of GSS. These constructs as identified in this study, provide a clear means of developing green collaboration among relevant stakeholders. By fostering and developing collaboration, the main construct of GRC, institutions can successfully integrate green sustainability into their supplier selection process, leading to long-term benefits for both the environment and the institution.

Collaboration toward integration of green sustainability into supplier selection necessitates engagement with various relevant green stakeholders, including suppliers, customers, government bodies, colleagues in sister institutions and environmental advocacy groups. This fosters a sense of shared responsibility and collective action toward sustainability goals.

This study offers empirical evidence on the impact of collaboration on supplier selection and green sustainability performance, contributing to the existing body of literature. By analyzing collaboration, a perspective of GRC, toward the integration of green sustainability into supplier selection is considered as a novel study.

The transition to green growth goals requires the concerted efforts of the whole society. Enterprises, as important players in the market, play a key role in promoting green and sustainable development. The rise of the concept of sustainable development has enabled more enterprises to disclose environmental, social and governance (ESG) information, and ESG behaviour is regarded as a positive strategic behaviour to implement the new development concept. This paper aims to explore the influence of ESG performance on enterprise green innovation.

This study applies a fixed effect model and the regulation effect of empirical analysis to explore the influence of ESG performance on enterprise green innovation. The object of investigation is 2014–2021 Shanghai and Shenzhen A-share listed companies.

The results of an empirical analysis outline the following conclusions: (1) ESG performance has a significant effect on enterprise green innovation, mainly by easing the pressure of the financing enterprise, fitting stakeholders’ environmental protection concept and obtaining employee organizational identity that influences enterprise green innovation. (2) Government regulation positively regulates the role of ESG performance in promoting the green innovation of enterprises. (3) Heterogeneity analysis found that the strengthening role of ESG performance on the green innovation of enterprises is stronger in green invention patents, state-owned enterprises and nonheavily polluting industries.

Despite the valuable findings, this study has a few limitations. Thus, it is necessary to extend the object of investigation by adding other Asian countries, which allows for comparison analysis and allocating best practices for promoting green innovation. Besides, innovation and ESG performance depend on the quality of institutions. In this case, the future study should incorporate the indicators that reveal the quality of institutions (corruption, transparency, digitalisation, voice, accountability, etc.).

According to the above conclusions, this paper proposes suggestions at the level of enterprises, government and investors. At the enterprise level, ESG responsibility should be strengthened, ESG information should be consciously disclosed and the quality of ESG disclosure should be improved. Government departments should play the role of supervisors, improve the construction of ESG information disclosure systems and promote the formation of ESG systems. At the social level, investors should improve the ESG information status and pay more attention to the ESG performance of enterprises.

This study fills the scientific gaps in the analysis impact of ESG performance on the green innovation of enterprises. This paper contributes to the theoretical landscape of ESG efficiency by developing approaches based on two empirical models: testing the impact of enterprise ESG performance on green innovation and testing whether government regulation plays a regulatory role in the relationship between ESG performance and green innovation. Besides, this study analysed the ESG performance and green innovation within the following categories: heavy and nonheavy polluter industries; state and nonstate-owned enterprise groups.

This study investigates the impact of experimentally priming a maximizing decision-making style on individuals’ likelihood of using artificial intelligence (AI) advisors for making complex financial decisions, such as building an investment portfolio for their retirement. It examines whether individuals with stronger maximizing tendencies are more likely to perceive algorithms as effective, thereby reducing their algorithm aversion, and ultimately increasing the likelihood of using AI advisors in their financial decision-making.

A qualitative pre-study amongst individuals differing in their maximizing tendencies to learn more about the existing usage patterns of AI advisors for financial decisions was combined with a quantitative study to experimentally test our hypotheses. For both studies, US participants were recruited through Prolific. The data were analyzed using thematic analysis in NVivo and regression analysis in the SPSS Process macro.

The results show that individuals primed with a maximizing mindset demonstrated a higher likelihood of using AI advisors for their financial decisions. This effect was serially mediated by the perception of enhanced algorithm effectiveness and reduced algorithm aversion.

This study provides actionable insights for financial service providers such as banks, pension funds and insurance companies into strategies on how to reduce algorithm aversion and encourage greater AI usage in decision-making amongst their (potential) clients. In particular, to increase the likelihood that consumers will rely on AI advisors for financial decisions, financial service providers can induce a maximizing mindset in these individuals by adjusting the wording of their marketing communications material.

This study extends our understanding of how maximizing tendencies influence the likelihood of using AI advisors. It contributes to the literature by highlighting the role of perceived effectiveness and algorithm aversion and by demonstrating that experimentally inducing a maximizing mindset can increase AI usage for financial decisions; doing so is important as AI can help provide consumers with personalized advice in a cost-effective way.

The NHS long-term plan outlined that mental health services in the UK will be based on the trauma-informed approach in the next 10 years. Staff in leadership roles in those services will face a responsibility to lead the implementation of TIC; however, little is known about the experiences of these staff attempting to create change. Therefore, this study aims to gain an understanding of mental health staff (MHS)’s experiences in implementing trauma-informed care (TIC) in the NHS.

In total, 14 mental health staff (MHS), comprising ten psychologists and four multidisciplinary clinicians, were recruited through purposive and snowball sampling. Semi-structured individual interviews were conducted via Microsoft Teams to provide qualitative data. Interviews were transcribed verbatim and analysed using Braun and Clarke’s (2006) reflexive thematic analysis.

Five themes were found: 1) having a visionary outlook and high expectations for change, 2) professional growth and personal development, 3) affirmation of the role’s importance and impact, 4) psychological discomfort and 5) ways of coping. Participants strongly believed that TIC should be standard practice for all health-care staff, noting that implementing TIC led to their professional and personal growth. While they found satisfaction in influencing others to value TIC, they experienced negative emotions when their efforts were unsuccessful and used various strategies to overcome barriers and manage psychological discomfort.

To the best of the authors’ knowledge, this study is the first to explore the experiences of MHS implementing TIC in the NHS. Support recommendations for staff in the role are made and future research is identified.

This innovative study aims to combine an AI-based cognitive intervention known as AI painting with a traditional behavioral approach to retirement financial planning, specifically through personalized pension projection (PPP). This integration is intended to address the design limitations inherent in the traditional method.

To evaluate the effectiveness of AI painting, a randomized control trial was conducted, focusing on its impact on retirement goal clarity and risk tolerance among a sample of Chinese working adults. Participants were divided into two groups: the experimental group received both the PPP and AI painting interventions, while the control group was given only the PPP intervention.

The results indicated that AI painting significantly enhanced risk tolerance, although it unexpectedly led to a reduction in retirement goal clarity. Interestingly, the study also found that AI painting facilitated the transformation of retirement goal clarity into increased risk tolerance.

These findings carry important theoretical implications for the development of the capacity-willingness-opportunity model in retirement financial planning. It deepens our understanding of the CWO model by identifying a conditional effect of retirement goal clarity on risk tolerance, a relationship that has not been previously modeled. It also expands the CWO model by proposing and testing additional pathways from new interventions to critical psychological constructs within the framework.

The promising results encourage business banks to incorporate AI painting into their counseling practices, enabling them to capitalize on the rapid advancements in text-to-visual AI technologies to enhance their marketing strategies.

To our knowledge, we are the first team in the world to integrate AI painting into retirement financial planning. As advancements in text-to-painting technology continue to progress rapidly, the value of this study will increase accordingly, as it sets a foundation and inspires further research in this area.

Building defects are becoming recurrent phenomena in most high-rise buildings. However, little research exists on the analysis of defects in high-rise buildings based on data from real-life projects. This study aims to develop dashboards and models for revealing the most common locations of defects, understanding associations among defects and predicting the rectification periods.

In total, 15,484 defect reports comprising qualitative and quantitative data were obtained from a company that provides consulting services for the construction industry in Victoria, Australia. Data mining methods were applied using a wide range of Python libraries including NumPy, Pandas, Natural Language Toolkit, SpaCy and Regular Expression, alongside association rule mining (ARM) and simulations.

Findings reveal that defects in multi-storey buildings often occur on lower levels, rather than on higher levels. Joinery defects were found to be the most recurrent problem on ground floors. The ARM outcomes show that the occurrence of one type of defect can be taken as an indication for the existence of other types of defects. For instance, in laundry, the chance of occurrence of plumbing and joinery defects, where paint defects are observed, is 88%. The stochastic model built for door defects showed that there is a 60% chance that defects on doors can be rectified within 60 days.

The dashboards provide original insight and novel ideas regarding the frequency of defects in various positions in multi-storey buildings. The stochastic models can provide a reliable point of reference for property managers, occupants and sub-contractors for taking measures to avoid reoccurring defects; so too, findings provide estimations of possible rectification periods for various types of defects.

Generative artificial intelligence (GenAI) can potentially improve supply chain management (SCM) processes across levels and verticals. However, despite its promise, the implementation of GenAI for SCM remains challenging, mainly due to the lack of knowledge regarding its key drivers. To address this gap, this study examines the factors driving GenAI implementation in an SCM environment and how these factors optimize SCM performance.

A thorough literature review was followed to identify the drivers. The resultant model from the drivers was validated using a quantitative study based on partial least squares structural equation modeling (PLS-SEM) that used responses from 315 expert respondents from the field of SCM.

The results confirmed the positive effect of performance expectancy, output quality and reliability, organizational innovativeness and management commitment to GenAI usage. Further, they showed that successful GenAI usage improved SCM performance through improved transparency, better decision-making, innovative design, robust development and responsiveness.

This study reports the potential drivers for the contemporary development of GenAI in SCM and highlights an action plan for GenAI’s optimal performance. The findings suggest that by increasing the rate of GenAI implementation, organizations can continuously improve their strategies and practices for better SCM performance.

This study establishes the first step toward empirically testing and validating a theoretical model for GenAI implementation and its effect on SCM performance.

Artificial intelligence (AI) is a powerful and promising technology that can foster the performance, and competitiveness of micro, small and medium enterprises (MSMEs). However, the adoption of AI among MSMEs is still low and slow, especially in developing countries like Jordan. This study aims to explore the elements that influence the intention to adopt AI among MSMEs in Jordan and examines the roles of firm innovativeness and government support within the context.

The study develops a conceptual framework based on the integration of the technology acceptance model, the resource-based view, the uncertainty reduction theory and the communication privacy management. Using partial least squares structural equation modeling – through AMOS and R studio – and the importance–performance map analysis techniques, the responses of 471 MSME founders were analyzed.

The findings reveal that perceived usefulness, perceived ease of use and facilitating conditions are significant drivers of AI adoption, while perceived risks act as a barrier. AI autonomy positively influences both firm innovativeness and AI adoption intention. Firm innovativeness mediates the relationship between AI autonomy and AI adoption intention, and government support moderates the relationship between facilitating conditions and AI adoption intention.

The findings provide valuable insights for policy formulation and strategy development aimed at promoting AI adoption among MSMEs. They highlight the need to address perceived risks and enhance facilitating conditions and underscore the potential of AI autonomy and firm innovativeness as drivers of AI adoption. The study also emphasizes the role of government support in fostering a conducive environment for AI adoption.

As in many emerging nations, the AI adoption research for MSMEs in Jordan (which constitute 99.5% of businesses), is under-researched. In addition, the study adds value to the entrepreneurship literature and integrates four theories to explore other significant factors such as firm innovativeness and AI autonomy.