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To analyse the reasons for acceptance and rejection of insect consumption among urban Mexican consumers based on their perceptions and levels of food neophobia and neophilia.

A questionnaire was distributed online to 415 people. The Food Neophobia Scale was used, measured on a four-point Likert scale, and consumers were grouped according to their level of neophobia. The Free Word Association technique was used to determine participants’ perceptions of “edible insects”, and the words obtained were categorised according to synonyms. The Chi-square test per cell made it possible to determine the statistical significance of each group in relation to the categories and was confirmed by a correspondence analysis. The economic aspects of the groups were analysed using the Chi-square and the Z-test with the Bonferroni method.

Three groups of consumers have been identified: neophilic, intermediate and neophobic. Participants mentioned 1,235 words, which were grouped into 16 categories. For neophilics, edible insects are a familiar product; intermediates are curious and neophobics are afraid of consumption.

The paper contributes to the analysis of the aspects that influence the acceptance and rejection of insect consumption among urban consumers by considering the degree of food neophobia and the participants’ perceptions.

The purpose of the study is to investigate the impact of artificial intelligence (AI), machine learning (ML), and data science (DS) on unemployment rates across ten high-income economies from 2015 to 2023.

This study takes a unique approach by employing a dynamic panel data (DPD) model with a generalised method of moments (GMM) estimator to address potential biases. The methodology includes extensive validation through Sargan, Hansen, and Arellano-Bond tests, ensuring the robustness of the results and adding a novel perspective to the field of AI and unemployment dynamics.

The study’s findings are paramount, challenging prevailing concerns in AI, ML, and DS, demonstrating an insignificant impact on unemployment and contradicting common fears of job loss due to these technologies. The analysis also reveals a positive correlation (0.298) between larger government size and higher unemployment, suggesting bureaucratic inefficiencies that may hinder job growth. Conversely, a negative correlation (−0.201) between increased labour productivity and unemployment suggests that technological advancements can promote job creation by enhancing efficiency. These results refute the notion that technology inherently leads to job losses, positioning AI and related technologies as drivers of innovation and expansion within the labour market.

The study’s findings suggest a promising outlook, positioning AI as a catalyst for the expansion and metamorphosis of employment rather than solely a catalyst for automation and job displacement. This insight presents a significant opportunity for AI and related technologies to improve labour markets and strategically mitigate unemployment. To harness the benefits of technological progress effectively, authorities and enterprises must carefully evaluate the balance between government spending and its impact on unemployment. This proposed strategy can potentially reinvent governmental initiatives and stimulate investment in AI, thereby bolstering economic and labour market reliability.

The results provide significant perspectives for policymakers and direct further investigations on the influence of AI on labour markets. The analysis results contradict the common belief of technology job loss. The study’s results are shown to be reliable by the Sargan, Hansen, and Arellano-Bond tests. It adds to the discussion on the role of AI in the future of work, proposing a detailed effect of AI on employment and promoting a strategic method for integrating AI into the labour market.

Interest-free promotions are a prevalent strategy employed by credit card lenders to attract new customers, yet the research exploring their effects on both consumers and lenders remains relatively sparse. Selecting an optimal promotion strategy is intricate, involving the determination of an interest-free period duration and promotion-availability window, all within the context of market dynamics and complex consumer behaviour. The purpose of this study is to develop an agent-based model to assist with determining optimal promotion strategies.

In this paper, we introduce a novel agent-based model that facilitates the exploration of various credit card promotions under diverse market scenarios.

Our experiments reveal that, in the absence of competitor promotions, lender profit is maximised by an interest-free duration of approximately 12 months, while market share is maximised by offering the longest duration possible. In the context of concurrent interest-free promotions, we identify that the optimal lender strategy entails offering a more competitive interest-free period and a rapid response to competing promotional offers. Notably, a delay of three months in responding to a rival promotion corresponds to a 2.4% relative decline in income.

Our model consists of multiple lender and consumer agents that interact through a novel set of mechanisms based on well-studied consumer behaviours. Distinct from previous works, our model adopts a realistic billing cycle with a focus on interest charged to revolving accounts and supports a range of lender promotion strategies. It is calibrated to historical benchmarks and validated against both stylised facts and time-series data, ensuring a realistic reflection of market behaviour, which has been neglected in prior studies.

This study examines the impact of Economic Policy Uncertainty (EPU) on US Real Estate Investment Trust (REIT) sectors from 1994 to 2024, focusing on dynamic spillover effects across varying economic environments.

Employing a time-varying parameter vector autoregression (TVP-VAR) model, the research analyzes sectoral interconnectedness and spillover dynamics, capturing the evolving relationships between REIT subsectors and EPU over time.

The analysis reveals that US REIT subsectors are highly interconnected, indicating substantial internal spillovers. Retail, regional malls and shopping centres are primary shock transmitters, while self-storage, manufactured homes and industrial REITs act as net receivers, demonstrating resilience. Over the study period, Economic Policy Uncertainty (EPU) generally functions as a net receiver of spillovers from REITs. However, pairwise time-varying analysis shows that EPU transmitted shocks during the economic uncertainties of the 1990s but became a net receiver during the 2008 financial crisis and the COVID-19 pandemic, suggesting that disruptions in REIT markets contributed to policy uncertainties during crises. Additionally, the sectors driving volatility shifted over time: residential and apartment REITs were predominant during the 2008 crisis, while disruptions in the office sector were significant during the pandemic.

Policymakers ought to design targeted interventions to stabilize the sectors that are primary transmitters of shocks during economic downturns and monitor closely economic indicators and adopt policies to mitigate adverse spillover effects.

This study offers novel insights into the time-varying interconnections between REIT sectors and economic uncertainty, illustrating how these relationships evolve with economic conditions and enhancing understanding of REIT resilience under uncertainty.

The purpose of this study is to provide evidence to support an emerging theory that interpersonal conflict is the primary cause of workplace stress among a self-selected sample of Irish construction managers.

Eighteen construction managers working in Ireland were recruited for this study. Using semi-structured interviews and interpretative phenomenological analysis as the research methodology, the causes of their workplace stress were investigated.

Participants reported that the principal cause of their workplace stress was high levels of interpersonal conflict between colleagues. The effects of this interpersonal conflict included avoidance behaviour, ill health, absences from the workplace and loss of productivity issues. Deadlines, penalty clauses, lack of appreciation, cliques, costs, communication, temporary contracts and delays were also reported stressors.

A limitation of the study is the small sample of 18 construction managers and the limited geographical area.

The social implications of this study could be to clearly identify that interpersonal conflict may be under reported in the construction industry, and there is a possibility that it is being misclassified as other workplace behaviours such as bullying, harassment and workplace violence. If this is so, this could aid future researchers in addressing this challenging workplace behaviour.

The current consensus in the literature is that the three main causes of workplace stress are bullying, harassment and violence. However, the role and importance of interpersonal conflict as reported in this study, with the exception of North America and China, is not reflected in the wider health and safety research literature. In addition, interpersonal conflict and its reluctance to be reported is largely absent from construction safety research. The findings of this study may be explained if the workplace stress research community is currently misclassifying interpersonal conflict as a manifestation of bullying, harassment or violence. If this is the case, interpersonal conflict needs further research. This is to establish if this cause of construction-related workplace stress needs to be reconsidered as a standalone phenomenon in the wider family of challenging workplace behaviours.

The basis for this study on electronic transferable records (ETRs) in the context of digitalizing cross-border trade is established in this chapter. It illustrates how the growing trend toward paperless trade and technological improvements is changing trade operations. This chapter focuses on the need to look into ETRs because of their capacity to influence business transactions while navigating complex legal issues. The specific goals of the study are outlined, including a review of the advantages, legality, difficulties and best practices of ETRs. This study aims to shed light on the possible advantages and disadvantages of ETRs, the legal framework that controls their use and the best practices for their efficient implementation. This study also seeks to provide informative recommendations for businesses and people that are considering using ETRs.

This study explores the evolving world of ETRs and their crucial function in international trade. Multidimensional technique is used to examine the transformative potential of ETRs from a variety of research angles. The research design is based on a comprehensive evaluation of the literature that includes a wide range of reliable sources, including academic papers, business reports and legal documents. The comprehensive retrieval of essential material is ensured through keyword searches in renowned academic databases and industry resources. The qualitative synthesis of secondary sources further enhances this methodology and allows for a complex examination of the implications of ETRs. The case study analysis provides practical information on the benefits, hazards and practical applications of ETRs. Multifaceted aspects are uncovered via a thematic approach and qualitative investigation, including potential advantages, hazards, implementation plans and regulatory frameworks.

ETRs offer a range of potential advantages for cross-border trade, encompassing augmented efficiency, reduced costs and heightened security. Nonetheless, their implementation also presents legal challenges and risks, spanning security and privacy concerns, legal ambiguities and technical complexities. Consequently, it is crucial for individuals and businesses to meticulously assess and mitigate these risks through the integration of robust security protocols, staying informed about legal developments and adhering to pertinent regulatory stipulations. In spite of these hurdles, the trajectory of ETR adoption is anticipated to remain on an upward trajectory, driven by increasing recognition of their potential benefits and the concurrent evolution of legal frameworks and technical standards.

Research limitations included the following: lack of adoption of ETRs internationally; and legal diversity and different legal systems results in different consideration of the ETRs. It makes reaching a unified ETR system more difficult.

It is necessary to develop clear policies and procedures and establish well-defined policies and procedures governing ETR use. These should encompass security guidelines, data protection measures and adherence to legal mandates. Regular review and updates are imperative. Stay current on legal developments: In light of the continuously evolving legal and regulatory landscape pertaining to ETRs, businesses and individuals must stay abreast of pertinent changes and seek professional counsel when necessary. Collaborate with partners and stakeholders: To ensure harmonization and standardization in ETR deployment, active collaboration with partners, regulators and industry associations is vital.

Enhance awareness and education: Investment in awareness and educational initiatives is crucial. Decision-makers should organize training programs, workshops and seminars to enhance understanding of ETRs’ potential benefits in cross-border trade among stakeholders. Socially, the use of ETR can achieve several political advantages for the society. It minimizes risks of corruption through enhancing tracing and auditing abilities for relevant authorities making it more difficult to engage in corrupt practices. That can promote integrity within government and public procurement system.

The development of standardized technical frameworks and interoperable platforms for ETRs could enhance their seamless integration into existing trade systems. Additionally, investigating the integration of emerging technologies like blockchain, IoT and AI into ETR ecosystems could unlock innovative solutions to security, authenticity and data management concerns. This study examines how ETRs can radically alter how trade is conducted on a global scale. This paper examines ETRs’ role in improving cross-border trade digitization by examining their advantages, legal difficulties and implementation techniques. The conclusions will aid firms, decision-makers and attorneys in navigating the constantly changing world of trade agreements. The study’s ultimate goal is to offer takeaways that support effective, secure and legally compliant integration of ETRs, ensuring that they operate as a catalyst for improved global trade efficacy and efficiency.

This study aims to investigate academic integrity considerations of Generative Pre-Trained Transformer (ChatGPT) usage in assessment activities among the finalist student teachers at the University of Dar es Salaam. The study builds upon other previous studies on the topic of artificial intelligence (AI) tools. However, it is unique in terms of its context and the focus on integrity.

A questionnaire was used to collect data from 383 finalist student teachers, and other 30 students were involved in focus group discussions (FGDs). Data for this study were analysed using IBM-SPSS Statistics Versions 25 to obtain descriptive statistics for the quantitative part and thematic analysis was used along with QDA Miner Lite for the qualitative part to generate themes.

Student teachers consider ChatGPT as a transformative AI technology to ease the accomplishment of their assessment tasks. However, a large percentage of them did not pay much attention to academic integrity while using the AI tool. About 199 (52%) out of 383 respondents said ChatGPT is a useful tool in generating answers for their assessment tasks compared to 192 (50.1%) of respondents who had negative opinions.

Given the sensitivity of the topic on academic integrity some participants were hesitant to share information until when the researchers clarified the purpose of the study. Participants were told that the information they would provide was purely for academic purpose. Also, this study was conducted when students’ regular classes were in progress, such that it was challenging to set universal time to conduct FGDs where all intended participants could attend. The researcher was flexible enough to find hours which were conducive for participants to participate in FGDs.

For practitioners, given the fact that ChatGPT is a relatively new AI tool, training for raising awareness of its use should be conducted. Besides, specific academic integrity guidelines for its ethical use should be formulated and implemented. Likewise, instructors should set questions that prompt students original thinking which the AI tool cannot easily give contextualized solutions.

The university where the AI tool is used is an open system that is within the society, and the impacts of AI technologies are inevitable in social domains. The society, therefore, should be informed about the emerging AI tools with their associated potentials and shortcomings to safeguard the norms and interests of the society.

The study provides a comprehensive overview of student teachers’ ChatGPT use and its implication on academic integrity. Given the novelty nature of ChatGPT use in education, course instructors and students may benefit from insights into responsible utilization of this AI technology without compromising integrity and quality of education.

Agency theory motivated this study, posing that leverage mitigates the agency problem. The aim was to examine whether leverage influences the relationship between executive-employee pay gaps (EEPGs) and firm performance. The study was conducted in the mining and retail sectors between 2012 and 2021.

Two EEPGs were featured based on their executive fixed pay and variable incentives accumulation. Proxies of firm performance were headline earnings per share; return on assets; earnings before interest, tax, depreciation and amortisation; and return on stock price. Data were collected from 76 JSE-listed firms in the retail and mining sectors and analysed using the two-step generalised method of moments.

The results revealed the hybrid implication of the pay gap for firm performance in the retail and mining sectors of South Africa, depending on the performance measures emphasised. More importantly, the study shows that with the moderating effects of leverage, firms can improve their performance while shrinking the pay gap.

The results have implications for policy addressing income inequality, debt management, executive compensation and regulatory reforms in South Africa concerning productivity and remuneration decisions.

The article provides specific literature for retail and mining industries on pay gaps, shows that it is possible to reduce the pay gap without compromising performance and suggests a new measure of performance that is more attuned to pay gap effect measurement.

The aim of this study is to investigate the application of advanced language models, particularly ChatGPT-4, in identifying and utilizing industrial symbiosis opportunities within the circular economy. It examines how the model can aid in promoting sustainable industrial practices by processing data from the MAESTRI project database, which includes various symbiotic relationships, as well as randomly selected waste codes not included in the database. The research involves structured queries related to industrial symbiosis, circular economy, waste codes and potential opportunities. By assessing the model’s accuracy in response generation, the study seeks to uncover both the capabilities and limitations of the language model in resource efficiency and waste reduction, emphasizing the need for ongoing refinement and expert oversight.

The study adopts a mixed-methods approach, combining qualitative and quantitative analyses to explore the potential of ChatGPT-4 in identifying industrial symbiosis opportunities. Data from the EU-funded MAESTRI project database, which includes existing symbiotic relationships, as well as randomly selected waste codes not included in the database, are used as the primary sources. The language model is queried with structured questions on industrial symbiosis, circular economy and specific waste codes utilizing the model’s advanced functions such as file upload. Responses are evaluated by comparing them with the MAESTRI database and official European Waste Catalogue (EWC) codes.

The study finds that ChatGPT-4 possesses a solid understanding of fundamental concepts related to industrial symbiosis and the circular economy. However, it encounters challenges in accurately describing EWC codes, with a notable portion of descriptions found to be incorrect. Despite these inaccuracies, the model shows potential in suggesting symbiotic opportunities, although its effectiveness is limited. Interestingly, the study reveals that the model can occasionally identify correct symbiotic relationships even with initial inaccuracies. These findings highlight the need for expert oversight and further development of the language model to improve its utility in complex, regulated fields like industrial symbiosis.

This study’s originality lies in its exploration of advanced language models, particularly ChatGPT-4, for identifying industrial symbiosis opportunities within the circular economy framework. Unlike previous research, which primarily focuses on specific sectors and AI’s role in general resource efficiency, this study specifically examines the capabilities and limitations of the language model in handling specialized and regulated information, such as EWC codes across various sectors. It employs a novel approach by comparing AI-generated responses with an established symbiosis database, which is comprehensive and spans all sectors rather than being limited to a single industry, as well as with randomly selected waste codes not included in the database. The study contributes to understanding how AI tools can support sustainable industrial practices, emphasizing the importance of refining these models for practical applications in environmental and industrial contexts.

This study aims to investigate the mediating role of different types of intellectual capital (human, structural and relational) in the relationship between artificial intelligence-driven supply chain analytics capability (AI-SCAC) and various supply chain decision-making processes, specifically rational, bounded and tacit decision-making.

The study used a quantitative survey strategy to collect the data. A total of 320 valid questionnaires were received from manufacturing companies. The data were analysed using structural equation modeling with partial least squares (PLS-SEM) approach through SmartPLS software.

The results indicate that human and structural capital significantly mediate the relationship between AI-SCAC and rational and bounded decision-making processes. However, structural capital does not mediate the relationship between AI-SCAC and the tacit decision-making process. Moreover, relational capital does not show a significant mediating effect on all of the decision-making processes. Notably, structural capital has the strongest impact on rational and bounded decision-making, while human capital plays a critical role across all three decision-making processes, including tacit decision-making.

This study contributes to the literature by providing a nuanced understanding of the differentiated impact of intellectual capital components on various decision-making processes within the context of AI-SCAC. While previous studies have broadly acknowledged the role of intellectual capital in decision-making, this research provides more understanding of how specific types of intellectual capital interact with AI to influence distinct decision-making processes. Notably, the differential impact of structural capital on rational and bounded decision-making versus tacit decision-making highlights the need for organisations to adopt a more tailored approach in leveraging their intellectual capital.