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This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

The purpose of this paper is to investigate the relative effects of individual environmental, social and governance pillars on the performance of international mergers. We examine whether performance complementarity exists among the environmental, social and governance pillars.

We draw on a global set of cross-border merger data covering 2002 to 2018 and employ pooled least squares (PLS) and instrumental variable (IV) approaches to gain novel and robust insights into the short- and long-term effects of the relationships among the three CSR pillars on acquisition returns. To study the examined interrelationships across three pillars, we rely on theories related to super-modular optimization and games.

We find positive and significant effects of the environmental pillar and the social pillar on changes in ROA and Tobin’s Q of acquirers during the three years following acquisitions. Furthermore, the governance pillar only impacts short-run ROA change. We found complementarities among the three pillars of CSR to attain performance synergy. The effects of CSR on post-merger performance are more profound in the long run.

We analyze the relative importance of CSR individual pillars (i.e. the environmental, social and governance pillars) and the nature of the relationship among them. This research question remained unexplored in the previous literature. Our analysis sheds light on prevailing concerns regarding the mutual relations among the different pillars of CSR; e.g. previous literature argued that the governance component is different from the social and environmental pillars.

In higher education, the appreciation and implementation of artificial intelligence (AI) has led to debate and polarization. This study examines how the institutional environment at universities impacts the value perception and intention to adopt AI in higher education. It seeks to optimize the adoption pathway by identifying essential “must-have factors” and “value drivers.”

Employing the technology-organization-environment (TOE) framework alongside the technology acceptance model (TAM) framework and perceived value concepts, this research utilizes a partial least squares structural equation modeling (PLS-SEM) approach complemented by necessary condition analysis (NCA), a novel approach in this context, which allows us to distinguish between critical antecedents and value drivers in the evaluation and adoption of AI technology.

Results indicate that organizational complexity, readiness, competency, compatibility and competitive advantage significantly affect the perceived value of AI, mediated by stakeholders’ perceptions of its ease of use and usefulness. These factors enhance the intention to adopt AI in higher education. Perceived usefulness has the highest effect size and the importance of ease of use differs between Europe and South America. External factors like partner support and competitive advantage are necessary considerations to be met, while competitive advantage and some internal factors are sufficient conditions for AI adoption.

The study underscores the importance of certain institutional factors, setting priorities for management actions in AI adoption. It differentiates between the general appreciation of AI and the intention of practical implementation, highlighting the role of partner support as a necessary condition, although a non-value-driving factor.

This research examines the complex relationship between usage of Chat Generative Pre-Trained Transformer (ChatGPT) amongst student and their creativity, learning and assessment using empirical data collected from postgraduate students. In addition, the study explores the student’s intrinsic motivation for usage to understand student categories. This research seeks to provide further insights into this artificial intelligence tool in enhancing the educational ecosystem for all stakeholders concerned.

The target population of this research – the students of post-graduation in diverse fields of science and management. A five-point Likert scale-structured questionnaire adapted from earlier literature relevant to the research questions was adopted for data collection. The data were collected for two months, resulted in 403 usable responses. Ethical considerations of assurance of confidentiality to the participants were strictly adhered to. Structured equation modelling (SEM) was employed to explore the relationships between the constructs of the study for the assessment of latent relationships. SmartPLS 4 was used to explore these relationships.

Usage has a negative impact on a student’s creativity, but increased usage of ChatGPT encourages a student’s adoption due to its perceived usability. Pedagogical applications of ChatGPT aid students as a learning tool but require controlled usage under supervision.

This study is innovative in the context of postgraduate students, where very little evidence of creativity exists. Through this research, the authors illuminate how ChatGPT use affects academic performance, benefiting educators as a tool but for evaluation and assessment, policymakers and students. The findings of the study provide implications that help to create effective digital education strategies for stakeholders.

Artificial intelligence (AI) has the potential to address significant challenges in education, innovate learning and teaching practices and achieve SDG 4. However, existing literature often overlooks the behavioural aspects of students regarding AI in education, focusing predominantly on technical and pedagogical dimensions. Hence, this study aims to explore the significant relationships among AI literacy, AI usage, learning outcomes and academic performance of generation Z students in the Indian educational context.

The study used structural equation modelling (SEM) on Gen Z students born in the years 1997–2012 as a sample population for the research in the north Indian states like Punjab, Haryana, Himachal and regions like Chandigarh and N.C.R. Delhi.

The results established significant positive relationships between AI literacy, AI usage, AI learning outcomes and academic performance. Specifically, higher levels of AI literacy were associated with increased engagement with AI technologies and tools for learning purposes, leading to better learning outcomes and academic performance. The findings demonstrated that AI literacy plays a crucial role in providing effective learning experiences and fostering skills such as problem-solving and critical thinking among Gen Z students.

The implications of the study include the significance of integrating AI education initiatives into curricula, prioritising professional development programmes for educators and making sure that every student has equitable access to AI technologies.

The study introduces a novel perspective by examining variables such as AI literacy, AI usage, AI learning outcomes and academic performance and developing a model that has not been previously studied. It provides a new discourse and proposes a framework uniquely combining AI-infused curriculum design, educator empowerment, robust assessment mechanisms and sustainable practices.

Political factors play a crucial role in China's initial public offering (IPO) market due to its distinctive institutional context (i.e. “economic decentralization” and “political centralization”). Given the significant level of IPO underpricing in China, we examine the impact of local political uncertainty (measured by prefecture-level city official turnover rate) on IPO underpricing.

Using 2,259 IPOs of A-share listed companies from 2001 to 2019, we employ a structural equation model (SEM) to examine the channel (voluntarily lower the issuance price vs aftermarket trading) through which political uncertainty affects IPO underpricing. We check the robustness of the results using bootstrap tests, adopting alternative proxies for political uncertainty and IPO underpricing and employing subsample analysis.

Local official turnover-induced political uncertainty increases IPO underpricing by IPO firms voluntarily reducing the issuance price rather than by affecting investor sentiment in aftermarket trading. These relations are stronger in firms with pre-IPO political connections. The effect of political uncertainty on IPO underpricing is also contingent upon the industry and the growth phase of an IPO firm, more pronounced in politically sensitive industries and firms listed on the growth enterprise market board.

Local government officials in China usually have a short tenure and Chinese firms witness significantly severe IPO underpricing. By introducing the SEM model in studying China IPO underpricing, this study identifies the channel through which local government official turnover to political uncertainty on IPO underpricing.

This study aims to investigate the association between corporate social responsibility (CSR) performance and the cost of debt financing (CODF) in Malaysia. It further explores whether the potential impact of CSR performance on debt pricing is moderated by the females’ representation on board and female directors’ foreign experience.

The authors use a sample of 845 firm-year observations from 2017 to 2021 and apply various regression techniques, including the pooled ordinary least squares (POLS), the Heckman two-stage self-selection model, propensity score matching (PSM) and quantile regression, to test the study’s hypotheses.

The results show that socially responsible firms incur lower costs of debt. Similarly, female directors and female directors with foreign exposure are negatively associated with CODF. However, their impact becomes positive when these two variables are interacted with CSR performance. The study findings are robust across alternative measures of board gender diversity, different model specifications and approaches addressing the endogeneity problem. In additional analyses, we find that the positive implication of CSR on CODF is more pronounced for firms with higher CSR performance and less financial constraint. Nevertheless, the results reveal that only firms with lower CSR performance but a high proportion of female directors and female directors with foreign experience exhibit lower CODF. This underscores the likelihood that female directors and their foreign exposure may substitute CSR practices in mitigating the cost of debt.

Existing literature generally emphasises the importance of CSR performance to corporate financing decisions, often neglecting the role of female directors and their attributes in financial institutions’ creditworthiness evaluation. This study is among the first to address this gap by examining the moderating effect of female directors and their characteristics on CSR–CODF relationship within an emerging economy context. The findings contribute to the literature on CSR and board gender diversity, indicating that CSR performance and board gender diversity function more as substitutes than complements. Despite the unexpected consequences of interacting with female directors and their foreign experience with CSR, the study affirms the significance of CSR practices and board gender diversity in shaping borrowers’ financial decisions.

The purpose of this study is to investigate the potential adoption of AI-powered tools by Chinese Generation Z (Gen Z) consumers in e-commerce. It aims to understand how factors, such as performance expectancy, effort expectancy, social influence and facilitating conditions, affect behavioral intention and user behavior towards AI-enhanced e-commerce platforms.

The study employed the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A survey with 24 questions across six constructs was designed and distributed to Chinese Gen Z consumers aged 18–28. The research used convenience and quota sampling methods in four commercial complexes in a populous southwestern Chinese city, with 280 valid responses collected. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).

It is found that performance expectancy, effort expectancy and facilitating conditions positively influence behavioral intention to use AI-powered e-commerce tools. Surprisingly, social influence shows a negative correlation with behavioral intention, suggesting that Chinese Gen Z consumers may not be swayed by others’ opinions in adopting these technologies. Facilitating conditions and behavioral intention both significantly affect user behavior. Gender differences can be observed, particularly in the impact of effort expectancy on behavioral intention.

This study extends the application of the UTAUT model to the rapidly evolving e-commerce sector, focusing on AI-powered tools and the unique characteristics of Chinese Gen Z consumers. By highlighting gender differences and the specific preferences of this generation, the research contributes to a more nuanced understanding of technology acceptance in e-commerce, guiding future marketing strategies and platform development.

This study aims to investigate the impact of customer willingness to adopt self-service technologies (SST) in the Nigerian fuel station context. Specifically, it explores the interplay between corruption mitigation at fuel stations, customer trust and customer intimacy in shaping the attitudes towards SST adoption.

The research employs a quantitative approach; partial least squares structural equation modelling (PLS-SEM) software was conducted using an online survey sample of 233 participants from Nigeria.

The research reveals that customer willingness to use SST positively correlates with customer trust, corruption mitigation and customer intimacy when introduced at fuel stations. However, as anticipated by the study, corruption mitigation at fuel stations and customer trust significantly mediate the relationship between willingness to use SST and customer intimacy. This suggests that an increase in customers’ willingness to use SST at a fuel station not only enhances the chances of mitigating corruption but also strengthens customer trust.

This study contributes to both academic understanding and practical implications. It advances the literature on technology adoption by exploring the unique context of Nigerian fuel stations. The study’s originality lies in its examination of these dynamics within a specific cultural and operational setting, presenting valuable knowledge for both researchers and fuel station managers seeking to improve customer experiences and service quality.

Teaching models in higher education entrepreneurship programs affect students’ entrepreneurial intentions. Yet evidence related to their effects on long-term venture creation remains limited. Past research on the effects of teaching models on entrepreneurship has focused narrowly on entrepreneurship education programs. The present study moves beyond this past narrow focus to study teaching model effects on new venture creation across a broad array of higher education programs.

We assess the effects of teaching models on new venture creation across 35 educational programs (entrepreneurship and other business programs) at a large Danish business school, tracing venture creation data for 5 years after graduation (N = 4,717).

Competence-based teaching models positively impact graduates’ long-term new venture creation across all programs, with no differences between entrepreneurship vs other types of programs.

These findings carry implications for both education and entrepreneurship research, as well as policymakers and educators, by pointing toward ways of impacting postgraduate business venturing through teaching model reforms extending beyond entrepreneurship education.

For the first time, we find effects of competence teaching models on postgraduation new venture creation across a broad array of business administration programs. The results are novel in documenting that teaching models generally impact venture creation in the long term, and that this occurs regardless of whether the program content centers on entrepreneurship or on other business administration content.