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The primary purpose of this study is to explore the effect of technical changes on provincial-level income inequality in Vietnam. The authors also investigate whether the quality of institutions and human capital level moderate this relationship.

This research applies the fixed-effect and random-effect models on a balanced panel data set of 63 Vietnamese provinces/cities from 2010 to 2020.

The study’s empirical results show that technical improvement has a nonlinear influence on income disparity in Vietnamese localities. When the local level of technology is limited, technological change can mitigate income disparity. However, as local technological levels increase, inequality tends to rise. Moreover, the study also reveals that the quality of a province’s institutions and the level of human resources are factors that moderate the correlation between technological change and income inequality. For provinces with better institutional quality and/or better human resources, inequality tends to decline under the impact of technological change.

The results of this study suggest that while encouraging technology advancement, localities should also ensure sustainable development, reduce income inequality and focus on improving institutional quality and human resources development.

There are increasing concerns about the impact of technical change on inequality in income distribution; however, empirical evidence on this relationship in developing countries remains scarce. This study is among the few attempts to examine this issue at the provincial level of a developing country considering the moderation effect of institutional quality and human capital level.

A number of recent artificial intelligence (AI)-enabled technologies, including summarisers, paraphrasers and the cutting-edge chatbots not only have outstanding potentials in modern educational systems but also could lead to a dramatic paradigm shift in the whole education process. This study aims to explore the factors that shape the academic community’s desire and intention to use AI conversational chatbot technology, with a particular focus on the leading ChatGPT.

This study uses a mixed method approach to explore the educators’ adoption of chatbots through an empirically validated model. The model, known as the “Educators’ Adoption of ChatGPT”, was developed by integrating the theoretical foundations of both the Unified Theory of Acceptance and Use of Technology and Status Quo Bias (SQB) frameworks, as well as insights gathered from interviews. The relationships within this model were then tested using a quantitative approach. The partial least squares-structural equation modelling method was used to analyse 243 valid survey responses.

The outcomes of the analysis indicated that perceived educators’ effort expectancy, educators’ autonomous motivation, perceived learners’ AI competency, perceived educators’ competency, innovative behaviour towards technological agility and perceived students’ engagement are significant determinants of educators’ intention to use chatbots. In contrast, perceived unfair evaluation of students, perceived students’ overreliance and perceived bias/inaccuracies were shown to have significant impacts on the resistance to use the technology, which typically implies a negatively significant influence on the educators’ use intention. Interestingly, perceived fraudulent use of ChatGPT was proven insignificant on the resistance to use chatbots.

This study makes a significant contribution to the field of educational technology by filling the gap in research on the use and acceptance of AI-enabled assistants in education. It proposes an original, empirically validated model of educator adoption, which identifies the factors that influence educators’ willingness to use chatbots in higher education and offers valuable insights for practical implementation.

The paper aims to explore the sided challenges facing the accounting profession in an advanced digitalised future where humans and robots will collaborate in working teams.

Employing a qualitative approach, the paper conducts a reflexive thematic analysis to identify challenges and associated socio-ethical risks of digitalisation; it then introduces an ethical decision-making model aimed at addressing these challenges.

Key professional accountants’ (PAs) sided challenges refer to autonomy, privacy, balance of power, security, human dignity, non-maleficence and justice, each of them possessing multifaceted dimensions that are interconnected dynamically to create a complex web of socio-ethical risks.

The ethical decision-making pathways corresponding to each detected challenges provide a useful reference and guideline for PAs in the digitalised future of the profession.

Using an anthropocentric perspective, the research addresses the sided challenges of accounting profession’s accelerated digitalisation; it contributes to fostering accountability and legitimacy of the accounting profession which serves the public interest.

By innovatively intertwining ethical positions with decision-making pathways, the paper offers a potential solution to address digitalisation’s sided challenges that might interfere with practitioners’ professional judgement and identity.

This study aims to analyse the ethical implications associated with the development of artificial intelligence (AI) technologies and to examine the potential ethical ramifications of AI technologies.

This study undertakes a thorough examination of existing academic literature pertaining to the ethical considerations surrounding AI. Additionally, it conducts in-depth interviews with individuals to explore the potential benefits and drawbacks of AI technology operating as autonomous ethical agents. A total of 20 semi-structured interviews were conducted, and the data were transcribed using grounded theory methodology.

The study asserts the importance of fostering an ethical environment in the progress of AI and suggests potential avenues for further investigation in the field of AI ethics. The study finds privacy and security, bias and fairness, trust and reliability, transparency and human–AI interactions as major ethical concerns.

The implications of the study are far-reaching and span across various domains, including policy development, design of AI systems, establishment of trust, education and training, public awareness and further research. Notwithstanding the potential biases inherent in purposive sampling, the constantly evolving landscape of AI ethics and the challenge of extrapolating findings to all AI applications and contexts, limitations may still manifest.

The novelty of the study is attributed to its comprehensive methodology, which encompasses a wide range of stakeholder perspectives on the ethical implications of AI in the corporate sector. The ultimate goal is to promote the development of AI systems that exhibit responsibility, transparency and accountability.

Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.

The crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.

In controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.

The implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.

This first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.

This paper aims to provide insights into the environment needed for advancing a digitally enabled circular plastic economy in Africa. It explores important technical and social paradigms for the transition.

This study adopted an interpretivist paradigm, drawing on thematic analysis on qualitative data from an inter-sectoral engagement with 69 circular economy stakeholders across the continent.

The results shows that, while substantial progress has been made with regard to the development and deployment of niche innovations in Africa, the overall progress of circular plastic economy is slowed due to relatively minimal changes at the regime levels as well as pressures from the exogenous landscape. The study highlights that regime changes are crucial for disrupting the entrenched linear plastic economy in developing countries, which is supported by significant sunk investment and corporate state capture.

The main limitation of this study is with the sample as it uses data collected from five countries. Therefore, while it offers a panoramic view of multi-level synergy of actors and sectors across African countries, it is limited in its scope and ability to illuminate country-specific nuances and peculiarities.

The study underlines the importance of policy innovations and regulatory changes in order for technologies to have a meaningful contribution to the transition to a circular plastic economy.

The study makes an important theoretical contribution by using empirical evidence from various African regions to articulate the critical importance of the regime dimension in accelerating the circular economy transition in general, and the circular plastic economy in particular, in Africa.

This study aims to explore AI’s potential impact on the broader landscape of higher education in Tanzania. This study contributes to the ongoing discussion of AI’s potential to transform higher education and highlights the ethical considerations and challenges that must be addressed to ensure its successful implementation. This study informs future research and policy decisions in education and technology by providing a detailed understanding of AI’s perceived benefits and challenges in higher education.

A mixed-methods approach was used, which involves collecting and analyzing quantitative and qualitative data to understand the research problem comprehensively. This approach allowed data triangulation and led to a more robust and detailed understanding of this study.

In this study, it was discovered that enhanced assessment, time-saving, personalized learning, improved accessibility and detecting cheating are the perceived benefits of AI as a tool for enhancing learning in higher education, while cost and infrastructure, academic misconduct, data privacy and security, bias and ethical concerns and lack of human interaction are the perceived challenges of AI as a tool for enhancing learning in higher education. Further, it was revealed that students are more accepting of using AI tools in the classroom because they think they are more effective and engaging. On the other hand, faculty were more cautious and skeptical about employing AI tools in the classroom because they worried about how it would affect their teaching methods and job security.

The data collection was not conducted face-to-face. To fully capture respondents’ emotional responses, feelings, facial expressions, reactions, or body language was challenging. However, a sufficient number of individuals who participated were very cooperative, and their knowledge was very beneficial in understanding the topic.

A unique view of this study is a clear understanding of the perceived benefits and challenges of using AI as a tool for enhancing learning in higher education, as well as the variations in these perceptions among students and faculty. By examining the perspectives of both groups, this study provides a comprehensive understanding of the complex role of AI in higher education. Understanding the broader implications of AI in higher education can inform policy decisions and ensure that AI is used responsibly and ethically.

The present study aims to investigate the effects of sector 4.0 technologies, particularly Financial Technology (Fintech), on Sustainable Business Success (SBS) within the Indian garment sector. It aims to analyse the impact of Fintech Integration (IF) and Extent of Financial Knowledge (EFK) on sustainability performance, with a focus on understanding the mediating effect of Financial Accessibility (FA) in this relationship.

The study utilizes covariance-based structural equation modelling (CB-SEM) to analyse data collected from 683 enterprises in the Indian garment sector. The theoretical frameworks of Ecological Modernization Theory (EMT) and the Resource-Based View are employed to guide the research.

The investigation reveals that Fintech Integration (IF) and environmental friendliness knowledge significantly impact the promotion and maintenance of sustainability within the Indian garment sector. Moreover, the study highlights the moderating influence of financial Accessibility (FA) on the associations among fintech integration, Extent of Financial Knowledge, and sustainability attainment. Furthermore, sensitivity studies demonstrate that improved financial access positively affects a firm’s sustainability performance.

This study contributes to the existing literature by addressing significant knowledge gaps and offering practical insights for managers and policymakers in the Ready-Made Garments (RMG) industry. It provides a comprehensive approach that integrates fintech and financial expertise to enhance credit accessibility and foster long-term viability for enterprises within the Indian garment sector. The originality lies in its holistic perspective, combining technological integration with the Extent of Financial Knowledge to drive sustainability in a specific industrial context, thus providing valuable guidance for industry stakeholders.

This paper aims to clarify the relationship between digital transformation and labor structure from the perspectives of microenterprise business strategies and factor allocation efficiency. It attempts to address the gap in existing research by explaining the impact of digital transformation on multidimensional workforce structures and the positive effects of this structural adjustment on labor allocation efficiency. In addition, the study further explores the economic ramifications of digital transformation, clarifying the correlation between changes in labor force structure and enterprise human resource allocation, thus enhancing the employment mobility effects of digital innovation at the enterprise level.

In contrast to prior research, our approach uses text analytics to assess the internal labor structure, incorporating labor skill, position and age into the analytical framework. This approach yields a more comprehensive data set, shedding light on variations in multidimensional employment structures.

The paper asserts that digital transformation significantly influences labor structure changes, evidenced by increased proportions of high-skilled, non-routine and younger laborers, as well as decreased shares of low-skilled, routine and older-age workers. Furthermore, it captures internal labor structure impacts, influenced by enterprise size, ownership, industry density and regional digitization levels. Mechanism analysis indicates moderation of digital transformation effects on labor structure by innovative tasks, labor productivity and management shareholding.

The paper reveals the specific impact of corporate digital transformation on workforce structure, enriching the employment mobility effects of digital innovation at the enterprise level and providing theoretical support for the formulation and implementation of relevant policies.

First, this paper delves into the impact of digital transformation on the internal labor structure from a microlevel perspective, elucidating its mechanisms. Second, in contrast to prior research, it uses text analytics to assess the internal labor structure, incorporating labor skill, position and age into the analytical framework. This approach yields a more comprehensive data set, shedding light on variations in multidimensional employment structures. Lastly, the study investigates the economic ramifications of shifts in employment structures. The findings of this study furnish novel empirical evidence for the debate regarding whether digital transformation can indeed enhance labor allocation efficiency.

This study aims to examine the complex effects of foreign direct investment (FDI) on China’s agricultural total factor productivity (TFP) from 2005 to 2020. It also explores the role of absorptive capacity as a moderating factor during this period.

Employing provincial panel data from China, this research measures agricultural TFP using the Stochastic Frontier Approach (SFA)-Malmquist method. The impact of FDI on agricultural productivity is further analyzed using a nondynamic panel threshold model.

The results highlight technological progress as the main driver of agricultural TFP growth in China. Agricultural FDI (AFDI) seems to impede TFP development, whereas nonagricultural FDI (NAFDI) shows a distinct positive spillover effect. The study reveals a threshold in absorptive capacity that affects both the direct and spillover impacts of FDI. Provinces with higher absorptive capacity are less negatively impacted by AFDI and more likely to benefit from FDI spillovers (FDISs).

This study provides new insights into the intricate relationship between FDI, absorptive capacity and agricultural productivity. It underscores the importance of optimizing technological progress and research and development (R&D) to enhance agricultural productivity in China.