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The purpose of this study is to shed light on the relationships between the different types of ownership structure and tax avoidance activities and examine the moderating effect of audit quality.

This study used secondary data from the listed companies in Amman Stock Exchange (2009–2020). To obtain additional robust findings, this study used various proxies for measuring tax avoidance (effective tax rate [ETR] and cash flow effective tax rate [CFETR]).

Relying on various proxies for tax avoidance, the results reveal that family and managerial ownership lead to exacerbating tax avoidance activities. Although institutional and board ownership have a positive impact on ETR and CFETR, which indicate that these type of ownership have a negative impact on tax avoidance. Audit quality also has a significant role in moderating the ownership structure–tax avoidance relationships. Besides, the results reveal that audit firm size is not merely symbolic words, but it contributes to reducing and restricting tax aggressiveness.

This study has policy implications related to the policymakers in creating future tax policies to minimize and avoid tax avoidance activities. Results of this study can be used to improve awareness among the various owners and to reduce the tax avoidance practices in the developing countries. It also determines a good agenda for research in the relationships between ownership identities, audit quality and tax avoidance, which also can be used to encourage and guide future studies.

This research extends the existing literature by examining both the direct and indirect influence of ownership structure on tax avoidance in Jordanian firms by including audit quality as a moderating variable. This is a pioneering and unique study examining the joint influence of the different forms of ownership on tax avoidance. To the best of the author’s knowledge, this study is the first of its kind that examines the interaction influences between the various identities of ownership and audit quality on the tax avoidance activities in the Jordanian context.

The authors originally explore the factors for blockchain technology (BCT) adoption in agricultural supply chains (ASCs) to enhance circularity and understand the dependencies, hierarchical structure and causalities between these factors.

Based on an extant literature review and expert opinion, the present study identified ten enablers for adopting BCT to leverage the circular economy (CE) practices in the ASCs. Then, using an integrated interpretive structural modeling and decision-making trial and evaluation laboratory (ISM-DEMATEL) approach, hierarchical and cause–effect relationships are established.

It was observed that traceability is the most prominent enabler from the CE perspective in ASCs. However, traceability, being a net effect enabler, will be realized through the achievement of other cause enablers, such as seamless connectivity and information flow and decentralized and distributed ledger technology. The authors also propose a 12 Rs framework for enhancing circularity in ASC operations.

The paper identifies enablers to BCT adoption that will enhance circularity in ASC operations. The ISM hierarchical model is based on the driving and dependence powers of the enablers, and DEMATEL aids in identifying causal relationships among the enablers.

The study's findings and proposed 12 Rs framework may help the practitioners and policymakers devise effective BCT implementation strategies in ASCs, thereby empowering sustainability and circularity.

This study enriches the literature by identifying and modeling enablers for BCT adoption in ASCs. The study also proposes a new 12 Rs framework to help enhance ASC circularity.

This study aims to espouse the concept of sustainable environment, social and governance (ESG) practices as the proxies of sustainability reporting (SR). In the presence of smart technology adoption (STA), ESG drives total quality management (TQM) of sustainability matters in advanced medical device (AMD) companies post-pandemic.

This study uses two stages of rigorous data collection. Two focus groups comprising board members, investment advisers and senior managers of AMD were formed to establish the external validity of the constructs proposition. It then used a Web survey to solicit 240 respondents from AMD. Data were analysed using the partial least squares structural equation modelling (PLS-SEM) to provide robustness of predictive power in the model estimation.

Results show SR has positively impacted TQM. It reveals positive relationships between SR and ESG and ESG and TQM. Findings indicate that STA moderates the relationships between ESG and TQM with large effect sizes.

This study offers direction to expedite strategies and action plans by sustainability practitioners in an asymptotic quest for ESG and TQM best practices. Future research should focus on the protection of sustainable social using qualitative methodology.

Using the lens of corporate sustainability, this study develops a framework that integrates ESG, TQM and STA to examine the synergistic effects post pandemic. It provides evidence that ESG practices and STA adoption drive TQM in transition to attain sustainability among the AMD at the country level.

Drawing on dual process theory as the overarching framework, this study investigates how different types of incidental vocabulary learning yield different performance, repetition, and continuance intention outcomes and uncovers the underlying mechanism.

We identify four popular types of incidental learning: traditional, a murder mystery game, noneducational live streaming, and VTuber. We propose that the underlying mechanism is the mediating role of perceived novelty as heuristic processing, and effort and performance expectancy as systematic processing. We conduct a between-subject experiment with four groups for the four types of incidental learning. From a total of 220 subjects, 55 valid responses were collected from each group. Analysis of variance and a partial least squares structural equation model are employed to examine the differences and mechanism.

The results show that noneducational live streaming performs significantly best for all three outcomes. The mechanism test demonstrates that perceived novelty and performance expectancy play significantly positive mediating roles, whereas effort expectancy has a null mediating effect.

The research provides both theoretical and practical implications.

This study aims to examine the association between related-party transactions (RPT) and tax avoidance. The study further investigates whether government ownership improves scrutiny of tax aggressiveness activities among Taiwanese group companies.

The authors used 16,061 firm-year observations derived from the Taiwan Economic Journal Database (TEJ) from 2005 to 2021. The authors applied GLS fixed-effect regression. Additional tests, such as a difference-in-difference examination, propensity score matching (PSM) analysis and other tests were performed to obtain more robust results.

The results show different consequences between eliminated and non-eliminated RPT toward tax avoidance. RPT enhances tax benefits aligned with the efficient contracting hypothesis. Under varying degrees of government control, this paper empirically reveals that government ownership has a role in mitigating tax avoidance. This implies that government control improves corporate governance by balancing opportunistic and efficiency-based tax avoidance.

This paper provides substantial practical implications since using the strategy of reducing taxes through RPT will result in greater tax savings at the business group level. Therefore, RPT is beneficial for enhancing business efficiency. Furthermore, government control increases corporate governance quality, which could lead to balancing tax aggressiveness activity.

Using a unique setting for RPT reporting in Taiwan, this paper divides RPT into eliminated and non-eliminated RPT. The findings offer significant insight for policymakers, investors and managers regarding the utilization of RPT to enhance efficiency in business groups. Additionally, this paper highlights the role of government control in preserving a harmonious balance in tax planning practices.

This study aims to evaluate the performance of LLMs with various prompt engineering strategies in the context of health fact-checking.

Inspired by Dual Process Theory, we introduce two kinds of prompts: Conclusion-first (System 1) and Explanation-first (System 2), and their respective retrieval-augmented variations. We evaluate the performance of these prompts across accuracy, argument elements, common errors and cost-effectiveness. Our study, conducted on two public health fact-checking datasets, categorized 10,212 claims as knowledge, anecdotes and news. To further analyze the reasoning process of LLM, we delve into the argument elements of health fact-checking generated by different prompts, revealing their tendencies in using evidence and contextual qualifiers. We conducted content analysis to identify and compare the common errors across various prompts.

Results indicate that the Conclusion-first prompt performs well in knowledge (89.70%,66.09%), anecdote (79.49%,79.99%) and news (85.61%,85.95%) claims even without retrieval augmentation, proving to be cost-effective. In contrast, the Explanation-first prompt often classifies claims as unknown. However, it significantly boosts accuracy for news claims (87.53%,88.60%) and anecdote claims (87.28%,90.62%) with retrieval augmentation. The Explanation-first prompt is more focused on context specificity and user intent understanding during health fact-checking, showing high potential with retrieval augmentation. Additionally, retrieval-augmented LLMs concentrate more on evidence and context, highlighting the importance of the relevance and safety of retrieved content.

This study offers insights into how a balanced integration could enhance the overall performance of LLMs in critical applications, paving the way for future research on optimizing LLMs for complex cognitive tasks.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2024-0111

The aim of this study is to examine the availability, utilization and effectiveness of various information sources in enhancing the knowledge and practices of Lakadong turmeric farmers.

This study focuses on 125 Lakadong turmeric farmers in Myntkung and Biar villages, Meghalaya, India. A purposive sampling technique was used to select participants, ensuring the representation of various ages, experiences and agricultural practices. A mixed-methods approach was used, including structured interviews and focus group discussions, to gather information on the farmers.

The study found that most of the Lakadong turmeric farmers (35.2%) needed information on farming techniques and practices and 93.6% of farmers approached their friends, relatives or fellow farmers to access any information. For most farmers, 77 (62%) relied on word-of-mouth to get information. After using the information effectively and efficiently, most farmers (46.4%) agreed that there was an increase in Lakadong turmeric yields, improved crop quality and reduced pest infestation.

The study highlights informational gaps in the agricultural sector, empowering farmers to explore alternative sources. It suggests that understanding farmers’ unique needs can improve agricultural extension agencies’ programs and resources. Policymakers should invest in digital literacy programs and regional information resources. The study also suggests integrating information literacy training into agricultural operations, fostering collaboration among farmers, institutions and information providers. The geographical indication tag on Lakadong turmeric can also empowers local communities by fostering economic opportunities through the promotion and protection of this prized spice.

Existing research tends to focus more broadly on agricultural information dissemination or other crops, leaving a notable gap in the literature regarding the unique informational needs and access challenges faced by Lakadong turmeric farmers.

This research examines the long-held belief in the adaption-related literature that a firm’s ability to adapt organizational structure to changing environments is related to superior performance. We create and test a construct that reflects an organization’s ability to change structure, which we call Supply Chain Structural Adaptability (SCSA), rather than relying on proxies (e.g. structural form or organizational modularity) used in prior studies.

Quantitative data was collected from 218 firms to test our conceptual model.

We find that SCSA has a mixed effect on profitable growth under various environmental conditions.

We find evidence that refutes two widely held assumptions in organization research, namely, that structural form serves as a reasonable proxy for structural adaptability and that the benefits of adaptive capabilities always increase as environmental dynamism increases.

Capital markets are the backbone of an economy. COVID-19 has created an unacceptable and unpredictable economic environment worldwide, resulting in a significant setback for securities exchanges. India also experienced two waves of this pandemic, which led to a significant downturn in the capital market.

Researchers have endeavored to study the impact of the first and second waves of COVID-19 on the performance of various sectoral stocks in India. The performance of selected sectoral indices of the Bombay Stock Exchange was compared with the market performance of the S&P BSE 100. An event study was conducted to analyze the normal return, abnormal return (A.R.), and t-statistics of A.R. for various sectoral stocks. In addition, the abnormal returns of sectoral stocks between the first and second waves of COVID-19 in India were compared

The results of the tests showed heterogeneous A.R. between different sectors in both the first and second waves of COVID-19 in India. Positive investor outlook and government financial support programs for various sectors helped them recover from the second wave of COVID-19.

The study analyzed the impact of the peak of the first and second waves of COVID-19 on selected sectoral indices. There may be several reasons for the performance of this particular stock index. However, we have tried to analyze the best possible reasons for this turbulence in the performance of stocks of selected sectoral indices. The study can be further analyzed to examine the long-term impact of such a pandemic on other sectors.

The study is based on the panic behavior of investors during such a pandemic. No one was prepared for this and expected this pandemic to last this long. This pandemic has taught so many lessons to everyone involved. Investors need to be prepared and cautious for such unforeseen disasters before making any investment decision. They need to analyze which industry can survive under such circumstances, and then they should invest there. Industries and enterprises must adapt and improve by honestly looking at their weaknesses and trying to meet investors' expectations.

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.