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This paper aims to present a systematic literature review and bibliometric analysis of research papers published on chat generative pre-trained transformer (ChatGPT), an OpenAI-developed large-scale generative language model. The study’s objective is to provide a comprehensive assessment of the present status of research on ChatGPT and identify current trends and themes in the literature.

A total of 328 research article data was extracted from Scopus for bibliometric analysis, to investigate publishing trends, productive countries and keyword analysis around the topic and 34 relevant research publications were selected for an in-depth systematic literature review.

The findings indicate that ChatGPT research is still in its early stages, with the current emphasis on applications such as natural language processing and understanding, dialogue systems, speech processing and recognition, learning systems, chatbots and response generation. The USA is at the forefront of publishing on this topic and new keywords, e.g. “patient care”, “medical”, “higher education” and so on are emerging themes around the topic.

These findings underscore the importance of ongoing research and development to address these limitations and ensure that ChatGPT is used responsibly and ethically. While systematic review research on ChatGPT heralds exciting opportunities, it also demands a careful understanding of its nuances to harness its potential effectively.

Overall, this study provides a valuable resource for researchers and practitioners interested in ChatGPT at this early stage and helps to identify the grey areas around this topic.

This study investigates the role of market concentration and efficiency in banking system stability during the COVID-19 pandemic. We empirically test the hypothesis that market concentration and efficiency are significant determinants of bank performance and stability during the time of crises, using a sample of 575 banks in 20 countries in the Middle East and North Africa (MENA).

The main sources of bank data are the BankScope and BankFocus (Bureau van Dijk) databases, World Bank development indicators, and official websites of banks in MENA countries. This study combined descriptive and analytical approaches. We utilize a panel dataset and adopt panel data econometric techniques such as fixed/random effects and the Generalized Method of Moments (GMM) estimator.

The results reveal that market concentration negatively affects bank profitability, whereas improved efficiency further enhances bank performance and contributes to the banking sector’s overall stability. Furthermore, our analysis indicates that during the COVID-19 pandemic, bank stability strongly depended on the level of market concentration, but not on bank efficiency. However, more efficient banks are more profitable and stable if the banking institutions are Islamic. Similarly, Islamic banks with the same level of efficiency demonstrated better overall financial performance during the pandemic than their conventional peers did.

The main limitation is related to the period of COVID-19 pandemic that was covered in this paper (2020–2021). Therefore, further investigation of the COVID-19 effects on bank profitability and risk will require an extended period of the pandemic crisis, including 2022.

This study provides information that will enable bank managers and policymakers in MENA countries to assess the growing impact of market concentration and efficiency on the banking sector stability. It also helps them in formulating suitable strategies to mitigate the adverse consequences of the COVID-19 pandemic. Our recommendations are useful guides for policymakers and regulators in countries where Islamic and conventional banking systems co-exist and compete, based on different business models and risk management practices.

The authors contribute to the banking stability literature by investigating the role of market concentration and efficiency as the main determinants of bank performance and stability during the COVID-19 pandemic. This study is the first to analyze banking sector stability in the MENA region, using both individual and risk-adjusted aggregated performance measures.

First, with real-life examples and current research, this study aims to demonstrate the existence of various forms of ostracism (linguistic, gender, social and workplace). Second, following the “need-threat model,” this research addresses the previously unaddressed topic of coping with, reducing, mitigating or curbing workplace ostracism. Moreover, the researchers also proposed a “multiplying effect model” of ostracism.

Data was gathered from 199 service sector employees. The NVivo software is used for the thematic analysis of qualitative data(suggestions) gathered using open ended question on how to mitigate/reduce/curb ostracism.

The results generated were the suggestive measures, which were further categorized under three major themes: individual, society and organizational. The measures to reduce, mitigate and stop the practices of workplace ostracism can be initiated on all these three levels.

This is the only study that addresses the subject of decreasing, alleviating or eliminating workplace ostracism and explains the compounding effect of ostracism by suggesting a multiplying effect model. The study will pique the interest of the government and legislators to propose legal measures to prevent ostracism and achieve sustainable development goals (gender equality and reduced inequalities. The study’s practical, social, theoretical and managerial utility are discussed in the implications section.

The present study undertakes an extensive review of the causes of service failures in artificial intelligence (AI) technology literature.

A hybrid review has been employed which includes descriptive analysis, and bibliometric analysis with content analysis of the literature approach to synthesizing existing research on a certain topic. The study has followed the SPAR-4-SLR protocol as outlined by Paul et al. (2021). The search period encompasses the progression of service failure in AI from 2001 to 2023.

From identified theories, theoretical implications are derived, and thematic maps direct future research on topics such as data mining, smart factories, and among others. The key themes are being proposed incorporates technological elements, ethical deliberations, and cooperative endeavours.

This research study makes a valuable contribution to understanding and reducing service defects in AI by providing insights that can inform future investigations and practical implementations. Six key future research directions are derived from the thematic and cluster discussions presented in the content analysis.

This study aims to investigate the relative impacts of the cognitive dissonance on brand switching (BS) and satisfaction level (SAT) by considering the mediation role of purchase regret (PRG) in these relationships in an online shopping context.

The data was collected from 354 participants, and structural equation modeling and mediation analysis were conducted to test the conceptual model hypotheses.

The findings revealed that the cognitive dissonance positively affects PRG. Also, the study indicated a negative and nonsignificant relationship between cognitive dissonance and SAT and a negative significant relationship between cognitive dissonance and BS. In addition, a positive and significant correlation was found between PRG and BS. Furthermore, the results revealed that PRG mediates the relationship between cognitive dissonance and SAT.

This research provides a novel contribution by examining the impact of the cognitive dissonance on BS and SAT and uncovering the underlying mechanism of how and under what conditions cognitive dissonance promotes BS and SAT in an online shopping context.

By examining the impact of the day of the week during the COVID-19 pandemic and the subsequent economic recession, it is possible to provide insights into market behaviour during volatile times that can be furnished to investors and policymakers for informed decisions.

This study investigates the day-of-the-week effect on the Colombo Stock Exchange (CSE), with particular emphasis on the variations in this effect during the COVID-19 pandemic and the subsequent economic crisis.

The study applies the Exponential Generalised Autoregressive Conditional Heteroskedasticity (EGARCH) model, allowing for the evaluation of asymmetric responses to positive and negative shocks. The data span from January 2006 to December 2022 and are segmented into different periods: the entire sample, war and post-war periods, the COVID-19 pandemic and the economic crisis period, each reflecting distinct market conditions.

The study uncovers a significant day-of-the-week effect on the CSE. Mondays and Tuesdays typically show a negative effect, while Thursdays and Fridays display a positive impact. However, this pattern shifts notably during the COVID-19 pandemic, with all weekdays exhibiting significant positive impact, and varies further across different waves of the pandemic. The economic crisis period also shows unique weekday effects, particularly before and after an important political event.

India’s rapid economic growth has triggered a significant transformation in its logistics sector, fueled by comprehensive reforms and digital initiatives outlined in the National Logistics Policy. Smart warehouses, equipped with cutting-edge technologies such as IoT, AI and automation, have taken center stage in this evolution. They play a pivotal role in India’s digital journey, revolutionizing supply chains, reducing costs and boosting productivity. This AI-driven transformation, in alignment with the “Digital India” campaign, positions India as a global logistics leader poised for success in the industry 4.0 era. In this context, this study highlights the significance of smart warehouses and their enablers in the broader context of supply chain and logistics.

This paper utilized the ISM technique to suggest a multi-tiered model for smart warehouse ecosystem enablers in India. Enablers are also graphically categorized by their influence and dependence via MICMAC analysis.

The study not only identifies the 17 key enablers fostering a viable ecosystem for smart warehouses in India but also categorizes them as linkage, autonomous, dependent and independent enablers.

This research provides valuable insights for practitioners aiming to enhance technological infrastructure, reduce costs, minimize wastage and enhance productivity. Moreover, it addresses critical academic and research gaps contributing to the advancement of knowledge in this domain, thus paving the way forward for more research and learning in the field of smart warehouses.

The qualitative modeling is done by collecting experts' opinions using the ISM technique solicits substantial value to this research.

This study examines the associations of a buyer’s online buying intentions, followed by shopping cart abandonment. The objective of the study is to investigate the determinants that trigger a buyer for abandoning the shopping cart, despite the consumers’ intent to buy. This study aims to examine the factors that leads a consumer to abandon the shopping cart, despite the intention to purchase.

A cross-sectional data from 354 millennials was undertaken for the current study. These young consumers were identified to examine the conceptual model, which was conceived using the theories of planned behavior, expectation disconfirmation, along with a few seminal works of consumer behavior and psychology.

The results show perceived risk to be a mediator to a buyer’s online buying intention and shopping cart abandonment, and process satisfaction moderates the association among the two variables in the presence of perceived risk.

This study was conducted with a sample of Indian millennial consumers; hence, its findings may not be generalizable.

This study concludes with important implications for both academicians and e-tailers, which could prove useful in reducing cases of abandonment of shopping carts, due to which, e-tailers do tend to lose out young consumers resulting thereby in revenue loss.