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Talent management is conducted to maximize an organization's overall performance and efficiency which helps to serve as a competitive advantage. Human resource management is a concept which includes human-related activities, but talent management is a strategy which helps to get new talent, develop their skill sets and provide better employee engagement and experience to retain the top potential employees in an organization. Improvement in recruiting and retention of a workforce results from a well-executed talent acquisition approach. In the 21st century, employee retention has become a primary concern for the organizations specially with work from home and hybrid models.

The workforce for tomorrow is going to be very different from what it has been. Technology is transforming the way people work within organizations. The workplace is rapidly evolving in terms of people and processes and is going through a lot of technological changes. The terminologies such as automation, artificial intelligence, augmented reality and block chain technologies are slowly becoming part of the workplace and everyday activities of the organization.

The challenges are many and especially post-pandemic organizations are going through some major changes such as a mindset shift of employees to take up more remote working opportunities, building virtual teams, increase in the gig economy workers (contractual workers) and a diverse workforce which makes it even more challenging for the organization to manage and retain talent.

The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI).

Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research.

The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices.

This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields.

The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

Knowledge is the source of competitive advantage, but when shared at all levels. Unfortunately, there is a universal unruly present in the form of knowledge hiding at employees’ level, but the causes and remedies are still vague as past studies have rarely investigated the causes of daily knowledge hiding behavior. Against this backdrop, this study aims to entail a daily diary method investigation of the role of daily abusive supervision in daily employees’ knowledge hiding through the mediation of dehumanization and moderation of psychological capital.

The data for this study is collected using a daily diary method approach, which estimates the daily workplace events and their continuous influence on employees’ feelings (i.e. dehumanization) and actions (knowledge hiding). The daily responses of 279 respondents were considered useful for analysis purposes.

The findings of the study revealed that the daily events of abusive supervision have both direct and indirect (through dehumanization) influence on employees’ daily knowledge hiding behavior. Moreover, psychosocial capital has a significant conditional influence in the relationships of negative workplace treatments (abusive supervision and dehumanization) and their outcomes (i.e. knowledge hiding).

The study provides some theoretical and practical insights by providing the explanatory and coping mechanism between continuous abusive supervision and daily knowledge hiding behavior.

There is a dearth of literature that has focused on daily episodes of abusive supervision, dehumanization and knowledge hiding behavior. Furthermore, the moderating role of psychological capital has also been rarely investigated.

Using the theoretical lens of appraisal theory, this research aims to investigate the interrelationship between employees' organizational justice perceptions and counterproductive work behaviours (CWBs) through the mediation of negative emotions.

To this end, a sample comprised of 207 banking sector employees of Pakistan was utilized to test hypothesized relationships. The collected data were analyzed through the partial least structural equation modelling technique.

Results show that counterwork behaviours are influenced by distributive and procedural justice perceptions. The mediating effects of negative emotions were also statistically significant between procedural, interpersonal and informational justice perceptions and counterwork behaviours. No gender differences were found between distributive, interpersonal and informational justice perceptions and counterwork behaviours. However, the authors found that procedural justice perceptions of female employees are strongly related to CWBs as compared to male employees.

This research contributes to the existing organizational behaviour literature by empirically testing the hypothesized relationships using the theoretical lens of appraisal theory with advanced quantitative data analysis techniques.

This research assesses the present condition of Circular Economy (CE) practices in green hotels situated in two leading Middle Eastern nations, Saudi Arabia and Egypt. Furthermore, it investigates the crucial impact of Industry 4.0 innovations in facilitating the relationship between the adoption of CE initiatives and hotel performance (HP).

Data from 59 green hotels in Saudi Arabia and 76 in Egypt were collected online, resulting in 400 complete forms, providing a diverse sample size suitable for PLS-SEM analysis using JASP software as a leading machine learning application.

The results demonstrate a notable correlation between CE strategies, including redesign, production, reuse and recovery in hotels and HP. Additionally, the impact of Industry 4.0 performance on the relationship between CE and HP is facilitated by four distinct CE loops.

This study offers practical insights into implementing CE practices in the hotel industry, aiding policymakers and establishments in enhancing sustainability and performance. Key stakeholders can use these findings to develop strategies that integrate CE initiatives, improve operational outcomes and align marketing efforts with sustainable practices.

This study is groundbreaking in its exploration of the relationship between CE and the transition to Industry 4.0 in the MENA region, analyzing the specific CE practices that contribute to high HP. The tested model expands on the Natural Resource-Based View and stakeholder theory by incorporating the critical role of Industry 4.0 innovations in the CE–HP nexus.

The study aims to identify and understand the underlying behavioral tendencies and motivations influencing investor sentiments and examines the relationship between these underlying factors and investment decisions during the COVID-19-induced financial risks.

The study uses the primary data and information collected from 300 Indian retail equity investors using a nonprobability sampling technique, specifically purposive and snowball sampling. This research uses the insights from Phuoc Luong and Thi Thu Ha (2011) and Shefrin (2002) to delineate behavioral factors influencing investment decisions. Structural equation modeling estimates the causal relationship between underlying behavioral factors and investment decisions during the COVID-19-induced financial risks.

The study establishes that the “Regret Aversion,” “Gambler’s Fallacy” and “Greed” significantly influence investment decisions, and provide a comprehensive understanding of how psychological motivations shape investor behavior. Notably, “Mental Accounting” and “Conservatism” exhibit insignificance, possibly influenced by the unique socioeconomic context of the pandemic. The research contributes to 35% of variance understanding and prompts the researchers and policymakers to tailor investment strategies aligned to these behavioral tendencies.

The findings hold policy implications for investors and policymakers and provide tailored recommendations including investor education programs and regulatory measures to ensure a resilient and informed investment community in the context of India's evolving financial landscapes.

Theoretically, behavior tendencies and motivations have been strongly linked to investment decisions in the stock market. Yet, empirical evidence on this relationship is limited in developing countries where investors focus on risk management. To the best of the authors’ knowledge, this study is among the first to document the influence of underlying behavioral tendencies and motivation factors on investment decisions regarding retail equity in a developing country.

The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps.

This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC.

This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies.

The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.

The proliferation of lean principles in the construction industry is offset by the enduring uncertainty among industry stakeholders regarding their respective roles in lean implementation. This uncertainty is further compounded by the scarcity of empirical investigations in this area. Consequently, this study undertakes the task of bridging this knowledge gap by identifying the critical roles of lean learners and their indispensable contributions to achieving successful lean implementation.

A qualitative exploratory approach informed by an interpretivism perspective was adopted. The case study strategy was employed to gather data from three contracting organisations that had implemented lean practices. Empirical data was collected through in-depth semi-structured interviews with fifteen industry experts and complemented by document reviews. To analyse the data, a code-based content analysis approach was employed using NVivo software, while Power BI software was utilised to develop a comprehensive force-directed graph visualisation.

The research findings substantiated nine lean learners and unveiled a set of seventy-three roles associated with them. The force-directed graph facilitated the identification of lean learners and their connections to the emerged roles. Notably, the graph highlighted the pivotal role played by project managers and internal lean trainers in ensuring the success of lean implementation, surpassing the contributions of other lean learners.

The implications of findings extend to industry professionals seeking to establish a robust lean learning framework to expedite lean implementation within the construction sector. This study not only provides a comprehensive definition of lean learners’ roles but also transcends specific construction types, making it a significant catalyst for global impact.

Considering information processing theory, this study aims to examine how big data analytics (BDA) mediates the influence of e-procurement coordination (EPC) and e-procurement transactional (EPT) applications on transparency and accountability (TA) in the procurement processes of firms within the United Arab Emirates' private sector. Furthermore, it investigates the moderating role of information processing capabilities (IPCs) in the relationships among EPC, EPT and BDA to clarify their collective impact on enhancing TA and procurement performance.

Data were collected from procurement and information technology professionals in the UAE’s private sector through a Web-based survey. Established scales were used to assess e-procurement, BDA, TA, procurement performance and IPCs. Data were analyzed using partial least squares structural equation modeling.

Integrating e-procurement with BDA demonstrates the potential to improve TA and procurement performance in the UAE’s private sector. BDA is positively associated with EPC and EPT applications use, contributing to increased procurement TA and enhancing overall procurement performance.

Organizations can enhance procurement TA by adopting e-procurement and BDA technologies.

This study identifies the mediating role of BDA in the relationship between e-procurement and procurement TA. In addition, it investigates the moderating role of IPCs in the relationship between e-procurement and BDA.