Human resources (HR) analytics is garnering increasing interest each year and is set to play a pivotal role in the development of human resources. In the present era, numerous companies are harnessing the power of analytics to gain a competitive advantage by comprehending all the vital aspects of their workforce by enhancing employee retention through leveraging HR analytics to inform strategic HR choices. Many companies are now incorporating analytical tools into their HR function as a fact-based approach to develop relevant strategies and make informed decisions in managing their workforce more effectively. However, HR faces several challenges in implementing data analytics. Talent management commonly utilizes data analytics to enhance employee engagement, including retention rates, recruitment, job satisfaction, and happiness. This paper discusses the adoption of HR data analytics to enhance employee retention in the workplace. This study delves into the significance of HR data analytics in the realm of employee retention, aiming to assess the efficacy of data-driven decisions. A thorough examination of scholarly publications was undertaken, encompassing both indexed and non-indexed papers sourced from reputable electronic databases to gain insights into the present understanding of HR analytics and its influence on employee retention. The discussion uncovers that HR analytics has a noteworthy impact on improving employee retention in the workplace.

In today’s fast-paced digital world, generative AI, especially OpenAI’s ChatGPT, has become a game-changing technology with significant effects on education. This study examines public sentiment and discourse surrounding ChatGPT’s role in higher education, as reflected on social media platform X (formerly Twitter). Employing a mixed-methods approach, we conducted a thematic analysis using Leximancer and Voyant Tools and sentiment analysis with SentiStrength on a dataset of 18,763 tweets, subsequently narrowed to 5655 through cleaning and preprocessing. Our findings identified five primary themes: Authenticity, Integrity, Creativity, Productivity, and Research. The sentiment analysis revealed that 46.6% of the tweets expressed positive sentiment, 38.5% were neutral, and 14.8% were negative. The results highlight a general openness to integrating AI in educational contexts, tempered by concerns about academic integrity and ethical considerations. This study underscores the need for ongoing dialogue and ethical frameworks to responsibly navigate AI’s incorporation into education. The insights gained provide a foundation for future research and policy-making, aiming to enhance learning outcomes while safeguarding academic values. Limitations include the focus on English-language tweets, suggesting future research should encompass a broader linguistic and platform scope to capture diverse global perspectives.

The cars industry has undergone significant technological advancements, with data analytics and artificial intelligence (AI) reshaping its operations. This study aims to examine the revolutionary influence of artificial intelligence and data analytics on the cars sector, particularly in terms of supporting sustainable business practices and enhancing profitability. Technology-organization-environment model and the triple bottom line technique were both used in this study to estimate the influence of technological factors, organizational factors, and environmental factors on social, environmental (planet), and economic. The data for this research was collected through a structured questionnaire containing closed questions. A total of 327 participants responded to the questionnaire from different professionals in the cars sector. The study was conducted in the cars industry, where the problem of the study revolved around addressing artificial intelligence in its various aspects and how it can affect sustainable business practices and firms’ profitability. The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs. However, in order to fully benefit from these advantages, new technologies need to be implemented while maintaining moral and legal standards and continuously developing them. This approach has the potential to guide the cars industry towards a future that is environmentally friendly, economically feasible, and socially responsible. The paper’s primary contribution is to assist professionals in the industry in strategically utilizing Artificial Intelligence and data analytics to advance and transform the industry.

Purpose: This study empirically investigates the effect of big data analytics (BDA) on project success (PS). Additionally, in this study, the investigation includes an examination of how intellectual capital (IC) and (KS) act as mediators in the correlation between BDA and KS. Lastly, a connection between entrepreneurial leadership (EL) and BDA is also explored. Design/Methodology- Using a sample of 422 senior-level employees from the IT sector in Peru. The partial least squares structural equation modeling technique tested the hypothesized relationships. Findings- According to the findings, the relationship between BDA and PS is mediated by structural capital (SC) and relational capital (RC), and BDA demonstrates a positive and noteworthy correlation with PS. Furthermore, EL is positively associated with BDA in a significant manner. Practical implications- The finding of this study reinforce the corporate experience of BDA and suggest how senior levels of the IT sector can promote SC, RC, and EL. Originality/Value- This study is one of the first to consider big data analytics as an important antecedent of project success. With little or no research on the interrelationship of big data analytics, intellectual capital and knowledge sharing the study contributes by investigating the mediating role of intellectual capital and knowledge sharing on the relationship between big data analytics and project success.

Purpose: Drawing on the Resource Based View (RBV) and Dynamic Capabilities Theory (DCT), the study seeks to investigate the impact of Big Data Analytics (BDA) on Project Success (PS) through Knowledge Sharing (KS) and Innovation Performance (IPF). Design/Methodology: Survey data were collected from 422 senior-level employees in IT companies, and the proposed relationships were assessed using the SMART-PLS 4 Structural Equation Modeling tool. Findings: The results show a positive and significant indirect effect of big data analytics on project success through knowledge sharing. IPF significantly mediated the relationship between BDA and PS in IT companies. Originality/Value: This study is one of the first to consider big data analytics as an essential antecedent of project success. With little or no research on the interrelationship of big data analytics, knowledge sharing, innovation performance, and organizational performance, the study investigates the mediating role of knowledge sharing and innovation performance on the relationship between BDA and PS. Implications: This study, grounded in RBV and DCT, investigates BDA’s influence on PS through KS and IPF. Implications encompass BDA’s strategic role, KS and IPF mediation, and practical and research-based insights. Findings guide BDA integration, collaborative cultures, and sustained success.

Strategically managing production systems is crucial for creating value and enhancing the competitive capabilities of companies. However, research on organizational culture within these systems is scarce, particularly in the Colombian context. This research aims to evaluate cultural profiles and their impact on the performance of production systems in Colombian firms. The regional focus is vital as cultural and contextual factors can vary significantly between regions, influencing organizational behavior and performance outcomes. To achieve this, we make a study in a sample of Colombian companies, with participation from working students of the Universidad Nacional Abierta y a Distancia (UNAD). We used a data analytics approach to collected data. The results will be relevant to both the scientific community and business practitioners. This research seeks to determine whether the perception of the work environment within a company influences the perceived performance of the company. The findings will provide a deeper understanding of the relationship between organizational culture and production system performance, offering a foundation for business decision-making and enhancing competitiveness in Latin American context.