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.

Background: Kangyang tourism, a wellness tourism niche in China, integrates health preservation with tourism through natural and cultural resources. Despite a growing interest in Kangyang tourism, the factors driving tourist loyalty in this sector are underexplored. Methods: Using a sample of 413 tourists, this study employed Covariance-Based Structural Equation Modeling (CB-SEM) to examine the influence of destination image, service quality, tourist satisfaction, and affective commitment on tourist loyalty. Results: The findings reveal that destination image and service quality positively affect tourist satisfaction, affective commitment, and loyalty. Tourist satisfaction and affective commitment are identified as critical drivers of tourist loyalty. Notably, affective commitment plays a stronger role in fostering loyalty compared to satisfaction. Conclusion: These results highlight the importance of a positive destination image and high service quality in enhancing tourist loyalty through increased emotional and psychological attachment. The findings inform strategies for stakeholders to improve Kangyang tourism’s growth by focusing on emotionally engaging experiences and service excellence.

Working Capital Management (hereafter WCM) is the strategic tool that helps a company navigate through challenging economic growth, and influence its competitive performance. Thus, this study examines the impact of WCM on the competitiveness of firms operating in the non-financial sectors in Pakistan. We use the Generalized Method of Moments (GMM) technique to ensure the robustness of our results. The study findings reveal that both a large net trade cycle and surplus working capital have a substantial negative impact on firms’ competitiveness within their respective industries. These results suggest that companies should streamline their investments in working capital accounts and concentrate more resources on long-term projects that maximize value to improve their competitiveness compared to other companies. Therefore, firms that are effectively managing their short-term financial affairs are experiencing much better performance in all aspects of firm performance. The research findings highlight the urgent need for governmental initiatives designed to improve WCM practices in these industries. It is imperative for the management of  companies with excess net working capital to maximize their working capital efficiency, aligning it with industry standards to enhance competitiveness. Moreover, policymakers should prioritize easing access to financial alternatives that allow enterprises to maintain an efficient working capital structure without relying on excessive measures. Furthermore, policymakers should be cautious when determining minimum cash balance requirements in a cash-strapped economy where external financing is relatively more expensive than in other regional economies.

This research was conducted using a survey research method to investigate the influence of Artificial Intelligence (AI) on Nigerian students' academic performances in tertiary institutions. Nigerian tertiary institutions have an estimated population of about 2.5 million students across the universities, polytechnics, monotechnics, and colleges of education. A sample size of 509 was used. The researchers adopted an online questionnaire (Google Form) to administer questions to respondents across Nigeria to elicit responses from the respondents bordering on their awareness and the use of AI and its attendant impacts on their academic performance. Five research objectives were raised for the proper investigation of this study. From the findings of the study, the researchers found that the majority of Nigerian students use AI and that AI has positive impacts on the educational performance of Nigerian students. It was also found that Nigerian students have training on the use of AI for educational purposes and that they are more familiar with Snapchat AI and ChatGPT. Conclusively, AI is useful to students in the sense that it enhances their knowledge of their courses, improves their learning and speaking skills, and helps them to have a quick understanding of their course by way of simplifying technical aspects of their courses. The researchers therefore recommend as follows: Nigerian tertiary institutions should formally train students as well as teachers on the use of AI for academic purposes so that they can understand the ethical implications of the use of AI. Using AI for writing could be interpreted to mean examination malpractice, and this should not be condoned in the educational sector; however, at the moment, a small number of students used AI for examinations. Albeit, the appropriate use of AI should be fully integrated into Nigerian tertiary institutions' curricula.

In today's changing world of work, Strategic Human Resource Management (SHRM)) still focuses on making workers more productive. This study systematically examines the mediating function of incentives both monetary and non-monetary between antecedent characteristics (e.g., leadership, organizational culture) and employee productivity using a systematic literature review (SLR) of papers published from 2010 to 2024. The review adheres to PRISMA principles and integrates 18 peer-reviewed studies chosen through a stringent screening and quality evaluation process from Scopus and Google Scholar. The results show that the success of incentives depends a lot on things like the ideals of the business, the style of leadership, and the demographics of the workforce. Thematic analysis, informed by the Ability-Motivation-Opportunity (AMO) theory and Strategic Human Resource Management (SHRM) frameworks, delineates four principal processes by which incentives affect productivity: goal alignment, perceived equity, motivational pathways, and cultural congruence. The research emphasizes the necessity of customizing incentive systems to specific organizational contexts and offers practical guidance for HR professionals. Recognizing limitations and publishing bias, suggestions for future incentive system design are presented.

This paper uses quantitative research methods to explore the differences in the impact of virtual influencers on different consumer groups in the context of technological integration and innovation. The study uses DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering technology to segment consumers and combines social media behavior analysis with purchase records to collect data to identify differences in consumer behavior under the influence of virtual influencers. Consumers' emotional resonance and brand awareness information about virtual influencers are extracted through sentiment analysis technology. The study finds that there are significant differences in the influence of virtual influencers on different consumer groups, especially in high-potential purchase groups, where the influence of virtual influencers is strong but short-lived. This paper further explores the deep integration of virtual influencer technology with new generation information technologies such as 5G and artificial intelligence, and emphasizes the importance of such technological integration in enhancing the endogenous and empowering capabilities of virtual influencers. The research results show that technological integration and innovation can not only promote the development of virtual influencers, but also provide new technical support for infrastructure construction, especially in the fields of smart cities and industrial production. This paper provides a new theoretical perspective for the market application of virtual influencers and provides practical support for the application of virtual technology in infrastructure construction.