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This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance.

In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employee’s performance meets expectations or needs improvement.

All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce.

The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the model’s effectiveness across various contexts.

The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment.

Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance.

This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach.

Environment, social and governance (ESG) criteria are a quantum of a company’s performance in the environmental, social and governance aspects. A company’s worth may be determined not only by its earnings but also by its knowledge and sensitivity towards its stakeholders and society. The study aims to rank the companies and determine which company is superior based on ESG criteria. The authors employed the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in this study. The companies are ranked with this standardized method comprehending which company is the best taking into consideration the various environmental, social and governance factors. The authors have evaluated four companies in the electric utilities and IPPs industry. The results of the study rank these four companies on the basis of ESG criteria. Interestingly, the rankings calculated for ESG criteria are identical to the rankings calculated by a well-known ESG rating agency. To the best of author’s knowledge, this work is among the first to use the TOPSIS method to find rankings of the companies on the basis on ESG criteria. The work provides practical implications regarding convenient to use when finding ESG rankings for companies. This might be the most effective way for investors or other parties to learn which firm is the greatest for sustainable investing.

In the face of escalating urban populations, the quest for seamless mobility in cities becomes increasingly complex, even in regions where transit options are presumably accessible within the developing world. The imperative to confront urban mobility challenges and forge sustainable cities equipped with adept transportation and traffic management systems cannot be overstated. This study delves into the technological paradigms employed by developed nations and evaluates their pertinence in the current milieu for mitigating urban mobility challenges. Simultaneously, it scrutinizes the deployment of smart city technologies (SCTs) within developing nations, investigating potential technological strides that can be harnessed to achieve sustainable urban transportation. By dissecting the intricacies of SCTs in developing countries, the study aims to unearth viable technological advancements that can be judiciously implemented to foster sustainable urban mobility. It aspires to provide nuanced recommendations for the integration of latent SCTs, unlocking untapped potential to augment the sustainability of urban transportation in the developing world. The research also elucidates strategies geared towards fostering international collaborations which are instrumental in propelling the development of cities characterized by equity and inclusivity. The study underscores the significance of a global alliance in overcoming urban challenges, emphasizing the need for shared knowledge, resources and experiences to propel the evolution of cities towards a more sustainable and equitable future. This research serves as a comprehensive exploration of the intricate interplay between technology, urbanization and international cooperation, offering insights and recommendations pivotal to steering the trajectory of urban development in developing nations.

The Metaverse and Internet of Things (IoT) have emerged like a tidal wave, and it is creating a transformative impact on society and industry. The metaverse and IoT changed the way companies were operating earlier and customers were living their lives. On the other hand, Metaverse enriches the customer experience by offering a matchless virtual experience using augmented reality and state-of-the-art technology. The metaverse and the IoT can be used in various sectors such as manufacturing, transportation, retailing, health care, banking, and automobiles to make cities smart. Metaverse and IoT provide real-time data, reduces operational cost and errors, improves efficiency, and helps industries to make intelligent decisions. Although the IoT and Metaverse offer significant benefits, it is not free from limitations. Ethical dilemmas, privacy issues, data breaches, and difficulty in extracting relevant data impose serious challenges that need to be addressed. There is an urgent and dire need to create a trade-off between the interest of the business and the privacy and security of customers. This chapter aims to discover the potential of Metaverse and IoT in various sectors (e.g., healthcare, transportation, and electronics). This study will bring significant insights to researchers and policymakers by exploring the likely benefits of IoT and metaverse in diverse sectors to develop smart cities. This chapter will also explain the challenges of metaverse and IoT, which can be addressed by integrating data analytics tools optimally and efficiently.

This chapter aims to comprehend the challenges of urban mobility in smart cities and the measures to mitigate these challenges. This chapter also tries to study how sustainable mobility can be achieved to improve the quality of life in smart cities. In this direction, this chapter reviews various newspapers, academic reports, travel reports, government portals, government websites and research papers. Results and discussions are then carried out based on such data. So, the sources of data are secondary in nature. This chapter presented an overall comprehensive discussion on urban mobility, its challenges and the measures to combat these challenges. Further, this chapter confirmed that sustainable mobility helps in improving the quality of life. Practically, this chapter offers many implications to urban transport companies, providers, government and policymakers. Urban transport companies, providers, government and policymakers may be able to understand that the path to leading a quality life in today's smart cities lies in sustainable mobility. This chapter is original in the sense that the researchers, to their limited knowledge, could not find a chapter that discusses the challenges posed by smart cities in the form of urban mobility, and that sustainable mobility is the only path to enhance the quality of life by making the environment sustainable.

Celebrity endorsers are usually considered to bring positive effects to associated nodes, such as brands and corporations. However, limited evidence suggests that brands and corporations are equally responsible for affecting celebrities and their credibility. Drawing on associative network theory, this study explores the effects of brand credibility and corporate credibility on celebrity credibility, both directly and through the mediating and moderating effects of advertising credibility. The research addresses three main issues: (1) whether brand credibility, corporate credibility and advertising credibility have significant effects on celebrity credibility; (2) whether advertising credibility has a significant mediating effect on the effects of brand credibility and corporate credibility on celebrity credibility and (3) whether advertising credibility has a significant moderating effect on the effects of brand credibility and corporate credibility on celebrity credibility.

The study used a quantitative approach involving structural equation modelling. Data were collected from 675 participants from London and focussed on four leading international brands, corporations and celebrity endorsers.

The findings show that brand credibility and advertising credibility have positive direct effects on celebrity credibility; and that advertising credibility mediates the effects of both credibility constructs on celebrity credibility. Furthermore, moderating effects of advertising credibility are also found.

This study will help managers to understand the reverse effects, i.e. the effects of brand credibility and corporate credibility on celebrity credibility. They will be able to understand that a credible brand and corporation like a credible celebrity can also bring significant effects on the associated elements. This will help them to recruit celebrity endorsers who have historically earned their credibility from previous endorsements of credible brands and corporations. Further, these findings will help managers to understand that credibility of the brand and corporation can also affect the credibility of the associated advertising, resulting in having a significant effect on the credibility of the celebrity. This on the consumers’ side will enhance their preferences, attitudes and behaviours, while for the corporation, it will enhance their economic and commercial performance.

This is the first study in the literature, where a conceptual model based on the reverse effects of both credibility constructs on celebrity credibility is examined, directly and based on the moderating and mediating effects of advertising credibility. Hence, the contributions to the literature are threefold: first, the study examines the reverse effect of celebrity endorsement, whereby the credibility of a brand or corporation is transferred to a celebrity endorser; second, the study examines the mediating and moderating effects of advertising credibility on this reverse effect and finally, associative network theory is used to examine the importance of the model.

Space tourism is currently experiencing significant attention because of its rapid and burgeoning development in the present era. This surge has resulted in an unprecedented growth in publications dedicated to unravelling the intricacies of space tourism. However, there is a conspicuous absence of a large-scale bibliometric analysis focusing on space tourism research from 1993 to 2022. Therefore, the aim of this study is to fill this research gap by examining and mapping the scholarly output published across the world in the spectrum of space tourism over the past 30 years (1993–2022).

A corpus of 7,438 publications pertaining to space tourism published from 1993 to 2022 was gathered from the Web of Science Core Collection. Accordingly, bibliometrix package in R and VOSviewer software were used to conduct a comprehensive bibliometric analysis.

The current study highlights a significant surge in publications related to space tourism, indicating a heightened scholarly interest and a significant paradigm shift in its exploration. Scott M. Smith, affiliated with National Aeronautics Space Administration Johnson Space Center, emerges as the most prolific author. Leading journals in disseminating space tourism research are Acta Astronautica and Aviation Space and Environmental Medicine. Keyword analysis revealed hotspots such as “space flight”, “simulated microgravity”, “weightlessness” and “stress”, while research gaps include “skylab”, “shuttle”, “cartilage”, “herpes virus” and “herniation”, offering potential avenues for exploration.

This study’s implications empower stakeholders with actionable insights and deepen the understanding of the evolving landscape of space tourism research, fostering an environment conducive to continuous exploration and innovation in this burgeoning field.

This study enriches the understanding of global space tourism research and offers valuable insights applicable to a diverse audience, including researchers, policymakers and industry stakeholders. The broad applicability of the study’s findings underscores its significance, serving as a guide for strategic decision-making and shaping research agendas in the dynamic realm of space tourism.