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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.

This study aims to examine the impact of online travel reviews (OTR) on tourists’ intention to travel based on the stimulus–organism–response (SOR) model. Further, it explored the mediating effects of tourist trust in OTR.

In this direction, this study proposes and empirically validates a conceptual model after collecting data from 299 Indian consumers. Proposed hypotheses were tested by applying the structural equation modelling technique. Bootstrapping method was used for mediation testing.

The findings revealed that various attributes of OTR exert differential impacts on travel intention. The study also confirmed the mediating role of tourist trust in OTR.

This study offers significant practical implications for travel marketers. To capitalize on OTR, travel marketers are recommended to develop an effective and efficient online reviews management system. This will improve the quality, valence, quantity and consistency of OTR, which in turn will enhance tourist trust in OTR, leading to improved travel intention.

No empirical evidence has been traced on how OTR enhances tourist trust in OTR and their travel intention. In support of this, the present study proposes and empirically validates an extensive model to comprehend the role of various drivers of OTR in improving tourist trust in OTR, leading to enhanced travel intention based on the SOR model.

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.

The aim of this study is to perform a systematic literature review on retail agglomeration literature and present an agenda of future research in this domain.

To synthesize and evaluate the retail agglomeration literature, the study adopts a structured systematic literature review approach. Additionally, the study employs the Theory-Context-Characteristics-Methodology (TCCM) framework to present future research directions in the retail agglomeration domain.

This review proposes a conceptual framework showing the relationships between the antecedents, mediators and consequences reported in the retail agglomeration literature. Lexicometric analysis shows that the key themes of retail agglomeration research are retail store selection and retail performance, retail location strategy and store format, customer perceptions, behavior and expectations.

Specific criteria used for the inclusion of literature limits articles considered for the current systematic review. Also, only those articles published in English were considered.

Based on the proposed model, this review presents strategies to enhance the performance of retail agglomeration.

This study has systematically synthesized the retail agglomeration literature to explore its development over time and proposes a research framework which provides a comprehensive understanding of retail agglomeration literature.

This study aimed to explore the impact of Artificial Intelligence (AI) characteristics, namely Perceived Animacy (PAN), perceived intelligence (PIN), and perceived anthropomorphism (PAI), on user satisfaction (ESA) and continuous intentions (CIN) by integrating Expectation Confirmation Theory (ECT), with a particular focus on Generation Y and Z.

Using a quantitative method, the study collected 495 data from Gen Y (204) and Z (291) respondents who were users of digital banking apps through structured questionnaires that were analysed using PLS-SEM. The latter helped investigate the driving forces of AI characteristics and user behavioural intentions as well as reveal generation-specific features of digital banking engagement.

The study revealed that PAN and PIN have significant positive effects on the anthropomorphic perceptions of digital banking apps, which in turn increases perceived usefulness, satisfaction, and continuous intentions. In particular, the influence of these AI attributes varies across generations; Gen Y’s loyalty is mostly based on the benefits derived from AI features, whereas Gen Z places a greater value on the anthropomorphic factor of AI. This marked a generational shift in the demand for digital banking services.

The specificity of Indian Gen Y and Z users defines the scope of this study, suggesting that demographic and geographical boundaries can be broadened in future AI-related banking research.

The results have important implications for bank executive officers and policymakers in developing AI-supported digital banking interfaces that appeal to the unique tastes of millennial customers, thus emphasising the importance of personalising AI functionalities to enhance user participation and loyalty.

This study enriches the digital banking literature by combining AI attributes with ECT, offering a granular understanding of AI’s role in modulating young consumers' satisfaction and continuance intentions. It underscores the strategic imperative of AI in cultivating compelling and loyalty-inducing digital banking environments tailored to the evolving expectations of Generations Y and Z.

Smartphone apps collect users' personal information, which triggers privacy concerns for app users. Consequently, app users restrict apps from accessing their personal information. This may impact the effectiveness of in-app advertising. However, research has not yet demonstrated what factors impact app users' decisions to use apps with restricted permissions. This study is aimed to bridge this gap.

Using a quantitative research method, the authors collected the data from 384 app users via a structured questionnaire. The data were analysed using AMOS and fuzzy-set qualitative comparative analysis (fsQCA).

The findings suggest privacy concerns and risks have a significant positive effect on app usage with restricted permissions, whilst reputation, trust and perceived benefits have significant negative impact on it. Some app-related factors, such as the number of apps installed and type of apps, also impact app usage with restricted permissions.

Based on the findings, the authors provided several implications for app stores, app developers and app marketers.

This study examines the factors that influence smartphone users' decisions to use apps with restricted permission requests. By doing this, the authors' study contributes to the consumer behaviour literature in the context of smartphone app usage. Also, by explaining the underlying mechanisms through which the principles of communication privacy management theory operate in smartphone app context, the authors' research contributes to the communication privacy management theory.