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This study aims to provide empirical insights pertaining to the impact of trait emotional intelligence on the mental well-being of students in higher education institutions (HEIs) in India.

In the current study, responses from a total of 252 students were randomly taken from different universities of Uttarakhand (India). The analysis was done using structural equation modelling AMOS 23.

The current study empirically established the positive impact of trait emotional intelligence (TEI) on the mental well-being of students and highlighted the relevance of TEI in curbing the psychological distress in students of HEIs.

This study endeavours to bridge the empirical and population gap by examining the emotional intelligence and its impact on mental well-being of the students of Indian HEIs, where studies are still scant and demand massive exploration of the perceptions of students. Strong emotional intelligence is pivotal in strengthening the mental well-being of students so that they can make appropriate decisions pertaining to their career and personal life.

This study aims to address the existing knowledge gap by investigating teachers’ adoption of ChatGPT for educational purposes. The study specifically focuses on identifying the factors that motivate and inhibit teachers in adoption of ChatGPT in higher education institutions (HEIs).

This research has used interpretative phenomenological analysis – a qualitative approach. Through in-depth interviews among the teachers, data was collected to identify the motivating and inhibiting factors that impacted teachers’ willingness to adopt ChatGPT. The data was collected from 48 teachers working across HEIs of Uttarakhand region in India.

The analysis revealed seven themes under motivating factors that encourage teachers to adopt ChatGPT for their educational purposes. These include time factor, tool for competitive edge, learning enhancement tool for students, research facilitator, benefits in educational settings, troubleshooter and easy to use. On the other hand, inhibiting factors comprise five themes, which include technical difficulties, limited features for educational and research purposes, tool for handicapping innovation and creativity, lack of personal touch and ethical considerations.

The findings will be valuable for HEIs in establishing policies that promote the appropriate and effective use of ChatGPT. Moreover, the study provides recommendations to ChatGPT solution providers for improving ChatGPT services for effective adoption of ChatGPT among teachers and implementation at HEIs. Further, it contributes to the body of literature by filling a knowledge gap about teacher adoption of ChatGPT in the HEIs. Through qualitative research, the study has pinpointed specific motivating and inhibiting factors that affect teacher adoption of ChatGPT.

Unlike previous studies that primarily explored the potential advantages and drawbacks of ChatGPT in education, this research study delves deeper into the topic. It makes a substantial contribution to our understanding of ChatGPT adoption among teachers by identifying distinct factors that either motivate or inhibit teachers from adopting ChatGPT for job related purposes. The study provides novel insights that were previously mislaid, thereby introducing a fresh perspective to the existing literature

Due to government policies, accreditation demands, competition, digital India reforms and National Education Policy (NEP) 2020, the need for electronic human resource management (e-HRM) has increased considerably in the Indian higher education (HE) sector, but the literature has revealed that the adoption of e-HRM practices in Indian HE institutions (HEIs) is still in its embryonic stage; therefore, the purpose of the current qualitative study is to explore the challenges and facilitators of e-HRM adoption in the Indian HE sector through interpretative phenomenological analysis (IPA).

The present study incorporates IPA, to capture the personal lived experiences of the HR executives employed in the Indian HEIs. Using purposive sampling, semi-structured interviews were conducted with the HR executives employed in Indian universities and institutions to know the perspectives on the adoption of e-HRM practices in Indian HEIs.

The study identified two superordinate themes, namely, challenges and facilitators of e-HRM adoption in the Indian HE sector. The superordinate theme “challenges” comprises eight sub-themes. Further, the theme “facilitators” consists of six subthemes.

The study has implications for the stakeholders of the HE sector, i.e. HR practitioners, top executives of the HE sector, government and HE regulators and other stakeholders of the HE sector.

This study has given deep insights into the challenges and facilitators in the adoption of e-HRM practices in the Indian HE sector, and to the best of the authors’ knowledge, no study till date has filled this knowledge gap through qualitative exploration using IPA.

Performance appraisal (PA) is one of the most indispensable human resource management practices as many critical decisions regarding employees’ performance are heavily based on the PA results as they are responsible for various attitude-related outcomes. This study aims to investigate the relationship between PA justice and affective commitment (AC) of employees in the Indian banking sector, and to examine the moderating effects of age and gender on this relationship.

A total of 298 employees from the banking sector were randomly selected to examine the relationship between PA justice and AC along with the moderating role of age and gender on this relationship through structural equation modelling using AMOS 21.

The findings of the study supported hypothesized relationships as PA justice significantly predicted the AC of bank employees in India. The moderating effects of age and gender on the relationship of PA justice and AC are found to be high for older employees and female employees, respectively.

As the present study was cross-sectional, so any inferences regarding causality are limited. Theoretical and managerial implications have been discussed in the context of banking sector.

The paper contributes new insights to the existing literature by examining the moderating effects of age and gender on the relationship of PA justice and AC in the context of Indian banking sector.

The present study aims to gauge the perspectives of students on the difficulties they faced during online learning during the outbreak of coronavirus disease 2019 (COVID-19) through the interpretative phenomenological analysis (IPA) technique.

The current study employed IPA, and it was conducted among the students enrolled for professional courses in the government, private and deemed-to-be universities in Uttarakhand, India, using semi-structured interviews for the purpose of data collection.

The findings have been grouped under five parts referred to as “superordinate themes” or “barriers” which comprises learning in an online class environment, online learning in the home environment, student–teacher relationship in online learning, technical hindrances in online learning and health issues in online learning. These superordinate themes were further grouped under sub-themes.

The present study focused on the students of various private, public and deemed-to-be universities of the Uttarakhand region and represents the higher education sector only and did not tap the primary, secondary and vocational education. The students of academic courses or degree courses like arts, commerce, basic sciences and humanities, etc. were not included in the research study. Hence, the study lacks generalizability.

The research findings of the present study have implications for higher education institutions (HEIs), teachers, students and policymakers.

The present study addresses the methodological gap by offering a new line of research where IPA has been used as the methodology to determine the barriers of online learning in the COVID-19 situation, and to the best of the authors’ knowledge, none of the studies have used it so far to ascertain the barriers to online learning from the student perspective.

International Journal of Emerging Markets (IJoEM) is a leading journal that publishes high-quality research focused on emerging markets. In 2020, IJoEM celebrated its fifteenth anniversary, and the objective of this paper is to conduct a retrospective analysis to commensurate IJoEM's milestone.

Data used in this study were extracted using the Scopus database. Bibliometric analysis, using several indicators, is adopted to reveal the major trends and themes of a journal. Mapping of bibliographic data is carried using VOSviewer.

Study findings indicate that IJoEM has been growing for publications and citations since its inception. Four significant research directions emerged, i.e. consumer behaviour, financial markets, financial institutions and corporate governance and strategic dimensions based on cluster analysis of IJoEM's publications. The identified future research directions are focused on emergent investments opportunities, trends in behavioural finance, emerging role technology-financial companies, changing trends in corporate governance and the rising importance of strategic management in emerging markets.

To the best of the authors' knowledge, this is the first study to conduct a comprehensive bibliometric analysis of IJoEM. The study presents the key themes and trends emerging from a leading journal considered a high-quality research journal for research on emerging markets by academicians, scholars and practitioners.

This study evaluates the effect of the media image content of business to business (B2B) organizations to accelerate social media engagement. It highlights the importance of strategically designing image content for business marketing strategies.

This study designed a computation extensive research model based upon the stimulus-organism-response (SOR) theory using 39,139 Facebook posts of 125 organizations selected from Fortune 500 firms. Attributes from images and text were estimated using deep learning models. Subsequently, inferential analysis was established with ordinary least squares regression. Further machine learning algorithms, like support vector regression, k-nearest neighbour, decision tree and random forest, are used to analyze the significance and robustness of the proposed model for predicting engagement metrics.

The results indicate that the social media (SM) image content of B2B firms significantly impacts their social media engagement. The visual and linguistic attributes are extracted from the image using deep learning. The distinctive effect of each feature on social media engagement (SME) is empirically verified in this study.

This research presents practical insights formulated by embedding marketing, advertising, image processing and statistical knowledge of SM analytics. The findings of this study provide evidence for the stimulating effect of image content concerning SME. Based on the theoretical implications of this study, marketing and media content practitioners can enhance the efficacy of SM posts in engaging users.