Department of Management

Business travellers spending some leisure time during their business trips is not a new phenomenon, but this aspect has grasped the attention of tourism practitioners in recent times owing to its consistent economic returns. Both hoteliers and destination managers are focusing on this segment of business travellers aspiring to have vivid leisure experiences too as a part of their business trip and have segmented them as 'bleisure travellers'. This scenario opened a lucrative and challenging market to hoteliers and travel companies pushing them to create unique service offerings to this segment. Handling bleisure travellers can be challenging owing to the dual-faced travel motive. For example, a bleisure traveller may have to attend a meeting for a whole day but doesn't want his family to miss out on the fun and excitement keeping them idle. These kinds of tricky instances put a lot of weight on the hoteliers and travel companies as they need to plan and deliver satisfactory experiences to the entire bleisure travel group. Exclusive study indicating satisfactory and dissatisfactory attributes of bleisure travellers is sparse till date. Current study tried to contribute to the literature by narrowing down the satisfactory and dissatisfactory attributes pertaining to bleisure travellers. Six thousand reviews of 35 Indian heritage were extracted from Tripadvisor. Reviews were scrapped using python. Data was analysed using LDA. Findings of this study indicate major implications for the hoteliers to improve and customise their service offerings.

University entrepreneurial ecosystem (UEE) provides students and academic staff with the resources and environment where they are supported to take up entrepreneurship. Studies have indicated that student and academic-staff-run entrepreneurship contributes to regional development through university spinoffs and knowledge spillover. However, the complete framework of developing UEE has not been discussed in the literature, and scholars have focused on examining the impact of certain aspects of UEE, such as entrepreneurial education and technology transfer. The measurement scale for the dimensions of UEE is scarce. In this article, we address this gap by conceptualizing the UEE as analogous to the ecology ecosystem and identifying the dimensions of UEE as entrepreneurial skill development, entrepreneurial resources, and entrepreneurial culture. Moreover, we strengthen the UEE framework by finding out the components and subcomponents. Finally, we develop measurement scales for UEE by following a rigorous four-step methodology followed by ensuring the nomological validity of the scale. The article not only contributes to the entrepreneurial ecosystem literature by conceptualizing UEE and developing a way to measure UEE but it also helps managers by suggesting a tool for comparison of performance of various UEEs.

Amid growing environmental concerns, natural fibers such as date palm stems (DPS) emerge as viable alternatives in polymer composites. This study investigates the mechanical and physical properties of short-length DPS-Glas epoxy composites, incorporating E-glass hybridization and alkali treatment. Unlike previous studies that focused on long fibers or fiber dust, this research employs short DPS fibers through the hand layup method to enhance composite processability. The impact of fiber content and alkali treatment on physical, mechanical, and water-absorption properties was explored. Results show that tensile strength improved significantly with alkali treatment and was further enhanced through glass fiber hybridization. Similar trends are observed in both flexural strength and microhardness also. SEM analysis revealed superior fiber-matrix adhesion in hybrid composites, while hybridization and surface treatment reduced water absorption. However, fiber clustering at 20 wt % led to surface imperfections and compromised mechanical properties. These findings highlight the potential of DPS fibers in sustainable composites and emphasize the importance of optimizing fiber content for enhanced performance.

Driving comfort assessment is a prerequisite to improve the journey experience for the drivers as well as the passengers. In this work, we proposed an advanced approach for the measurement of driving comfort in real-time. Different types of environmental features are considered along with the traditionally used Comfort Index (CI), and an Environment-specific Comfort Index (EsCI) is proposed. EsCI is also inversely proportional to the drivers' comfort level, just like CI. We also developed an android application named QDCL (Quantification of Driver Comfort Level) for overall data collection and computation of EsCI from the same. A series of driving experiments at different times of the day and different traffic conditions have been performed in Indian urban road scenarios to assess the performance of QDCL and the relevance of EsCI. We extended the work by studying the effects of different external stimuli on the computed driving comfort level. The performance of EsCI is observed to outperform the traditionally used CI (Comfort Index) in terms of accuracy for the quantification of overall driving comfort.

Driver behaviour modelling is essential for reducing driver workload and enhancing vehicle safety systems. To create more personalized and intelligent vehicles, efficient driver behaviour models must be integrated into vehicle dynamic systems. This paper focuses on understanding driver responses to unexpected events, with a particular emphasis on acceleration profiles due to their sensitivity to driving anomalies. Notable abnormal driving manoeuvres, such as hard braking, sharp U-turns, and sudden lane changes, often necessitate stopping the vehicle. The study employs KDE of histograms, KL divergence, ANOVA, and F-test analyses to identify and validate these findings. Additionally, time-frequency plots are utilized to predict various driving anomalies and their causes. A Kalman filter-based jerk estimation algorithm is proposed to estimate changes in jerk behaviour, aiding in the prediction of driving anomalies. The applications of this research include developing advanced driving safety systems by evaluating driving behaviour changes before and after anomalies occur.

Purpose – The study aims to investigate the relationship between financial development and the growth dynamics of the manufacturing industry, offering empirical evidence from India. Design/methodology/approach – This study employs the time series autoregressive distributed lag (ARDL) model to empirically examine the effectiveness of financial development on the growth performance of India’s manufacturing industry in both the long run and short run. Findings – Financial development includes specific variables such as commercial banks’ credit, foreign direct investment inflows, market capitalization, real exchange rate and foreign trade openness have a significant positive effect, while money supply has a significant negative effect on the growth performance of the manufacturing industry in the long run. In the short run, money supply has a significant positive impact, whereas commercial banks’ credit has a significant negative effect on the growth performance of the manufacturing industry. Thus, an advancement in financial development will enhance the manufacturing industry’s growth performance in India over the short run as well as the long run. Research limitations/implications – The outcomes of this study are of significant importance to the Government of India (GoI) for fiscal consolidation and to the Reserve Bank of India (RBI) for effective monetary policy transmission, particularly in their efforts to promote financial inclusion and financial development in relation to the growth of the manufacturing industry amidst challenging market conditions. Originality/value – As a developing economy, India faces the challenge of advancing its financial infrastructure to boost national output and employment, particularly through the manufacturing industry. However, there has been a dearth of research focused on this crucial intersection. So, the study aims to provide a renewed perspective on the interconnection, offering valuable insights.

The advent of Metaverse offers tremendous avenues for businesses specially marketers, metamorphosing engagement with consumers and implementation of corporate strategies. The present chapter tries to delve into the exploration of sustainability development goals (SDGs) and practices of marketing in the Metaverse environment. It also unearths as to how business can bank upon the trending technologies viz augmented reality (AR), virtual reality (VR), blockchain (BC) and artificial intelligence (AI) aiding to change the perception and behaviour of consumers and provide superior customer engagement and experiences. It is evident that metaverse’s impact is profound on customer engagement and experiences whether its healthcare, medicine, entertainment, retail by proposing customization, personalization, unique and innovative techniques that breaks the jinx between virtual and real world. The chapter provides insight to various stakeholder such as academia, industry and practitioner about the advancement of Metaverse and hinderances towards successful implementation of SDGs into marketing practices and business.

In this study we explore the determinants of job satisfaction using 68,000 Indeed reviews of hotel employees from 27 countries, using Structural Topic Modeling (STM). We compare these determinants with the job description index (JDI). We also conducted covariate analysis based on Hofstede’s cultural dimensions and employment status. Work culture, work-life balance, peer support, and job security were key determinants of job satisfaction. Toxic work culture, compensation, hire & fire, and abusive supervision were key determinants of job dissatisfaction. Results of cultural dimensions highlighted cultural heterogeneity and indicated that national culture is a key determinant of employees’ online voice behavior. Results signify the use of employee reviews for evidence-based decision-making to boost job satisfaction.

The paper aims to understand anthropomorphism concerning people’s perceptions, opinions, and emotions toward AI-based virtual influencers. PRAW (Python Reddit API Wrapper), a Python package was used to facilitate access to Reddit’s API to retrieve the comments and posts from Reddit servers. The comments from November 2018 to March 2024 were collected. A search feature was employed to search for the keywords ‘virtual influencers’, and ‘AI influencers.’ Based on 50,939 comments that were extracted were used for analysis. The findings show that audiences anthropomorphize AI Influencers’ marketing activities and share a common passion for online games. The findings also show that the audience perception of AI-based virtual influencers has seven key categories: uncanniness, promotional activities, esthetics, entertainment, gaming and recreation, identity and social life, and shared passion. The findings also show that the audience has a high level of trust and anticipation.

Global studies indicate that consumers’ environmental apprehensions have progressively altered their purchasing behavior and prompted a re-evaluation of their purchases. The study uses the web-based advertising value framework, uses and gratification theory, and elaboration likelihood model to examine the effects of social media advertising on young female consumers’ purchase intentions of sustainable products. We collected the data through 501 offline surveys administered to young women who have encountered social media advertising, providing valuable insights into their perceptions and behaviors. We analyzed and validated the data through structural equation modelling using SmartPLS 4.0. The framework explains 51% of the variability in advertising attitude (R2 = 0.51) and 31% of the variance in purchase intention (R2 = 0.31). The results reveal that eWOM, advertising entertainment, and informativeness are the most critical factors influencing advertising attitudes, surpassing the effects of credibility and emotional appeal. The study recommends promoting sustainable practices by highlighting the role of positive word-of-mouth in generating public interest for sustainable purchasing. By integrating eWOM, incentives, and emotional appeal into the advertising value model, this research offers a comprehensive framework to understand the factors influencing consumers’ purchase intentions in social media advertising. These insights advance theoretical understanding and offer actionable recommendations for marketers seeking to align their advertising strategies with responsible consumption, production, and climate action. By leveraging social media advertising to promote sustainable consumption, businesses can contribute to global sustainability efforts while achieving their marketing objectives.

Purpose – The objective of this study is to analyse the integration of technology in Human Resources Management (HRM) with a special focus on Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT) and Big Data. Design/methodology/approach – This study aims to contribute to the understanding of these trends by conducting a thorough bibliometric analysis using the Scopus database, encompassing research on HRM and Technology from 1991 to 2022. By employing citation analysis, co-citation analysis and co-word analysis, the study uncovers key patterns and trends in the field. Findings – The findings indicate that AI, Big Data and ML are the focal points of research when exploring the intersection of Technology and HRM. These technologies offer promising prospects for enhancing Human Resource processes, such as Talent Acquisition, Performance Management and Employee Engagement. Research limitations/implications – In our study, we showcase the practical implications that offer guidance for HR researchers and professionals, enabling them to make informed decisions regarding the adoption and implementation of Information Technology. Practical implications – This research can provide valuable insights to HR managers on the use of cutting-edge technology in HRM. It aims to enhance the manager’s awareness of how technology-enabled HRM can improve HR performance. Originality/value – This study adds to the existing body of knowledge on how Modern Technology empowers HRM. It also proposes a conceptual framework for the use of Modern Technology along with Strategic Management and Knowledge Management to improve Human Resource Performance.

Purpose – This study aims to provide a comprehensive review of existing literature on consumer savings. Design/methodology/approach – A bibliometric analysis was conducted on 1, 078 journal articles, followed by a cluster analysis and an in-depth review of 250 key articles. Findings – The study identified 5 major thematic clusters: financial literacy, savings pattern, drivers of savings, personality traits and Fintech and policy. It also highlighted key antecedents, moderators and theories discussed in the literature. Research limitations/implications – This review provides a structured agenda for future research. Originality/value – Despite extensive research on consumer savings, there is no review paper in this domain.

This study investigates the impact of financial and other factors, including profitability, firm value, board size, firm size, and the influence of COVID, on the quality of integrated reporting (IR). The sample comprises 46 listed Indian corporates observed over a three-year period (2019–2021). A scoring system was devised to assess IR quality through content analysis of annual reports. Empirical estimation employed pooled ordinary least square, fixed effect, and random effect models. The findings revealed no significant relationship between profitability, firm value, and IR quality. However, board size, firm size, and the COVID dummy variable exhibited a positive impact on IR quality. Robustness checks provided further support to the panel regression estimates.

This paper’s objective is to scientifically identify the present research trends for future research agenda in Human Resource Analytics (HRA) domain by conducting a comprehensive literature study. Our proposed article applied the existing SPAR-4-SLR protocol for the systematic assessment of the HRA literature. Based on search parameters including subject, function, and language, the authors discovered 226 publications from the Scopus database. We performed a systematic literature review by considering the top 60 highly cited publications and identified five key themes such as HR analytics, Training and Development, Performance Management, Big Data, AI and ML, and Human Resource Information System and Organisation Development. This study extends the HRA research by incorporating systematic assessment to build a systematic grasp of the field of study and highlights the emerging relevance of HRA for effective, objective, and transparent Human Resource Development (HRD).

Teachers need to replace the traditional approach of the teaching and learning (TL) process by the integration of Information and Communication Technology (ICT)-based education to meet the global requirement. This paper aims to map the role of technology in the teaching and learning and the challenges associated with the technology implementation in the domain of language education. Therefore, the study has conducted a systematic literature review to synthesize the knowledge in this field based on the documents from the Scopus database. Fifty-three documents were finalized for the systematic review based on the SPAR 4 SLR protocol and inclusion and exclusion criteria. The study identified four technological themes such as virtual reality, multimedia and digital tools, online learning, and information and communication technology. This review has implications for the students, teachers, educators, EdTech companies, and regulators.

Corporate social responsibility (CSR) has become a significant focus in the corporate world, emphasizing ethical practices and sustainable development. CSR is important because it ensures that businesses act ethically, contribute positively to economic growth, and improve societal well-being, addressing stakeholder interests while fostering trust and reputation. It is imperative in India due to legal mandates under the Companies Act, of 2013, requiring companies to allocate a portion of profits to CSR, thus aligning corporate goals with national priorities like education, healthcare, and sustainability. CSR's role in addressing climate change is crucial, especially in India, a country vulnerable to extreme weather, where companies contribute to reforestation, renewable energy, and community resilience projects, supporting India’s commitments to global climate goals such as the Paris Agreement and net-zero targets. This study explores the interplay between CSR and CFP within BSE 500 companies in India, offering insights into their interconnected impact on business sustainability and profitability. The study analyzed data from the annual reports of 204 Indian firms from 2016 to 2023, yielding a dataset of 1,632 observations. The research design was explanatory, and the results indicate that CSR has no significant impact on CFP. The findings that CSR has no significant impact on CFP suggest several policy implications. Policymakers should focus on creating frameworks that encourage companies to align CSR activities with their core business strategies, ensuring these initiatives are both impactful and economically beneficial. Additionally, there is a need to enhance stakeholder awareness and engagement, so that CSR efforts are more valued in the market. Regulatory bodies might also consider offering incentives for innovative and sustainable CSR projects that demonstrate measurable economic and social benefits. Lastly, fostering transparency and improved reporting standards for CSR activities could help bridge the gap between CSR investments and their perceived financial returns.

Background: The health of employed individuals has micro- and macroeconomic implications for individuals, nations, and economies. Therefore, the importance of healthy human capital for sustained growth deserves more attention. Preventive healthcare is crucial for maintaining a healthy and productive workforce, as it can mitigate health risks and the impact of chronic health conditions and reduce healthcare costs. This study examined the factors that influence preventive healthcare decisions among IT and ITES working-age adults. This study established a conceptual model using health consciousness, social interaction, public service advertisements, risk perception, perceived value, and health governance as antecedents; attitude as a mediator; and intention to spend as an outcome. Method: Using an online survey, this study gathered data from 601 working-age participants of IT and ITES companies from Hyderabad, Visakhapatnam and Chennai via purposive sampling in a cross-sectional design. Subsequently, analysed the data using R. Lavaan and reported via structural equation modelling, measurement model comparison, and mediation analysis. Results: The results showed that health consciousness, social interaction, risk perception, public service advertisement, and perceived value exhibited favourable influences on attitudes, whereas health governance negatively affected attitudes but positively affected intention. The implications of these findings indicate a nuanced understanding of beneficial interventions, that shall extend beyond merely fostering positive attitudes via health education, boosting social support, and utilizing promotional strategies. They must also tackle issues related to health governance, potentially by enhancing communication and transparency, to facilitate the primary goal of promoting behavioural change. Conclusion: In order to promote preventive healthcare behaviours among individuals of working age, this study emphasizes the necessity for customized policies and interventions that deal with health consciousness, social interaction, and risk perception. The study demonstrated that enhancing perceived value (benefits outweighing barriers) in preventive healthcare services can increase self-efficacy and, consequently, foster stronger intentions. The study also highlighted the impact of health governance, focusing on policy development and regulation, community engagement and participation, and building trust in health systems.

Researchers have associated a dark side with metaverse adoption, suggesting that metaverse use may hurt the natural environment, society, and users' health. This study proposes a framework to measure the users' perceptions about the negative impacts of the metaverse on the environment, society, and users' health. The study also examined the effect of negative perceptions on metaverse adoption intention for three distinct segments of consumers, i.e., general consumers, technology enthusiasts, and socio-environmentally conscious consumers. Three studies were conducted. Study 1 was aimed at developing scales to measure the perceived negative impact of metaverse use on the environment (PIME), society (PIMS), and users' health (PIMH). Study 2 examined the role of PIME, PIMS, and PIMH in the consumers' metaverse adoption. Study 3 revalidated the proposed model on two segments of technology enthusiasts and socio-environmentally conscious consumers separately. PIMH was observed to be crucial for the metaverse adoption by general consumers and socio-environmentally conscious consumers. PIMS was observed to be a significant predictor of the metaverse adoption by socio-environmentally conscious consumers. The three study constructs were not observed to be significant predictors of metaverse adoption by technology enthusiasts.

The present study empirically verifies the potential determinants of capital flows into the emerging countries. The study mainly focuses on the role of recently proposed variables viz economic policy uncertainty, geopolitical risk, and environmental performance index. We use balanced panel data for 13 countries spanning from the year 2002 to 2021. The study employs recently developed Machado and Silva (2019) method of moments quantile regression analysis to examine the drivers of capital flows. The study also employs dynamic panel data estimators which contain fully modified ordinary least square (FMOLS), and dynamic ordinary least square (DOLS), models to check the robustness and consistency of the results. The overall results show that lagged FDIt-1, market size (GDPG), trade openness, economic freedom index, GPRC, and EPI have positive and significant effects on FDI inflows in the sample countries. The findings of the study bear significant implications for government, policy makers, and investors in several ways. The market size plays an important role in determining FDI inflows. Policymakers need to mainly focus on economic conditions of the developing countries. The findings also help to formulate the economic and monetary policies that can boost the FDI inflows. Finally, foreign investors (home countries) carefully look at the state-of-affairs of the economic policy uncertainty, geopolitical risk, and environmental sustainability before they park their money in the host countries.

In recent years, there has been an enormous increase in the number of companies and of customers for almost every industry. The increment in the number of companies has also provided the choices to the customer but in turn it has also created new challenges. Thus, the companies must work not only to improve their products or services but to sustain customers in the competitive world. Churn prediction is the prediction of customers who are at a potential risk of discontinuing the product or service of the company. Thus, in today's competitive world, churn prediction is more relevant. In the present work, we have employed various machine learning models for an early prediction of churns, to mitigate the potential risk of losing the customers. The authors have chosen ensemble models for this task. Finally, the models are trained on the dataset. The results for various models are compared using accuracy, precision, recall, and F1 score. Moreover, it is also observed that for our dataset XGBoost outperformed over other models.