Consumer confidence surveys are key indicators of households' economic expectations and support monetary and policy decisions. However, prior studies mainly rely on traditional statistical methods, leaving a research gap in capturing the complex factors shaping public perception. This study employs machine learning techniques to uncover patterns in consumer sentiment regarding India's general economic conditions over a one-year horizon. Data collected from Reserve Bank of India's Consumer Confidence Survey from Jan 2022 to June 2024. Three were 90,000 responses with 28 variables. The data classified participants' perceptions of future economic conditions into three imbalanced classes: worsened, improved, and remained the same. Machine learning algorithms; Decision Tree, Random Forest, Gradient boosted algorithms, Multilayer Perceptron and Probabilistic neural network were chosen from the literature review. This study compared machine learning models' performance before and after treating imbalanced class problems. The model's classification accuracy had improved when we address imbalanced class problem using SMOTE. Among all classifiers, Random Forest algorithm outperformed the rest with an F1-score of 0.77. In addition, the general profile of respondents with different perceptions of India's general economic conditions were identified. This data driven insights could be helpful to the policymakers to design economic policies to cater various consumers.
This study proposes a regime-based fractal framework for exploring the global equity-system interdependencies under seven clearly-defined market functional role segments- global benchmark, US liquidity, Japanese export-industrial, European banking-fragility, Chinese state-guided, Indian market-oriented growth, Brazilian commodity-sensitive regimes. We employ a 3025 matched daily observation from 11th April 2011 to 30th March 2026, with data processed with detrended fluctuation analysis (DFA), multifractal detrended fluctuation analysis (MF-DFA), multifractal detrended cross-correlation analysis (MF-DCCA), and rolling 252-day correlation robustness check. We conclude that global equity regime is not efficient systems with homogenous random walks, and there is significant heterogenity. DFA reveals that market memory is almost efficient but heterogenous. While US liquidity regime presents the best effiency(H=0.5036), Brazil commodity-sensitive regime exhibits the strongest memory persistence(H=0.5548). MF-DFA highlights significant multifractality within equity regimes. Brazilian commodity-sensitive and Chinese state-guided markets exhibit the largest(Delta alpha=0.6572) and smallest (Delta alpha=0.4457) spectrum width, respectively.MF-DCCA indicate that global benchmark to US liquidity link serves as the most effective fractal transfer channel(avg rho =0.9583), whilst banking-fragility to state-guided Chinese markets have the least coupling(avg rho =0.2154).Rolling correlation confirm the robustness of dominant linkage pair but exhibits weaker conventional dependencies for state-guided and commodity-sensitive regimes.This work is a contribution in that it replaces geographic-based market grouping with a functional regime architecture which ties memory, multifractality, and transmission together within a single scale-aware framework.
Paid employment and caring for a variety of paid and non-paid care, more or less regularly, has an impact on employee well-being, engagement and productivity. This study introduces a framework for the Organizational Decision System based on the machine learning concept, to aid the support of human resources for caregivers. The proposed project differs from traditional management assessments by defining stress, flexibility in the workplace, support from supervisors, disengagement indicators, and positive indicators of enthusiasm as indicators that predict productivity indicators. This analysis employs a robust and easy-to-interpret main predictive algorithm: Random Forest classifier, suitable for the variety of employee data. Kaggle's Work from Home Employee Burnout dataset is the most useful benchmark for dataset-driven implementation, as it provides caregiver-sensitive workforce modeling relevant behavioural, workload, and burnout related variables. In order to improve productivity predictions, model features related to schedule control, emotional strain, work-care conflict and access to support were engineered. The findings of the experiment indicated that the model achieved an accuracy rate of 95.2% in predicting the category of the employees' productivity risk and well-being status. This work contributes to the unification of caregiver-responsive HR constructs and explainable machine learning for early-risk detection, intervention design and data-informed policy optimization. Thus, the suggested system provides a technically robust and performance-scalable solution for intelligent caregiver workforce management.
Learning Management Systems (LMS) are now a vital source of information for evaluating student learning and predicting academic success. Students' involvement can now be evaluated using metrics such as time spent on learning assignments, quiz attempts, and course activities, thanks to the growing use of LMS platforms. Nevertheless, a lot of current research assumes that these engagement metrics have comparable predictive power across academic fields without considering the variations in learning situations among domains. In an adaptive Outcome-Based Education (OBE) setting, this study examines whether LMS engagement indicators show domain-dependent prediction patterns. Sixty undergraduate Business Analytics students who took two courses via Moodle LMS — NoSQL/MongoDB as a technical course and Applied Business Statistics as an analytical course — were the participants of a within-subjects study. As indicators of student achievement, engagement metrics such as activity completion, quiz attempts, and LMS time were investigated. The investigation employed exploratory machine learning classification, Steiger's Z-test, and multiple linear regression. The findings show that the technical course (R 2 = 0.62) has a higher predictive strength than the analytical course (R2 = 0.42). Steiger's Z-test indicated that the variation in prediction strength was statistically significant (Z = 2.12, p = 0.034). Additionally, the technical domain's AUC was significantly higher according to the exploratory classification analysis. These results show that different academic domains exhibit different predicting behaviour of LMS engagement metrics. Therefore, rather than using a standard method for engagement indicators, LMS-based predictive models used in adaptive OBE systems should be calibrated based on the characteristics of individual domains.
The study explores the research agenda in relation to chatbots' adoption in the banking industry against the backdrop of intense digital transformation and increasing incorporation of artificial intelligence into the financial services. The study is carried out within the framework of the bibliometric analysis in order to evaluate scholarly works related to chatbot technologies, conversational artificial intelligence and digital banking transformation. In total, 206 peer-reviewed articles have been selected by the means of systematic review and analyzed through VOSviewer employing various methods (co-authorship analysis, keyword co-occurrence analysis and thematic evolution mapping). The Findings identified few thematic areas like technology adoption and user behavior, customer experience and service quality, operational efficiency and financial performance, AI technologies along with security and ethical concerns and fintech innovations. The study reveals the structured body of knowledge regarding drivers, enactment mechanisms and outcomes of chatbot adoption and the strategic importance of conversational AI in the digital age.
In this analysis, the author analyses the combined influences of industrialisation, growth in the service sector and investment in fixed capital toward economic growth of different regions in Uzbekistan using a panel of fourteen administrative regions from 2015 - 2024. To evaluate these variables, the author employs a pooled ordinary least squares analysis with robust standard error estimators for heteroscedasticity, in addition to performing Pearson correlation analysis and conducting a tier-based comparison on the elasticities of investment. The results demonstrate that fixed capital invested per capita served to predict regional gross product growth at the highest (β =0.0629 for each dollar invested or $100/person, p=0.0015), while the relationship between the share of industrial value-added and investment intensity was shown to be very strong (r =0.733). Additionally, the steepest slope in the investment-growth relationship was observed in the regions of the highest industrial tier, indicating that capital has a greater influence on growth compared to other forms of capital when based on an established industrial base.
Corporate governance and its elements in the recent decades play a vital role while value investors choosing a stock. The study focuses on how value investors gain confidence while choosing firms to invest in. This study aims to provide a comprehensive framework of how corporate governance influences value investors selecting a stock, mediated by investment confidence and perceived information asymmetry, and Perceived Risk of Governance Failure (PRGF). Using a systematic literature review and PRISMA based approach of publications indexed in Scopus and Web of Science between the period of 2010 and 2025. The paper comprises findings on five core governance dimensions like Board Independence (BI), Executive Compensation Transparency (ECT), Shareholder Protection Mechanisms (SPM), Financial Reporting Quality (FRQ), and Audit Committee Quality (ACQ), and connects them to value relevant outcomes such as earnings quality, information asymmetry, and investor behaviour. Based on the reviewed findings of journals, a conceptual Value Stock Selection Likelihood (VSSL) model is proposed. The review shows that BI, ECT, SPM, FRQ, and ACQ affect VSSL through Investment Confidence (IC), information asymmetry perception (IAP), and perceived Risk of Governance Failure (PRGF). The paper concludes with theoretical and practical implications and outlines directions for future empirical research using primary data from value investors.
The growing use of digital healthcare platforms has changed the way healthcare is delivered by making medical services more accessible and giving patients greater convenience. It is important to understand the factors that lead to people continuing to use these platforms if digital healthcare programmes are to be maintained. This study looks at how perceived usefulness and perceived ease of use affect the frequency with which consumers use digital healthcare platforms in India's business-to-consumer (B2C) healthcare area. A quantitative research approach was used, involving the distribution of a structured questionnaire to 400 respondents who were selected by purposive sampling. Simple linear regression was applied to the data to assess the relationship between the Technology Acceptance Model (TAM) constructs and the frequency of use of healthcare platforms. The results show that both perceived usefulness and perceived ease of use have a positive and statistically significant effect on how frequently users engage with digital healthcare platforms. At the same time, the findings suggest that these factors account for only a small part of the variation in users' behaviour, implying that ongoing engagement is probably also affected by other behavioural and contextual factors. Additional contributions: The research builds on existing research in this field as it focuses on the use of real, and not proposed, behaviours. Therefore, these findings have practical implications for healthcare platform designers and service providers who are willing to increase user engagement of their platforms by designing practical, understandable, and reachable online services that are also accessible for the target users.
Manufacturing's aggregate growth and its internal composition both influence the shaping of industrial development. In this analysis, the structural transformation of the manufacturing sector across 14 regions in Uzbekistan between 2014 and 2024 is then examined. The sectors of light manufacturing, heavy manufacturing, and extractive industries were distinguished from one another. We utilize a Lilien-type structural change index and pooled OLS regression with robust standard errors in order to test if within a region, the reallocation of sectors and the increase in heavy manufacturing weight correlate with gross regional product (GRP) growth and labor productivity. The structural change index (β = 0.880, p = 0.003) and share of heavy manufacturing (β = 15.255, p < 0.001) significantly projected GRP growth and explained 41.8% of the variance in GRP respectively. Our stratified analysis by tier revealed that the productivity dividends associated with reallocation were greatest in regions with low to moderate levels of heavy industry orientation (r = 0.61), whereas the dividends decreased with increasing rates of industrialization (r = 0.32). Therefore, these results indicate that the rate and not just the direction of structural change will have an impact on regional economic growth outcomes.
Sustainable tourism balances environmental protection and economic growth in ecologically sensitive areas. This paper analyses digital interventions to raise awareness of sustainable tourism in Ranipuram, a biodiverse hill tourism site in the Kasaragod district of Kerala. Apart from its features, the area lacks digital promotion and sustainable practices. The study used mixed methods, including an empirical survey (n = 115), secondary data and statistical analysis. Descriptive statistics, independent sample t-test, and chi-square tests were conducted as quantitative tools. As for the hypothesis testing, a relationship between the number of digital interventions and the development of sustainable tourism awareness was statistically significant (p < 0.05). Individuals aged 18 to 35 years often use Instagram and YouTube, followed by Facebook, for responsible tourism awareness campaigns. Challenges highlighted include a poor digital media presence, inadequate localised content, and the absence of digital marketing campaigns. This research indicates that awareness of responsible tourism can be fostered through interactive content, collaboration with digital influencers, virtual tours, and digital marketing campaigns. It is suggested that government agencies, the tourism department, and digital marketers come together to develop sustainable digital marketing campaigns for this new eco-sensitive destination.
Food waste is a global problem in the food supply chain that affects the environment, has more than one billion tons of losses annually, and contributes to the social inequity. Consumers' attitude towards food waste reduction is central to behavioural factors. The aim of this research is to extend the Theory of Planned Behaviour by integrating the constructs of emotions, perceived ability, food literacy, environmental concern and food choice behaviour into the model to better capture the food waste reduction intentions of food court customers. A validated online questionnaire (Cronbach's alpha = 0.863) is employed in the study which explores waste reduction intentions and behaviours among 367 young customer of food courts in Coimbatore, India, through an extended theory of planned behaviour. Results indicate that intention is a strong predictor of behaviour, environmental concern was the strongest predictor of intention, emotions was the next strongest predictor, perceived ability was the next strongest predictor and that food choice was the weakest predictor. Intention to reduce food waste is explained by emotions, self-efficacy, environmental concern, and food choice behaviour, which then predict food waste reduction behaviour. The study builds on the original Theory of Planned Behaviour by integrating sustainability-related and contextual factors in a food court context, providing practical information for food service operators aiming to implement behavioural strategies for a more conscious consumption and reduce food waste
The relationship between institutional commitment and motivational factors among management academicians in higher education institutions is investigated in this study. Using Self-Determination Theory and Social Exchange Theory, the study examines professional fulfillment as a mediating element between institutional commitment and motivational drives. The researchers collected data from management academicians employed by higher education institutions using a quantitative study design. The researchers examined the connections between institutional commitment, professional fulfillment, and motivational factors using structural equation modeling (SEM). The study shows that academicians' institutional commitment and professional fulfillment are influenced by motivating factors. The statistical findings show that motivational factors have a major impact on professional fulfillment, which in turn increases management academicians' institutional commitment. The findings show that supportive academic settings, professional development opportunities, and institutional recognition help faculty members feel more fulfilled and committed to their institution. The study adds to the body of knowledge regarding faculty motivation and organizational commitment by demonstrating the psychological pathways through which motivational practices affect institutional commitment among faculty members in higher education institutions. Academic administrators and legislators can apply the researchers' practical recommendations to improve faculty retention and engagement in their institutions
This study aims at investigating the influence of digital transformation (DT) on the marketing performance of textile small and medium enterprises (SMEs) in South India, which is mediated by the capability of social media and moderated by the market turbulence. Although digital transformation is a key factor in firm competitiveness, the empirical literature on the impact of digital transformation on textile SMEs in emerging economies focuses on cluster-based economies is still limited. This paper aims to fill this void by examining how DT maturity, social media capability and marketing performance are related under different levels of environmental instability. A quantitative, cross-sectional design was used and data collected from the textile SMEs in the major clusters of South India were analysed using Reliability test, Descriptive statistics, One Way ANOVA, Multiple linear regression and Partial Least Square Structural Equation modelling. Results of pilot samples support the strong positive relationship between DT and marketing performance, with a significant mediation effect of social media capability (β = 0.58; β = 0.40, both p < 0.01). Two boundary conditions, market turbulence and being primary market oriented, were found to be significant. The results highlight the need for more than just investing in digital technologies, but for establishing disciplined digital marketing routines like content planning, leveraging digital analytics and being responsive to digital data. Theoretically, this study provides contributions to the DT-performance literature, and practically, it provides guidance and recommendations for textile SME managers and policy makers.
Supply Chain Contracts (SCC) have emerged as an innovative approach to managing uncertainties in project environments. This study presents the development of link sampling plans integrated with SCC using the simulation software GoldSim (14.0) and the proposed plans are designed based on lognormal distribution. The optimal sampling parameters are obtained by minimizing the sample size while maintaining the desired level of quality control. Comprehensive tables are presented to assist in selecting appropriate link sampling plan parameters for various proportions of defectives. Additionally, illustrative examples demonstrate the practical application of the proposed plans, providing clear guidance for implementation.
This research study aims to explore the effect of Emotional Intelligence (EI) on the performance of employees in four mid-sized economy hotels in Bangalore based on Goleman's model of EI. In light of the hospitality sector's high dependence on emotional labour, emotional competencies play a significant role in determining employee performance. In this study, a mixed-methods design was used, including a stratified random sample survey (N=92) using the Wong and Law Emotional Intelligence Scale, performance ratings from supervisors, and interviews. The empirical data revealed a significant positive relationship between EI and employee performance (r = .61, p < .01), where self-management and social awareness have emerged as significant predictors of employee performance (adjusted R 2 = .52). It has been found that the relationship exists more strongly in guest-related departments, indicating that department type acts as a moderator variable. The qualitative findings highlight the importance of empathy, emotion regulation, teamwork, and organisational commitment for improving work performance.
The tradition in gold is that it serves as a safe haven in times of financial and geopolitical crisis. The latest trend is the Gold Exchange Traded Fund (Gold ETFs) as an alternative investment vehicle which follows the physical gold prices, however, it is differentiated by the liquidity, transparency and convenience of investing. The paper compares the performance, risk-return profile and market efficiency of physical gold, and Gold ETFs, in both the stable and crisis periods between the period 2021 and 2023. The two periods are analysed, pre-crisis period (2021-2022) and the period of geopolitical crisis (2022-2025) connected with the war in Russia and Ukraine. This paper evaluates the validity of gold ETFs to the price of physical gold and characteristics of a safe haven in the times of geopolitical uncertainty, using risk-adjusted performance ratios of the Capital Asset Pricing Model (CAPM) and correlation and regression analysis based on an Efficient Market Hypothesis (EMH). The result of this research will offer information to the investors, regulators and other financial institutions on the hedging ability and efficiency of Gold ETFs over time or whether the property of safe haven does actually anticipate higher returns.
This study aims to explore the intellectual structure, collaboration networks and emerging research trends of the gig economy and digital adoption using bibliometric analysis. The bibliographic data published from 2016 to 2025 were obtained from Lens.org and analyzed using VOSviewer (Version 1.6.20). Co-authorship analysis, keyword cooccurrence analysis and citation analysis were carried out to identify dominant authors, predominant research topics and knowledge networks of the discipline. The results indicate a significant growth in the level of academic interest in the gig economy and digital adoption intersection. The co-authorship analysis revealed the active cooperation between the academics. The key topics emerging from keyword analysis were gig economy, digital adoption, digital transformation, artificial intelligence, platform economy, employment, technology adoption and workforce development. The citation analysis revealed the most productive authors and the most cited papers, which had an important role in development of this research field. The study contributes to the understanding of the growing intersection between the gig economy and digital adoption by providing a holistic intellectual map and suggesting future research themes for sustainable digital labour ecosystems.
Modern Free Economic Zones (FEZs) are extremely relevant assets of regional development policy, but there is still very limited systematic performance evaluation across countries globally. The main focus of this paper is evaluating the performance of FEZs located in China, United Arab Emirates (UAE), Poland and Uzbekistan, based on a set of composite indicators that include: foreign direct investment (FDI) attraction, export growth, job creation, technology transfer, the quality of infrastructure, and regulatory efficiency. We used a multi-criteria assessment framework along with panel data for the time period of 20182023 in order to analyze performance outcomes for each of the four countries (China, UAE, Poland and Uzbekistan). The findings indicate that both the UAE FEZ and China FEZ performed at significantly higher composite performance levels (8.8 and 8.1 respectively) than the other countries studied as a result of their superior regulatory environments and geographic location relative to other global geographic regions. In contrast, the Navoi FEZ in Uzbekistan has produced early-stage growth, but has not yet achieved comparable composite performance levels (5.1). These results can inform future actions and policies for the governance of FEZs in emerging economies and assist with future regional development planning.
The purpose of this conceptual paper is to transform Sustainable HRM to Regenerative HRM (R-HRM). It also explains the need for regeneration among the human resources in the current AI driven world. Guided by the theories of regeneration in ecology, the author had generated possible concepts, that help in the regeneration of human resources. The shift from sustainable HRM to R-HRM is needed as the employees are exhausted of "wellness apps", gym discounts and snacks. From being productive, regeneration transforms our work and workspace to be more vibrant and rejuvenating. A systematic literature review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) protocol, to propose a four-pillar model for adopting regenerative HR practices by the organization. As the study is conceptual, further research is needed to test the proposed hypotheses constructed.
Researches based on Employee engagement has grown significantly since Kahn (1990) first defined the construct, and till date there are very minimal studies which systematically mapped the field's intellectual structure has evolved over the past two decades. Around 2,490 articles from Scopus and Dimensions.ai-indexed articles published between 2002 and 2023 are gathered for this study. Bibliometric technique was the main approach used for this study, which concentrated to trace the trends of publication, patterns involved in citations, authorship networks and thematic development in this particular field. VOSviewer-based mapping and bibliographic coupling identify the authors, the institutions and the country collaborations that anchor the discipline. Keyword cooccurrence clustering surfaces six groupings. These six thematic groupings span leadership, being and organizational commitment. These six thematic groupings tie employee engagement to psychology, human resource management and organizational behaviour. Publication output increased consistently after 2010. Then dropped in 2023. We interpret this drop not as a sign of importance but as evidence of a more mature theoretical foundation. Still bibliometric mapping alone cannot prove the reason, behind this change.Taken together, the findings give researchers a structured map of where employee engagement scholarship has concentrated and where its next contributions are likely to emerge.
