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The purpose of this paper is to quantify several measures to examine the determinants of profitability for the listed Indian banks. The authors include both public sector (PSUs) and private sector’s banks in the study. The authors have taken all the banks that are registered on the Bombay stock exchange (BSE) in the sample. This paper also intends to identify the association between the net profit margin (PM) and return on assets (ROA) with the several other independent variables of the Indian banking sector including private banks and public banks over the past six years starting from April 1, 2012 to March 31, 2017. Therefore, a sample of 39 listed banking companies and total 195 balanced observations are selected for the analysis purpose.

The authors have used profitability as a dependent variable represented by net PM, ROA and several financial ratios as independent variables. Financial statement and income statement of all listed banks were obtained from BSE and particular company’s website. Panel data regression has been analyzed with both the descriptive research techniques, i.e., fixed effects and random effects. The authors also verified both panel techniques with Hausman’s specification test, which is a widely used procedure for selecting a panel effect. The authors applied PP – Fisher χ², PP – Choi Z-statistics and Hadri to testing whether the data set is free from unit root problem and data set is a stationary series.

Results imply that interest expended interest earned (IEIE) and credit deposit ratio (CRDR) reduced the profitability of private banks in India. IEIE, CRDR and quick ratio (QR) reduced the profitability of public banks in India, while cash deposit ratio (CDR) and Advances to Loan Funds (ALF) increased the effectiveness of public banks. Under the total banks IEIE, CRDR reduced the profitability, on the other side, CDR, ALF and Total Debt to Owners Fund (TDOF) increased the profitability of total banks in India. Under the dependency of ROA, CRDR and TDOF reduced the return of private banks in India, while CDR, ALF and QR enhanced the profitability of private banks.

No variables found significant under public banks while taking ROA as a dependent variable. Under the overall banking data, CRDR reduced the profitability. On the other side, capital adequacy ratio and ALF increased the profitability of total banks in India. The findings of this study will support policy creators, financial executives and investors in constructing investment decisions.

With a growing preference for active, authentic, and cultural experiences over traditional ones, creative tourism has garnered significant academic interest. This study offers a comprehensive review of creative tourism research, delineating its evolution, prominent contributors, pivotal areas, and prospective trajectories through a bibliometric analysis.

Employing a bibliometric analysis using the biblioshiny and VOSviewer software, this study systematically reviews 198 articles on creative tourism identified and retrieved from the Scopus database.

A notable increase in creative tourism research is witnessed in recent times, with Portugal and the Netherlands leading in publications and citations, respectively. This review also pinpoints key authors, countries, institutions, and journals shaping the field, and presents emerging themes such as authenticity and creative experience, culture and heritage, urban and rural contexts, and co-creation in creative tourism.

Identifying core research contributors (authors, countries, institutions, journals) and contributions (themes, topics) assists academics in seeking collaborations and shaping future research. Practitioners are advised to adapt these trends (authenticity, co-creation, sustainability) into their strategic planning to meet market demands.

This study offers a seminal review of creative tourism through a bibliometric analysis, a technique that leverages the power of technology (data, software) to engage in retrospection and projection—the hallmark of benchmarking studies across fields, including tourism. Noteworthily, this study provides a detailed summary of the field’s trajectory and significant trends, positioning itself as an essential reference for academic scholars, industry professionals, and policymakers with a keen interest in creative tourism.

This study aims towards developing a conceptual framework by systematically reviewing the available literature with reference to job crafting under the lens of an emerging economy from South Asia, i.e. India, which is the largest country and the largest economy in the South Asian region.

The study employs a hybrid methodology of a systematic literature review (SLR) and bibliometric analysis using VOSviewer and Biblioshiny. Bibliometric analysis provides glimpses into the current state of knowledge like-trend of publication, influential authors, collaboration with foreign authors, the major themes and studied topics on job crafting in India etc. Further, a detailed SLR of the selected articles led to the development of the conceptual framework consisting of the enablers and outcomes of job crafting.

It discusses implications for academia, business and society at large, and also provides valuable insights to policymakers and practitioners paving the way for better adoption, customization and implementation of job crafting initiatives.

Owing to its own unique social, cultural, and economic characteristics, the dynamics of job crafting in India may vary from other countries and regions which can also be reflective of how job crafting operates in South Asia in general. As job crafting was conceptualized and later evolved mostly in the western context, our study assumes greater significance as it is the first study which attempts to systematically review the job crafting literature to understand how job crafting manifests in the Indian context and presents a conceptual framework for the same.

Operational risks can cause considerable, atypical disturbances and impact food supply chain (SC) resilience. Indicatively, the COVID-19 pandemic caused significant disruptions in the UK food services as nationwide stockouts led to unprecedented discrepancies between retail and home-delivery supply capacity and demand. To this effect, this study aims to examine the emergence of digital platforms as an innovative instrument for food SC resilience in severe market disruptions.

An interpretive multiple case-study approach was used to unravel how different generations of e-commerce food service providers, i.e. established and emergent, responded to the need for more resilient operations during the COVID-19 pandemic.

SC disruption management for high-impact low-frequency events requires analysing four research elements: platformisation, structural variety, process flexibility and system resource efficiency. Established e-commerce food operators use partner onboarding and local waste valorisation to enhance resilience. Instead, emergent e-commerce food providers leverage localised rapid upscaling and product personalisation.

Digital food platforms offer a highly customisable, multisided digital marketplace wherein platform members may aggregate product offerings and customers, thus sharing value throughout the network. Platform-induced disintermediation allows bidirectional flows of data and information among SC partners, ensuring compliance and safety in the food retail sector.

The study contributes to the SC configuration and resilience literature by investigating the interrelationship among platformisation, structural variety, process flexibility and system resource efficiency for safe and resilient food provision within exogenously disrupted environments.

This study aims to predict the Indian stock market (Nifty 50) by employing macroeconomic variables as input variables identified from the literature for two sub periods, i.e. the pre-coronavirus disease 2019 (COVID-19) (June 2011–February 2020) and during the COVID-19 (March 2020–June 2021).

Secondary data on macroeconomic variables and Nifty 50 index spanning a period of last ten years starting from 2011 to 2021 have been from various government and regulatory websites. Also, an artificial neural network (ANN) model was trained with the scaled conjugate gradient algorithm for predicting the National Stock exchange's (NSE) flagship index Nifty 50.

The findings of the study reveal that Scaled Conjugate Gradient (SCG) algorithm achieved 96.99% accuracy in predicting the Indian stock market in the pre-COVID-19 scenario. On the contrary, the proposed ANN model achieved 99.85% accuracy in during the COVID-19 period. The findings of this study have implications for investors, portfolio managers, domestic and foreign institution investors, etc.

The novelty of this study lies in the fact that are hardly any studies that forecasts the Indian stock market using artificial neural networks in the pre and during COVID-19 periods.