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The volume of international patients has been growing in the past 15 years, with developing countries gaining a larger market share. The international patients market is lucrative, given that hospitals may be able to attract an affluent clientele, and many patients from foreign countries who seek care require complicated procedures and treatments. The purpose of this paper is to build on previous work in the international business and health services fields, to develop a model that predicts a hospital's ability to attract international patients.

The paper is a synthesis of the literature on export ventures and patient choice to predict a hospital's “export” performance.

It is estimated that around 70,000 foreign patients travel each year to the USA to receive inpatient medical care. These patients not only benefit hospitals and medical professionals but also benefit the local community through money spent in hotels, restaurants, shopping, etc. Strategic management, international business, and health services research fields can help us understand how hospitals can be more competitive in attracting international patients. The authors propose that the following dimensions affect a hospital's export performance: hospital resources (hospital attributes, international competence, management commitment, and strategy); institutional environment; and domestic hospital industry factors.

More empirical work is required to test the authors' model.

Most of the studies done on international patients were descriptive in nature. This is the first paper that builds a conceptual model to help us understand what determines a hospital's performance in the international patients market.

This paper aims to examine the work-life balance (WLB) experiences of tribal working women belonging to the matrilineal Khasi and Jaintia communities of Meghalaya, India, using an identity-based approach.

Semi-structured interviews conducted with 18 tribal women working in the formal sector helped generate descriptions of the subjective subliminal tensions they experienced in their efforts to balance work and home life.

Six key themes emerged: webs of role-based responsibilities; reframing family around work; revising self-identity through work; challenges and coping tactics; traditional community influences on management of work and home life; and enacting womanhood as problem-solving.

This study contributes to the literature on women and WLB in that it expands the theoretical understanding of the impact of identity work on women’s WLB.

A healthy WLB is crucial for enhanced intrinsic motivation and consequently women’s psychological empowerment and career satisfaction. This has important social and practical implications for enriching tribal women’s quality of life in India and facilitating their contribution towards the betterment of their communities and the economy at large. To this end, policymakers should launch awareness campaigns pertaining to tribal women’s WLB, to aid organizations in rolling-out contextually relevant work-life management programmes for these women.

This study extends an identity-based approach as a general theory of the self to examine matrilineal tribal women's WLB construction as a distinct form of “doing” and “being”.

The intent to start an entrepreneurial venture is predisposed by certain personality traits. The study aims to analyze the impact of various identified personality traits and entrepreneurial education on entrepreneurial intentions of management students.

The data for the study were gathered from 440 students of top 5 management institutes in India. The study used a cross-sectional design and structured questionnaire based on seven-point Likert scale and was administered employing a purposive sampling method. Structural equation modeling (SEM) was utilized to validate posited hypotheses in the study.

The study discerns that apart from agreeableness dimension of personality traits, all remaining identified dimensions along with entrepreneurial education had a significant influence on entrepreneurial intentions of management students with need for achievement emerging as the most important enabler. Conscientiousness was the major dimension among big five personality traits bearing a positive influence, while neuroticism registered a negative impact on entrepreneurial intentions. In addition, results bespeak that entrepreneurial education partially mediates the effect of need for achievement, locus of control and innovativeness on entrepreneurial intentions.

The compendious model proffered in the study might be helpful for students, educators, consultants, financial institutions and policymakers in appreciating the gravity of underlying personality traits.

There is a dearth of research on big five personality traits and entrepreneurial education as enablers of entrepreneurial intentions. The study attempts to integrate big five personality traits model with dimensions of need for achievement, locus of control, innovativeness and entrepreneurial education for management students in India.

The study examines the key drivers of the adoption of disruptive technologies (DTs) in the digital supply chain (DSC) in developing nations.

The data were collected using well-established measures grounded in the diffusion of innovation (DOI) theory. The hypotheses were tested using the structural equation modeling (SEM) approach using SmartPLS. The authors control for the demographics and apply the required statistical diagnostics for robust findings.

The compatibility and IT expertise were the two key factors in adopting the DTs in DSC in developing nations. The organizations with higher compatibility and internal IT expertise and competence witness a higher level of adoption of DT. The perceived cost and complexity were not found statistically significant. This may be probably because developing nations such as India do not perceive the technology adoption complex.

The research enhances DTs adoption, assuming it is organizational innovation. This study makes a theoretical contribution to the DOI literature.

The practicing managers should pay attention to addressing the existing technology compatibility issues and spend efforts on training employees to increase the IT expertise to improve the adoption of DT.

The greater adoption of the DTs in DSCs can reduce wastages in supply chains by a faster sense and response and greater technological flexibility with transparency and information sharing.

The key antecedent to the acceptance of the DTs in developing nations is compatibility than complexity and IT expertise than the cost. The study's originality lies in the fact that most studies on technology adoption study a single technology, but this study captures a holistic view on a group of technologies under industry 4.0.

This paper aims to identify various macroeconomic variables that affect the stock market performance of developed and emerging economies. It also investigates the effect of these factors on the stock markets of both economies. The impact of these variables on broad market indices and sectoral indices is investigated and compared too.

The publications for the study were retrieved from databases such as Emerald Insight, EBSCO, ScienceDirect and JSTOR using the keywords “Macroeconomic variables” and “Stock market” or “Stock market performance.” The result demonstrated a growing corpus of scholarly work in the domain of stock market. The study was carried out separately for each macroeconomic indicator. Given a large number of articles under consideration, the authors began by reading the titles and abstracts of all publications to identify those that were relevant. The papers are evaluated in Excel and the articles for review range from 1972 to 2021.

The authors found that gross domestic product (GDP), FDI (Foreign Direct Investment) and FII (Foreign Institutional Investment) have a positive effect on both emerging and developed economies’ stock market while gold price has a negative effect. Interest rates had a negative impact on both economies except for a few developing countries. The relationship with oil prices was positive for oil exporting countries while negative for oil importing countries. Inflation, money supply and GDP are the macroeconomic variables that have the same effect on sectoral indices as they do on broad market indices. The impact was sector-specific for the remaining variables.

This paper gives an overview of relation and effect covering variety of macroeconomic variables and stock market indices. Still, there is a scope for further research to analyze the effect on thematic, strategy and sectoral indices. A longer time horizon with new variables, such as bank deposit growth rate, nonperforming assets of banks, consumer confidence index and investor sentiment, can be studied using high-frequency data. This research may help stakeholders adopt and manage their policies during a crisis or economic slump.

This study will assist investors, researchers and educators in the fields of economics and finance in understanding how macroeconomic factors affect the stock market. Furthermore, this study can guide in portfolio diversification strategy across multiple sectors by examining the impact of macroeconomic factors specific to sectoral indices. This paper provides insight into society and researchers since it integrates a number of macroeconomic variables and their interaction with the stock market. It may also help pension funds and mutual fund firms to hedge their funds and allocate equity portfolios.

With respect to India, this study looked at new macroeconomic variables and sectors. It contrasted the impact of these variables in developed and developing economies. The effect of broad and sectoral stock indexes was also investigated and compared. The authors examined how these variables responded during crisis and economic downturns by using articles from a longer time frame. This research also looked into how changing the frequency of data for the variables altered stock performance. This paper emphasized the need for more research into thematic, strategy and broad market indices, such as small-cap and mid-cap indices.

This work aims to review past and present articles about data-driven quality management (DDQM) in supply chains (SCs). The motive behind the review is to identify associated literature gaps and to provide a future research direction in the field of DDQM in SCs.

A systematic literature review was done in the field of DDQM in SCs. SCOPUS database was chosen to collect articles in the selected field and then an SLR methodology has been followed to review the selected articles. The bibliometric and network analysis has also been conducted to analyze the contributions of various authors, countries and institutions in the field of DDQM in SCs. Network analysis was done by using VOS viewer package to analyze collaboration among researchers.

The findings of the study reveal that the adoption of data-driven technologies and quality management tools can help in strategic decision making. The usage of data-driven technologies such as artificial intelligence and machine learning can significantly enhance the performance of SC operations and network.

The paper discusses the importance of data-driven techniques enabling quality in SC management systems. The linkage between the data-driven techniques and quality management for improving the SC performance was also elaborated in the presented study.

Consumers can spend their disposable income on hedonic consumption or save for the future. Their preferences were altered by the prolonged life and livelihood-threatening experiences of the pandemic. This paper aims to study the spillover effect of the pandemic experience on consumer savings attitudes and hedonic purchase preferences in the new normal.

The authors conducted 35 in-depth interviews with consumers in India. The data were analysed thematically.

The results showed that when fear of life and negative emotions of the pandemic persisted, consumers became short-term focused, moved towards materialism and increased hedonic spending. Alternatively, individuals who faced substantial financial hardships resorted to an increased preference for savings. The relationship between changes in savings orientation and hedonic consumption was found to be moderated by consumer's individual differences in financial vulnerability and life history strategies.

As the trend towards increased hedonic consumption and preference for luxury products continues, the study findings can be used to devise effective marketing strategies to tap the emerging segment of mass luxury consumption.

Despite ample work being conducted in the hedonic consumption domain, it has not been studied in conjunction with savings orientation, a significant determinant. This research links personal savings orientation with hedonic spending and substantiates that purchase decisions are cognitively weighted as a choice of discretionary spending against the opportunity to save.

The study aims to overview the different types of internal-led cyber fraud that have gained mainstream attention in recent major-value fraud events involving prominent Indian banks. The authors attempted to identify and classify cyber frauds and its drivers and correlate them for optimal mitigation planning.

The methodology opted for the identification and classification is through a detailed literature review and focus group discussion with risk and vigilance officers and cyber cell experts. The authors assessed the future of cyber fraud in the Indian banking business through the machine learning–based k-nearest neighbor (K-NN) approach and prioritized and predicted the future of cyber fraud. The predicted future revealing dominance of a few specific cyber frauds will help to get an appropriate fraud prevention model, using an associated parties centric (victim and offender) root-cause approach. The study uses correlation analysis and maps frauds with their respective drivers to determine the resource specific effective mitigation plan.

Finally, the paper concludes with a conceptual framework for preventing internal-led cyber fraud within the scope of the study. A cyber fraud mitigation ecosystem will be helpful for policymakers and fraud investigation officers to create a more robust environment for banks through timely and quick detection of cyber frauds and prevention of them.

Additionally, the study supports the Reserve Bank of India and the Government of India's launched cyber security initiates and schemes which ensure protection for the banking ecosystem i.e. RBI direct scheme, integrated ombudsman scheme, cyber swachhta kendra (botnet cleaning and malware analysis centre), National Cyber Coordination Centre (NCCC) and Security Monitoring Centre (SMC).

Structured and effective internal-led plans for cyber fraud mitigation proposed in this study will conserve banks, employees, regulatory authorities, customers and economic resources, save bank authorities’ and policymakers’ time and money, and conserve resources. Additionally, this will enhance the reputation of the Indian banking industry and extend its lifespan.

The innovative insider-led cyber fraud mitigation approach quickly identifies cyber fraud, prioritizes it, identifies its prominent root causes, map frauds with respective root causes and then suggests strategies to ensure a cost-effective and time-saving bank ecosystem.

The purpose of this study is to measure the impact of technology spend on bank profitability focusing on Indian public and private sector commercial banks. The study also assesses the longevity duration of impact for both public and private sector banks. The trade-off is created between the bank’s profitability and technology spent across four identified columns: increase product sales, reduce cost, enhance employee efficiency and optimally use existing resources.

The metrics of four columns converted to measurable 11 financial alarming indicators and impact is again checked on profitability indicator ROE. The data used for empirical analysis for the study are between the year 2003 and 2019, across 12 public and 15 private banks. The interface between technology spend and profitability is measured using panel vector autoregression (VAR) and panel vector error correction model (VECM) and further the link between 11 financial indicators and profitability measures is established using panel data analysis.

The study found that there is a mixed effect of technology spent on profitability and performance of Indian banks, where public sector banks were found to be more unstructured over private sector banks. The study advises the optimal technology spend strategies to gain enhanced productivity for banking business which are to name a few – planned technology reserves, customer awareness campaigns for products, robust employee-customer motivation policy and customized technologies aligned to existing infrastructure.

The data was original and extracted from the Reserve bank of India website and respective banks’ annual reports.