Search results

One of the major goals of sustainable development is creating employment opportunity among all. Despite its largest demographic dividends, the whole world faces challenges in employment generation among youth. The growing number of unemployed youths is one of the important problems faced by developed as well-developing countries. Youth unemployment is the situation of young people who are looking for a job but cannot find a job in the age between 15 and 24. Mismatch between education and employability resulted in high unemployment rates among the youth. A key research question is that how we can bridge the gap and equip the youth for job field. Although eminent economists, newspapers, international statistical bodies continuously put fingers towards this vulnerability, research work in this field is really scant. On this backdrop, this chapter wants to explore the intensity of youth unemployment in India; keeping in mind, India has the largest youth population in the world. Based on data sources from World Development Indicators, the chapter studies the time series since globalisation to COVID periods. This chapter also tries to search the macroeconomic variables related anyway to the youth unemployment rate. As research methodology, we use vector autoregressive (VAR) Granger causality test. Based on our results, the author has concluded that human development index in India and GDP both ways causes each other. And youth unemployment rate in India causes HDI. However, our econometric investigations can be useful to better assessment of youth unemployment in India from liberalisation to pandemic. At the end of this chapter, some final considerations and policy implications on youth labour market dynamics are analysed and discussed.

We were intrigued by the question of whether the convergence of businesses across nations in search of flexibility to survive economic crisis led to a convergence of the annual rate of change of union membership. The question emerged because the convergence theory was controverted, especially when the neo-capitalist idea failed to withstand the test of time during the economic crisis.

By adopting the model from Bain and Elsheik (1976) and using time-series data from 1990 to 2014 for Finland and India that survived economic crisis during this period, whereby union membership remained steady in Finland but declined in India, we assessed the empirical distinction between the changes in union membership.

We argued that when hit by an economic crisis, different nations had divergent responses and chose different means of economic recovery because of which the countries have not withstood the crisis in one specific way/direction that at all times, marginalises unions. Our main finding is that in both the countries, the annual rate of change of union membership during the years of economic recovery was determined by the policy response. And, policy responses were determined not only by the causes of economic crisis but also by the strength of unique national institutional configurations and history of the country.

The annual rate of change of union membership during the years of economic recovery was determined by the policy response. And, policy responses were determined not only by the causes of economic crisis but also by the strength of unique national institutional configurations and history of the country.

Healthcare services have enormous potential for growth in the country. However, the drivers of clinical productivity are yet to be explored. The purpose of this paper is to examine the effect of clinician buy-in, clinician ease of use, clinician work stream flexibility and device placement comfort on clinician’ s involvement. The study further examines the effect of converged devices implementation and wireless portable devices adoption on care delivery agility. Lastly, the authors examine the influence of clinician’s involvement and care delivery agility on clinical productivity.

Measures for the constructs were developed from extant literature and expert feedback complemented with pre-testing. Further, perceptual responses were collected from 221 doctors through internet surveys and were analyzed using partial least squares.

The study identified clinician’s ease of use and workflow streamlining flexibility as key enablers of clinician’s involvement. Findings further reveal that converged devices implementation and wireless portable adoption as prominent enablers of care delivery agility. Further, the authors found clinician’s involvement and care delivery agility to have a positive contribution in enhancing clinical productivity.

The study has collected perceptual responses from doctors practicing both in hospitals and private clinics and their perceptions have been based on their experiences at those specific institutions or sites, which may limit generalizability of the current study findings. This study incorporated only doctors and ignored other assistant staffs’ perceptions who assisted and aided doctors in executing treatment procedures either in private clinics or hospitals.

The study has practical implications for enhancing clinical productivity.

The study findings highlight the role of process focus and infrastructure focus on the human elements of involvement and agility which often play pivotal role for motivation for individual physicians and their productivity.

The study is the foremost to develop perceptual measures for several constructs in the healthcare services.