Work from home or remote working has been seen as a buzzword during pre-Covid-19 times has become a reality during Covid-19 Pandemic starting March 2020 and will stay for a longer period in India. In this empirical study the researchers... more
Work from home or remote working has been seen as a buzzword during pre-Covid-19 times has become a reality during Covid-19 Pandemic starting March 2020 and will stay for a longer period in India. In this empirical study the researchers present outcome an empirical study carried out surveying the employees of International Agricultural Research Institute, Hyderabad. This research was carried out during the pandemic period because of its nature-novelty, innovation and challenging and this is the first research study that was carried out in the agricultural research sector. The predictor variables of remote working challenges-social/ workplace isolation, career development, work schedule, ergonomic issues, infrastructure dependencies, personal habits, additional costs to employee, the factors that influence the work-life balance a dependent variable on employees working in an international agricultural research institute employee are presented. The seven said independent variables that effect the work-life balance a dependent variable because of remote working of an employee are measured. using a five-point Likert-type scale. The work-life balance was measured with a modified questionnaire based on the survey instrument by Lisa Yang and Hock Tan and Cook. The multiple regression analysis reveal that employee personal habits, ergonomic issues and work schedules are significantly influencing the employee work-life balance. The most important concern and challenge expressed by employee is Post-Covid-19 to work back plan is explained.
Research on women’s experiences with work schedules and flexibility tend to focus on professional women in high-paying careers, despite women's far greater prevalence in low-wage jobs. This paper seeks to contribute to our understanding... more
Research on women’s experiences with work schedules and flexibility tend to focus on professional women in high-paying careers, despite women's far greater prevalence in low-wage jobs. This paper seeks to contribute to our understanding of the work-hours problems faced by low-wage women relegated to part-time work. We address how work-on-demand scheduling and other features of part-time labor in the neoliberal economy limit women’s ability to make ends meet. Using data from17 in-depth interviews with women precariously employed in low-wage jobs, we identify four themes—unpredictable schedules, inadequate hours, time theft, and punishment-and-control via hours-reduction—and the problems they present. Results suggest that much-championed flexible work policies that seek to encourage women’s career advancement may have little bearing on the work-hours dilemmas faced by low-wage women workers. We conclude that social change efforts need to encompass work policies geared to low-wage workers, such as guaranteed minimum hours and increases in the minimum wage.
The quality of service and efficiency of labour utilization in emergency service fleets, such as police, fire departments, and emergency medical services (EMS), depends, among other things, on the efficiency of work break scheduling. The... more
The quality of service and efficiency of labour utilization in emergency service fleets, such as police, fire departments, and emergency medical services (EMS), depends, among other things, on the efficiency of work break scheduling. The workload of such fleets usually cannot be forecasted with certainty and its urgency requires an immediate response. However, prolonged focused work periods decrease efficiency with related decline of attention and performance. Therefore, break schedule should be regularly updated as the work shift progresses to allow frequent and sufficiently long time for rest. In this paper, we propose a distributed and dynamic work break scheduling algorithm for crews in emergency service vehicle fleets. Based on the historical intervention data, the algorithm rearranges vehicles' crews' work breaks in a manner considering individual crews' preferences. Moreover, it dynamically reallocates stand-by vehicles for best coverage of a region of interest. We analyze the proposed algorithm and show its performance on a simple use-case.
