Purpose The recent COVID-19 pandemic has (triggered) lots of interest in work from home (WFH) practices. Many organizations in India are changing their work practices and adopting new models of getting the work done. The purpose of the study to look at the boundary-fit perspective (Ammons (2013) and two factors, namely, individual preferences (boundary control, family identity, work identity and technology stress) and environmental factors (job control, supervisor support and organizational policies). These dimensions are used and considered to create various clusters for employees working from home. Design/methodology/approach K-mean clustering was used to do the cluster analysis. Statistical package for social sciences 23 was used to explore different clusters based on a pattern of characteristics unique to that cluster, but each cluster differed from other clusters. Further analysis of variance test was conducted to see how these clusters differ across three chosen outcomes, namely, work-family conflict, boundary management tactics used and positive family-to-work spillover effect. The post hoc test also provided insights on how each cluster differs from others on these outcomes. Findings The results indicated four distinct clusters named boundary-fit family guardians, work warriors, boundary-fit fusion lovers and dividers consistent (with previous) research. These clusters also differ across at least two major outcomes like boundary management tactics and positive spillover. The high control cluster profiles like Cluster 3 (boundary-fit fusion lovers) and Cluster 4 (dividers) showed low technostress and higher use of boundary management tactics. Cluster 3 (boundary-fit fusion lovers) and Cluster 1 (boundary-fit family guardians) having high environmental influencers also showed higher positive family-to-work spillover. Research limitations/implications Because this study is very specific to the Indian context, a broad generalization requires further exploration in other cultural contexts. The absence of this exploration is one of the limitations of this study. On the culture continuum, countries may vary from being individualistic on one extreme to being collectivistic on the other extreme. Interaction of these two cultural extremities with the individual and the environmental dimension, as espoused in this research, can be examined further in a different cultural setting. Originality/value This study has extended the work of Ammons (2013) and added external influencers as a dimension to the individual preferences given by (Kossek 2016), and created the cluster for employees in the Indian context. This study has demonstrated the importance of reduced technostress, and the use of boundary management tactics (temporal and behavioral) leads to positive family-to-work spillover. It has also emphasized the relevance of organization policies and supervisor support for better outcomes in WFH.

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Purpose Big data analytics capability is the new source of competitive advantage. This is very axiomatic and it is also known that this capability covers the domain of descriptive, diagnostic, predictive and prescriptive analytics. Apart from the capability of managing data coming with high velocity, huge volume, and high variety the capability to analyze data with the same or more speed is a not only a hygiene factor but a compulsion for any organization. Ease of use of data visualizing tools which help in describing the data is driving the consumption of visualization tools and Tableau has taken a clear lead, and is witnessing mass adoption. The paper is a detailed account of creating a interactive visualization of heath care infrastructure data using Tableau Publix as a business intelligence tool. Methods: The Visualization is done on data taken from Open Government Data (OGD) Platform India. The paper is a demonstration of the use of visualization for exploration of data, generation of relevant questions and thereafter finding the right answers. Result: The papers explores the data and does the following through visualization 1. Maps the geography (Indian states) with total number of patients) 2. Maps the state name with segregated number of patients suffering from acute and chronic disease. 3. Maps the state name with segregated number of patients suffering from chronic disease. 4. Maps the state name with segregated number of patients suffering from acute disease. 5. Builds all the above in an interactive dashboard.