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The purpose of this study is to investigate the relationship between role overload (RO) and the work–family interface (work–life conflict and work–life enrichment) among bank employees and the moderating effects of personal life characteristics and commitments on this relationship. It aimed to bring out the importance of contextual factors in individual's interactions across various roles.

A structured questionnaire based on validated instruments was designed and administered to 279 employees from the banking sector in India. The instrument was adapted to the local language to ensure ease of comprehension.

RO was positively correlated with both work interference with personal life (WIPL) and personal life interference with work (PLIW), and negatively correlated with work–personal life enrichment (WPLE). Gender, number of children and age of the youngest child significantly moderated the relationship between RO and WIPL.

This study was limited by the use of self-reported data and its cross-sectional nature. Future studies will need to include a larger sample with people from across the workplace hierarchy.

This paper provides valuable insight into the influence of personal life characteristics and commitments on RO and the work–family interface.

The banking sector is among the top 10 most stressful workplaces in India due to high work pressure and the threat of competition. These working conditions make it important to understand employee perceptions of RO and its impact on the work–family interface.

Little attention has been paid to restructuring existing massive amounts of literature data such that evidence-based meaningful inferences and networks be drawn therefrom. This paper aims to structure extant literature data into a network and demonstrate by graph visualization and manipulation tool “Gephi” how to obtain an evidence-based literature review.

The main objective of this paper is to propose a methodology to structure existing literature data into a network. This network is examined through certain graph theory metrics to uncover evidence-based research insights arising from existing huge amounts of literature data. From the list metrics, this study considers degree centrality, closeness centrality and betweenness centrality to comprehend the information available in the literature pool.

There is a significant amount of literature on any given research problem. Approaching this massive volume of literature data to find an appropriate research problem is a complicated process. The proposed methodology and metrics enable the extraction of appropriate and relevant information from huge quantities of literature data. The methodology is validated by three different scenarios of review questions, and results are reported.

The proposed methodology comprises of more manual hours to structure literature data.

This paper enables researchers in any domain to systematically extract and visualize meaningful and evidence-based insights from existing literature.

The procedure for converting literature data into a network representation is not documented in the existing literature. The paper lays down the procedure to structure literature data into a network.