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Background: Human resource management (HRM) is the tactical method for a business enterprise’s optimistic and systemic administration. This study aims to identify the common and major triggering attributes and the knowledge gap between HRM and an organisation’s employee attrition rate.

Method: The employee Attrition Case Study Dataset used is an anecdotal data set that tries to figure out relevant variables that determine employee behavioural aspects towards attrition. This study investigates why attrition occurs, the major triggering attributes for employee turnover, and how it might be anticipated to employ artificial intelligence (AI) to avert corporate losses.

Results: Employees’ monthly income, age, average monthly hours, distance from home, total working years, years at the company, per cent of salary hike, number of companies worked, stock options level, job role and other factors are taken into consideration. A feature importance extraction framework was devised to investigate the various dormant factors. The findings also show feasible hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels of risk decrease attrition rate.

Implications: Employees’ monthly income, age, average monthly hours, distance from home, etc., are all major variables in employee attrition in the Indian IT business. This research adds to the theory development of behavioural elements in people analytics based on AI.

Purpose: Can we predict employee attrition through employee behavioural patterns advancement using AI tools.

Using the two waves of the India Time Use Survey, 1998–1999 and 2019, we document a 110-minute (30%) increase in average daily learning time. The largest offsetting decrease was in work time: 61 minutes. The composition of leisure changed, with television rising by 19 minutes, while talking fell by 10 minutes and games by 17 minutes. We then implement a Gelbach decomposition, showing that 68 minutes of the unconditional learning increase are predicted by demographic covariates. Of these predictors the most important are a child's state of residence and usual principal activity, which captures extensive-margin transitions into schooling.

High-technology firms often institute cross-functional teams to manage product innovation initiatives. The assumption is that (a) creative and successful products from new technologies must reflect the integration of multiple talents, therefore the innovation process will require integration among people who possess them, and (b) when people with multiple talents are placed in teams, they will interact, cross-fertilize ideas, and collaborate to produce creative new products from new technologies faster and cheaper than those produced by alternative structural arrangements. While teams are easy to institute, fostering high levels of collaboration among participants has proved harder in practice. While some teams achieve high levels of collaboration, others merely replicate rivalries that exist in the organization and breed cynicism.

Based on our study of product innovation processes in high-technology industrial manufacturers, this chapter discusses the differences between high- and low-collaboration teams. Specifically, we report the key (a) developmental milestones in the process by which groups of people from diverse functional areas become high-collaboration teams and create new products faster and cheaper, and (b) factors associated with participants, team leaders, senior management, and the organizational culture that seem to shape the emergence of collaboration in teams.