Predicting knowledge workers’ participation in voluntary learning with employee characteristics and online learning tools

Abstract

Purpose -This paper aims to explore predicting employee learning activity via employee characteristics and usage for two online learning tools.Design/methodology/approach -Statistical analysis focused on observational data collected from user logs. Data are analyzed via regression models.Findings -Findings are presented for over 40,000 employees' learning activity for one year in a multinational technology company. Variables including job level and tool use yielded a predictive model for overall learning behaviors. In addition, relevant differences are found for managers and nonprofessional learning.Research limitations/implications -Importantly, how well employees learned content was not measured. This research is also limited to observational relationships: for example, the online tools were used by self-selected users, instead of randomly assigned. Future research which randomly assigns tool use to employee subgroups could explore causal relationships.Practical implications -This paper presents implications for business analysts and educational technology: how predictive analytics can leverage data to plan programs, the significant challenges for the adoption and usage for online learning tools, and the distinct needs of managers engaging with these tools.Originality/value -Given a growing emphasis on using employee data, it is important to explore how learning behaviors can be made visible in people analytics. While previous research has surveyed employee cultures on learning or explored the socio-psychological factors which contribute to this learning, this paper presents novel data on employee participation in learning programs which illuminates both how HR metrics can productively use this data to reify learning patterns, and how workplace technology designers can consider important factors such as internal hierarchies.

Workplace learning and information exchange among gig workers: crowdsourcing and the social media advantage

“…social ties, trust, perceived competition and friendship among peer workers) that could provide a more comprehensive explanation behind a seemingly compassionate and understanding group of food delivery workers. Our study also confirms Hicks (2018) research of 40,000 employees’ learning activities that found that employees at a lower job level were more likely to participate in online learning opportunities.…”

The dark sides of people analytics: reviewing the perils for organisations and employees

“…Finally, several studies stress that the three types of analytics offer increasing analytical power (Frederiksen, 2017;Leicht-Deobald et al, 2019). Among the empirical studies in this theme (Aral et al, 2012;Hicks, 2018;, we find that most studies only examine the use of descriptive and predictive analytics techniques (e.g., Bekken, 2019;Chalutz, 2019;Dahlbom et al, 2019). However, recent technological advances in the context of people analytics illustrate the need to expand current research and emphasise the use of more advanced forms of people analytics.…”

Human Resources Analytics: A systematic Review from a Sustainable Management Approach

“…HRA and Learning and Development. Only one article is included in this section (Hicks, 2018). The author used a regression model to predict employee learning activity, and found that a significant percentage of professional learning activity is predictable, based on job level, organizational function, overall satisfaction score, the use or non-use of the Profile tool, and the use or non-use of real time feedback (RTF) tool, F(20, 39.849) = 1.742.92, adj.…”