Search results

The purpose of this article is to explore the importance of workplace learning in the context of performance measurement on an organisational level. It shows how workplace learning analytics can be grounded on professional identity transformation theory and integrated into performance measurement approaches to understand its organisation-wide impact.

In a conceptual approach, a framework to measure the organisation-wide impact of workplace learning interventions has been developed. As a basis for the description of the framework, related research on relevant concepts in the field of performance measurement approaches, workplace learning, professional identity transformation, workplace and social learning analytics are discussed. A case study in a European Public Employment Service is presented. The framework is validated by qualitative evaluation data from three case studies. Finally, theoretical as well as practical implications are discussed.

Professional identity transformation theory provides a suitable theoretical framework to gain new insights into various dimensions of workplace learning. Workplace learning analytics can reasonably be combined with classical performance management approaches to demonstrate its organisation-wide impact. A holistic and streamlined framework is perceived as beneficial by practitioners from several European Public Employment Services.

Empirical data originates from three case studies in the non-profit sector only. The presented framework needs to be further evaluated in different organisations and settings.

The presented framework enables non-profit organisations to integrate workplace learning analytics in their organisation-wide performance measurement, which raises awareness for the importance of social learning at the workplace.

The paper enriches the scarce research base about workplace learning analytics and its potential links to organisation-wide performance measurement approaches. In contrast to most previous literature, a thorough conceptualisation of workplace learning as a process of professional identity transformation is used.

To help workers make the right decision, over the years, technological solutions and workplace learning analytics systems have been designed to aid this process (Ruiz-Calleja et al., 2019). Recent developments in artificial intelligence (AI) have the potential to further revolutionise the integration of human and artificial learning and will impact human and machine collaboration during team work (Seeber et al., 2020).

Complex problem-solving has been identified as one of the key skills for the future workforce (Hager and Beckett, 2019). Problems faced by today's workforce emerge in situ and everyday workplace learning is seen as an effective way to develop the skills and experience workers need to embrace these problems (Campbell, 2005; Jonassen et al., 2006).

In this commentary the authors argue that the increased digitization of work and social interaction, combined with recent research on workplace learning analytics and AI opens up the possibility for designing automated real-time feedback systems capable of just-in-time, just-in-place support during complex problem-solving at work. As such, these systems can support augmented learning and professional development in situ.

The commentary reflects on the benefits of automated real-time feedback systems and argues for the need of shared research agenda to cohere research in the direction of AI-enabled workplace analytics and real-time feedback to support learning and development in the workplace.

This paper aims to explore predicting employee learning activity via employee characteristics and usage for two online learning tools.

Statistical analysis focused on observational data collected from user logs. Data are analyzed via regression models.

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.

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.

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.

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.

Purpose: In the contemporary knowledge economy, organisations mainly derive a competitive advantage by leveraging their intangible assets. Competent and motivated employees are the primary strategic resources to attain innovation and business continuity. Consequently, workplace learning and development (L&D) is at the forefront of the human resource management (HRM) discipline. At the same time, with the changing technology landscape, organisations are transforming their L&D function to be sustainable. Against this backdrop, the main objective of this chapter is to illustrate how artificial intelligence (AI) contributes to a specific HRM sub-function, that is, workplace L&D.

Design/Methodology/Approach: Grounded on intense scrutiny of literature, this chapter construes AI as intelligent machines that think and work like humans and have the potential for enhancing learning processes. Different themes have been presented, which suggest the capabilities of AI systems to fuel employee learning at the workplace.

Findings: Findings demonstrate that AI-enabled workplace learning is rooted in improved knowledge management (KM) capabilities, developmental feedback, personalised education, learning for a diverse pool of learners, virtual mentoring, and chatbot-based learning.

Research Limitations/Implications: This conceptual study suffers from a lack of empirical support.

Practical Implications: This chapter contributes to expanding scholarship on integrating AI and the HRM domain, particularly L&D. Further, it highlights how L&D professionals should integrate AI into employee learning journeys to evoke effective learning outcomes.

Originality/Value: This chapter provides a gestalt approach to integrating AI with employee L&D

The purpose of this paper is to examine the evolution of the concept of human resource analytics (HRA) and propose an operational framework demonstrating the sources generating data for HRA, as well as the impact of HRA on multiple levels in the organization.

A review of literature was conducted to present the existing body of knowledge and build upon for the development of an operational framework for successful implementation of HRA as a human resources (HR) process.

Building upon the existing literature, this paper presents an operational HRA framework, positioning HRA as an analytical process through integrating advanced statistical methodology. HRA presents a tool to obtain evidence-based analytical results for improving people-related performance, operational effectiveness, and ultimately the impact of the business strategy. By using HR big data, HRA impacts multiple organizational levels, from individual employees to HR functions and the organizational strategy.

While research on data analytics has recently flourished across various management fields, this has not been the case for the broader field of HR. This is especially a growing concern as the lack of understanding of the basics and fundamentals of people analytics in the field of HR may delay the effective implementation and operationalization of HRA and present additional barriers impacting on-going HR activities, as well as HR’s role as a strategic business partner. HR practice may greatly benefit from gaining an understanding of HRA and the multi-levels of impact it may have on the organization.

This paper explores various concepts related to HRA by examining terms such as “HR metrics” vs “HR” and “HR big data” vs “big data.” Furthermore, the comprehensive HRA operational framework presented in this paper provides HR professionals and researchers with a better understanding of HRA in the age of data analytics and artificial intelligence.

The potential capabilities and benefits that learning analytics can provide are not fully utilized. A current stream of research suggests that learning analytics has more to offer for continuous improvement of higher education institutions. This study aims to explore the opportunities that data analytics stand to offer higher education and the challenges that plays down its role, adoption and usage in different areas of higher education institutions.

This study adopts a systematic literature review approach in answering the research questions. The critical role of learning analytics and the exploratory nature of research questions justify the use of systematic literature review. The current study used systematic research process adapted and presented by Hallinger (2013) to be used in social sciences in general and in educational leadership and management in particular. A standard process of finding relevant articles and examining reference lists is followed using articles from higher education which is the research context.

An examination of the literature showed that the majority of studies within the sample of articles are empirical representing 53 per cent, 32 per cent are conceptual, while only 15 per cent of the articles are a systematic literature review. Results also show that 58 per cent of the articles are teaching and learning related, 34 per cent are management related, while only 8 per cent are research related. Several challenges and opportunities of learning analytics in the three areas highlighted are presented and discussed.

The benefits and challenges of learning analytics are numerous and scattered in the literature. In this study, a typology related to different educational domains is developed to shed light on the benefits and challenges of learning analytics within particular higher education areas that are relevant to specific stakeholders. Benefits and challenges of learning analytics are classified into being management related, teaching and learning related and research related.