Search results

Tasks in today’s global marketplace are becoming increasingly reliant on the work of teams. As creativity and innovation are frequently required for organizational success, work teams are becoming more and more prominent within all types of organizations. With the rise of organizational teams, the purpose of this paper is to develop appropriate tools to measure how well these teams work together and how well they perform required tasks.

This paper outlines a measure of teamwork, a transactive memory system (TMS), and proposes new methods for using TMSs to measure team structures, processes, and performance. These new methods include dispersion models and social network analysis.

Dispersion models and social network analysis hold promise for the future evaluation of TMS and other team constructs.

This paper provides a summary of two novel approaches to the measurement of TMS and other team constructs.

The purpose of this paper is to describe the dispersion models, where within-team variance is the outcome of interest, and propose the application of these models to the measurement of the transactive memory system (TMS). As teams become increasingly prominent in educational contexts and within organizations, it is important to evaluate how various measures of individual and team attributes relate to team performance. One measure that has been evaluated by a number of previous empirical studies is TMSs.

In past studies of TMS and in most teams research, team-level data are collected and correlated with performance, or individual-level data are collected, aggregated to the team-level data and then correlated with performance. While this is appropriate in situations where data are isomorphic or similar across levels of measurement, there are often important differences among within-team responses that lead to a discrepancy between the sum of individual attributes and a team-level measure.

Preliminary results demonstrate that within-team variance in reported levels of TMS has an inverse relationship with team performance.

Future research should further evaluate the ability for dispersion models of TMS to predict team performance, especially in organizational settings with professional rather than student teams.

This paper provides a new approach to measuring TMS and relating TMS to team performance.

Human resource management (HRM) processes are increasingly artificial intelligence (AI)-driven, and HRM supports the general digital transformation of companies' viable competitiveness. This paper points out possible positive and negative effects on HRM, workplaces and workers’ organizations along the HR processes and its potential for competitive advantage in regard to managerial decisions on AI implementation regarding augmentation and automation of work.

A systematic literature review that includes 62 international journals across different disciplines and contains top-tier academic and German practitioner journals was conducted. The literature analysis applies the resource-based view (RBV) as a lens through which to explore AI-driven HRM as a potential source of organizational capabilities.

The analysis shows four ambiguities for AI-driven HRM that might support sustainable company development or might prevent AI application: job design, transparency, performance and data ambiguity. A limited scholarly discussion with very few empirical studies can be stated. To date, research has mainly focused on HRM in general, recruiting and HR analytics in particular.

The four ambiguities' context-specific potential for capability building in firms is indicated, and research avenues are developed.

This paper critically explores AI-driven HRM and structures context-specific potential for capability building along four ambiguities that must be addressed by HRM to strategically contribute to an organization's competitive advantage.