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Even with the recognized impact organizational leaders have on the outcome of digital transformation (DT), a comprehensive scholarly understanding of the competencies that leaders must possess to lead a DT to success is lacking.

To derive and list the competencies considered by experts as necessary for managing DT, the authors recruited 18 international senior managers with relevant experience and applied the Delphi method to survey the managers. Upon the completion of three survey rounds and the authors modifying the response list until consensus was reached, 39 items were shortlisted as constituting key competencies for managing DT. Furthermore, the authors engaged in inductive theorizing to derive propositional statements using these findings.

The practitioners agreed on visionary thinking, agility, understanding the value of data, data-driven decision-making, knowledge of strategy and accepting change as the most important requirements for managing DT. Through inductive theorizing, the authors further derived that the seven discovered clusters fell into two broader competencies – behavioral and strategic – and that each behavioral competency would have varying importance depending on the country and industry that the organization operates in.

As is typical for Delphi studies that involve multiple survey rounds, the study participant response rate was moderate. The implications of this study, in finding that a variety of leadership competencies are needed to ensure successful DT, validate prior research that people, not technology, drive DT.

This study helps mitigate assumptions that successful DT processes are only possible by hiring technological experts, as doing so highlights the importance of behavioral leadership competencies.

The study is one of the first to interlink digital leadership with DT by inductively theorizing behavioral and strategic competencies. The authors also establish that contexts are vital in determining which aspects of leadership competencies are deemed most important in driving DT.

This research employed two neurophysiological techniques (electroencephalograms (EEG) and galvanic skin response (GSR)) and machine learning algorithms to capture and analyze relationship-oriented leadership (ROL) and task-oriented leadership (TOL). By grounding the study in the theoretical perspectives of transformational leadership and embodied leadership, the study draws connections to the human body's role in activating ROL and TOL styles.

EEG and GSR signals were recorded during resting state and event-related brain activity for 52 study participants. Both leadership styles were assessed independently using a standard questionnaire, and brain activity was captured by presenting subjects with emotional stimuli.

ROL revealed differences in EEG baseline over the frontal lobes during emotional stimuli, but no differences were found in GSR signals. TOL style, on the other hand, did not present significant differences in either EEG or GSR responses, as no biomarkers showed differences. Hence, it was concluded that EEG measures were better at recognizing brain activity associated with ROL than TOL. EEG signals were also strongest when individuals were presented with stimuli containing positive (specifically, happy) emotional content. A subsequent machine learning model developed using EEG and GSR data to recognize high/low levels of ROL and TOL predicted ROL with 81% accuracy.

The current research integrates psychophysiological techniques like EEG with machine learning to capture and analyze study variables. In doing so, the study addresses biases associated with self-reported surveys that are conventionally used in management research. This rigorous and interdisciplinary research advances leadership literature by striking a balance between neurological data and the theoretical underpinnings of transformational and embodied leadership.

Emerging technologies are capable of enhancing organizational- and individual-level outcomes. The organizational behavior (OB) field is beginning to pursue opportunities for researching emerging technologies. This study aims to describe a framework consisting of white, black and grey boxes to demonstrate the tight coupling of phenomena and paradigms in the field and discusses deconstructing OB’s white box to encourage data-driven phenomena to coexist in the spatial framework.

A scoping literature review was conducted to offer a preliminary assessment of technology-oriented research currently occurring in OB.

The literature search revealed two findings. First, the number of published papers on emerging technologies in top management journals has been increasing at a steady pace. Second, various theoretical perspectives at the micro- and macro- organizational level have been used so far for conducting technology-oriented research.

By conducting a scoping review of emerging technologies research in OB literature, this paper reveals a conceptual black box relating to technology-oriented research. The essay advocates for loosening OB’s tightly coupled white box to incorporate emerging technologies both as a phenomenon and as data analytical techniques.

As conventional practices of working continue to be modified in the gig economy, more theoretical work examining the experiences of gig workers is needed. Relying on person-based fit and levels of analysis literature, this paper proposes an adaptation to the traditional Attraction-Selection-Attrition (ASA) framework to the gig economy.

Drawing on the ASA framework, this conceptual paper explores how gig workers join, leave and could be retained by gig employers.

The authors recognize an intermediary “organizing” phase within the ASA framework for gig workers. Using examples of appwork and crowdwork, the authors show that workers tend to self-organize through third-party websites to help gig work become economically sustainable, avoid being exploited and enhance gig workers' sense of community and identity.

The practical implications of this research lie in gig employers understanding how workers experience gig employment and in helping employers be successful in attracting, selecting and retaining quality workers and thereby lowering permanent attrition.

The authors propose a novel adaptation to the conventional ASA framework to include organizing as a phase in gig worker employment. This research defines gig attraction and attrition at the individual-level, selection at the individual- and task-levels based in person-job (PJ)-fit and the various aspects of gig organizing as encompassing fit with one's job, organization, and environmental (i.e., PJ-, PO-, PE-fit) at the individual-, task-, and network-levels.

This paper aims to apply the theoretical perspective of job embeddedness to delineate how organizations could bundle and implement specific HRD practices that cater to fit, connections and the psychological costs of leaving to influence employees’ organizational commitment.

Using a dual-study approach, the current research uses survey responses collected from two samples of working adults to test the theorized framework using structural equation modelling.

Replicated results reveal that on-the-job embeddedness predicts affective commitment. There was no association between embeddedness at the community level and organizational commitment in either study.

This research offers a fresh perspective to explore the direct influence that embeddedness has on organizational commitment in the context of HRD practices.