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This paper aims to clarify under which conditions, and via what mechanisms, power asymmetry is likely to affect team learning. This work is part of a two-paper series. Part I presents the theoretical arguments linking power asymmetry to team learning via egalitarianism and the moderating role of environmental hardship. In Part II, the authors provide an empirical evaluation of the conceptual model presented in Part I.

Data was gathered on 4,637 military personnel nested in 143 ongoing teams. Multiple regression analysis was used to analyze the proposed moderated mediation model. The results show that under higher levels of environmental hardship, teams with higher power asymmetry (greater hierarchy) show greater team egalitarianism and higher team learning.

The results show that under higher levels of environmental hardship, teams with higher power asymmetry (greater hierarchy) show greater team egalitarianism and higher team learning.

The empirical examination of the proposed relationships is based on a large sample of military teams in the real world. Future research would benefit from testing the model on different samples across industries and adopting different operationalizations for environmental hardship relevant to each industry.

This work provides insights to help practitioners to preserve the coordination benefits of hierarchy, while still promoting more egalitarianism and team learning in hierarchical teams.

This paper aims to understand the effects of team power asymmetry (hierarchy) on team learning.

Literature suggests that power asymmetry can hurt team learning due to unequal interactions. The authors integrate the situated focus theory of power and the theory of adversarial growth to propose that environmental hardship can moderate this relationship. Such that, under environmental hardship there is a shift in power relations within hierarchical teams, such that power asymmetry positively relates to team learning via increased team egalitarianism (interactional equality).

The study is presented in two parts. Part 1 reviews the literature and builds the theoretical arguments for the conceptual model, while Part 2 empirically examines the model on a sample of military teams. In Part 1, the authors propose a theoretically derived model and directions for future research in team power, dynamics and learning.

It provides directions to empirically validate a contingency-based model to resolve the dilemma of creating equality and high levels of team learning in hierarchical teams.

The conceptual model and hypotheses contribute to the team learning literature by theoretically clarifying the conditions under which power asymmetry is likely to improve team learning.

This chapter introduces electroencephalography (EEG), a measure of neurophysiological activity, as a critical method for investigating individual and team decision-making and cognition. EEG is a useful tool for expanding the theoretical and research horizons in organizational cognitive neuroscience, with a lower financial cost and higher portability than other neuroimaging methods (e.g., functional magnetic resonance imaging). This chapter briefly reviews past work that has applied cognitive neuroscience methods to investigate cognitive processes and outcomes. The focus is on describing contemporary EEG measures that reflect individual cognition and compare them to complementary measures in the field of psychology and management. The authors discuss how neurobiological measures of cognition relate to and may predict both individual cognitive performance and team cognitive performance (decision-making). This chapter aims to assist scholars in the field of managerial and organizational cognition in understanding the complementarity between psychological and neurophysiological methods, and how they may be combined to develop new hypotheses in the intersection of these research fields.

The study aims to propose and test how leadership styles (learning-oriented, transformational and transactional leadership) and a new construct, psychological equality, help overcome the typically negative effect of rank disparity on team learning.

Militaries have a rigid hierarchy, and rank disparity (hierarchy) inhibits team learning. However, little (quantitative) attention has been paid to understanding the factors that might help overcome the inhibiting effect of hierarchy on military team learning. This study evaluates how learning-oriented leadership helps military teams to learn by improving a sense of psychological equality.

Learning-oriented leadership supported greater psychological equality and team learning than either transformational or transactional leadership. Additionally, psychological equality significantly improved team learning. Together, learning-oriented leadership and psychological equality were found to support team learning within hierarchical teams. The findings show that team rank disparity does not inevitably stifle team learning.

Cross-sectional archival and self-report data limits drawing causal conclusions; further, longitudinal studies should be undertaken to extend and test the proposed causal relationship modeled in this study.

Generating team learning within the military does not require dismantling traditional military command, communication and control structures; instead, specific leadership behaviors (e.g., sharing information, coaching and avoiding blame or shame) can support psychological equality and increased team learning within military’s established command and control structures.

This study answered recent calls to identify the contingencies shaping team learning; improving psychological equality enhances team learning while maintaining the benefits of a clear hierarchical structure (e.g. military command and control).

Learning organizations are often theorized at a team level, yet there is a lack of team-level studies. This study aims to evaluate if team-level dimensions of a learning organizational questionnaire (DLOQ) measures are reliable and reflect real team properties and if both individual-level and team-level DLOQ leadership mediates the effect of rank on other DLOQ measures.

An empirical analysis evaluated if team-level DLOQ measures are reliable and reflected real team properties and if DLOQ learning-oriented leadership mediates the effect of rank or team hierarchy on all other DLOQ measures. A novel approach (random group resampling) was used to evaluate if team level measures reflected either a real team property or a statistical artifact. Next, a series of mediation models evaluated if learning-oriented leadership was isomorphic, namely, displays a similar pattern at both individual and team levels.

The analysis found team-level DLOQ measures reflected real properties of the teams and were reliable and learning-oriented leadership mediates between rank and team hierarchy and the other six dimensions at both individual and team-levels (i.e. DLOQ team and individual level were isomorphic).

The results show that hierarchical teams’ learning capacities can be improved by focusing on the learning-oriented leadership, which overcomes the typically negative effect of hierarchical differences within teams.

This study provides a significant step forward by applying an innovative analysis that shows that the DLOQ team level measures reflect real team properties and DLOQ leadership displays isomorphic characteristics.

This paper aims to explore the factors influencing the behavioural intention of Gen Y consumers to avail omnichannel service and to identify the relative influence of predictors in explaining the behavioural intention of Gen Y consumers to use omnichannel service.

Data collected through surveys from 287 Gen Y consumers has been analysed through structural equation modelling to examine direct and mediated relationships between the constructs influencing behavioural intention to use omnichannel service.

Findings indicate that perceived ease of use, social influence, perceived trust, and personal innovativeness positively affect behavioural intention to use omnichannel service, with the result accounting for 48% of the variance. We also demonstrate that perceived value and perceived ease of use mediate the association between personal innovativeness and behavioural intention to use omnichannel service.

The study provides valuable insights into adopting technology-based offerings for Gen Y customers. The presented model can be extended for analysing consumers' behavioural intentions by considering additional variables, such as consumer personality traits and diverse cultural settings. The study may help managers and policymakers formulate a consumer-focussed strategy to win over modern retail consumers.

This study explores the behavioural intention of Gen Y consumers in availing omnichannel services. Further, the study contributes to the technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT) or UTAUT2 theories that may need to be extended in the omnichannel shopping context.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.