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It is largely acknowledged that arbitrating the flow of knowledge can help firms strategically leverage tacit and explicit internal knowledge. However, despite the apparent scholarly and managerial acceptance of the criticality of the flow of knowledge between various stakeholders, the academic understanding of knowledge arbitrage remains coarse-grained. There are practically no empirical insights available to unravel the consequences of firms’ knowledge arbitrage choices regarding rewards and risks. This study aims to identify the risks that emerge as firms channel the flow of knowledge from surplus to deficit areas within organizational boundaries. To this end, the authors investigate several subsumed subprocesses in knowledge arbitrage to map the associated risks.

This study used an exploratory qualitative approach to examine the risks that emerge as firms attempt to support knowledge flows within their organizational boundaries. The data were collected through open-ended essays via an online research platform from 45 full-time employees of firms operating in different sectors. The collected data were analyzed inductively through open, axial and selective coding.

The research findings identified three key subprocesses of knowledge arbitrage: knowledge diffusion, knowledge brokering and knowledge absorption. These subprocesses are susceptible to various risks arising the form of channels, champions, sharers and receivers of knowledge flows. In general, the study showed that a firm’s decision regarding knowledge flows, such as structured or random flows, or the presence or absence of designated coordinators to broker the flow carries specific risks for both sharers and receivers. In particular, while the risks of knowledge hiding, misinformation and disinformation manifest in all three subprocesses, low employee engagement, loss of knowledge and information overload also emerged as key risks in any two of the three subprocesses.

This study offers valuable insights by uncovering the hitherto unexplored risks in intrafirm knowledge arbitrage. Given that knowledge is a crucial organizational tool for driving performance, innovation and competitive advantage, understanding the risks associated with intrafirm arbitrated knowledge flows can help firms anticipate and mitigate the associated adverse consequences. The findings make a novel contribution by offering (a) a comprehensive categorization of the risks associated with knowledge arbitrage rooted in processes, people and structures and (b) a macro overview of knowledge arbitrage risks associated with the processes of knowledge diffusion, knowledge brokering and knowledge absorption.

This study based on the conservation of resources (COR) theory examines the role of employees’ voice behavior in the form of a mediator, linking abusive supervision (AS) and turnover intention. It also investigates the moderating role of workplace friendship in the mediated AS–turnover intention relationship through voice behavior.

A two-wave data collection method was used to collect data from the 324 respondents employed in various companies with a geographical spread across northern India. The study used PROCESS macro to test the hypothesized model.

The findings of the study supported the meditated moderation hypothesis suggesting workplace friendship reduces the mediating effect of AS on employees’ intention to exit employment relationships.

The study yields important implications for organizations with respect to developing a disciplinary framework for AS. It focuses on the need for promoting and implementing psychological well-being-related interventions at the workplace for subordinates as well as supervisors, which in turn can help them apply healthy coping strategies in stressful situations and prevent them from indulging in counterproductive work behaviors.

The utilization of COR as a framework to explain the role of voice behavior and workplace friendships with respect to AS is thus far scant.

As the global financial ecosystem grapples with the complexities of modernization, blockchain technology emerges as a pivotal catalyst, offering the banking, financial services, and insurance (BFSI) industry unprecedented opportunities for secured digital transformation and enhanced customer trust. To gain a comprehensive understanding of blockchain technology adoption, this study aims to identify the factors and establish the contextual interrelationships among them.

The authors have identified the factors affecting blockchain technology adoption in BFSI industry through extensive literature review and experts’ interviews. After identification of factors, contextual relationship has been established based on experts’ opinion and total interpretive structural modeling (TISM) approach. Furthermore, factors are categorized into autonomous, dependent, linkage and driving variables using cross-impact matrix multiplication applied to classification analysis.

The TISM-based structural model is divided into eight different hierarchal levels in which Government support is placed on the lower most layer (level 8) which indicates that this is the most crucial factor in blockchain adoption. Further social influence and security are placed on seventh and sixth level in the hierarchy.

The results of this study will help the policymakers to direct the resources from the most crucial factor to other factors in the hierarchy as per their relevance. In essence, this study serves as a guiding compass, steering the course of blockchain technology adoption in the BFSI sector toward a more secure and digitally transformed future.

In the current landscape, blockchain technology remains in its nascent stage, leaving ample room for exploration and innovation. This study stands as the pioneering effort to comprehensively identify and establish the contextual relationships among the adoption factors of blockchain technology within BFSI industry. Through rigorous TISM analysis, this paper enriches the existing body of knowledge on blockchain technology adoption.

This paper aims to identify the impacts of sociodemographic covariates on behavioural biases (BB) scores; the psychometric evidence of the BB measurement instruments; and the main BB that influences the decision-making of individual investors.

Papers were retrieved through search using keywords in ten databases. This systematic review is based on 69 peer-reviewed papers, most of which were published between 2017 and 2021. The relevance of the included papers was assessed through the analysis of statistical/psychometric methods used, and content analysis of the BB literature and its sociodemographic correlations.

Overconfidence is higher in men and not related to age. There was no consensus regarding the relationship between BB and other sociodemographic variables. Most measuring instruments are ad hoc, showing ≤ 4 types of psychometric evidence and assessing ≤ 9 BB. Therefore, the findings demonstrate that there is no gold standard instrument for measuring investors’ BB. Furthermore, 37 BB were cited as influencers of individual investors’ decision-making and overconfidence, herding, anchoring, representativeness and loss aversion were the most prevalent.

Considering that very few systematic reviews have been published in the behavioural finance area, this paper highlights the current state-of-the-art and identifies significant gaps in the literature that can be explored by further research.

To the best of the authors’ knowledge, this is the first systematic review that analyses the psychometric properties of instruments used for individual investors BB assessment.