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Blockchain technology offers numerous venues for supply chain applications and research. However, the connections between specific blockchain features and future applications have been unclear to date in its evolution. The purpose of this study is to fill this void.

The authors advance the understanding of blockchain in supply chain management by providing a new research framework built on unique blockchain features as applied across core supply chain functions.

This study’s framework is a feature-function matrix that integrates four overarching supply chain functions (i.e. supplier management, logistics, production processes and customer management) with nine blockchain features (i.e. traceability/provenance, accessibility, visibility, immutability, distributed/shared ledger, validity, peer-to-peer transacting, pseudonymity and programmability). This study’s feature-function framework is supported by a structured, systematic review of reviews using PRISMA methods. The authors use the framework to present a future blockchain research agenda in supply chain management.

The authors provide a new blockchain feature/supply chain function framework and provide a structured path for future research.

The purpose of this paper is to analyse how novel homicide defences predicated on contemporary neuroscience align with legal insanity.

Doctrinal analysis, systematic investigation of relevant statutes and cases, was used to elucidate how the law of insanity is evolving. Cases represent the first recorded instance of a particular neuroscientific defence. US appellate cases were categorised according to the mechanism of action of neurotransmitter relied upon in court. A case study approach was also used to provide a contextualised understanding of the case outcome in depth.

Findings broadly depict how the employment of expert testimony runs parallel with our contemporary understanding of key neurotransmitters and their function in human behaviour. Generally, medico-legal evidence concerning neuromodulating agents and violent behaviour was inconclusive. However, the outcome of defence strategy may depend on the underlying neurotransmitter involved.

This study shows that as more discoveries are made about the neurobiological underpinnings of human behaviour; this new knowledge will continue to seep into the US court system as innovative defence strategies with varying success. Medical and legal practitioners may gauge the success of a defence depending on the neuromodulating agent.

Many scholars have focused on the role of neuroimaging as neuroscientific evidence and how it is used is shaping US criminal jurisprudence. To the best of the author’s knowledge, no study has incorporated the true origin of neuroscientific evidence as being underpinned by the understanding of neurotransmitters.

Emotional intelligence (EI) is a critical component of leadership that reflects the ability of leaders to understand how their emotions and actions affect the people around them in the organization. This paper aims to deliver state-of-the-art insights on EI and leadership.

This paper leverages on bibliometric analysis to unpack 25 years of EI and leadership research.

This paper reveals the bibliometric profile (e.g. trends in publication activity and top articles, authors, countries and journals) and intellectual structure (e.g. themes and topics) of EI and leadership research, shedding light on EI manifestation in leadership, EI and leadership congruence, EI role in leadership and EI and leadership for human resource management.

This paper offers several noteworthy implications. First, EI is a leadership competency that can be cultivated and leveraged to improve leadership effectiveness. Second, the need for EI and leadership congruence indicates that leadership effectiveness is vital to human resource management (HRM). Taken collectively, these theoretical implications, and by extension, practical implications, suggest that increased investment in EI and leadership effectiveness is critical for organizations and their HRM.

This paper sheds light on current trends and ways forward for leading by feeling, showcasing the role and manifestation of EI in leadership, its value for HRM and the importance of its congruence for effective leadership in shaping the future of work.

Historically, graduate education’s goal was to prepare academics; now most science, technology, engineering and/or mathematics (STEM) graduate students (GSs) go on to nonacademic careers. STEM GSs must be equipped for success regardless of career aspirations, which can be done by strengthening GSs’ professional identities. This study aims to explore an interdisciplinary partnership designed to strengthen STEM GS professional identity.

The STEM Partnership Project (SPP), asked STEM GSs to serve as disciplinary experts and teach STEM content to elementary teacher candidates (TCs) so the TCs could design and teach an elementary science lesson. GSs also enrolled in a one-credit course to support SPP participation and activities. Over five semesters, the authors collected data from 28 STEM GSs across different disciplines and degree programs in the form of course assignments, surveys and interviews.

The SPP supported the development of a professional identity by having GSs serve as and feel like experts; increasing GSs’ sense of belonging in their field; increasing GSs’ self-confidence that they could (learn to) teach a wide variety of audiences; and raising GSs’ awareness of their ability to serve others via their field.

The SPP’s outcomes were consistent across STEM disciplines, did not require GSs to take on large amounts of coursework, nor did it cost much beyond materials for the various lessons. Furthermore, the key components that strengthened GSs’ professional identities could be adapted for different contexts and institutions.

India is an emerging economy and one of the preferred investment destinations for environmental, social and governance (ESG) fund issuers. Institutional investors invest retail investors’ money, and hence, it becomes imperative for ESG fund managers to understand the social investment preferences of retail investors. This study aims to compare the Indian socially responsible (SR) investors and conventional investors in terms of their socially responsible investment (SRI) awareness level, opinions about broad and specific ESG issues, investment behavior and demographics. In addition, this paper makes an attempt to have a deeper insight into Indian investors’ behavior toward SRI by segmenting the Indian retail investors based on their SRI awareness level, attitude toward ESG issues and intention to accept lower financial returns, and choices made by them as consumers.

After collecting the data through the survey method an independent t-test is used to compare SR investors with conventional investors. Chi-square has been used to analyze the data related to demographics, and cluster analysis is used to identify segments among Indian retail investors.

The results indicated that Indian SR investors’ SRI awareness level is more, they are more concerned about broad and specific ESG issues, they are more into faith-based investing, and are responsible consumers vis-à-vis conventional investors. As per demographic, SR investors are in the middle age group of 30–40 years, male, hold a postgraduate degree and have an annual income of 10–20 lakhs in comparison to conventional investors. The results of cluster analysis indicated that Indian retail investors can be classified into three groups based on their SRI awareness, intention to sacrifice financial return, attitude toward ESG issues and choices made by them as consumers.

Results have implications for national and international fund managers, policymakers, regulators and society. These results will help mutual fund companies to provide curated SR mutual funds as per the behavior and choice of retail investors and penetrate the Indian investment market more deeply.

This research study contributes to the literature on SRI by identifying the differentiating characteristics of Indian SR and conventional investors and segmenting Indian retail investors on the basis of their SRI awareness, the importance of ESG issues and choices made by them as investors and consumers.

This study aims to develop a unified theoretical framework that presents a cohesive picture of workplace cyberbullying to better understand the interplay between cyberbullying, its effects on organizations and organizational controls enacted to contain these effects.

The study conducts a theoretical review of the workplace cyberbullying literature between 2005 and 2021 drawing upon existing literature and two important theories, the routine activities theory and control theory. The final sample of 54 empirical papers represents a comprehensive body of literature on cyberbullying published across various disciplines.

A theoretical model of workplace cyberbullying is developed, which highlights major antecedents to workplace cyberbullying and its impact on individual employees as well as organizations.

As firms increasingly rely on information and communication technologies (ICTs), the misuse of ICTs in the form of cyberbullying is also increasing. Workplace cyberbullying severely hurts an organization’s employees and compromises the efficacy of its information systems. Fortunately, various controls can be utilized by firms to minimize workplace cyberbullying and its attendant costs. In all, eleven propositions are offered, providing a robust agenda for future research. The authors also offer insights for practitioners on how to minimize cyberbullying in the workplace and its damaging effects.

The study's purpose is to investigate the price volatility of four dairy commodities (skim milk powder [SMP], whole milk powder [WMP], butter and cheddar cheese) in the three most significant regional markets (EU, Oceania and US) in the international dairy market.

The study uses a panel-Generalized Autoregressive Conditional Heteroskedastic (panel-GARCH) modeling technique and data from January 12, 2001, to April 28, 2017.

Conditional volatility was higher during subperiods 2007–2010 and 2014–2016 when conditional cross-correlations between prices had the lowest values. In some cases, they were negative (i.e. between the EU and the USA and between Oceania and the USA for both butter and cheese). Interdependence across the three dairy markets, especially for SMP and WMP markets and for the butter market between EU and Oceania is also strongly evidenced. Interdependence is responsible for the spillover of price shocks across the three regions.

The data set used should be extended to cover the COVID-19 pandemic period.

This is the first study to use panel-GARCH to examine international dairy prices and volatility linkages, where previous studies mainly used multivariate GARCH models. Panel-GARCH allows a high-dimensional data series (i.e. 12 dairy prices) and generates potential efficiency gains in estimating conditional variances and covariances by incorporating information about heterogeneity across markets and considering their interdependence.

This study aims to develop a model of learning-by-hiring in which knowledge gains may occur at the time of recruitment but also after recruitment when other incumbent organizational members assimilate a recruit’s knowledge. The author’s model predicts that experienced recruits are more likely to catalyze change to their organization’s core technological capabilities.

The continuous-time parametric hazard rate regressions predict core technological change in a long panel (1970–2017) of US biotechnology industry patent data. The author uses over 140,000 patents to model the evolution of knowledge of over 52,000 scientists and over 4,450 firms. To address endogeneity concerns, the author uses the Heckman selection method and does robustness tests using a difference-in-difference analysis.

The author finds that a hire’s prior research and development (R&D) experience helps overcome inertia arising from her or his new-to-an-organization “distant” knowledge to increase the likelihood of core technological change. In addition, while the author finds that incumbent organizational members resist technological change, experienced hires may effectively induce them to adopt new ways of doing things. This is particularly the case when hires collaborate with incumbents in R&D projects. Understanding the effects of hiring on core technological change, therefore, benefits from an assessment of hire R&D experience and its effects on incumbent inertia in an organization.

First, the author does not recommend managers to hire scientists with considerable distant knowledge only as this may be detrimental to core technological change. Second, the author recommends organizations striving to effectuate technological change to hire people with considerable prior R&D experience as this confers them with the ability to influence other members and socialize incumbent members. Third, the author recommends that managers hire people with both significant levels of prior experience and distant knowledge as they are complements. Finally, the author recommends managers to encourage collaboration between highly experienced hired scientists and long-tenured incumbent organizational members to facilitate incumbent learning, socialization and adoption of new ways of doing things.

This study develops a model of learning-by-hiring, which, to the best of the authors’ knowledge, is the first to propose, test and advance KM literature by showing the effectiveness of experienced hires to stimulate knowledge diffusion and core technological change over time after being hired. This study contributes to innovation, organizational learning and strategy literatures.

The aim of this paper is to better understand European consumers' behaviour in relation to Short Food Supply Chains (SFSCs), so as to provide insights to support their development as part of a sustainable food system. Specifically, it aims to analyse consumer purchase patterns, motivations and perceived barriers and to identify patterns of behaviour amongst different consumer groups.

An online consumer survey was conducted in 12 European countries (n = 2,419). Quantitative data analysis, including principal component analysis (PCA) and cluster analysis, was undertaken using SPSS.

Four consumer clusters are named according to their behavioural stage in terms of SFSC engagement: Unaware Unengaged, Aware Unengaged, Motivationally Engaged and Executively Engaged. Unaware Unengaged and Aware Unengaged are in the non-engagement phase of behaviour. Motivationally Engaged are motivationally activated to engage in the behaviour but fail to do so consistently. Executively Engaged is the fully engaged cluster, being motivated to act and purchasing local food on a frequent basis. The results show an interesting interplay between motivations and barriers, i.e. higher scores for motivations and lower scores for barriers do not necessarily translate into higher purchase frequency.

The research gleans insights into the contextual factors that may inhibit SFSC purchases in different consumer segments. It offers practical implications for policymakers and others seeking to develop SFSCs as part of a sustainable food system.

In the uncertain business environment, the supply chains are under pressure to balance routine operations and prepare for adverse events. Consequently, this research investigates how artificial intelligence is used to enable resilience among supply chains.

This study first analyzed the relationship among different characteristics of AI-enabled supply chain and how these elements take it towards resilience by collecting the responses from 27 supply chain professionals. Furthermore, to validate the results, an empirical analysis is conducted where the responses from 231 supply chain professionals are collected.

Findings indicate that the disruption impact of an event depends on the degree of transparency kept and provided to all supply chain partners. This is further validated through empirical study, where the impact of transparency facilitates the mass customization of the procurement strategy to Last Mile Delivery to reduce the impact of disruption. Hence, AI facilitates resilience in the supply chain.

This study adds to the domain of supply chain and information systems management by identifying the driving and dependent elements that AI facilitates and further validating the findings and structure of the elements through empirical analysis. The research also provides meaningful implications for theory and practice.