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This study aims to explore how to overcome and address the loss of exploratory innovation, thereby achieving greater success in exploratory innovation. This phenomenon of loss occurs when enterprises decrease their investment in and engagement with exploratory innovation, ultimately leading to an insufficient amount of such innovation efforts. Drawing on dynamic capabilities, this study investigates the relationship between organizational foresight and exploratory innovation and examines the moderating role of breakthrough orientation/financial orientation.

This study used survey data collected from 296 Chinese high-tech companies in multiple industries and sectors.

The evidence produced by this study reveals that three elements of organizational foresight (i.e. environmental scanning capabilities, strategic selection capabilities and integrating capabilities) positively influence exploratory innovation. Furthermore, this positive effect is strengthened in the context of a high-breakthrough orientation. Moreover, the relationships among environmental scanning capabilities, strategic selection capabilities and exploratory innovation become weaker as an enterprise’s financial orientation increases, whereas a strong financial orientation does not affect the relationship between integrating capabilities and exploratory innovation.

Ambidexterity is key to successful enterprise innovation. Compared with exploitative innovation, it is by no means easy to engage in exploratory innovation, which is especially important in high-tech companies. While the loss of exploratory innovation has been observed, few empirical studies have explored ways to promote exploratory innovation more effectively. A key research implication of this study pertains to the role of organizational foresight in the improvement of exploratory innovation in the context of high-tech companies.

This paper contributes to the broader literature on exploratory innovation and organizational foresight and provides practical guidance for high-tech companies regarding ways of avoiding the loss of exploratory innovation and becoming more successful at exploratory innovation.

The purpose of this study was to investigate the elements of the triadic framework (e.g. consumer motives, activities, resources and capabilities) and their influence on consumer attitudes and the theory of reasoned action (TRA) elements of subjective norms and eventual purchase intention of secondary sneakers.

The framework draws from two models, the triadic framework for collaborative consumption and the TRA. Data was collected using an online survey, which consisted of demographic questions and reliable scales. The hypothesized relationships were tested through structural equation modeling.

The findings indicate that reducing risks and the customer activity of providing information was significant in influencing consumers' attitudes. However, the customer motives of economic and environmental as well as technical skills were not significant in influencing attitudes.

This study attempts to inform secondary and full-priced retailers offering used products strategies to provide the best experience to millennial and generation Z consumers to ensure they can attract and retain them.

It can be stated with a high degree of certainty that sneaker resale remains one of the most permanent trends in the market today with sneaker resale is significantly outperforming the broader ecommerce ecosystem suggesting more research into the motivations of secondary sneaker consumption.

There are uncertainties concerning how innovators can successfully venture into disruptive innovations and how incumbents can react to the emergence of such innovations. Disruptive digital innovations, which use information technology to disrupt business contexts and can evolve rapidly to either successes or failures, have unique challenges. The literature has largely remained silent concerning these. Also, existing studies often focus on innovations originating in developed economies and just on successful cases. There is a lack of comparative focus on successful and failure cases emerging across economies. The purpose of this paper is to fill these gaps.

This paper assesses the evolution of disruptive digital innovations in various contexts through a financial management-motivated conceptual framework. Contrary to existing works, this paper focuses on both successful and failure cases and regards the influence of various stakeholders further to innovators and incumbents to explain the successes or failures of the innovation.

There are some common success factors for disruptive digital innovation. These include an inherent focus on social value, alignment to financiers' interests and rivals' actions and strategic collaborations to create a synergy effect.

Innovators can cause effective digital disruption by focusing on social and financial values. Success can also largely depend on strategic partnerships rather than actions by an individual entity. Thus, venturing and managing disruptive digital innovation is not an isolated but a social process.

This paper recommends propositions for innovators and incumbents to venture into and confront disruptive digital innovations effectively. Its originality lies in focusing on both successful and failure cases, unexplored in literature, to develop the propositions.

A novel method for modelling permanent magnets is investigated based on numerical approximations with rational functions. This study aims to introduce the AAA algorithm and other recently developed, cutting-edge mathematical tools, which provide outstandingly fast and accurate numerical computation of potentials and vector fields.

First, the AAA algorithm is briefly introduced along with its main variants and other advanced mathematical tools involved in the modelling. Then, the analysis of a circular Halbach array with a one-pole pair is carried out by means of the AAA-least squares method, focusing on vector potential and flux density in the bore and validating results by means of classic finite element software. Finally, the investigation is completed by a finite difference analysis.

AAA methods for field analysis prove to be strikingly fast and accurate. Results are in excellent agreement with those provided by the finite element model, and the very good agreement with those from finite differences suggests future improvements. They are also easy programming; the MATLAB code is less than 200 lines. This indicates they can provide an effective tool for rapid analysis.

AAA methods in magnetostatics are novel, but their extension to analogous physical problems seems straightforward. Being a meshless method, it is unlikely that local non-linearities can be considered. An aspect of particular interest, left for future research, is the capability of handling inhomogeneous domains, i.e. solving general interface problems.

The authors use cutting-edge mathematical tools for the modelling of complex physical objects in magnetostatics.

This study aims to investigate the configurational effects of five rules – artificial intelligence (AI)-based hiring decision transparency, consistency, voice, explainability and human involvement – on applicants' procedural justice perception (APJP) and applicants' interactional justice perception (AIJP). In addition, this study examines whether the identified configurations could further enhance applicants' organisational commitment (OC).

Drawing on the justice model of applicants' reactions, the authors conducted a longitudinal survey of 254 newly recruited employees from 36 Chinese companies that utilise AI in their hiring. The authors employed fuzzy-set qualitative comparative analysis (fsQCA) to determine which configurations could improve APJP and AIJP, and the authors used propensity score matching (PSM) to analyse the effects of these configurations on OC.

The fsQCA generates three patterns involving five configurations that could improve APJP and AIJP. For pattern 1, when AI-based recruitment with high interpersonal rule (AI human involvement) aims for applicants' justice perception (AJP) through the combination of high informational rule (AI explainability) and high procedural rule (AI voice), there must be high levels of AI consistency and AI voice to complement AI explainability, and only this pattern of configurations can further enhance OC. In pattern 2, for the combination of high informational rule (AI explainability) and low procedural rule (absent AI voice), AI recruitment with high interpersonal rule (AI human involvement) should focus on AI transparency and AI explainability rather than the implementation of AI voice. In pattern 3, a mere combination of procedural rules could sufficiently improve AIJP.

This study, which involved real applicants, is one of the few empirical studies to explore the mechanisms behind the impact of AI hiring decisions on AJP and OC, and the findings may inform researchers and managers on how to best utilise AI to make hiring decisions.

Gender bias in artificial intelligence (AI) should be solved as a priority before AI algorithms become ubiquitous, perpetuating and accentuating the bias. While the problem has been identified as an established research and policy agenda, a cohesive review of existing research specifically addressing gender bias from a socio-technical viewpoint is lacking. Thus, the purpose of this study is to determine the social causes and consequences of, and proposed solutions to, gender bias in AI algorithms.

A comprehensive systematic review followed established protocols to ensure accurate and verifiable identification of suitable articles. The process revealed 177 articles in the socio-technical framework, with 64 articles selected for in-depth analysis.

Most previous research has focused on technical rather than social causes, consequences and solutions to AI bias. From a social perspective, gender bias in AI algorithms can be attributed equally to algorithmic design and training datasets. Social consequences are wide-ranging, with amplification of existing bias the most common at 28%. Social solutions were concentrated on algorithmic design, specifically improving diversity in AI development teams (30%), increasing awareness (23%), human-in-the-loop (23%) and integrating ethics into the design process (21%).

This systematic review is the first of its kind to focus on gender bias in AI algorithms from a social perspective within a socio-technical framework. Identification of key causes and consequences of bias and the breakdown of potential solutions provides direction for future research and policy within the growing field of AI ethics.

The peer review history for this article is available at https://publons.com/publon/10.1108/OIR-08-2021-0452

Conflicts between herders and farmers in Nigeria in the last five years have been destructive to the corporate existence of Nigerian society and the Nigerian State, especially in Northcentral, Northwestern and Southern Nigeria. This paper aims to investigate the relationships between state responses and peace-building in rural grazing communities in Nigeria using a National Survey on Peace-building in Nigeria conducted by this team using a cross-sectional survey of 1,711 farmers and herders.

The study adopted the political economy of the herder–farmers conflict in Africa to contextualise the problem. Data generated from the study were analysed using chi-square test and binary logistic regression model.

The results showed that protection of victims of herder–farmers conflict (P = 0.024), blockage of sources of illicit weapons (P = 0.000), arrest of leaders (P = 0.043), provision of shelter (P = 0.030), provision of food (P = 0.037), protection of women from sexual exploitation and abuse (P = 0.019) and use of the media were positively related to peace-building in the rural grazing areas. The study further found that when the Federal Government (ß= 0.452, P = 0.018), State Government (ß= 0.522, P = 0.018), private individuals (ß = 0.855, P = 0.000) and cooperative societies (ß = 0.744, P = 0.021) established ranches, peace was likely to be guaranteed as opposed to where herders (ß= –0.355, P= 0.029) were allowed to establish ranches in the rural grazing communities in Nigeria implying that the Federal and State Government must be cautious in the implementation of the Livestock Transformation Plans not to create an impression that it is designed to favour the herders.

To the best of the authors’ knowledge, this study is original and the paper has not been submitted to any journal.

Although scholars argue that artificial intelligence (AI) represents a tool to potentially smoothen the bullwhip effect in the supply chain, only little research has examined this phenomenon. In this article, the authors conceptualize a framework that allows for a more structured management approach to examine the bullwhip effect using AI. In addition, the authors conduct a systematic literature review of this current status of how management can use AI to reduce the bullwhip effect and locate opportunities for future research.

Guided by the systematic literature review approach from Durach et al. (2017), the authors review and analyze key attributes and characteristics of both AI and the bullwhip effect from a management perspective.

The authors' findings reveal that literature examining how management can use AI to smoothen the bullwhip effect is a rather under-researched area that provides an abundance of research avenues. Based on identified AI capabilities, the authors propose three key management pillars that form the basis of the authors' Bullwhip-Smoothing-Framework (BSF): (1) digital skills, (2) leadership and (3) collaboration. The authors also critically assess current research efforts and offer suggestions for future research.

By providing a structured management approach to examine the link between AI and the bullwhip phenomena, this study offers scholars and managers a foundation for the advancement of theorizing how to smoothen the bullwhip effect along the supply chain.