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A spherical motor is a novel electromechanical device that has obtained worldwide attention for its attractive advantages. A general analysis of electromagnetic torque in double excited spherical motor has been completed on the calculation of its 3D electromagnetic field distributions. The analysis accounts for the effect of open‐end region in the stator. Double scalar magnetic potentials method has been used in the FEM numerical analysis. On the computation results, the other electromagnetic parameters can be calculated, which will be very significant in the design and performance prediction of the spherical motor. The calculation results indicate that the device is capable of continuous speed control and efficient torque production.

This paper briefly introduces the main characteristics of the Nd‐Fe‐B single phase synchronous motor, discusses the equivalence method of the Nd‐Fe‐B and the mathematical model of the FEM analysis, then reports the calculation results of no‐load and the armature reaction.

The exponential growth of artificial intelligence (AI) technologies, coupled with advanced algorithms and increased computational capacity, has facilitated their widespread adoption in various industries. Among these, the financial technology (FinTech) sector has been significantly impacted by AI-based decision-making systems. Nevertheless, a knowledge gap remains regarding the intricate mechanisms behind the micro-decision-making process employed by AI algorithms. This paper aims to discuss the aforementioned issue.

This research utilized a sequential mixed-methods research approach and obtained data through 18 interviews conducted with a single FinTech firm in France, as well as 148 e-surveys administered to participants employed at different FinTechs located throughout Europe.

Three main themes (ambidexterity, data sovereignty and model explainability) emerge as underpinnings for effective AI micro decision-making in FinTechs.

This research aims to minimize ambiguity by putting forth a proposition for a model that functions as an “infrastructural” layer, providing a more comprehensive illumination of the micro-decisions made by AI.

This research pioneers as the very first empirical exploration delving into the essential factors that underpin effective AI micro-decisions in FinTechs.

This paper examines how digitalization can affect three aspects of firm growth. The specific objectives are as follows: (1) to increase understanding of how digitalization affects pre-factors for growth, (2) to examine how digitalization transforms the growth process, especially growth strategies and (3) to examine how digitalization is apparent in the outcome of growth.

We explore six Finnish growth companies in order to understand the relationship between digitalization and growth. We used qualitative data collection and the Digimat measurement test for analyzing patterns, themes and best practices to generate a deeper understanding of the impact of digital technologies on business growth and growth strategies in these companies.

We propose that business growth includes three aspects of growth: pre-factors of growth, growth as a process and growth as an outcome. Digitalization may affect all of these aspects and strategic flexibility can affect business growth. Digitalization and strategic flexibility are intertwined; strategic flexibility enables the application of new technology, and digitalization enables flexibility.

Building on the results of the case studies, this research identifies relationships between digitalization, business growth and strategic flexibility.

This paper contributes to the growing literature on digitalization, providing new insight into its relation to business growth.

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

In an era marked by artificial intelligence (AI), virtual reality (VR) and augmented reality (AR), this study presents a research paradigm centered on nurturing fundamental skills crucial for effective digital leadership in a paradoxical age where leaders are ambitious as well as skeptical for the adoption of such technologies. This study offers a strategic framework to seamlessly integrate diverse technologies into leadership development; the objective is to bridge the divide between theoretical understanding and practical implementation, especially through the lens of paradox theory.

This conceptual study delineates essential attributes that digital leaders must cultivate, drawing insights from the corpus of literature encompassing leadership, technology and organizational advancement. Synthesizing theoretical perspectives, the study proposes a comprehensive research framework that provides a systematic approach to harnessing the potential of AI, VR and AR to enhance leadership competencies. This conceptual study significantly contributes to paradox theory through method of “theory adaptation” as elaborated in the literature.

The study unveils a spectrum of foundational proficiencies, including technological acumen, adaptability, strategic acumen, effective communication, collaborative aptitude and ethical acumen, among others. These competencies underscore the multifaceted skill set required of digital leaders. To adeptly traverse the intricate digital terrain, foster innovation and align technological advancements with organizational objectives, these proficiencies are imperative for digital leaders to possess.

The distinctiveness of this study lies in its all-encompassing approach to digital leadership development by offering a paradoxical perspective and hence making a contribution to the body of knowledge for paradox theory. By amalgamating AI, VR and AR into a cohesive framework, the study enhances the comprehension of how these technologies collaboratively nurture leaders capable of cultivating organizational triumph in the digital age. This proposed paradigm serves as a bridge between cutting-edge digital technology usage and leadership proficiency paradox, furnishing pragmatic insights to benefit both academic researchers and industry practitioners.

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.

This study aims to empirically investigate how the interaction effect of occasion-setting cues and consumers’ cognitive styles (e.g. field dependence levels) influences their food intake intention.

In this research, several scenario simulation studies were conducted to verify the hypotheses. A total of 646 participants were recruited for the experiments, and samples were obtained through well-established online research platforms.

In the occasion-setting cue advertisement condition, field-dependent (vs field-independent) consumers displayed increased cravings for food and purchase intention, with mental simulation playing a mediating role and cognitive load playing a moderating role.

The influence of others (e.g. servers and other consumers) was not taken into consideration in this study. Future research can extend this study by conducting field experiments.

The research conclusions can help various organisations reduce consumers’ food overconsumption intention and encourage healthier food choices by adjusting occasion-setting cues in marketing stimuli and identifying the target consumers’ cognitive styles.

Based on embodied cognition theory, this study reveals the influence and internal mechanism of the interaction effect between occasion-setting cues and individual cognitive style on eating desire.

This paper aims to attempt to explore the influence of network position on innovation performance, specifically for companies from a less-developed area in China.

The paper uses a social network analysis method based on the data of high and new tech companies from Jiangxi province, China. It relies on Ucinet 6.212 software for data processing.

The present work, on the one hand, reveals that a high and new tech company from Jiangxi province tends to achieve better innovation performances if it is in the central position of its social network. On the other hand, it indicates that the same type of company from the same area does not always accomplish better innovation performance, even it possesses more structural holes.

The conclusions of this paper suggest that a high and new tech company from a less-developed area in China should build up its position closer to the center when constructing a social network. Meanwhile, the company should also exert more effort into managing its networks, as only building structural holes offers little help with operations besides showing that it has a broad social network.

The purpose of this paper is to present a mobile robot with an olfactory capability for hazardous site survey. Possible applications include detection of gas leaks and dangerous substances along predefined paths, inspection of pipes in factories, and mine sweeping.

The mobile sentry is equipped with a transducer array of tin oxide chemical sensors, compliant with the standard interface IEEE 1451, which provides odour‐sensing capability, and uses differential drive and spring‐suspended odometric trackballs to move and localize in the environment. The monitoring strategy comprises two stages. First, a path learning operation is performed where the vehicle is remotely controlled through some potential critical locations of the environment, such as valves, pressure vessels, and pipelines. Then, the robot automatically tracks the prerecorded trajectory, while serving as an electronic watch by providing a real‐time olfactory map of the environment. Laboratory experiments are described to validate the approach and assess the performance of the proposed system.

The approach was shown to be effective in experimental trials where the robot was able to detect multiple odour sources and differentiate between sources very close to one another.

One limitation of the methodology is that it has been specifically designed for odour detection along a well‐defined path in a highly structured environment, such as that expected in the industrial field. The problem of detection of leakages outside the search path is not addressed here.

This mobile robot can be of great value to detect hazardous fluid leakages in chemical warehouses and industrial sites, thus increasing the safety level for human operators.

The paper describes a mobile robotic system, which employs an odour‐sensing capability to perform automated monitoring of hazardous industrial sites. A dynamic model of the mobile nose is also discussed and it is shown that it well describes the behaviour of the system.