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Previous research has found that individuals with social phobia differ from controls in their processing of emotional faces. For instance, people with social phobia show increased attention to briefly presented threatening faces. However, when exposure times are increased, the direction of this attentional bias is more unclear. Studies investigating eye movements have found both increased as well as decreased attention to threatening faces in socially anxious participants. The current study investigated eye movements to emotional faces in eight patients with social phobia and 34 controls. Three different tasks with different exposure durations were used, which allowed for an investigation of the time course of attention. At the early time interval, patients showed a complex pattern of both vigilance and avoidance of threatening faces. At the longest time interval, patients avoided the eyes of sad, disgust, and neutral faces more than controls, whereas there were no group differences for angry faces.

Obesity is a significant public health issue. With obesity increasing worldwide, risk factors for obesity need to be better understood and require careful examination. This study aims to examine mental health as a risk factor for obesity using longitudinal data from Australia.

The main identification strategy relies on the recent death of a close friend and a serious injury or illness to a family member as exogenous shocks to mental health.

The authors’ preferred estimates, which account for the endogeneity of mental health, suggest that mental health has a significant negative impact on obesity. This result proves to be robust to a suite of sensitivity checks. Further investigations reveal that poor mental health leads to increased smoking, which also has an effect on obesity.

The study’s findings provide a new perspective on how good mental health helps curb obesity.

The General Data Protection Regulation (GDPR) introduces significant data protection obligations on all organizations within the European Union (EU) and those transacting with EU citizens. This paper presents the GDPR privacy label and uses two empirical studies to examine the effectiveness of this approach in influencing consumers' privacy perceptions and related behavioral intentions.

The paper tests the efficacy of two GDPR privacy label designs, a consent-based label and a static label. Study 1 examines the effects of each label on perceptions of risk, control and privacy. Study 2 investigates the influence of consumers' privacy perceptions on perceived trustworthiness and willingness to interact with the organization.

The findings support the potential of GDPR privacy labels for positively influencing perceptions of risk, control, privacy and trustworthiness and enhancing consumers' willingness to transact and disclose data to online organizations.

The findings are useful for organizations required to comply with the GDPR and present a solution to requirements for transparent communications and explicit consent.

This study examines and demonstrates the efficacy of visualized privacy policies in impacting consumer privacy perceptions and behavioral intentions.

The empirical study of factors related to digital transformation (DT) in the banking sector is still limited, even though the importance of the topic is universally evident. To bridge that gap, this paper aims to explore the role of digital leadership (DL), innovative culture (IC) and technostress inhibitors (TI) to support engagement for improved digital innovation (DI). Based on the literature, these variables are crucial aspects of digitalisation, even though there is no agreement on their conclusiveness.

This quantitative study tested a new conceptual model using survey data from five major banks in Libya. Partial least squares structural equation modelling was used to analyse the data from the 292 usable responses.

The results showed that DL and IC positively affect DI. Techno-work engagement (TE) mediated the relationship between leadership, culture and innovation. TI played a significant moderating role in leadership, culture and engagement relationships.

The research findings highlight critical issues about how leadership style and fostering organisational support in the banking sector can enhance DT. Leaders must demonstrate a commitment to long-term resource allocation to avoid possible negative effects from digital stress while pursuing DI through work engagement.

The study suggests that fostering organisational support can enhance DT in retail banks, potentially leading to improved customer experiences and increased access to financial services. These programs will help banks contribute to societal and economic development.

This timely study examines predictor mechanisms of innovation in retail banking that resonate within the restrictions of organisational and DI frameworks and the social exchange theory. Exploring the intervening effect of TE in the leadership, culture and innovation associations is unprecedented.

The purpose of this paper is to assess the impact and effectiveness of the LearnOvation leadership development program in the welfare services sector in Sweden.

LearnOvation was based on ambidexterity theory for the program content and the research study design. A mixed-method design was applied, using questionnaires among staff (n = 523) and written evaluations with the management teams (n = 60).

Quantitative analysis of the questionnaires indicated little change in managers' and staffs' innovation behaviors, though employee exploration behaviors were strongly and positively correlated with their innovation behaviors. Qualitative leader-written evaluations reported increased understanding of innovation management and the use of exploration and exploitation activities to involve staff in the implementation of creative ideas within the organization.

The authors argue that innovating is about creating a fertile ground for exploration and exploitation processes of learning that support staff's willingness to meet goals, as well as their capability to explore new ideas and experiment in new ways of working. Leadership development activities that engage the entire management team can build the necessary capacity and power to lead innovation processes in highly structured welfare services and free the employees' innovativeness, potentially leading to improved services and employee satisfaction.

With the goal of enhancing the innovation capacity in daily practice, this study adds to the scarcity of research in welfare services on how to actually support management's work on leading successful implementation of creative ideas.

The purpose of this research is to obtain empirical evidence about the impact of auditor’s integrity and organizational commitment to the prevention of fraud.

This research was conducted using questionnaires distributed to all internal auditors who worked at East Java Representatives Office of Indonesia’s National Government Internal Auditor. One hundred and thirteen questionnaires were distributed, and fifty-seven questionnaires were received, and all have validity eligible to use in this research. The hypothesis of this research was tested using the partial least square analysis with WarpPLS version 6.0 software.

The result of this research found that auditor’s integrity and organizational commitment affect positively to fraud prevention.

Using government internal auditors, this study believed, brings a new insight into government internal auditor behavior.

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.

This paper aims to explore consumer perception of “brand voice” authenticity, brand authenticity and brand attitude when the source of text is disclosed as either artificial intelligence (AI)-generated or human-written.

A 3 × 3 experimental design using Adidas marketing texts disclosed as either “AI” or “human”, or not disclosed was applied to data gathered online from 624 English-speaking students.

Text disclosed as AI-generated is not perceived as less authentic than that disclosed as human-written. No negative effect on brand voice authenticity and brand attitude results if an AI-source is disclosed.

Findings offer brand managers the potential for cost and time savings but emphasise the strong effect of AI technology on perceived brand authenticity and brand attitude.

Results show that brands can afford to be transparent in disclosing the use of AI to support brand voice as communicated in product description or specification or in chatbot text.

This paper presents a comparative study of different stator-segmentation topologies of a permanent magnet synchronous machine (PMSM) used in traction drives and their effect on iron losses. Using stator segmentation allows one to achieve more significant copper fill factors, resulting in increased power densities and efficiencies. The segmentation of the stators creates additional air gaps and changes the soft magnetic material’s material properties due to the cut edge effect. The aim of this paper is to present an in-depth analysis of the influence of stator segmentation on iron losses. The main goal was to compare various segmentation methods under equal excitation conditions in terms of their influence on iron loss.

A transient finite element method analysis combined with an extended iron-loss model was used to evaluate discussed effects on the stator’s iron losses. The workflow to obtain a homogenized airgap length accounting for cut edge effects was established.

The paper concludes that the segmentation in most cases slightly decreases the iron losses in the stator because of the overall reduced magnetic flux density B due to the additional air gaps in the magnetic circuit. An increase of the individual components, as well as total power loss, was observed in the Pole Chain segmentation design. In general, segmentation did not change the total iron losses significantly. However, different segmentation methods resulted in the different distortion of the magnetic field and, consequently, in different iron loss compositions. The analysed segmentation methods exhibited different iron loss behaviour with respect to the operation points of the machine. The final finding is that analysed stator segmentations had a negligible influence on the total iron loss. Therefore, applying segmentation is an adequate measure to improve PMSMs as it enables, e.g. increase of the winding fill factor or simplifying the assembly processes, etc.

The influence of stator segmentation on iron losses was analysed. An in-depth evaluation was performed to determine how the discussed changes influence the individual iron loss components. A workflow was developed to achieve a computationally cheap homogenized model.