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This article shows how different data sources can be combined to learn about the evolution of gender norms over time. First, data on job advertisements from 1950 up to 2020 reveal that there was a significant change among Swiss employers' stated preferences regarding their prospective employees' gender. More specifically, the proportion of gender-neutral job posts increased from five to almost 95% within the observation period. To further corroborate and contextualize this finding, I complement it with time series on the relative frequency of several specific queries, such as equality between men and women, from Google's German language book corpus. These additional series are broadly consistent with the evolution of the share of gender-neutral job posts. However, it also appears that there are two distinct narratives, one concerned with the personal sphere, identity, and intimate relationships, the other with the political and public realm. Interestingly, the narrative on personal relations set off considerably earlier than the change in the proportion of gender-neutral job ads. Overall, the evidence from the different data series shows that gender norms have changed substantively, yet in a complex manner, over the past decades.

Understanding the restorative potential of built environments is essential for promoting mental well-being. However, existing studies often rely on static image-based methods, which are inherently limited in capturing the temporal and spatial dynamics of environmental perception. These methods frequently introduce biases, such as selective framing and abrupt transitions, failing to reflect natural viewing behavior. Addressing these limitations, this study investigates the restorative qualities of campus environments using dynamic VR stimuli and eye-tracking technology. By providing continuous temporal and spatial information, dynamic VR stimuli offer a more immersive and ecologically valid approach to understanding how specific environmental features contribute to psychological restoration.

This study investigates the restorative qualities of campus environments using VR eye-tracking technology and dynamic stimuli. Campus environments were filmed through walking sequences and paired with PRS audio prompts. About 40 university students participated in the experiment, with eye-tracking data processed using computer vision-based semantic segmentation and the concept of relative areas of interest, followed by correlation analysis with restorative quality scores.

The results revealed that natural elements such as “sky,” “tree,” “waterscape” and “landscape corridor” were significantly positively correlated with the being-away and fascination dimensions, indicating their role in capturing attention and supporting psychological recovery. Conversely, architectural elements like “architectural corridor” and “building facade” were negatively correlated with the extent dimension, while “architectural open space” positively correlated, enhancing spatial perception and exploration.

These findings underscore the importance of natural elements and open spaces in built environments while also revealing the complex influence of architectural features. The study provides valuable insights for optimizing campus design to support students’ mental health and well-being.

In this era of rapid technological advancement, Artificial Intelligence (AI) has emerged as a crucial factor in reshaping organisational dynamics, notably in the realm of leadership. This systematic literature review (SLR) aims to investigate the emerging relationship between AI and leadership, focussing on defining AI-powered leadership, identifying prevalent themes, exploring challenges, and uncovering research gaps within the relevant literature.

A sample of 73 papers was chosen after carefully applying the inclusion and exclusion criteria to 1,387 research articles that were initially sought. Using the methodological framework presented by Denyer and Tranfield (2009), our study adopted a four-step procedure to obtain insights from the corpus of literature. The papers were analysed by employing content and thematic analysis to address four key questions.

The review explores various definitions of AI-powered leadership proposed in the literature based on real-world situations. The study further synthesises significant themes in the existing literature, such as the past, present and future of AI and AI in various facets of organisational leadership, transitional management, and urban management. The review revealed a range of key challenges in AI-powered leadership, including ethical dilemmas, complications in human-AI interactions, hurdles in AI implementation within leadership contexts, and long-term risks associated with AI integration. In addition, this study identified areas within AI-powered leadership research that require further investigation by revealing significant research gaps in the papers.

By adopting a comprehensive approach, this research advances understanding of the complex relationship between AI and leadership dynamics, thus facilitating comprehension of the current body of knowledge and enabling future scholarly investigations in the AI-powered leadership domain.

Intelligence transformation has hugely influenced business operation in many industries and countries, such as in the emerging market. Generative Artificial Intelligence (GAI) adoption by organisations is a significant result of transformation. However, the influence of GAI adoption on small and medium-sized enterprises (SMEs) has been given less attention in business and management studies. In particular, managing the relationship of employees in GAI adoption is a focal point during the transformation from an ethical, responsible and sustainable perspective. Drawing on organisational socialisation and technology adoption theories, this chapter develops a process of socialising newcomers and/or existing employees in the development of GAI adoption in their workflow with identification of challenges and strategies to the adapt to the change. This discussion can help managers and other key persons to effectively manage the relationship and interactions between employees and technology (GAI) in a more ethical, responsible and sustainable manner.

Artificial intelligence (AI) will transform various processes by utilizing and sharing data and information. This transformation brings new opportunities and challenges to organizations. Effective leadership is essential to handle these changes. However, there is no scientific research on how AI affects the everyday lives of managers. Therefore, this paper aims to identify how AI can affect changes in the skills and personality traits of managers using AI.

A structured literature review identified leadership competencies relevant to the AI era. Three scientific databases were included in the search: (I) Scopus, (II) EBSCO Business Source Complete, and (III) Web of Science. A total of 730 articles were identified from the three databases under the topics “Digital Leadership,” “Leadership” AND “Artificial Intelligence,” “Future Leadership,” “Algorithm Leadership,” “AI Leadership,” “Artificial Leadership,” and “Data-driven Leadership.”

A total of 24 leadership competencies, including 12 personality traits and 12 skills, were identified from the literature. To adapt effectively to AI, leaders should focus on developing communication skills and forming high-performance teams working cross-functionally and in a symbiosis of humans and machines.

The article adds knowledge to leadership theories and provides a basis for future management education.

Previous studies highlighted a dearth of scale to measure algorithm aversion among working professionals. Accordingly, this study tried to adopt and validate an algorithm aversion scale for employees.

The scale was developed and validated in three stages. In the first stage, a five-item xenophobic scale was adopted. Its items were rephrased to match the algorithm aversion-related workplace context. In the second stage, the sample was divided into two subsamples using random sampling. The exploratory factor analysis (EFA) applied on the first subsample. The confirmatory factor analysis (CFA) was conducted on the second sample to check the model’s fitness proposed by EFA. In the third stage, a sample of 378 respondents was collected. The Cronbach’s alpha (CA) and composite reliability (CR) values checked internal consistency reliability of the proposed algorithm aversion scale.

The EFA yielded a one-factor solution with acceptable factor loading (greater than 0.50) of all (five) statements. All values of model indices in CFA were in the acceptable range, which recommended the factorial structure of the scale. The acceptable (greater than 0.70) values of CA and CR concluded the internal consistency reliability. The convergent validity was suggested by appropriate average variance extracted (AVE) values (greater than 0.50). In addition, as expected, the proposed algorithm aversion scale reported a statistically significant positive correlation with neuroticism and a considerable negative relationship with trust. These findings concluded the criterion validity of the scale.

This study uses primary data to adopt and validate algorithm aversion scale.

Steam explosions are a major safety concern in many modern furnaces. The explosions are sometimes caused by water ingress into the furnace from leaks in its high-pressure (HP) cooling water system, coming into contact with molten matte. To address such safety issues related to steam explosions, risk based inspection (RBI) is suggested in this paper. RBI is presently one of the best-practice methodologies to provide an inspection schedule and ensure the mechanical integrity of pressure vessels. The application of RBIs on furnace HP cooling systems in this work is performed by incorporating the proportional hazards model (PHM) with the RBI approach; the PHM uses real-time condition data to allow dynamic decision-making on inspection and maintenance planning.

To accomplish this, a case study is presented that applies an HP cooling system data with moisture and cumulated feed rate as covariates or condition indicators to compute the probability of failure and the consequence of failure (CoF), which is modelled based on the boiling liquid-expanding vapour explosion (BLEVE) theory.

The benefit of this approach is that the risk assessment introduces real-time condition data in addition to time-based failure information to allow improved dynamic decision-making for inspection and maintenance planning of the HP cooling system. The work presented here comprises the application of the newly proposed methodology in the context of pressure vessels, considering the important challenge of possible explosion accidents due to BLEVE as the CoF calculations.

This paper however aims to optimise the inspection schedule on the HP cooling system, by incorporating PHM into the RBI methodology, as was recently proposed in the literature by Lelo et al. (2022). Moisture and cumulated feed rate are used as covariate. At the end, risk mitigation policy is suggested.

In this paper, the proposed methodology yields a dynamically calculated quantified risk, which emphasised the imperative for mitigating the risk, as well as presents a number of mitigation options, to quantifiably affect such mitigation.

The study’s objective is to assess the impact of employer branding (EB) on person–job fit (PJF), work engagement (WE), internal communication (IC) and employee performance (EP) in tourism businesses. In addition, the study evaluated the mediating roles of PJF, WE and IC between EB and EP.

To gather data, the questionnaire was distributed to 1,024 employees in travel agencies and 746 questionnaires valid for statistical analysis were received.

The findings indicated the positive influence of EB on PJF, WE, IC and EP. These findings also revealed the positive effects of PJF, WE and IC on EP. The partial mediating functions of PJF, WE and IC in the nexus between EB and EP, and the mediating function of PJF in the nexus between EB and WE are highlighted.

General managers should invest in creating a strong EB that highlights the organization's core values, culture and unique features, such as personal development opportunities and a supportive environment.

This study presents and empirically examines an innovative theoretical model that thoroughly evaluates the effects EB, PJF, WE and IC on EP in the lens of Social Exchange Theory (SET).

This study aims to explore the eye movement behavior of preadolescent children accessing and diagnosing information.

The researchers tracked the eye movements of 30 children with an eye-tracking apparatus. Using the kit of factor-referenced cognitive tests to measure perceptual speed and associative memory, they measured information-searching behavior with screen recordings, the data of which were analyzed by IBM SPSS Statistics 26.

Regarding information accessibility, there was a correlation between the child’s age, associative memory and the number of round-trip choices, and there were differences in the total fixation area among children of different age groups. Regarding diagnosticity, perceptual speed was positively correlated with the total fixation area, and the number of round-trip choices was negatively correlated with fixation duration.

Empirical evidence suggests that during information encoding, perceptual speed is the most important influencing factor. Extensive research indicates that children predominantly rely on recall and familiarity when searching for new information, both of which play roles in associative memory. Through an examination of the psychological and behavioral indicators of children, the study elucidated the cognitive processes involved in information processing and how children engage with information at both visual and cognitive levels.