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This study aims to develop a framework that explains how and when telework is related to emotional exhaustion and task performance, by conceiving work overload as a mediator and self-leadership as a moderator. For this purpose, two studies were conducted. Study 1 aims to understand whether telework would be related to emotional exhaustion and task performance and if work overload would mediate such relationships. Study 2 aims to analyze whether self-leadership was a significant moderator of the mediated relations found in Study 1.

The hypotheses were tested in a sample of 207 (in Study 1) and 272 (in Study 2) participants, which were exclusively teleworking. The results were analyzed using PROCESS macro in SPSS.

The results of Study 1 showed that telework dimensions were negatively related to work overload, which consequently decreased emotional exhaustion and increased task performance. In Study 2, self-leadership moderated the indirect effect of work overload on the relationship between telework and emotional exhaustion, such that the indirect effect was stronger for those who scored higher in self-leadership. However, it was not significant for task performance.

This paper adds to research on telework by focusing on the employee's mental health and performance, in the context of mandatory confinement. The authors identified telework dimensions that may act as resources to cope with the increased work overload inherent to telework, as well as the importance of personal resources in these relationships.

A better understanding of the characteristics and capabilities of women entrepreneurs can significantly improve their chances of success. Therefore, three studies were conducted for this exploratory paper. We have discovered the characteristics of entrepreneurial intelligence among female entrepreneurs through semi-structured interviews based on conventional content analysis. According to the second study, qualitative meta-synthesis was utilized to identify characteristics of women's entrepreneurial intelligence at the international level. As a third study, we examined the evolutionary relationships of entrepreneurs' intelligence components following the discovery and creation of opportunities.

The present paper was based on three studies. In the first study, 15 female entrepreneurs were interviewed using purposive sampling in the Guilan province of Iran to identify the characteristics of entrepreneurial intelligence at the national level. An inductive content analysis was performed on the data collected through interviews. Using Shannon entropy and qualitative validation, their validity was assessed. In the second study, using a qualitative meta-synthesis, the characteristics of women's entrepreneurial intelligence were identified. Then the results of these two studies were compared with each other. In the third study, according to the results obtained from the first and second studies, the emergence, priority and evolution of entrepreneurial intelligence components in two approaches to discovering and creating entrepreneurial opportunities were determined. For this purpose, interviews were conducted with 12 selected experts using the purposeful sampling method using the fuzzy total interpretive structural modeling (TISM) method.

In the first research, this article identified the components of entrepreneurial intelligence of women entrepreneurs in six categories: entrepreneurial insights, cognitive intelligence, social intelligence, intuitive intelligence, presumptuous intelligence and provocative intelligence. In the second study, the components of entrepreneurial intelligence were compared according to the study at the national level and international literature. Finally, in the third study, the evolution of the components of entrepreneurial intelligence was determined. In the first level, social intelligence, presumptuous intelligence and provocative intelligence are formed first and social intelligence and provocative intelligence have an interactive relationship. In the second level, entrepreneurial insight and cognitive intelligence appear, which, in addition to their interactive relationship, take precedence over the entrepreneur's intuitive intelligence in discovering entrepreneurial opportunities. With the evolution of the components of entrepreneurial intelligence in the opportunity creation approach, it is clear that intuitive intelligence is formed first at the first level and takes precedence. At the second level, there is cognitive intelligence is created. At the third level, motivational intelligence and finally, at the last level, entrepreneurial insight, social intelligence and bold intelligence.

This study has the potential to discover credible and robust approaches for further examining the contextualization of women's entrepreneurial intelligence at both national and international levels, thereby advancing new insights. By conceptualizing various components of entrepreneurial intelligence for the first time and exploring how contextual factors differ across nations and internationally for women's entrepreneurship, this paper challenges the assumption that the characteristics of women's entrepreneurial intelligence are uniform worldwide. It also depicts the evolution of the components of entrepreneurial intelligence.

The COVID-19 pandemic has contributed to making workers more uncompromising with respect to issues such as quality of workplace relations and work-life balance. Hence, motivation and leadership style assume a key relevance for keeping the workforce engaged. We hypothesize that individuals may exhibit different preferences for motivational drivers and for leadership style, and that these two sets of preferences might be correlated with each other and with employees’ personality traits.

Here, we empirically investigate the relationship between leadership style and motivation, by also hypothesizing the possible contribution of personality traits. An online survey was developed and distributed to 150 employees or interns/trainees to collect measures related to their preference for leadership, their motivational drivers, as well as their personality traits. The data were analyzed by means of mediation and moderation analyses to disentangle the three-level relationship existing between these constructs.

Our results suggest that indeed there exists a relationship between preferences for leadership style and motivational drivers. Furthermore, one of these relationships appears to be critically mediated by specific personality traits.

This work is the first, to our knowledge, empirically testing the existence of a three-level relationship between leadership preferences, motivation and personality traits of employees and to contribute to disentangle their reciprocal influences.

Land value varies at a micro level depending on the location’s economic, geographical and political determinants. The purpose of this study is to present a comprehensive assessment of the determinants affecting land value in the Indian city of Thiruvananthapuram in the state of Kerala.

The global influence of the identified 20 explanatory variables on land value is measured using the traditional hedonic price modeling approach. The localized spatial variations of the influencing parameters are examined using the non-parametric regression method, geographically weighted regression. This study used advertised land value prices collected from Web sources and screened through field surveys.

Global regression results indicate that access to transportation facilities, commercial establishments, crime sources, wetland classification and disaster history has the strongest influence on land value in the study area. Local regression results demonstrate that the factors influencing land value are not stationary in the study area. Most variables have a different influence in Kazhakootam and the residential areas than in the central business district region.

This study confirms findings from previous studies and provides additional evidence in the spatial dynamics of land value creation. It is to be noted that advanced modeling approaches used in the research have not received much attention in Indian property valuation studies. The outcomes of this study have important implications for the property value fixation of urban Kerala. The regional variation of land value within an urban agglomeration shows the need for a localized method for land value calculation.

Material extrusion-based additive manufacturing is a prominent manufacturing technique to fabricate complex geometrical three-dimensional (3D) parts. Despite the indisputable advantages of material extrusion-based technique, the poor surface and subsurface integrity hinder the industrial application of this technology. The purpose of this study is introducing the hot air jet treatment (HAJ) technique for surface treatment of additive manufactured parts.

In the presented research, novel theoretical formulation and finite element models are developed to study and model the polishing mechanism of printed parts surface through the HAJ technique. The model correlates reflow material volume, layer width and layer height. The reflow material volume is a function of treatment temperature, treatment velocity and HAJ velocity. The values of reflow material volume are obtained through the finite element modeling model due to the complexity of the interactions between thermal and mechanical phenomena. The theoretical model presumptions are validated through experiments, and the results show that the treatment parameters have a significant impact on the surface characteristics, hardness and dimensional variations of the treated surface.

The results demonstrate that the average value of error between the calculated theoretical results and experimental results is 14.3%. Meanwhile, the 3D plots of Ra and Rq revealed that the maximum values of Ra and Rq reduction percentages at 255°C, 270°C, 285°C and 300°C treatment temperatures are (35.9%, 33.9%), (77.6%,76.4%), (94%, 93.8%) and (85.1%, 84%), respectively. The scanning electron microscope results illustrate three different treatment zones and the treatment-induced and manufacturing-induced entrapped air relief phenomenon. The measured results of hardness variation percentages and dimensional deviation percentages at different regimes are (8.33%, 0.19%), (10.55%, 0.31%) and (−0.27%, 0.34%), respectively.

While some studies have investigated the effect of the HAJ process on the structural integrity of manufactured items, there is a dearth of research on the underlying treatment mechanism, the integrity of the treated surface and the subsurface characteristics of the treated surface.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Golestan province in the northern part of Iran has been affected by devastating floods. There has been a significant change in the pattern of rainfall in Golestan province based on an analysis of the seven heaviest rainfall events in recent decades. Climate change appears to be a significant contributing factor to destructive floods. Thus, this paper aims to assess the susceptibility of this area to flash floods in case of heavy downpours.

This paper uses a variety of computational approaches. Following the collection of data, spatial analyses have been conducted and validated. The layers of information are then weighted, and a final risk map is created. Fuzzy analytical hierarchy process, geographic information system and frequency ratio have been used for data analysis. In the final step, a flood risk map is prepared and discussed.

Due to the complex interaction between thermal fluctuations and precipitation, the situation in the area is further complicated by climate change and the variations in its patterns and intensities. According to the study results, coastal areas of the Caspian Sea, the Gorganrood Basin and the southern regions of the province are predicted to experience flash floods in the future. The research criteria are generalizable and can be used for decision-making in areas exposed to flash flood risk.

The unique feature of this paper is that it evaluates flash flood risks and predicts flood-prone areas in the northern part of Iran. Furthermore, some interventions (e.g. remapping land use and urban zoning) are provided based on the socioeconomic characteristics of the region to reduce flood risk. Based on the generated risk map, a practical suggestion would be to install and operate an integrated rapid flood warning system in high-risk zones.

Introduction: The COVID-19 pandemic was unprecedented, and none of the world’s think tanks could have predicted how far it would spread. People visualise the pandemic as a classic example of a VUCA environment (an environment characterised by volatility, uncertainty, complexity, and ambiguity). Managing COVID-19 requires unique and different leadership qualities.

Purpose: This study’s rationale emphasises that VUCA demands strategic and agile leadership. Leaders across the globe must acknowledge the fact that the elements of VUCA, i.e. volatility, uncertainty, complexity, and ambiguity, are not synonymous and hence need differential treatment. Also, recommend adopting agile leadership to tackle the VUCA world.

Methodology: This is a conceptual study focusing on agile leadership. The study addresses the challenge of leading in the VUCA environment. The constituting components of VUCA are differentiated, followed by an extensive literature review of agile leadership.

Findings: VUCA is the new normal and is there to stay. Business leaders still need to counter VUCA due to their incapacity to distinguish and treat the components equally. Furthermore, there is an emergent need to develop an agile workforce, agile organisations, and agile leadership to combat VUCA. The study indicates that business leaders must be agile enough to handle the COVID-19 pandemic and future crises. There is a constant need to ensure agility. This study will provide a deep insight into VUCA and help business leaders emerge victorious in turbulent times.

We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.

In order to address these correlated conflict problems with uncertain information, considering the interactive influence and mutual restraints among agents and portraying their attitudes toward the conflict issues, we utilize grey numbers and three-way decisions to propose a grey three-way conflict analysis model with constraints. Firstly, based on the collected information, we introduced grey theory, calculated the degree of conflict between agents and then analyzed the conflict alliance based on the three-way decision theory. Finally, we designed a feedback mechanism to identify key agents and key conflict issues. A case verifies the effectiveness and practicability of the proposed model.

The results show that the proposed model can portray their attitudes toward conflict issues and effectively extract conflict-related information.

By employing this approach, we can provide the answers to Deja’s fundamental questions regarding Pawlak’s conflict analysis: “what are the underlying causes of conflict?” and “how can a viable consensus strategy be identified?”

The rapid advancement of technology has transformed the health-care industry and enabled the emergence of m-Health solutions such as health apps. The viability and success of these apps depends on the definition of a monetization model appropriate to their specificities. In this sense, the purpose of this paper is to study the mechanisms of monetization of health apps, to stablish how alternative revenues determine if a health app is to be free or paid.

Probability models are used to identify the factors that explain if a health app is free or paid.

Results show that the presence of alternative monetization mechanisms negatively impacts the likelihood of a health app being paid for. The use of personal data to customize advertising (the monetization of “privacy capital”) or the inclusion of ads on the app are alternative means of monetization with potential to decrease the likelihood of a health app being paid for. The possibility of in-app purchases has a lower negative impact on the probability of a health app being paid for. The choice of platform to commercialize an app is also a strategic decision that influences the likelihood of an app being paid for.

This work stands out for bringing together the two largest platforms present in Portugal and for focusing on the perspective of revenue and monetization of health apps and not on the perspective of downloads.