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The purpose is to examine the relationship between entrepreneurs’ exit strategies and modes of entry. The topic of exit strategies in the context of approaching retirement warrants further attention.

We apply logistic regression to analyse 1,192 responses to an online survey of firms with entrepreneurs aged over 55.

Family successors are more likely to choose family succession and buyers to choose to sell, but the association between founding and exit mode cannot be confirmed. Firm size is also significant. Our findings suggest that entry and exit via a business transfer are linked. Entrepreneurs might be influenced by their form of entry when choosing their exit strategy.

The data were collected from a single European country, limiting generalisation. Future research should incorporate intervening variables not controlled for here, such as, entrepreneurial experience. Future studies should also seek to test the existence of imprinting directly, as it is implied rather than verified here.

If the entry mode has a lasting effect on the entrepreneur as our results suggest, thus influencing the exit strategy selected, entrepreneurs could benefit from greater awareness of the imprinting mechanism. Increasing awareness of imprinted biases could unlock the benefits of exit strategies previously overlooked.

The study is the first to consider sale, family succession and liquidation as exit strategies in relation to the original entry mode of ageing owners. It contributes to the understanding of exit strategies of ageing entrepreneurs and proposes using entrepreneurial learning and imprinting as lenses to clarify the phenomenon.

Simulations exist for the prediction of the behaviour of building structural systems under fire, including two-way coupled fire-structure interaction. However, these simulations do not include detailed models of the connections, whereas these connections may impact the overall behaviour of the structure. Therefore, this paper proposes a two-scale method to include screw connections.

The two-scale method consists of (a) a global-scale model that models the overall structural system and (b) a small-scale model to describe a screw connection. Components in the global-scale model are connected by a spring element instead of a modelled screw, and the stiffness of this spring element is predicted by the small-scale model, updated at each load step. For computational efficiency, the small-scale model uses a proprietary technique to model the behaviour of the threads, verified by simulations that model the complete thread geometry, and validated by existing pull-out experiments. For four screw failure modes, load-deformation behaviour and failure predictions of the two-scale method are verified by a detailed system model. Additionally, the two-scale method is validated for a combined load case by existing experiments, and demonstrated for different temperatures. Finally, the two-scale method is illustrated as part of a two-way coupled fire-structure simulation.

It was shown that proprietary ”threaded connection interaction” can predict thread relevant failure modes, i.e. thread failure, shank tension failure, and pull-out. For bearing, shear, tension, and pull-out failure, load-deformation behaviour and failure predictions of the two-scale method correspond with the detailed system model and Eurocode predictions. Related to combined load cases, for a variety of experiments a good correlation has been found between experimental and simulation results, however, pull-out simulations were shown to be inconsistent.

More research is needed before the two-scale method can be used under all conditions. This relates to the failure criteria for pull-out, combined load cases, and temperature loads.

The two-scale method bridges the existing very detailed small-scale screw models with present global-scale structural models, that in the best case only use springs. It shows to be insightful, for it contains a functional separation of scales, revealing their relationships, and it is computationally efficient as it allows for distributed computing. Furthermore, local small-scale non-convergence (e.g. a screw failing) can be handled without convergence problems in the global-scale structural model.

Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.

Urban digital transformation has become a key strategy in global countries. This study aims to provide a comprehensive and dynamic exploration of the intrinsic traits associated with urban digital transformation, in order to yield detailed insights that can contribute to the formulation of well-informed decisions and strategies in the field of urban development initiatives.

Through analysis of parallels between urban digital transformation and gyroscope motion in physics, the study developed the urban digital transformation gyroscope model (UDTGM), which comprises of seven core elements. With the balanced panel dataset from 268 cities at and above the prefecture level in China, we validate the dynamic mechanism of this model.

The findings of this study underscore that the collaboration among infrastructure development, knowledge-driven forces and economic operations markedly bolsters the urban digital transformation gyroscope’s efficacy.

This research introduces a groundbreaking framework for comprehending urban digital transformation, potentially facilitating its balanced and systemic practical implementation.

This study pioneers the UDTGM theoretically and verifies the dynamic mechanism of this model with real data.

This study investigates information flow of market constituents and global indices at multi-frequencies.

The study’s findings were obtained using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (I-CEEMDAN)-based cluster analysis executed for Rényi effective transfer entropy (RETE).

The authors find that significant negative information flows among sustainability equities (SEs) and conventional equities (CEs) at most multi-frequencies, which exacerbates diversification benefits. The information flows are mostly bi-directional, highlighting the importance of stock markets' constituents and their global indices in portfolio construction.

The authors advocate that both SE and CE markets are mostly heterogeneous, revealing some levels of markets inefficiencies.

The empirical literature on CEs is replete with several dynamics, revealing their returns behaviour for diversification purposes, leaving very little to know about the returns behaviour of SE. Wherein, an avalanche of several initiatives on Corporate Social Responsibility (CSR) enjoin firms to operate socially responsible, but investors need to have a clear reason to remain sustainable into the foreseeable future period. Accordingly, the humble desire of investors is the formation of a well-diversified portfolio and would highly demand stocks to the extent that they form a reliable portfolio, especially, amid SEs and/or CEs.

本研究擬審查多頻率的及為市場成份的信息流和全球指數。

研究人員使用基於改良完全集合經驗模態分解自適應噪聲(Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)的聚類分析法,取得Rényi有效轉移熵,藉此得到研究結果。

我們發現、於大部份多頻率,在持續性股票和傳統股票間有顯著的負信息流動,這會增加多樣化的益處。這些信息流大部份是雙向的,這強調了股票市場成份及其全球指數在構建投資組合上的重要性。

我們認為持續性股票市場和傳統股票市場大多為異質市場,這顯示了市場的低效率,而且這低效率的程度頗大。

關於傳統股票的實證性文獻裡是充滿了變革動力的,這顯示了它們以多樣化為目的的回報行為。這使我們對關於持續性股票的回報行為、認識變得實在太少了。於此,大量的企業社會責任的新措施不斷提醒各公司、要本著企業社會責任的理念去營運;但投資者需清晰明白他們為何需在可見的將來保持可持續性。因此,他們卑微的願望是一個較好的多樣化投資組合得以形成,故此他們高度要求股票要有組成可靠投資組合的性質和能力,特別是在持續性股票和/或傳統股票當中。

The article aims to present the results of adapting the team boosting behaviors (TBB) scale to Polish cultural conditions and validating it.

The research methodology consisted of three steps. In the first step, I translated the TBB scale into Polish using a rigorous back-translation method. Next, to assess content validity, nine domain experts reviewed the initial version of the instrument for clarity and relevance. Finally, I applied the scale to a sample of 532 team members and underwent thorough psychometric testing to assess construct validity. I employed structural equation modeling (SEM) with the partial least squares (PLS) factor-based algorithm technique for confirmatory factor analysis to assess the scale’s reliability and validity.

After development, the Polish version of the TBB scale kept its three sub-scale structures. However, the validation process led to a slight reduction in the number of test items compared to the original scale.

The findings imply that the Polish version of the scale is a valid and reliable tool for assessing TBB. However, I recommend additional studies to confirm this instrument’s structure.

The results confirmed the reliability and relevance of the tool for measuring TBBs in Polish cultural conditions. The tool provides the basis for implementing further research with the TBB construct in Poland and internationally.

The purpose of this paper is to examine the association between consumers’ emotions towards emerging e-banking technology, perceived risk and subsequent intention to adopt emerging e-banking technology.

An online questionnaire was used to collect data, which were analysed in a quantitative study. The final sample of 224 educated young consumers, familiar with emerging e-banking technology, allowed testing of the research hypotheses by applying confirmatory factor analysis and structural equation modelling (SEM).

The empirical results indicate that deterrence emotions and hedonic motivation are associated with consumers’ perceived risk and, subsequently, their intention to adopt emerging e-banking technology. Additionally, analysing the moderating role of hedonic motivation in the association between consumers’ deterrence emotions towards emerging e-banking technology and their perceived risk highlights the significant association of deterrence emotions with perceived risk, regardless of the presence of hedonic motivation.

This study demonstrates the association between consumers’ emotions, perceived risk and subsequent intention to adopt emerging e-banking technology whilst underscoring the importance of distinguishing between different types of emotions and their corresponding appraisals.

Quality management practitioners have yet to cease the potential of digitalisation. Furthermore, there is a lack of tools such as frameworks guiding practitioners in the digital transformation of their organisations. The purpose of this study is to provide a framework to guide quality practitioners with the implementation of digitalisation in their existing practices.

A review of literature assessed how quality management and digitalisation have been integrated. Findings from the literature review highlighted the success of the integration of Lean manufacturing with digitalisation. A comprehensive list of Lean Six Sigma tools were then reviewed in terms of their effectiveness and relevance for the hybrid digitisation approach to process improvement (HyDAPI) framework.

The implementation of the proposed HyDAPI framework in an industrial case study led to increased efficiency, reduction of waste, standardised work, mistake proofing and the ability to root cause non-conformance products.

The activities and tools in the HyDAPI framework are not inclusive of all techniques from Lean Six Sigma.

The HyDAPI framework is a flexible guide for quality practitioners to digitalise key information from manufacturing processes. The framework allows organisations to select the appropriate tools as needed. This is required because of the varying and complex nature of organisation processes and the challenge of adapting to the continually evolving Industry 4.0.

This research proposes the HyDAPI framework as a flexible and adaptable approach for quality management practitioners to implement digitalisation. This was developed because of the gap in research regarding the lack of procedures guiding organisations in their digital transition to Industry 4.0.

There is little knowledge to date regarding the influence of the COVID-19 health crisis on tourists' intention to travel differently in the future. This paper addresses this and explores its determinants. The objective of the present study is to determine to what extent the Spanish tourists affected by COVID-19 may change the way they travel in the future, according to the perceived risk of travel in a pandemic context.

Between May and June 2020, the authors conducted a survey with a sample population of Spanish tourists who were resident in Spain during the COVID-19 pandemic, for the purposes of studying the role of attitudes and risk in the intention to change the way they want to travel in the future. Cluster analysis and one-way ANOVA were conducted to assess differences among the respondents. Finally, some models were built using the linear regression technique in order to evaluate the role of attitudes in the tourists' adaptive response to the perceived risk of travel.

Results confirm the formation of a new way of life influencing tourists' intentions to travel more sustainably. Accordingly, tourists with a previous environmental attitude are less interested in visiting mass tourism beach destinations in the future. However, changes in the way some tourists travel can also be read as an adaptive and temporary response to the perceived risk of contracting the disease, and do not point to a reduction of the vital importance of tourism in their lives.

The exploratory nature of the study and the lack of similar international analyses does not allow the authors to contrast its results at a global level, though it offers a starting point for future research in other countries. There are also methodological limitations, since the field work was carried out between the first and second waves of the disease, at a time when the pandemic was in remission, possibly affecting the orientation of some responses, given the desire to recover normalcy and “normal” travel, and this may have influenced the priority given to tourism.

This study gives new insights into the debate on the social transformation of the collective consciousness. Despite some signs of change, part of the Spanish tourists are still anchored in traditional tourism practices embedded in cultural factors, which can hinder sustainability in the Spanish tourism industry. The experience of the COVID-19 crisis has not been sufficient to change the declared travel habits of Spanish tourists. Therefore, progress towards the definition of a new tourism system that implies the effective transformation of demand will require applying policies and promoting institutional innovation and education to create paths that facilitate transformative experiences.

The study is focused on the analysis of the relationship between attitudes and risk perception, including novel elements that enrich the academic debate on social progress in the transformation of tourism and the possibilities of promoting a reset from the demand side. Moreover, it incorporates, for the first time, the COVID-19 as it was experienced as an explanatory variable to analyse the changing travel attitudes in a post-COVID-19 era. The analysis of the psychosocial mechanisms of risk offers a good opportunity for a better assessment of post-pandemic demand risk perception. Finally, the study offers empirical evidence on how Spanish tourists are reimagining their next and future holidays, which can be highly valuable for destination managers.

We conceptualized the novel phenomenon of COVID-induced virtual teams and its implications and provided researchers with the required information on how to conduct a phenomenon-based study for conceptualizing novel phenomena in relevant ways.

This article stems from phenomenon-based and, thus, theory-building and grounded qualitative research in the German industrial sector. We conducted 47 problem-centered interviews in two phases (February–July 2021 and February–July 2022) to understand how team members and team leaders experienced COVID-induced virtual teamwork and its subsequent developments.

Empirically, we found COVID-induced virtual teams to be characterized by a high relevance of shaping positive team dynamics via steering internal moderators; crisis is a novel external moderator and transformation becomes the key output factor to be leveraged. Work-from-home leads to specific configuration needs and interrelations between work-from-home and on-site introduce additional dynamics. Methodologically, the phenomenon-based approach is found to be highly suitable for studying the effects of such novel phenomena.

This article is explorative. Thus, we advocate further research on related novel phenomena, such as post-COVID-hybrid and work-from-home teams. A model of how to encourage positive dynamics in post-COVID-hybrid teams is developed and lays the groundwork for further studies on post-COVID teamwork. Concerning methodology, researchers are provided with information on how to conduct phenomenon-based research on novel phenomena, such as the COVID-induced virtual teams that we studied.

Companies receive advice on how to encourage positive dynamics in post-COVID teamwork, e.g. on identifying best practices and resilient individuals.

In a country such as Germany that faces labor shortages, our insights might facilitate better labor-market integration for those with care-work obligations and international workers.

We offer a first conceptualization of a relevant novel phenomenon, namely COVID-induced virtual teams. We exemplify the phenomenon-based approach as a suitable methodology that serves to build relevant theory using active categorization.