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Employees facing challenges in their careers – i.e. female, migrant, elderly and lower-educated employees – might expect job searches to have a low likelihood of success and might therefore more often stay in unsatisfactory positions. The goal of this study is to discover inequalities in job mobility for these employees.

We rely on a large sample of Dutch public sector employees (N = 30,709) and study whether employees with challenges in their careers are hampered in translating job dissatisfaction into job searches. Additionally, we assess whether this is due to their perceptions of labor market alternatives.

Findings show that non-Western migrant, elderly and lower-educated employees are less likely to act on job dissatisfaction than their advantaged counterparts, whereas women are more likely than men to do so. Additionally, we find that although they perceive labor market opportunities as limited, this does not affect their propensity to search for different jobs.

This paper is novel in discovering inequalities in job mobility by analyzing whether employees facing challenges in their careers are less likely to act on job dissatisfaction and therefore more likely to remain in unsatisfactory positions.

The purpose of this research is to analyze the influence of heuristics on Brazilian investors' behavior in the decision-making process.

The authors apply the partial least squares structural equation modeling methodology. This sample is composed of 220 investors.

The heuristics of overconfidence and anchoring positively influence investors' decision-making, while loss aversion negatively influences it. The herd effect exhibits no influence. The results also support the idea that decision-making positively influences investors' performance. Investors feel secure in their attitudes regarding financial decision-making, even if their decisions are not always rational as they are affected by biases.

This article explains the influence of heuristics on investors' decision-making and performance in the Brazilian context during the COVID-19 pandemic.

This research aims to model the existing system of destination competitiveness, identifies leverage points and develop revised model to achieve sustainable competitiveness.

System dynamics is used as method of modeling destination competitiveness. Structure of model utilizes 9-factor model as reference. Leverage points are identified using system archetypes. Revised model is built with resource-based view (RBV). Case study was conducted in Samosir, Toba Lake. Data used are secondary data and results of in-depth interviews.

There are 3 sub-systemic characteristics (archetypes) that hinder competitiveness. They are limit to growth quality gap, fix that fails infrastructure and promotion, tragedy of common lake pollution. Destination was unable to meet tourist expectations. Tourists spending decreased, demand size was small. Industries are unable to increase capabilities. Professionals, entrepreneurs, local workers, supporting industries are less interested in entering industry. Government policies do not match with destination's needs. Lake as main attraction is getting polluted. To achieve sustainable competitiveness, destination must utilize their valuable, rare and inimitable (VRI) resources and capabilities to design unique experiences for tourists, hence sustainable.

Government policy should be shifted to prioritizing development of valuable, rare, inimitable and well-organized resources and capabilities of destination, to produce unique tourist experience and achieve sustainable competitiveness.

Methods and findings, combining system dynamics, system archetype, 9-factor model and RBV to achieve sustainable competitiveness is novel and can enrich tourism sustainable competitiveness theory/concept.

The purpose of this study is to propose a precise and standardized strategy for numerically simulating vehicle aerodynamics.

Error sources in computational fluid dynamics were analyzed. Additionally, controllable experiential and discretization errors, which significantly influence the calculated results, are expounded upon. Considering the airflow mechanism around a vehicle, the computational efficiency and accuracy of each solution strategy were compared and analyzed through numerous computational cases. Finally, the most suitable numerical strategy, including the turbulence model, simplified vehicle model, calculation domain, boundary conditions, grids and discretization scheme, was identified. Two simplified vehicle models were introduced, and relevant wind tunnel tests were performed to validate the selected strategy.

Errors in vehicle computational aerodynamics mainly stem from the unreasonable simplification of the vehicle model, calculation domain, definite solution conditions, grid strategy and discretization schemes. Using the proposed standardized numerical strategy, the simulated steady and transient aerodynamic characteristics agreed well with the experimental results.

Building upon the modified Low-Reynolds Number k-e model and Scale Adaptive Simulation model, to the best of the authors’ knowledge, a precise and standardized numerical simulation strategy for vehicle aerodynamics is proposed for the first time, which can be integrated into vehicle research and design.

Interorganizational power dependence has become an increasingly important factor for small and medium-sized enterprises (SMEs) to improve product innovation. This paper examines the role of power dependence in SMEs' product innovation trade-offs between exploration and exploitation. The article further studies the mediating effect of supply chain adaptability and the moderating effect of knowledge acquisition on the relationship between power dependence and product innovation.

The study proposes a model to verify the impact of power dependence on SMEs' product innovation trade-offs based on social network theory. Two conceptually independent constructs, “availability of alternatives (ALTRN)” and “restraint in the use of power (RSPTW),” are used to evaluate the power dependence. The model also analyzed how these effects are mediated by supply chain adaptability and moderated by knowledge acquisition. The authors test these relationships using data collected from 224 SMEs in China.

The empirical analysis shows that ALTRN has a more substantial effect on exploration for product innovation, while RSTPW has a more significant impact on exploitation for product innovation. Moreover, empirical data indicate a partial mediating effect by supply chain adaptability between power dependence and product innovation of SMEs. The results also show that knowledge acquisition positively moderates the relationship between ALTRN/RSTPW, supply chain adaptability and product innovation.

Overall, the findings of the study advance the understanding of the roles of power dependence in product innovation for SMEs. In addition, the research also uncovers the impact mechanisms of existing theoretical frameworks and extends the boundaries of the theory.

The primary objective of this study is to tackle the enduring challenge of preserving feature integrity during the manipulation of geometric data in computer graphics. Our work aims to introduce and validate a variational sparse diffusion model that enhances the capability to maintain the definition of sharp features within meshes throughout complex processing tasks such as segmentation and repair.

We developed a variational sparse diffusion model that integrates a high-order L₁ regularization framework with Dirichlet boundary constraints, specifically designed to preserve edge definition. This model employs an innovative vertex updating strategy that optimizes the quality of mesh repairs. We leverage the augmented Lagrangian method to address the computational challenges inherent in this approach, enabling effective management of the trade-off between diffusion strength and feature preservation. Our methodology involves a detailed analysis of segmentation and repair processes, focusing on maintaining the acuity of features on triangulated surfaces.

Our findings indicate that the proposed variational sparse diffusion model significantly outperforms traditional smooth diffusion methods in preserving sharp features during mesh processing. The model ensures the delineation of clear boundaries in mesh segmentation and achieves high-fidelity restoration of deteriorated meshes in repair tasks. The innovative vertex updating strategy within the model contributes to enhanced mesh quality post-repair. Empirical evaluations demonstrate that our approach maintains the integrity of original, sharp features more effectively, especially in complex geometries with intricate detail.

The originality of this research lies in the novel application of a high-order L₁ regularization framework to the field of mesh processing, a method not conventionally applied in this context. The value of our work is in providing a robust solution to the problem of feature degradation during the mesh manipulation process. Our model’s unique vertex updating strategy and the use of the augmented Lagrangian method for optimization are distinctive contributions that enhance the state-of-the-art in geometry processing. The empirical success of our model in preserving features during mesh segmentation and repair presents an advancement in computer graphics, offering practical benefits to both academic research and industry applications.

As cross-cultural mergers and acquisitions (M&A) have learning effects on organisations, assessing their impacts on corporate performance is crucial. This study aims to explore the impact of inter-firm cultural differences on long-term post-M&A stock market performance.

The authors select domestic M&A transactions of Chinese listed companies during 2010–2021 as the sample. Then, the authors use the partial least squares structural equation model (PLS-SEM) to construct the latent variable of cultural differences in four dimensions to explore long-term stock market performance.

Cultural differences first positively and then negatively impact post-M&A performance. Three transmissions mechanisms are identified: investor sentiment, takeover premiums and information disclosure quality. Further analysis reveals that acquirer stock performance improves with higher analyst coverage and non-local shareholders but worsens if there are business affiliations between the acquirer and target firms.

This study can help optimise information disclosure systems in M&A transactions for regulatory authorities and aid investors’ understanding of post-M&A performance changes. Furthermore, it can improve acquirers’ understanding of the risks and opportunities in cross-cultural M&A, thereby facilitating the adaptation of management practices to the im-pacts of cultural differences.

By integrating the theories of resource dependence and transaction costs, this study examines the reversal effect of cultural differences between merging companies on post-M&A performance. The authors use a PLS-SEM to empirically analyse the main effects and reveal three transmission mechanisms.

This study aims to dissect the multifaceted impact of ISO/IEC 17025 accreditation, specifically within civil engineering testing and calibration laboratories. To achieve this, it intends to explore several key objectives: identifying the prominent benefits of accreditation to laboratory performance, understanding the advantages conferred through participation in proficiency testing schemes, assessing the role of accreditation in enhancing laboratory competitiveness, examining the primary challenges encountered during the accreditation process, investigating any discernible adverse effects of accreditation on laboratory performance and evaluating whether the financial cost of accreditation justifies the resultant profitability.

This study employs a qualitative approach through semi-structured interviews with 23 industry professionals—including technical managers, quality managers, external auditors and clients. Thematic analysis, guided by Braun and Clarke’s six-stage paradigm, was utilized to interpret the data, ensuring a comprehensive understanding of the accreditation’s impact.

Findings reveal that accreditation significantly enhances operational processes, fosters quality awareness and facilitates continuous improvement, contributing to greater client satisfaction. In addition, standardized operations and rigorous quality controls further result in enhanced performance metrics, such as staff capability and measurement accuracy. However, the study also uncovers the challenges of accreditation, including high resource costs and bureaucratic hurdles that can inhibit innovation and slow routine operations. Importantly, the research underscores that the impact of accreditation on profitability is not universal, but contingent upon various factors like sector-specific regulations and market demand. The study also highlights sector-specific variations in the role of accreditation as a marketing tool and differing perceptions of its value among clients. It further emphasizes the psychological stress of high-stakes evaluations during audits.

This study represents the first in-depth investigation into the impact of ISO/IEC 17025 accreditation on civil engineering testing and calibration laboratories, directly contributing to the enhancement of their quality and operational standards. Providing actionable insights for laboratories, it underscores the importance of weighing accreditation costs and benefits and the necessity for a tailored approach to the unique market and regulatory landscapes they operate in.

The purpose of this study is to identify the factors that create value for the customer in the area of e-commerce logistics and to verify whether these factors impact online retailers' performance through customer satisfaction and customer loyalty in a business to customer (B2C) area.

For the purpose of the study, a model of logistics value in e-commerce was introduced. The model was proposed based on the literature review. A total of 592 correctly filled questionnaires from telephone and web interviews [computer-assisted telephone interview (CATI) and computer-assisted web interview (CAWI)] were studied. The exploratory factor analysis (EFA) and structural equation modeling (SEM) were conducted in the study.

It was confirmed that four of five defined logistics value dimensions have positive impact on customer satisfaction. These dimensions are communication of delivery status, convenience of receipt, reception experience and convenience of return. The influence of speed of delivery on customer satisfaction was not confirmed. It was also confirmed that customer satisfaction has a positive impact on customer loyalty and that customer loyalty positively impacts online retailers' performance.

The study contributes to the current understanding of the influence of logistics factors on online retailers' performance through customer satisfaction and customer loyalty. There have been few studies that concern logistics value in e-commerce, with particular emphasis on above-mentioned relations. It is especially an important issue due to the fact that logistics can potentially lead to gain a competitive advantage in e-commerce.

This study aims to explore the application of emojis to mood descriptions of fiction. The three goals are investigating whether Cho et al.'s model (2023) is a sound conceptual framework for implementing emojis and mood categories in information systems, mapping 30 mood categories to 115 face emojis and exploring and visualizing the relationships between mood categories based on emojis mapping.

An online survey was distributed to a US public university to recruit adult fiction readers. In total, 64 participants completed the survey.

The results show that the participants distinguished between the three families of fiction mood categories. The three families model is a promising option to improve mood descriptions for fiction. Through mapping emojis to 30 mood categories, the authors identified the most popular emojis for each category, analyzed the relationships between mood categories and examined participants' consensus on mapping.

This study focuses on applying emojis to fiction reading. Emojis were mapped to mood categories by fiction readers. Emoji mapping contributes to the understanding of the relationships between mood categories. Emojis, as graphic mood descriptors, have the potential to complement textual descriptors and enrich mood metadata for fiction.