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This study provides a comprehensive overview of the research landscape of Kansei engineering (KE) within the domain of emotional clothing design. It explores the pivotal technologies, challenges and potential future directions of KE, offering application methodologies and theoretical underpinnings to support emotional clothing design.

This study briefly introduces KE, outlining its overarching research methodologies and processes. This framework lays the groundwork for advancing research in clothing Kansei. Subsequently, by reviewing literature from both domestic and international sources, this research initially explores the application of KE in the design and evaluation of clothing products as well as the development of intelligent clothing design systems from the vantage point of designers. Second, it investigates the role of KE in the customization of online clothing recommendation systems and the optimization of retail environments, as perceived by consumers. Finally, with the research methodologies of KE as a focal point, this paper discusses the principal challenges and opportunities currently confronting the field of clothing Kansei research.

At present, studies in the domain of clothing KE have achieved partial progress, but there are still some challenges to be solved in the concept, technical methods and area of application. In the future, multimodal and multisensory user Kansei acquisition, multidimensional product deconstruction, artificial intelligence (AI) enabling KE research and clothing sales environment Kansei design will become new development trends.

This study provides significant directions and concepts in the technology, methods and application types of KE, which is helpful to better apply KE to emotional clothing design.

This study aims to efficiently estimate the extremely small failure probability with high-dimensional inputs and multiple failure domains.

This paper proposed an adaptive stratified mixture importance sampling method. The proposed method first constructs an explicit and regular mixture importance sampling probability density function (M-IS-PDF) by taking the clustering centroids as the density centers. Then by the constructed M-IS-PDF, the proposed method explores the rare multiple failure domains by adaptively stratifying, thereby addressing the issue of estimating extremely small failure probability robustly and efficiently.

Compared with the existing cross-entropy based IS method, the constructed M-IS-PDF not only covers the domains significantly contributing to the failure probability through clustering centroids to reduce the variance of failure probability estimation, but also has no undetermined parameter set to optimize, enhancing the adaptability in high-dimensional problems. Compared with the subset simulation method, the adaptive stratified M-IS-PDF constructed is explicit, regular and easy sampling. It not only has high sampling efficiency but also avoids estimating conditional failure probabilities layer by layer, improving the algorithmic robustness for estimating extremely small failure probability.

Both numerical and engineering examples indicate that, under the similar failure probability estimation accuracy, the proposed method requires significantly smaller sample size and lower computational cost than subset simulation and cross-entropy based IS methods, demonstrating higher efficiency and robustness in addressing intractable reliability analysis problems with high-dimensional inputs, multiple failure domains and rare failure.

Defining and validating a map of related technologies is critical for managers, investors and inventors. Because of the increase in the applications of and demand for semiconductor lasers, analyzing the technological position of developers has become increasingly critical. Therefore, the purpose of this study is to adopt the technological position analysis to identify mainstream technologies and developments relevant to semiconductor lasers.

Correspondence analysis and k-means cluster analysis, which are data mining techniques, are used to reveal strategic groups of major competitors in the semiconductor laser market according to their Patent Cooperation Treaty (PCT) patent applications.

The results of this study reveal that PCT patent applications are generally obtained for masers, optical elements, semiconductor devices and methods for measuring and that technology developers have varying technological positions.

Through position analysis, this study identifies the technological focuses of different manufacturers to obtain information that can guide the allocation of research and development resources.

This study aims to examine the impact of supply chain digitalization (SCD) on supply chain performance (SCP) with the integration of supply chain agility (SCA) and supply chain resilience (SCR) in manufacturing firms. Additionally, it investigates the moderating role of environmental dynamism (ED) in these relationships in the context of manufacturing firms.

A quantitative research design was employed, utilizing a structured questionnaire to collect data. The sample consisted of 374 valid responses from various managerial levels within manufacturing firms in Jordan. A purposive sampling technique was used to ensure that participants had relevant expertise in supply chain management. The data were analyzed using structural equation modeling (SEM) through Smart PLS software to test the proposed hypotheses.

The study found that SCD positively impacts SCP by enhancing both SCA and SCR. Digital technologies were shown to improve supply chain visibility, speed of decision-making and resource efficiency, which are essential for maintaining performance under uncertain conditions. Furthermore, ED significantly moderates the relationship between digitalization and SCP, indicating that the benefits of digitalization on SCA and SCR are more pronounced in highly dynamic environments.

This study contributes to the literature by offering an investigation on the role of SCD in influencing SCA, SCR and ultimately SCP in manufacturing firms. It also sheds light on the moderating effect of ED, providing a deeper understanding of the conditions under which digital transformation in supply chains is most beneficial. The study was based on three theoretical foundations: the resource-based view (RBV), the dynamic capabilities theory (DCT) and the contingency theory (CT).

The study aims to understand digital transformation as a socially constructed process with multiple stakeholders, influenced by internal and external forces. This perspective stresses the importance of context, human interaction and narratives in the digital transformation within public sector.

The author uses a case study at a Swedish university undergoing digital transformation. Through first and secondary data, the author thematically analyzes the interaction between change agents, organizational realities and the digital transformation process.

The study finds that conflicts in pace and scope in the digital transformation drive self-defense mechanisms and the formation of a feedback loop of pending action. Contrary to previous studies, technological and external forces do not make digital transformation inevitable.

Limitations stem from the empirical selection of a Swedish university, affecting the international and intersectoral transferability of the study. The impact of digital transformation differs from previous IT changes, which has implications for the design of the digital transformation process.

Stakeholders should, instead of considering structural and cultural barriers as facts, pay attention to the narratives within the organization as potential excuses to avoid action.

This research contributes to original insights into digital transformation. It uncovers how change agents, despite longing for change, can inadvertently foster inaction in digital transformation. This finding enriches the literature by highlighting the complex dynamics between the desire for change and the social constructs that contribute to stagnation, offering an understanding of barriers to digital transformation.

The purpose of this study is to understand the situation of hotels and tourism industry in Malaysia during and in post Covid-19 and to mitigate indirect damage caused by COVID-19 to the hotel business and tourism industry by examining the factors that have an influence on hotel’s customer satisfaction rating and revisit intention through an integration of service quality (SERVQUAL) framework and expectation-confirmation theory (ECT).

The SERVQUAL and ECT were considered the underpinning theoretical models but are integrated and extended by including a few additional variables. Data were collected from 458 respondents of travelers and hotel customers in Malaysia and analysed by applying partial least squares structural equation model technique.

The empirical results established that significant positive relationships exist between the three newly emerged independent variables (IVs), namely, hygienic practice, greenness of service and digitalization and hotel customer satisfaction towards hotel revisit intention, and only two variables from SERVQUAL, namely, reliability and assurance, have a significant relationship with hotel customer satisfaction towards hotel revisit intention. The results reveal that customer satisfaction has significant direct effect between above-mentioned IVs and customers revisit intention.

The use of purposeful sampling method in only one country might limit the generalizability of the results. Future research should be planned to duplicate the current study using a sizable sample of participants from multiple countries and include other related factors related to the pandemic phenomena such as safety, hotel location and health value offered.

Theoretical findings imply that service quality is a dynamic theory that should be examined continuously to achieve sustainable and resilient performance in today’s competitive business environment, as some modifications inevitably occur over time and new factors could be emerged. Regarding practical implications, study findings proved the great significance of assurance, reliability, digitalization, greenness and hygienic practices on customer satisfaction towards intention to revisit to hotel. Therefore, it is critical for hotel management to retain hotel business industry in a way that fits and matches customer’s health protection, meets customer’s newly prompted expectations and needs and ensures resilience during unsettled times.

This study is unique as the newly emerged variables are included in the research framework, and thus it helps to close the literature gap by introducing an integrated SERVQUAL and ECT theoretical model, which rarely performs in this context and can be replicated or extended with validated scales. This study contributes to enhancing hotel and tourism sustainable service quality performance to achieve myriad economic and health values.

This research investigates the complex relationship between economic policy uncertainty (EPU), energy consumption and institutional factors in the Gulf region. The purpose of this study is to examine how institutional factors moderate the impact of EPU on energy consumption in Gulf countries.

This paper uses the dynamic panel autoregressive distributed lag (PARDL) method, over a period stretching from 1996 to 2021 in the Gulf countries.

The results show that, only in the long term, EPU has a positive and significant impact on energy consumption, suggesting that increased EPU leads to increased energy use. Furthermore, this study found that, only in the long term, government effectiveness and regulatory quality have positive and significant effect on energy consumption. Accordingly, the two institutional factors play a moderating role in the EPU−energy consumption nexus.

This study highlights the importance of considering the time dimension when formulating energy and economic policies in Gulf countries. Policymakers should take into consideration the nature of these relationships to make informed decisions that promote energy efficiency and economic stability in the region.

To the best of the authors’ knowledge, this is the first study examining the relationship between EPU and energy consumption in the Gulf countries while incorporating the role of institutional factors as potential mediators.

The agri-food industry is experiencing a revolutionary shift due to the introduction of Industry 4.0 technologies to improve efficiency, transparency and sustainability. The importance of agri-food supply chains (AFSC) in promoting sustainability is expanding as the globe struggles with issues including resource scarcity, climate change and population growth. In order to better understand how Industry 4.0 might improve sustainability in a world that is changing quickly, this work aims to focus on identifying various sustainability assessment factors influencing AFSC to increase overall sustainability, minimize resource consumption, cut waste and streamline operations.

Important sustainability assessment factors are identified from the past academic literature and are then validated using the fuzzy-Delphi method. A method called decision-making trial and evaluation laboratory (DEMATEL) is used to examine and analyze structural models with complex causal linkages. The results are then validated using sensitivity analysis.

The factors that emerged as the highest ranked for evaluating the sustainability of Industry 4.0 in AFSC are market competitiveness, and knowledge and skill development, followed by resource efficiency. Industry 4.0 technologies are essential for increasing the marketability of agricultural products because of the major implications of market competitiveness. The significance of knowledge and skill development draws attention to Industry 4.0’s contribution to the promotion of chances for farmers and agricultural employees to increase their capability.

By outlining the nexus between Industry 4.0 technologies and sustainability, the study presents a comprehensive framework that would be relevant for researchers, policymakers and industry stakeholders who want to leverage Industry 4.0 technology to build more sustainable AFSC in the future. The study findings can help the farmers or producers make sensible choices that adhere to sustainability standards and guarantee long-term financial viability.

The originality of this work lies in the identification of sustainability assessment factors especially for AFSC in the era of digitalization which has not been discussed previously.

With the rise in demand and adoption of smart city initiatives, it is imperative to plan the railway infrastructure, as it will have a huge positive impact if adequately integrated into the planning process. Given the complexities involved, a whole systems thinking framework provides a useful platform for rail transport planners.

This paper proposes a simple, adoptable framework utilising systems thinking concepts and techniques taking into cognisance the key stakeholders. Milton Keynes in the United Kingdom is the adopted case study.

Selected systems thinking tools and techniques are adopted to develop a framework for mapping stakeholders and attributes when developing sustainable rail transport systems, taking note of their core functionalities and the complex systems wherein they exist.

The desire to build future (smart) cities is to effectively match infrastructural resources with a rapidly growing population, and the railway sector can play a strategic role in building a much more competitive low-carbon-emission transport system, which is a driving force for sustainable development.

The urban rail service has become vital to urban development as railway stations serve as hubs for sustainable mobility to meet local requirements. Moreover, it takes extra effort to input railway development into smart city plans, as it is a herculean task to get governments to focus on it with clarity of purpose in passing legislation.

The developed framework reduces complexities when planning and designing rail transport systems compared to many of the existing reductionist planning approaches. The simplicity of the framework would also make it easily adoptable by a wide range of users.

As the onset of the Russia–Ukraine military conflict on February 24, 2022, individuals from Ukraine have been relocating in search of safety and refuge. This study aims to investigate how the influx of Ukrainian refugees has impacted the stock markets and exchange rates of Ukraine's neighboring states.

The authors focused on the neighboring countries that share a western border with Ukraine and have received the highest number of refugees: Hungary, Poland, Romania and Slovakia. The analysis covered the period from April 24 to December 31, 2022. After this period, the influence of the refugees is small, insignificant. Wavelet coherence, wavelet power spectrum and the time-varying parameter vector autoregressions method were used for data processing.

The key finding are as follows: a link exists between the dynamics of refugees from Ukraine and volatility of the stock indices and exchange rate of the host countries; volatility was significant in the first weeks after the start of the conflict in all the analyzed states; and the highest volatility was recorded in Hungary and Poland; the effect of refugees was stronger on stock indices than that on exchange rates.

To the best of the authors’ knowledge, it is the first research that presents the impact of refugees from Ukraine on stock markets and exchange rates volatility in the countries analyzed.