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For a better understanding of the preferences and differences of young consumers in emerging wine markets, this study aims to propose a clustering method to segment the super-new generation wine consumers based on their sensitivity to wine brand, origin and price and then conduct user profiles for segmented consumer groups from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences.

We first proposed a consumer clustering perspective based on their sensitivity to wine brand, origin and price and then conducted an adaptive density peak and label propagation layer-by-layer (ADPLP) clustering algorithm to segment consumers, which improved the issues of wrong centers' selection and inaccurate classification of remaining sample points for traditional DPC (DPeak clustering algorithm). Then, we built a consumer profile system from the perspectives of demographic attributes, eating habits and wine sensory attribute preferences for segmented consumer groups.

In this study, 10 typical public datasets and 6 basic test algorithms are used to evaluate the proposed method, and the results showed that the ADPLP algorithm was optimal or suboptimal on 10 datasets with accuracy above 0.78. The average improvement in accuracy over the base DPC algorithm is 0.184. As an outcome of the wine consumer profiles, sensitive consumers prefer wines with medium prices of 100–400 CNY and more personalized brands and origins, while casual consumers are fond of popular brands, popular origins and low prices within 50 CNY. The wine sensory attributes preferred by super-new generation consumers are red, semi-dry, semi-sweet, still, fresh tasting, fruity, floral and low acid.

Young Chinese consumers are the main driver of wine consumption in the future. This paper provides a tool for decision-makers and marketers to identify the preferences of young consumers quickly which is meaningful and helpful for wine marketing.

In this study, the ADPLP algorithm was introduced for the first time. Subsequently, the user profile label system was constructed for segmented consumers to highlight their characteristics and demand partiality from three aspects: demographic characteristics, consumers' eating habits and consumers' preferences for wine attributes. Moreover, the ADPLP algorithm can be considered for user profiles on other alcoholic products.

The current study aims to investigate the factors that impact the feedback received on answers to questions in social Q&A communities and whether the expertise-required question influences the role of these factors on the feedback.

To understand the antecedents and consequences that influence the feedback received on answers to online community questions, the elaboration likelihood model (ELM) is applied in this study. The authors use web data crawling methods and a combination of quantitative analyses. The data for this study came from Zhihu; in total, 353,775 responses were obtained to 1,531 questions, ranging from 49 to 23,681 responses per question. Each answer received 0 to 113,892 likes and 0 to 6,250 comments.

The answers' cognitive and emotional components and the answerer's influence positively affect user feedback behavior. In addition, the expertise-required question moderates the effects of the answer's cognitive component and emotional component on the user feedback, moderating the effects of the answerer's influence on the user approval feedback.

This study builds upon a limited yet growing body of literature on a theme of great relevance to scholars, practitioners and social media users concerning the effects of the connotation of answers (i.e. their cognitive and emotional components) and the answerer's influence on user feedback (i.e. approval and collaborative feedback) in social Q&A communities. The authors further consider the moderating role of the domain expertise required by the question (expertise-required question). The ELM model is applied to explore the relationships between questions, answers and feedback. The findings of this study add a new perspective to the research on user feedback and have implications for the management of social Q&A communities.

This paper aims to provide insight into the evolving relationship between humans and machines, understanding its multifaceted impact on our lifestyle and landscape in the past as well as in the present, with implications for the near future. It uses bibliometric analysis combined with a systematic literature review to identify themes, trace historical developments and offer a direction for future human–machine interactions (HMIs).

To provide thorough coverage of publications from the previous four decades, the first section presents a text-based cluster bibliometric analysis based on 305 articles from 2,293 initial papers in the Scopus and Web of Science databases produced between 1984 and 2022. The authors used VOS viewer software to identify the most prominent themes through cluster identification. This paper presents a systematic literature review of 63 qualified papers using the PRISMA framework.

Next, the systematic literature review and bibliometric analysis revealed four major historical themes and future directions. The results highlight four major research themes for the future: from Taylorism to advanced technologies; machine learning and innovation; Industry 4.0, Society 5.0 and cyber–physical system; and psychology and emotions.

There is growing anxiety among humankind that in the future, machines will overtake humans to replace them in various roles. The current study investigates the evolution of HMIs from their historical roots to Society 5.0, which is understood to be a human-centred society. It balances economic advancement with the resolution of social problems through a system that radically integrates cyberspace and physical space. This paper contributes to research and current limited knowledge by identifying relevant themes and offering scope for future research directions. A close look at the analysis posits that humans and machines complement each other in various roles. Machines reduce the mechanical work of human beings, bringing the elements of humanism and compassion to mechanical tasks. However, in the future, smart innovations may yield machines with unmatched dexterity and capability unthinkable today.

This paper attempts to explore the ambiguous and dynamic relationships between humans and machines. The present study combines systematic review and bibliometric analysis to identify prominent trends and themes. This provides a more robust and systematic encapsulation of this evolution and interaction, from Taylorism to Society 5.0. The principles of Taylorism are extended and redefined in the context of HMIs, especially advanced technologies.

Artificial intelligence (AI) is a significant game changer in human resource development (HRD). The launch of ChatGPT has accelerated its progress and amplified its impact on organizations and employees. This study aims to review and examine literature on AI in HRD, using a bibliometric approach.

This study is a bibliometric review. Scopus was used to identify studies in the field. In total, 236 papers published in the past 10 years were examined using the VOSviewer program.

The obtained results showed that most cited documents and authors are mainly from computer sciences, emphasizing machine learning over human learning. While it was expected that HRD authors and studies would have a more substantial presence, the lesser prominence suggests several interesting avenues for explorations.

This study provides insights and recommendations for researchers, managers, HRD practitioners and policymakers. Prioritizing the development of both humans and machines becomes crucial, as an exclusive focus on machines may pose a risk to the sustainability of employees' skills and long-term career prospects.

There is a dearth of bibliometric studies examining AI in HRD. Hence, this study proposes a relatively unexplored approach to examine this topic. It provides a visual and structured overview of this topic. Also, it highlights areas of research concentration and areas that are overlooked. Shedding light on the presence of more research originating from computer sciences and focusing on machine learning over human learning represent an important contribution of this study, which may foster interdisciplinary collaboration with experts from diverse fields, broadening the scope of research on technologies and learning in workplaces.

In product modular design tasks, especially in the high-tech manufacturing industry, buyers and supplies play distinct roles, which may have different impacts on product architectural and modular innovation. Prior research has tended to view product innovation as a holistic concept, overlooking the importance of this differentiated influence. This study, from a modular design perspective, aims to clarify the impact of black-box supplier involvement on product architectural and modular innovation, as well as the influence of product modularity on these relationships.

Based on the theory of product modular design, this study decomposes product innovation into architectural and modular innovation from the perspective of the product internal structure to conduct in-depth theoretical analysis and model construction. A total of 276 valid questionnaires are collected from typical Chinese high-tech manufacturing firms and used to empirically test the constructed theoretical model using multiple hierarchical regression analysis.

The results show that black-box supplier involvement positively affects modular innovation and takes an inverted U-shape, as moderated by product modularity. However, the impact of black-box supplier involvement on architectural innovation shows contradictory differences at different modularity levels. Under a low level of product modular design, black-box supplier involvement has a negative impact on architectural innovation, but under a moderate level of modular design, it has a positive impact. After the degree of modular design exceeds a certain threshold, the impact gradually weakens.

The results provide valuable insights for managers, highlighting the need to avoid oversimplifying the innovation impact of black-box suppliers solely based on overall product innovation. Instead, a more accurate assessment of the innovation contributions of both the buyer and supplier should be based on the degree of architectural and modular innovation. Additionally, the findings suggest that managers should consider the alignment between their company's product modular design features and innovation priorities (i.e. modular innovation or architectural innovation) when determining an appropriate supplier collaborative development strategy.

This study not only reveals the different impacts of black-box supplier involvement on architectural and modular innovation, but also proves the significant synergistic innovation effect of the relationship between black-box supplier involvement and product modularization. It constitutes an enriched and deepened exploration in the existing research on supplier involvement in product innovation.

The Chinese data setting allows researchers to explore the influence of local versus national (central) government ownership on companies. This study aims to examine the influence of government ownership (local versus national) and auditor choice (choosing larger or smaller firms) on audit pricing in China.

This study executed three panel data regressions to examine the two hypotheses using 19,626 observations from 2009 to 2017 in the Chinese data setting. This study also uses the Sobel test to investigate the moderating effect of auditor choice.

This study first examines whether choosing a large audit firm positively influences audit pricing and whether listed state-owned enterprises (SOEs) charge less audit fees to audit firms after controlling for various variables. However, the interaction influence of government ownership and audit firm size on audit pricing is positive, suggesting that a large audit firm charges a client company more, even if the client is an SOE. More importantly, when we divide SOEs into national- and local-SOEs, the results of the influence of auditor choice, government ownership and the interaction of government ownership on audit pricing are consistent (plus, minus, plus), and audit firms charge local-SOEs less than national-SOEs. Furthermore, from the additional analysis, this study finds that the strong auditor type has a moderate effect on the case of local-SOEs on audit pricing and local-SOEs choose smaller auditors.

Research on the differences between local and national government ownership is limited. This study adds empirical results from this perspective. In particular, the findings suggest a further audit pricing research direction to consider the influence of client companies’ ownership types and auditor choice, especially in countries with planned economies.

Poor mental health in men and women is attributable to disparities in physical traits, social roles, power and health-seeking behaviours. This study aims to examine the gender differences in mental health among Pakistan and German university students and focuses on their right to seek mental health care.

Data on depression, anxiety and stress symptoms, as well as positive mental health (PMH), resilience, social support and life satisfaction, were gathered from Pakistani and German students.

In contrast to the Pakistani group, where no such gender differences were seen, women in Germany reported higher degrees of stress, anxiety and depression, as well as a lower level of overall good mental health. In comparison to German men and women, Pakistani women scored equally high on resilience. While gender had no bearing on life happiness in either Pakistan or Germany, women in both countries perceived more social support than men did.

The study’s strengths include its large sample size and battery of mental health measures. The results of partial weak measurement Invariance (MI) on the stress subscale underlined the importance of using MI in cross-cultural studies. The validity of a direct comparisons on sum score between different language versions or country samples shall be cautious. Still, there are limitations. Firstly, the authors did not differentiate gender and biological sex, and there was no group of non-binary gender. Pakistani (N = 1,840) and German (N = 7,890) students were in unequal numbers. Again, only university students were sampled, so the results cannot be generalised to older (probably less educated) populations. Self-reported data that mainly obtained via online survey were the third limitation. This design is cost-effective and easy to administer for cross-cultural survey research. However, social desirability and memory bias are common in self-report inventories. Fourthly, although English is an official language in Pakistan and the medium of instruction in education, the authors recommend future study to use questionnaires that have been translated and validated into Urdu (Pakistan’s national language) and investigate gender differences in a general population. Fifthly, this is a cross-sectional survey; the authors were not able to explore the causality or risk factors that contribute to the poor mental well-being in Pakistan students in general or the relatively worse mental health in German women. Future studies may investigate the mechanism behind the phenomena observed in this study with longitudinal or experimental design. Last but not least, Germany and Pakistan differ in so many different aspects from culture, religions and history to social structure and economic status, which make it hard to claim whether the observed differences were due to national differences, cultural differences, economic differences, gender inequality differences or other effects. It would be helpful for future studies to include more country samples with clear definitions of different “culture” aspects for a better understanding of gender differences in other countries and in different mental well-being constructs.

The current study is the first attempt to compare the gender difference patterns in positive and negative mental health between European and South Asian counties and focuses on gender-specific approaches. Although Pakistani university students reported in general worse mental well-being, the differences between the two genders in mental health (e.g. depression, anxiety, general PMH) were not as pronounced as in the German student sample. Gender comparisons in these mental health constructs would help to improve protective factors against mental illness and to develop appropriate management programmes based on cultural differences. The results suggest that the gender differences found in western countries cannot always be directly translated into the South Asian cultural framework. Our results also highlight the importance of improving the general situation of Pakistan (students) instead of focusing on one gender. At the same time, in Germany, prevention and intervention plans are more warranted for women. It could be that once the general situation in Pakistan is improved, the gender-related differences in mental health will be clearly observed.

These findings imply the significance of cultural context when inferring gender variations in mental health. Moreover, it supports the advancement of comprehensive policies to reduce gender-related mental health inequalities and focuses on the equal rights of men and women to get mental health care.

Based on the attention-based view (ABV), this study examines the mechanism of external pressure and internal managerial interpretation affecting the promotion of green entrepreneurial orientation (GEO) of agricultural enterprises.

Based on data collected from 208 agricultural enterprises in China, the conceptual model was tested by using hierarchical regression.

The results show that managerial interpretation can affect the promotion of GEO. Command and control regulation, market-based regulation and green market pressure are important external pressures that affect the promotion of GEO. In addition, managerial interpretation mediates the relationship between command and control regulation and GEO, market-based regulation and GEO, as well as green market pressure and GEO.

This study proposes a key path for promoting the adoption and implementation of GEO by agricultural enterprises. The research results provide experience for emerging and developing countries to promote the GEO of agricultural enterprises, which is helpful to alleviate the environmental problems caused by the development of agricultural enterprises.

For the first time, this study introduced the ABV into the research of GEO. The research results enrich the theoretical perspective of GEO and expand the research field of the ABV. In addition, this study fills the research gap that existing research has not paid enough attention to the internal driving factors of GEO and opens the black box between the external pressure and GEO.

This study aims to investigate the fabrication of solid ankle foot orthoses (SAFOs) using fused deposition modeling (FDM) printing technology. It emphasizes cost-effective 3D scanning with the Kinect sensor and conducts a comparative analysis of SAFO durability with varying thicknesses and materials, including polylactic acid (PLA) and carbon fiber-reinforced (PLA-C), to address research gaps from prior studies.

In this study, the methodology comprises key components: data capture using a cost-effective Microsoft Kinect® Xbox 360 scanner to obtain precise leg dimensions for SAFOs. SAFOs are designed using CAD tools with varying thicknesses (3, 4, and 5 mm) while maintaining consistent geometry, allowing controlled thickness impact investigation. Fabrication uses PLA and PLA-C materials via FDM 3D printing, providing insights into material suitability. Mechanical analysis uses dual finite element analysis to assess force–displacement curves and fracture behavior, which were validated through experimental testing.

The results indicate that the precision of the scanned leg dimensions, compared to actual anthropometric data, exhibits a deviation of less than 5%, confirming the accuracy of the cost-effective scanning approach. Additionally, the research identifies optimal thicknesses for SAFOs, recommending a 4 and 5 mm thickness for PLA-C-based SAFOs and an only 5 mm thickness for PLA-based SAFOs. This optimization enhances the overall performance and effectiveness of these orthotic solutions.

This study’s innovation lies in its holistic approach, combining low-cost 3D scanning, 3D printing and computational simulations to optimize SAFO materials and thickness. These findings advance the creation of cost-effective and efficient orthotic solutions.

The authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data.

The authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months.

The authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures.

Proposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors.

Longitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs.

Timely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.

The study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.