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This paper aims to clarify the relationship between digital transformation and labor structure from the perspectives of microenterprise business strategies and factor allocation efficiency. It attempts to address the gap in existing research by explaining the impact of digital transformation on multidimensional workforce structures and the positive effects of this structural adjustment on labor allocation efficiency. In addition, the study further explores the economic ramifications of digital transformation, clarifying the correlation between changes in labor force structure and enterprise human resource allocation, thus enhancing the employment mobility effects of digital innovation at the enterprise level.

In contrast to prior research, our approach uses text analytics to assess the internal labor structure, incorporating labor skill, position and age into the analytical framework. This approach yields a more comprehensive data set, shedding light on variations in multidimensional employment structures.

The paper asserts that digital transformation significantly influences labor structure changes, evidenced by increased proportions of high-skilled, non-routine and younger laborers, as well as decreased shares of low-skilled, routine and older-age workers. Furthermore, it captures internal labor structure impacts, influenced by enterprise size, ownership, industry density and regional digitization levels. Mechanism analysis indicates moderation of digital transformation effects on labor structure by innovative tasks, labor productivity and management shareholding.

The paper reveals the specific impact of corporate digital transformation on workforce structure, enriching the employment mobility effects of digital innovation at the enterprise level and providing theoretical support for the formulation and implementation of relevant policies.

First, this paper delves into the impact of digital transformation on the internal labor structure from a microlevel perspective, elucidating its mechanisms. Second, in contrast to prior research, it uses text analytics to assess the internal labor structure, incorporating labor skill, position and age into the analytical framework. This approach yields a more comprehensive data set, shedding light on variations in multidimensional employment structures. Lastly, the study investigates the economic ramifications of shifts in employment structures. The findings of this study furnish novel empirical evidence for the debate regarding whether digital transformation can indeed enhance labor allocation efficiency.

Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.

China has experienced a fast economic growth and shown a significant rise in income inequality in the past decades. During the same period, fluid milk consumption in urban areas has rapidly expanded. The objective of this paper is to analyze and simulate the influence of income distribution changes on fluid milk consumption of households in urban China.

This study applies an inverse hyperbolic sine (IHS) double-hurdle model to modeling at-home fluid milk consumption of households across different income strata based on a sample of 11,861 urban households in five provinces in China, and simulating the impact of changing income distribution, including five income growth patterns, on fluid milk consumption of total households as well as specific household groups.

The fluid milk consumption in urban China will continue to increase, with the unconditional income elasticity being 0.334 for the full sample and 0.347, 0.335 and 0.162 for the low-, middle-, and high-income groups, respectively. The simulation results show an evidence that, compared with distribution-neutral and disparity-enlarging income growth patterns, a rising income equality would lead to a more significant increase in fluid milk consumption. And the inequality-reducing income growth pattern has a larger impact on fluid milk consumption of households with seniors and no children, as well as households having no local urban household registration (hukou).

The government should strengthen the supply measures of fluid milk in urban areas, enlarge domestic dairy production, and diversify the sources of milk imports. It is also necessary to subsidize low-income families, especially households with seniors or households migrated from other areas without getting local urban hukou, which could simultaneously improve nutritional benefits and alleviate financial pressures.

A simulation considering the evolution of income distribution as well as different household groups is conducted. Widely distributed data with a large sample size and detailed demographic information are used. The problems of zero consumption and non-normal distribution are addressed by the IHS double-hurdle model.

The unitized curtain wall system (UCWS), a symbol of modern architecture, is gaining popularity among prefabricated components. Previous studies have focused on both construction technology advances and material selection strategies to facilitate the UCWS. However, the topic of client satisfaction, which drives industry development by targeting clients' demands, has gone unnoticed. Therefore, the current study aims to investigate client satisfaction with UCWS products in Hong Kong by finding its influential factors.

A systematic review was employed to first identify the influential factors. A semi-structured interview was employed to validate the reliability of the extracted factors. The machine learning algorithm Extreme Gradient Boosting (XGBoost) and the Pearson correlation were then employed to rank the importance and correlation of factors based on the 1–5 Likert scale scores obtained through a questionnaire survey.

The findings revealed that “reduction in construction time” and “reduction in construction waste” are the most important factors and have a strong positive influence on client satisfaction.

Unlike previous studies, the present study focused on a novel research topic and introduces an objective analysis process using machine learning algorithms. The findings contribute to narrowing the knowledge gap regarding client preference for UCWS products from both individual and collaborative perspectives, providing decision-makers with an objective, quantitative and thorough reference before making investments in the curtain wall management development.

This paper aims to explore the impact of green finance on the heterogeneity of enterprise green technology innovation and the underlying mechanism between them.

Using the data of China's A-share listed enterprises from 2008 to 2020 and the fixed effect model, the authors empirically explore the relationship and mechanism between green finance and green technology innovation by constructing the green finance index while considering both the quality and quantity of innovation.

The study suggests that green finance is positively related to the quality and quantity of enterprise green technology innovation, while green finance is more effective in stimulating the quality of green technology innovation than quantity. In addition, alleviating financial mismatch and improving the quality of environmental information disclosure are core mechanisms during the process of green finance facilitating green technology innovation. Furthermore, green finance exerts a more positive effect on the quality and quantity of green technology innovation with large-size enterprises, heavily polluting industries and enterprises in the eastern region.

This paper enriches the literature on green finance and green technology innovation and provides practical significance for green finance implementation.

Three-dimensional (3D) food printing is an innovative technology used to customize food products through the integration of digital technology and food ingredients. The purpose of this study is to assess the current state of research in the field of 3D food printing, identify trending topics and identify promising future research directions.

This bibliometric review systematically evaluates the field of 3D food printing using data from published literature in the Web of Science database. After reference screening, 812 articles were included in the analysis.

The result reveals that research in 3D food printing primarily focuses on the optimization and characterization of mechanical and rheological properties of food inks and that post-printing processing, such as laser treatment, has emerged recently as an important consideration in 3D food printing. However, extant works lack animal and human studies that demonstrate the functionality of 3D-printed food.

This sophisticated bibliometric analysis uncovered the most studied current research topics and the leading figures in the area of 3D food printing, providing promising future research directions.

The purpose of this paper is to explore the effect of ZnO on the structure and properties of micro-arc oxidation (MAO) coating on rare earth magnesium alloy under large concentration gradient.

The macroscopic and microscopic morphology, thickness, surface roughness, chemical composition and structure of the coating were characterized by different characterization methods. The corrosion resistance of the film was studied by electrochemical and scanning Kelvin probe force microscopy. The results show that the addition of ZnO can significantly improve the compactness and corrosion resistance of the MAO coating, but the high concentration of ZnO will cause microcracks, which will reduce the corrosion resistance to a certain extent.

When the concentration of zinc oxide is 8 g/L, the compactness and corrosion resistance of the coating are the best, and the thickness of the coating is positively correlated with the concentration of ZnO.

Too high concentration of ZnO reduces the performance of MAO coating.

The MAO coating prepared by adding ZnO has good corrosion resistance. Combined with organic coatings, it can be applied in corrosive marine environments, such as ship parts and hulls. To a certain extent, it can reduce the economic loss caused by corrosion.

The effect of ZnO on the corrosion resistance of MAO coating in electrolyte solution was studied systematically, and the conclusion was new to the common knowledge.

The present study aims to ascertain the relationships between subordinate moqi and leader behaviors, by primarily discussing how and when subordinate moqi is associated with leadership empowerment.

A self-report study was conducted by recruiting 334 employees from 13 firms. All concepts were rated on a seven-point Likert-type response scale. Linear regression analysis (conducted in MPLUS 7) was conducted to verify the hypotheses.

First, subordinate moqi showed positive association with empowerment. Second, trust-in-supervisor mediated the relationships between subordinate moqi and empowerment. Third, subordinates' power distance orientation (PDO) could moderate the subordinate moqi – leader empowerment relationship. When subordinates reported higher PDO, the relationships between subordinate moqi and empowerment were more robust; likewise, subordinate moqi would have more significantly indirectly impacted empowerment via trust-in-supervisor.

Though researchers have discussed the impacts of subordinate moqi on subordinates' outcomes, the impact of subordinate moqi on supervisors' attitudes or behaviors remains unclear. The relationships between subordinate moqi and supervisor empowerment behaviors are empirically ascertained by emphasizing the leader-subordinate dyadic process. The findings here suggested that subordinate moqi boosted subordinates' trust-in-supervisor, and moqi would also predict the behaviors of leader empowerment. This study extended the PDO literature by identifying the moderating role of PDO in the subordinate moqi – leader empowerment behavior relationship.

The purpose of this paper is to conceptualize the indigenous concept of suzhi at individual and organizational levels, and identify its dimensions for human resource management (HRM) research and practice in China.

Based on a comprehensive review of suzhi literature, Chinese cultural and historical literature, as well as Western mainstream HRM research, a multidimensional suzhi framework is conceptualized.

As an indigenous expression, suzhi can be and has been adopted for Chinese HRM research and practice. As a multidimensional construct, one’s cognitive suzhi is jointly determined by corresponding moral suzhi, wenhua (knowledge-based) suzhi and zhuanye (professional) suzhi. Cognitive suzhi, in turn, determines one’s behavioral suzhi that drives employees to enhance organizational performance, and this relationship is moderated by psychological suzhi.

The proposed framework provides new insight for Chinese indigenous management research, particularly in developing suzhi measurement for different dimensions. It also informs HRM practices in recruiting, selection, performance analysis and employee career development.

The complexity of suzhi dimensions from an organizational HRM perspective is analyzed. The resulting suzhi framework offers new insight for HRM research and practices in China.

Ocean exploration is of importance and in great demand throughout the world. This results in a huge challenge in tribology in the marine environment. Moreover, polymeric materials with large molecules or macromolecules play an important role in marine equipment.

The tribological performance of sea-water-eroded polyether polyurethane (PU) was systematically studied by using a multi-specimen test machine for different durations from 0 to 60 days. Surface characterization technologies, such as scanning electric microscopy energy dispersive spectroscopy, were used to analyze the PU samples.

It can be found that the COF measured against 316 steel increases with the testing load because of the change of contact areas for the original PU samples. The effect of hydrodynamic lubrication and heat resulted in the decline of the COF with the increase in testing speed. The COF of PU sample immersed for 20 days was the lowest compared with other samples. With the immersion time increased to 60 days, the COF increased first and then decreased. The reduced COF of PU resulted in improved anti-wear performance of the PU sample.

These results enhanced the comprehension of the tribological performances of PU immersed in sea-water.