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The purpose of this study is to evaluate service robots as an alternative service provider that can reduce customers’ social discomfort in hospitality service encounters. Specifically, the authors discuss when and in what scenarios service robots can alleviate such social discomfort and explain this effect from the perspective of dehumanization.

Following a social constructivist paradigm, the authors adopt a qualitative research design, gathering data through 21 semistructured interviews to explore why the presence of service employees causes customers’ social discomfort in hospitality service encounters and how service robots alleviate such discomfort.

This study’s results suggest that both the active and passive engagement of service employees are sources of customers’ social discomfort in hospitality service encounters; thus, adopting service robots can help reduce such discomfort in some scenarios. Customers’ differentiating behaviors, a downstream effect of social discomfort, are also addressed.

Service robots can reduce customers’ social discomfort in certain scenarios and influence their consumption behaviors. This finding offers actionable insights regarding the adoption of service robots in hospitality service encounters.

This research enhances the understanding of social discomfort in hospitality service encounters and expands the research on service robots. To the best of the authors’ knowledge, it is the first attempt to reveal the bright side of robots in service encounters from a dehumanization perspective.

The purpose of this paper is to explore the high-quality development (HQD) strategy of Chinese mineral resource enterprises, which is important for Chinese mineral resource enterprises to improve the efficiency and benefit of resource utilization, reduce the intensity of resource and energy consumption and gradually form resource-saving and environment-friendly enterprises.

This study establishes an evaluation index system with four dimensions: economy, environment, society and management innovation. The entropy value method assigns weights to them and then uses the system dynamics (SD) model for case simulation.

The results of the SD simulation conclude that the fulfillment of social responsibility and the implementation of management innovation can accelerate the realization of HQD of mineral resource enterprises; profitability plays a crucial role in economic indicators; the improvement of energy-saving volume has the most significant impact on environmental benefits; the social contribution is the key element to measure social indicators; and the sales rate of core products has the most significant impact on the benefits of management innovation.

Based on the few studies on the evaluation of the development strategy of mineral resource enterprises, this study establishes an evaluation index system that considers the interactions between indicators, combines the entropy value method with SD and uses the SD model to comprehensively and systematically analyze the impact and degree of each factor on the HQD of mineral resource enterprises.

Driven by the development of the global digital economy, knowledge management in industrial enterprises offers more possibilities for green innovation. Based on the perspective of external knowledge sources, this study aims to construct a panel regression model to explore the relationship between digital economy and industrial green innovation efficiency.

Panel data from 30 regions in China from 2011 to 2020 were selected as research samples. All data are obtained from national and provincial statistical yearbooks. Coupling coordination degree analysis, entropy method, panel regression analysis, robustness test and threshold effect test by Stata 16.0 were used to test the hypotheses.

The empirical results demonstrate the hypotheses and reveal the following findings: the digital economy is positively related to industrial green innovation efficiency and external knowledge sources, and external knowledge sources mediate the relationship between them. Moreover, based on the threshold test results, the digital economy has a double-threshold effect on industrial green innovation efficiency.

Based on the perspective of external knowledge sources, the proposed mediating mechanism between the digital economy and industrial green innovation efficiency has not been established previously, further enriching the research on the antecedents and outcomes of external knowledge sources. Moreover, this study estimated the direct influence mechanism and double-threshold effect of the digital economy on industrial green innovation efficiency from theoretical and empirical analysis, thus responding to the call of scholars and adding to existing research on how the digital economy affects the green transformation of industrial enterprises.

The ancient town of Lijiang is a representative place of ethnic minorities in China’s southwest border area jointly built by many ethnic groups. Its rich and diversified history, culture and architecture as well as its artistic and spiritual values need to be better retained and explored.

The protection and inheritance of Lijiang’s cultural heritage will be improved through the construction of digital memory resources. To guide Lijiang’s digital memory construction, this study explores strategies of digital memory construction by analyzing four case studies of well-known memory projects from China and America.

From the case studies analysis, factors of digital memory construction were identified and compared. Factors led to the discussion of strategies for constructing the digital memory of Lijiang within its design, construction and service phases.

The ancient town of Lijiang is a famous historical and cultural city in China, and it is also a representative place of ethnic minorities in the border area jointly built by many ethnic groups. The rich culture should be preserved and digitalized to offer better use for the whole nation.

European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic.

The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach.

XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood.

This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.

The aim of this study was to analyze the effects of carbon reduction efforts and preservation efforts on system benefits in the cold chain industry of fresh products.

This study develops an optimal decision game model for the fresh products in the cold chain, incorporating the retailer's preservation effort and the supplier's carbon emission reduction effort. It quantifies the relationship between carbon emission reduction effort, preservation effort and system profit. The model considers parameters like carbon trading price, consumer low-carbon preference and consumer freshness preference, reflecting real-world conditions and market trends. Numerical simulations are conducted by varying these parameters to observe their impact on system profit.

Under the carbon cap-and-trade policy, the profit of the fresh cold chain system is higher than that of the fresh cold chain system without carbon constraints, and the profit of the supplier under decentralized decision-making is increased by nine times in the simulation results. The increase in carbon trading prices can effectively improve the freshness level of fresh products cold chain, carbon emission reduction level and system profit.

This study comprehensively considers the factors of freshness and carbon emission reduction, provides the optimal low-carbon production decision-making reference for the fresh food cold chain and promotes the sustainable development of the fresh food cold chain.