Search results

The advent of digital technologies has ushered in a new era of quality management (QM) known as Quality 4.0 (Q4.0). The successful implementation of Q4.0 requires the involvement of both leaders and employees. Leadership plays a critical role in motivating employees involved in quality practices. However, the mechanisms by which leadership inspires quality professionals to engage in continuous learning and remain vigorous in their roles are not well understood. This study aims to determine the impact of Q4.0 leadership on thriving at work (TAW) among quality professionals and to identify the underlying mechanisms.

Utilising the identity theory and social identity theory, a multilevel TAW model was developed. This study surveyed 63 team leaders and 243 subordinates, who are quality professionals working for companies implementing Q4.0. Multilevel structural equation modelling (MSEM) was applied to assess the hypotheses.

The study finds that Q4.0 leadership enhances TAW among quality professionals. The linkage between Q4.0 leadership and TAW is mediated by work group identification (WGI) at the group level and job identification (JI) at the individual level.

Insights from this study will enable organisations to make informed decisions regarding the leadership styles that best support TAW among quality professionals. By understanding the mechanisms linking Q4.0 leadership to TAW, organisations can foster both WGI and JI, ultimately enhancing engagement and performance in quality initiatives.

This study offers a novel contribution to the QM field by examining the role of Q4.0 leadership in motivating and sustaining the engagement of quality professionals. Exploring the relationships between Q4.0 leadership, WGI, JI and TAW helps to deepen our understanding of how Q4.0 leadership can enhance TAW among quality professionals.

The boom in livestreaming commerce (LSC) has brought significant changes to social interaction methods. Understanding customer engagement in LSC is critical for online sellers who try to enhance the social influence and improve marketing effectiveness of LSC. Based on the stimulus–organism–response (S–O–R) paradigm, this study aims to develop a model to investigate the effects of perceived attributes of LSC (real-time interaction, perceived proximity and perceived authenticity) on social support (informational and emotional support) and subsequent engagement.

An online survey is conducted to collect data from LSC customers, and data are analyzed using SPSS and SmartPLS.

The results indicate that informational and emotional support are positively affected by real-time interaction, perceived proximity and perceived authenticity. In turn, informational and emotional support enable and mediate the prediction of customer engagement intention in LSC.

Prior LSC studies tend to focus on the motivation influencing LSC engagement from the perspective of perceived value. This study confirms the importance of perceived attributes of LSC in driving customer engagement from the perspective of social support.

To face profound changes from decreasing funding, growing patient expectations and increasing competition in the health-care market, public hospitals began to implement effective quality management (QM) practices following manufacturing and other service industries. However, there was little knowledge relevant to the impact of QM practices on the performance of public hospitals. The study aims to shed some further light on this issue.

The paper extends the previous empirical research to the health-care sectors and suggests the research framework of QM practice-performance relationships in public hospitals. For validation purposes, a cross-sectional survey involving 204 quality managers and directors of large public hospitals was carried out between April and October 2013 in Zhejiang Province, China. Structural equation modeling was used to test the hypothesized relationship between QM practices and hospital performance.

Empirical results support that the implementation of QM practices can bring many benefits to sample hospitals. The dimensions of employee relations and process management are directly related to the health-care and non-health-care performance of public hospitals.

It explores the relationship between QM practices and hospital performance based on empirical results from Chinese public hospitals, whereas few studies have been conducted within the context of public health-care sectors in developing countries. The empirical results could enhance hospital managers’ understanding of the nature of QM practice-performance relationship and help mangers re-allocate more resources to those elements of the QM systems that have the most significant impact on hospital performance.

College badminton gymnasium is an important place where students take physical exercise. Student satisfaction index for badminton gymnasium is an important factor which influences the design of badminton gymnasium. To improve the design and construction level of college badminton gymnasium, student satisfaction index evaluation system for the design of college badminton gymnasium was constructed, and empirical study of the system was carried out. Delphi method was applied to screen evaluation indicators, and analytical hierarchy process (AHP) was used to confirm the weight of evaluation indicators. Finally, student satisfaction index evaluation system for the design of college badminton gymnasium was constructed, including 3 first-level indicators (external form design, internal layout design and application function design) and 12 second-level indicators. Hunan University of Finance and Economics was taken for example to demonstrate student satisfaction index evaluation system for the design of college badminton gymnasium. Besides, comprehensive evaluation score of the badminton gymnasium was gained. The student satisfaction index evaluation system is an effective tool to evaluate the design of college badminton gymnasium. During the design of college badminton gymnasium in China, the application function and internal layout should mainly be taken into account.

Given little light has been shed on the preference of Generation Z tourists or tourists from different cultural backgrounds toward service robot preference in hospitality contexts, this study aims to explore robot service preferences in the accommodation sector in the Chinese market, with a specific focus of Generation Z customers.

This study followed an exploratory sequential research design including two qualitative methods (i.e. projective techniques and semistructured interviews) to reach its objectives.

This study suggests that service robots are more preferred for routine tasks and gender differences exist in the preference of service robot’s anthropomorphism level. The preferences are driven by four factors based on different levels of hedonic and utilitarian values: experienced-hedonic value, utilitarian-hedonic value, task-relevant value and utilitarian value.

First, because of the exploratory nature of this study, the data interpretation is unavoidably subjective; therefore, the results can be confirmed by using a more rigorous research method. Second, this study only focuses on the preference of Generation Z customers in China in the hotel sector; as the cultural differences exist in different countries, this study’s findings cannot be readily generalized across populations and service contexts. Finally, this study fails to dig into the effect of gender differences on varying levels of anthropomorphic attributes.

As hotels adopt service robots more widely, managers need to identify customers’ service preferences and prioritize tasks between robots and human resources for more efficient service. Particularly important is understanding the benefits and challenges of specific robot implementations rather than looking for a one-size-fits-all mode of operation.

To the best of the authors’ knowledge, this is the first study to understand robot service preference with regard to specific tourist groups in illustrating their preference for service delivery. Drawing on self-determination theory, this study potentially provides a theoretical basis for hotel service process optimization.

本研究从接待业情景出发, 针对中国Z世代游客在住宿业环境下对机器人服务使用偏好进行了探索性研究, 并对导致该偏好所产生的关键因素进行了探索。

本研究遵循了探索性次序研究设计步骤, 通过投射技术和半结构化访谈相结合的定性方法对本文研究目标进行了回答。

研究表明, 顾客对服务机器人日常服务事项表现出明显偏好, 但是, 对于服务机器人的拟人化水平偏好存在性别化差异。根据享乐主义和实用主义价值的不同维度角来看, 游客偏好差异的产生主要受到四种价值取向驱动, 包括：1）享乐—体验价值驱动; 2）实用—享乐价值驱动; 3）任务相关价值驱动; 4）实用价值驱动。

由于本研究是探索性研究, 且对投射技术和半结构化访谈数据的解释具有一定主观性, 建议未来通过更加严格的研究方法来验证分析结果。此外, 由于是在中国酒店行业特殊的语境和背景下进行的研究, 存在一定跨国文化差异, 其结论在跨种族和跨服务背景的适用性仍待进一步验证。最后, 本研究未能进一步深入探讨性别差异对不同层次拟人化属性的影响。

随着服务机器人在酒店业的普及, 管理者需要识别顾客的服务偏好, 在具体任务分配过程中, 需要对服务机器人和员工的使用安排进行优先等级划分, 以此全面提高服务效率。最重要的是, 要充分了解使用机器人的利与弊, 而不是通过一刀切的方式管理酒店服务。

本研究首次对特定游客群体（Z世代）对于服务机器人使用偏好进行了研究, 从自主决策理论出发阐述了他们对服务提供者的使用偏好差异及其驱动因素, 为优化酒店服务流程提供了理论指导和实证证据。

Este estudio se diseñó bajo una investigación secuencial exploratoria que incluyó dos métodos cualitativos (es decir, técnicas proyectivas y entrevistas semiestructuradas) para alcanzar sus objetivos.

Dado que se ha arrojado poca luz sobre la preferencia de los turistas de la Generación Z o de los turistas de diferentes orígenes culturales hacia los servicios de robótica en el sector alojativo, este estudio tiene como objetivo explorar las preferencias de servicios de robots en el sector del alojamiento en China, con un enfoque específico hacia la Generación Z.

Este estudio sugiere que los robots de servicio son más preferidos para tareas rutinarias, existiendo diferencias de género en la preferencia del nivel de antropomorfismo del robot de servicio. Las preferencias están impulsadas por cuatro factores basados en diferentes niveles de valores hedónicos y utilitarios: 1) valor hedónico experimentado, 2) valor hedónico utilitario, 3) valor relevante para la tarea y 4) valor utilitario.

Primero, debido a la naturaleza exploratoria de este estudio, la interpretación de los datos es inevitablemente subjetiva, por lo tanto, los resultados pueden confirmarse utilizando un método de investigación más riguroso. Además, este estudio solo se enfoca en la preferencia de los clientes de la Generación Z para el sector hotelero en China, ya que las diferencias culturales existentes en diferentes países, no pueden generalizarse fácilmente entre poblaciones y contextos de servicio. Por último, este estudio no profundiza en el efecto de las diferencias de género en los distintos niveles de atributos antropomórficos.

A medida que los hoteles vayan adoptando servicios con robots, los gerentes deberían identificar las preferencias de servicio de los clientes y priorizar tareas para robots o para recursos humanos cara a un servicio más eficiente. Particularmente importante es comprender los beneficios y desafíos de las implementaciones de robots específicos, en lugar de buscar un modo de operación único para todos.

Este es el primer estudio que comprende la preferencia de servicios de robots con respecto a grupos de turistas específicos para ilustrar su preferencia por la prestación de servicios. Basándose en la teoría de la autodeterminación, este estudio proporciona potencialmente una base teórica para la optimización del proceso de servicio del hotel.

Rare earths are essential materials for many high-tech industries critical to both economic development and national defense. China, the world's dominant supplier of rare earths, has recently been imposing stricter controls over its production and export. The purpose of this paper is to examine the domestic roots of the changes in China's rare earth industry production and exports in its three-decade rise to the current global monopoly.

This paper adopts the historical institutionalism approach to analyze the trajectory of industry and trade development. The author analyzes data collected from government whitepapers and reputed scholarly and news sources.

This paper argues that the Chinese rare earth industry has gone through three periods of development, in which the state attempted to control the market and industry through reformulating rules and institutions to achieve state goals. Domestic state institutions, combined with macroeconomic environment and state governance strategy shaped the three-decade experience of rare earth industry and trade development in China.

This paper builds on existing findings about Chinese state regulations to provide a novel analytical framework to analyze the role of the state in industry and trade development in the rare earth industry. The focus on a single strategic industry seldom studied in the current literature also provides ample empirical value to further scholarly understanding about this industry.

Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO) and nitrogen oxide (NOx) emissions from gas turbines (GTs) to enhance emission prediction for GTs in predictive emissions monitoring systems (PEMS).

The hybrid model architecture combines convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM) networks called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data from a GT power plant was uploaded to Google Colab, split into training and testing sets (80:20), and evaluated using test matrices. The model’s performance was benchmarked against state-of-the-art emissions prediction methodologies.

The model showed promising results for GT CO and NOx emissions. CO predictions had a slight underestimation bias of −0.01, with root mean-squared error (RMSE) of 0.064, mean absolute error (MAE) of 0.04 and R² of 0.82. NOx predictions had an RMSE of 0.051, MAE of 0.036, R² of 0.887 and a slight overestimation bias of +0.01.

While the model demonstrates relative accuracy in CO emission predictions, there is potential for further improvement in future research.

Implementing the model in real-time PEMS and establishing a continuous feedback loop will ensure accuracy in real-world applications, enhance GT functioning and reduce emissions, fuel consumption and running costs.

Accurate GT emissions predictions support stricter emission standards, promote sustainable development goals and ensure a healthier societal environment.

This paper presents a novel approach that integrates CNN and Bi-LSTM networks. It considers both spatial and temporal data to mitigate previous prediction shortcomings.

The purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry.

In this study, a review was carried out on the Web of Science (WoS) database with the assistance of bibliometric analysis techniques. Cluster analysis was also employed for this to group important data to determine the relationships and to visualize the areas in which the studies are concentrated. The thematic content analysis method was used to reveal on which customer experiences and on which methods the focuses were.

On the subject of experiences of service robots, the greatest number of publications was in 2021. In terms of country, China has come to the fore in the distribution of publications. As a result of thematic content analysis, it was determined that the leading factor was the main dimension of emotional experience. In terms of sub-dimensions, social interactions attracted more attention. Most of the studies discussed were not based on any theory. Apart from these, the Technology Acceptance Model (TAM), the Service Quality Model (SERVQUAL) and Perceived Value Theory (PVT) were featured more prominently among other studies.

In this study, only the WoS database was reviewed. In future studies, it would be possible to make contextual comparisons by scanning other databases. In addition to quantitative research designs, social dimensions may be examined in depth following qualitative research methods. Thus, various comparisons can be made on the subject with mixed-method research designs. Experimental research designs can also be applied to where customers have experienced human-robot interactions (HRIs).

In the hospitality industry, it is critical to uncover every dimension of guests' robot acceptance. This study, which presents the current situation on this basis, guides future projections for the development of guest experiences regarding service robots in the hospitality industry.