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With recent advances in artificial intelligence, the hospitality industry has introduced the concept of unmanned smart hotels staffed by service robots instead of human employees. Research is needed to understand consumers’ receptivity to such an innovation. This paper examines factors associated with consumers’ potential resistance to using automated service hotels via two sequential studies. Given that younger generations of consumers are typically early adopters of advanced technology and innovative services, our sampling approach focused on this consumer group.

Two studies were conducted. Study 1 proposed and empirically tested a theoretical model. Results revealed that attitude, subjective norms and perceived behavioral control each positively influenced individuals’ intentions to use unmanned smart hotels. In Study 2, we further investigated aspects informing perceived security, a key variable in the use of unmanned smart hotels.

Findings showed how people’s beliefs about unmanned smart hotels and security control assurances led to perceived security. These perceptions were shaped by perceived physical risks, privacy concerns, website design and hotel reputation. Overall, this research provides theoretical and practical implications for various stakeholders associated with unmanned smart hotels.

Findings of this study suggested that managers of unmanned smart hotels should design user-friendly, secure processes and offer comprehensive support resources to enhance customer experience and usage.

The findings provide a holistic understanding of consumers’ receptivity to unmanned smart hotels.

As the market competition becomes increasingly intensive and the profit from traditional voice services margins gradually decline, 3G telecom operators must provide various value‐added services to enhance the ARPU (average revenue per user). The purpose of this paper is to recognize the factors infecting the adoption of 3G value‐added services.

The TAM (technology acceptance model) was used as the foundation to further understand consumer's behavioral intention to use 3G value‐added services. Structural equation modelling (SEM) was used to develop a conceptual model of customer adoption and nine important factors were proposed. The authors conducted a questionnaire investigation, using a large sample, in the Chinese 3G value‐added service market. SPSS and AMOS were applied to analyze the survey data.

The empirical findings are: security and social influence are two of the most important factors in 3G market; 3G value‐added services must be perceived as enjoyment and useful; using context should be friendly and compatible; and in a whole view, the influence from gender, ease of use and need for unique to adoption intention is not significant, but to separate as two models, the difference is huge – the female group considers more about price and usefulness, while the male group thinks about enjoyment and compatibility.

The sample of this study is limited at the range of empirical data; the development of 3G technology has not yet been perfected in China and this study fails to include all of the 3G value‐added services, some of which are still in the exploratory phase of development. At the same time, the cities in this study cannot be said to represent the present stage of the whole of China.

The paper develops a conceptual structural equation model on the area of 3G value‐added services in China. The findings in the paper would help 3G providers to improve their services.

The purpose of this study is to identify medical service quality factors that patients care about and establish a medical service quality evaluation index system by analyzing online reviews of medical and healthcare service platforms in combination with a questionnaire survey.

This study adopts a combination of review mining and questionnaire surveys. The latent Dirichlet allocation (LDA) model was used to mine hospital reviews on the medical and healthcare service platform to obtain the medical service quality factors that patients pay attention to, and then the questionnaire was administered to obtain the relative importance of these factors to patients' perception of service quality. Finally, the index system was established.

The medical service quality factors patients care about include medical skills and ethics, registration service, operation effect, consulting communication, drug therapy, diagnosis process and medical equipment.

The identification of medical service quality factors provides a reference for medical institutions to improve their medical service quality.

This study uses online review mining to obtain medical service quality factors from the perspective of patients, which is different from previous methods of obtaining factors from relevant literature or expert judgments; then, based on the mining results, a medical service quality evaluation index system is established by using questionnaire data.

This paper aims to investigate the theoretical and practical links between digital twin (DT) application in heritage facilities management (HFM) from a life cycle management perspective and to signpost the future development directions of DT in HFM.

This state-of-the-art review was conducted using a systematic literature review method. Inclusive and exclusive criteria were identified and used to retrieve relevant literature from renowned literature databases. Shortlisted publications were analysed using the VOSviewer software and then critically reviewed to reveal the status quo of research in the subject area.

The review results show that DT has been mainly adopted to support decision-making on conservation approach and method selection, performance monitoring and prediction, maintenance strategies design and development, and energy evaluation and management. Although many researchers attempted to develop DT models for part of a heritage building at component or system level and test the models using real-life cases, their works were constrained by availability of empirical data. Furthermore, data capture approaches, data acquisition methods and modelling with multi-source data are found to be the existing challenges of DT application in HFM.

In a broader sense, this study contributes to the field of engineering, construction and architectural management by providing an overview of how DT has been applied to support management activities throughout the building life cycle. For the HFM practice, a DT-cum-heritage building information modelling (HBIM) framework was developed to illustrate how DT can be integrated with HBIM to facilitate future DT application in HFM. The overall implication of this study is that it reveals the potential of heritage DT in facilitating HFM in the urban development context.

To improve insufficient management by artificial management, especially for traffic accidents that occur at crossroads, the purpose of this paper is to develop a pro-active warning system for crossroads at construction sites. Although prior studies have made efforts to develop warning systems for construction sites, most of them paid attention to the construction process, while the accidents that occur at crossroads were probably overlooked.

By summarizing the main reasons resulting for those accidents occurring at crossroads, a pro-active warning system that could provide six functions for countermeasures was designed. Several approaches relating to computer vision and a prediction algorithm were applied and proposed to realize the setting functions.

One 12-hour video that films a crossroad at a construction site was selected as the original data. The test results show that all designed functions could operate normally, several predicted dangerous situations could be detected and corresponding proper warnings could be given. To validate the applicability of this system, another 36-hour video data were chosen for a performance test, and the findings indicate that all applied algorithms show a significant fitness of the data.

Computer vision algorithms have been widely used in previous studies to address video data or monitoring information; however, few of them have demonstrated the high applicability of identification and classification of the different participants at construction sites. In addition, none of these studies attempted to use a dynamic prediction algorithm to predict risky events, which could provide significant information for relevant active warnings.

In the era of big data, data have become an essential factor of production. In the context of open government data (OGD), encouraging the commercial development of OGD is significant for promoting mass innovation and entrepreneurship. This study aims to explore the configurational impact of data supply, external environment and entrepreneurial foundation on data-driven entrepreneurship.

This research used a fuzzy set qualitative comparative analysis (fsQCA). Fourteen start-ups using OGD were taken as a case sample.

This study produces two paths to achieving high entrepreneurial performance, one is a financing-enhanced entrepreneurial path and the other is a data-driven entrepreneurial path. Besides, four conditions are necessary for high performance of OGD-based entrepreneurship: good data quality, mature legal environment, favorable market environment and abundant big data entrepreneurial talents.

The findings have important practical implications for formulating policies related to promoting the application of government open data and innovation and entrepreneurship in terms of strengthening top-level design, improving the legal environment, developing the data market and cultivating entrepreneurial talents.

Although many studies have been conducted on OGD, studies on the paths to successful entrepreneurship based on OGD are limited. In this study, this issue is investigated from a configurational perspective by using the fsQCA technique.

Environmental sustainability orientation has emerged to drive firms into eco-friendly production. Yet, the consequence of this new strategic thinking on firms’ green innovations, especially small- and medium-scale enterprises (SMEs), remains unresolved. Recognizing that the connection between environmental sustainability orientation and green innovation may not always be direct, the study theorizes that dynamic capability and entrepreneurial orientation may form part of the boundary conditions that strengthen its effect on small enterprises’ green innovation. The study adjoins the dynamic capability theory with the entrepreneurial orientation theory to test this relationship among small businesses within a developing economy. Results from the partial least squares–structural equation modeling (PLS-SEM) suggest that environmental sustainability orientation will result in green innovation when the SME’s dynamic capability can develop a creative reconfiguration of knowledge and new distinctive resources to support this new strategic direction. Similarly, findings from the study suggest that environmental sustainability orientation will translate into better green innovation outcomes when the SME entrepreneurial orientation has a solid attraction to protect the ecosystem and does not perceive green innovation as a risky enterprise.

In view of the difficulty in determining the key parameters d in the Corten-Dolan model, based on the introduction of small loads, damage degrees and stress states to the Corten-Dolan model and the existing improved model, the sequential effects of the adjacent two-stage load were further considered.

Two improved Corten-Dolan models were established on the basis of modifying the parameter d by two different methods, namely, increasing stress ratio coefficient as well as considering the effects of loading sequence and damage degree as independent influencing factors respectively. According to the test data of the welded joints of common materials (standard 45 steel), alloy materials (standard 16Mn steel) and Q235B steel, the validity and feasibility of the above two improved models for fatigue life prediction were verified.

Results show that, compared with the traditional Miner model and the existing Corten-Dolan improved model, the two improved models have higher prediction accuracy in the fatigue life prediction of welding materials whether under two-stage load or multi-stage load.

Because the mathematical expressions of the models are relatively simple and need no multi-layer iterative calculation, it is convenient to predict the fatigue life of welded structure in practical engineering.

This study aims to establish a rigorous model that can pragmatically evaluate the facilities management (FM) performance of hospitals.

Among the applicable performance indicators that were identified from extant literature, a focus group study shortlisted ten key performance indicators (KPIs) in four categories (safety, physical, financial and environmental) and verified their practicality. Using the analytic network process (ANP) method to process the focus group’s responses yielded importance weightings for the KPIs and developed the intended evaluation model. This model was then validated by a case study.

From the empirical data collected, two types of FM performance data and two scenarios of KPI scores were identified. To process these data and scores, a robust calculation method was devised and then proved useful in obtaining an overall score for holistic hospital FM performance. The case study confirmed the appropriateness and validity of the model developed.

Through illustrating how the ANP method could be applied to develop an FM performance evaluation model, the study contributes knowledge to the multi-criteria decision-making domain. Despite the geographical limitation of the model established (i.e. centered around a group of hospitals investigated in Hong Kong), the study can serve as a reference for developing performance evaluation models for other buildings or infrastructures globally.

The model constitutes a practical tool for evaluating the FM performance of hospitals. Using this model on a regular basis will enable performance benchmarking and hence, continuous improvement of FM services.

The ANP model established is the first of its kind tailored for evaluation of hospital FM performance.

This study is intended to investigate the relationship between supervisor bottom-line mentality (BLM) and employee workplace well-being. In addition, this study discusses the mediating roles of perception of organizational politics and job anxiety in this relationship.

Data were gathered from a two-wave survey of 301 full-time employees in southern China. The PROCESS macro in SPSS was applied to test the hypotheses.

Results showed that supervisor BLM was negatively related to employee workplace well-being. Moreover, perceptions of organizational politics and job anxiety played multiple mediating roles in the relationship between supervisor BLM and employee workplace well-being.

Drawing on the conservation of resource (COR) theory and cognitive-affective personality system (CAPS) theory, this study proposes a multiple mediation model to advance our understanding of how supervisor BLM affects employee workplace well-being.