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With the arrival of the big data era, governments have appointed a chief data officer (CDO) to meet the opportunities and challenges brought by big data. The existing research on the CDOs is very limited, and what does exist focuses primarily on what are CDOs do. Little research has explored how CDOs do. To fill this gap, this study employed ambidexterity theory to investigate the ambidexterity of CDOs’ impact on data-driven innovation.

To empirically test the model, a survey study was conducted to empirically test the model. Data were collected from 261 CDOs in government and government employees in big data management centers or bureaus. The collected data were analyzed quantitatively to answer hypotheses using a structural equation model.

The findings suggest that data exploitation and data exploration significantly influence data-driven leadership, culture and value propositions. Data-driven leadership and value propositions significantly impact government performance.

This study is one of the first attempts to investigate how CDOs work, especially when promoting data-driven innovation. In addition, this study extends ambidexterity theory into the issue of the CDO in government.

The residential sector is a principal contributor to global energy consumption, underscoring the critical importance of promoting green housing initiatives to mitigate energy use and environmental degradation. The prevalence of uncertainty in the natural environment, exemplified by phenomena like extreme weather events, highlights the urgent need for adaptive strategies and sustainable practices to mitigate the impact on human communities and ecosystems. Against this backdrop, this paper presents a theoretical framework examining the influence of natural environmental uncertainty on consumers' willingness to purchase green housing.

Through three experiments, this study modeled the mechanism by which the natural environment uncertainty affects consumers' willingness to purchase green housing, and then verified the mediating effect of the threat of ontological security and the moderating effect of the degree of consumers' natural connectedness.

This paper concludes (1) natural environmental uncertainty exerts a significant positive impact on the willingness to purchase green housing, with the threat to ontological security serving as a pivotal mediating variable; (2) the degree of natural connectedness significantly moderates the effect of ontological security threats on the purchasing intent for green housing.

This research contributes to the marketing literature by offering a novel perspective on the impact of natural environmental uncertainty on consumer behavior, augmenting the body of knowledge concerning the determinants of green housing purchase intentions, and provides new ideas for marketers.

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.

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.

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.

This study aims to examine how COVID-related corporate social responsibility (CSR) activities affect future Generation Z employees’ intention to join the hotel industry through experimental designs.

Based on signaling theory, construal level theory and value theory, this study established an integrated research framework to explain the mechanism of CSR communication. The proposed study conducted three online experiments on a total of 463 participants. ANCOVA test and PROCESS macro were performed to analyze the data for main, mediation and moderation effects.

The results of this study indicate that in-kind donation is more efficacious in improving Generation Z’s job pursuit intention, as compared to cause-related marketing (CRM). CSR messages framed in a “how” mindset are favored by Generation Z members who are either unemployed or eager to change their current job. The findings also confirm the effect of brand warmth as a mediator and other-regarding personal value as a moderator.

The present study contributes to the limited knowledge on CSR initiatives by addressing the research gap of future employees and examining CSR as a response to COVID-19. The findings also provide hotel executives actionable implications to plan and communicate future CSR programs, especially during times of crisis.

This study is one of the first studies to address Generation Z employees and to investigate the role of CSR initiatives on future hotel workers.

Online health communities (OHCs) are platforms that help health consumers to communicate with each other and obtain social support for better healthcare outcomes. However, it is usually difficult for community members to efficiently find appropriate peers for social support exchange due to the tremendous volume of users and their generated content. Most of the existing user recommendation systems fail to effectively utilize the rich social information in social media, which can lead to unsatisfactory recommendation performance. The purpose of this study is to propose a novel user recommendation method for OHCs to fill this research gap.

This study proposed a user recommendation method that utilized the adapted matrix factorization (MF) model. The implicit user behavior networks and the user influence relationship (UIR) network were constructed using the various social information found in OHCs, including user-generated content (UGC), user profiles and user interaction records. An experiment was conducted to evaluate the effectiveness of the proposed approach based on a dataset collected from a famous online health community.

The experimental results demonstrated that the proposed method outperformed all baseline models in user recommendation using the collected dataset. The incorporation of social information from OHCs can significantly improve the performance of the proposed recommender system.

This study can help users build valuable social connections efficiently, enhance communication among community members, and potentially contribute to the sustainable prosperity of OHCs.

This study introduces the construction of the UIR network in OHCs by integrating various social information. The conventional MF model is adapted by integrating the constructed UIR network for user recommendation.

This paper aims to explore various tensions related to the diffusion and reception of the New Qing History (NQH) in China, and more specifically, it aims at underlying a recurrent tension within the core of this debate, between a Global and a Nationalist historical narrative.

The author’s focus is to analyze various texts published in China between 2006 and 2018.

The author argues that the intensity of the current debate is partly related on the one hand, to the fact that NQH undermines various aspects of China’s Nationalist teleology and territorial claims and, on the other hand, to the basic difficulty of accepting the coexistence of various historical representations that are risking to weaken contemporary’s justifications of its rising schemes.

The text presents an original reading of some important issues raised by the NQH debate.

The purpose of this paper is to deal with a linear multi-state sliding window coherent system which generalizes the consecutive k-out-of-r-from-n:F system in the multi-state case. The system has n linearly ordered multi-state elements consisting of m parallel independent and identically distributed elements. Every element of the system can have two states: completely working or totally failed. The system fails if the sum of performance rate is lower than the given weight.

The authors proposed to compute the signature, MTTF and Barlow–Proschan index with the help of UGF technique of multi-state SWS which consists of m parallel i.i.d. components in each multi-state window.

In the present study, the authors have evaluated the signature reliability, expected lifetime, cost analysis and Barlow–Proschan index.

In this study, the authors have studied a linear multi-state sliding window system which consists of n ordered multi-state element, and each multi-state element also consists of m parallel windows. The focus of the present paper is to evaluate reliability metrices of the considered system with the help of signature from using the universal generating function.