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Existing hospital building operations involve numerous information technology applications and complex building systems; therefore, an intelligent facility management (FM) platform is required to ensure their continuous operation. To address the persistent issues of data silos, inefficient data interoperability, and workflow incoordination that have been identified in the current body of FM practice and literature, the present study develops a data-asset (DA) centric FM platform specifically designed for hospital buildings.

This study proposes a semi-customized approach to develop the DA-centric FM platform for hospital buildings. To elucidate the precise function requirements of the hospital FM platform, focus group interviews are employed. By seamlessly integrating the as-built BIM model, IoT sensor data and FM workflow data, the BIM-based DA model with a data transfer mechanism is developed. The development of the FM platform with function modules in a case study is guided by a five-tier architecture and the coordination theory (CT). The case study provides an in-depth introduction to the applications of DA management, space management and maintenance management modules.

The capabilities of the developed DA-centric hospital FM platform are validated through the case application and user satisfaction survey, which assess data quality, automation level, operation efficiency, flexibility and functionality. For hospital FM activities, this DA-centric FM platform realizes data integration and seamless transformation, optimizes workflow coordination and enhances operation performance.

The initial scholarly contribution is the establishment of the BIM-based DA model, which serves as the data middle platform for continuous data integration, transmission and sharing within the FM platform. Subsequently, under the guidance of the CT, the business process of function modules is designed, improving the intra-module and inter-module workflow coordination. The developed DA-centric FM system along with its performance benchmarking application, assists facility managers and decision-makers in implementing smart operations for hospital buildings and achieving the management goals of safety, efficiency, energy savings and convenience.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.

In recent years, closed social networking sites (SNSs) have become popular advertising media. Marketer-generated advertisements (MGAs) and user-generated advertisements (UGAs) are the two pillars of advertising businesses. The objective of this research is to investigate and compare how these ad types (i.e. MGA versus UGA) affect advertising effectiveness in closed SNSs.

The authors conducted a scenario-based experiment of 403 WeChat users in China and used partial least squares structural equation modeling (PLS-SEM) to examine the research model.

The study results indicate that UGAs perform better than MGAs in enhancing consumers' perceived informativeness, credibility and entertainment, while MGAs are more likely to make consumers feel irritated than UGAs in closed SNSs. Moreover, consumers' perceived informativeness, credibility and entertainment positively influence advertising effectiveness, whereas perceived irritation negatively affects it.

This study reveals consumers' psychological response mechanisms to MGA and UGA and sheds light on their differential effectiveness by extending the stimuli-organism-response model to the context of closed SNSs.

Psychological ownership has been a topic of intense debate for several decades, especially in the digital era. In addition, as part of the digital public domain, virtual communities shape our digital lives. Unfortunately, few studies have examined the communication process in virtual communities from the perspective of psychological ownership. Moreover, information and organization are key aspects of virtual communities. This research aimed to explore the impact of psychological ownership on communication satisfaction from these two perspectives.

I collected 471 responses using a questionnaire. In terms of empirical methods, I developed a structural equation model (SEM) to examine the relationship between psychological ownership and communication satisfaction as well as the mechanism underlying this relationship – namely, information behavior. Specifically, I first examined the relationship between psychological ownership and information behavior. I then developed a comprehensive framework for the double-edged impact of psychological ownership in virtual communities on communication satisfaction.

I found that psychological ownership has a double-edged effect on communication satisfaction based on two types of information behavior in virtual communities. Specifically, organization-based psychological ownership (OPO) positively influences communication satisfaction through information exchange. In contrast, information-based psychological ownership (IPO) negatively impacts communication satisfaction through information-hiding.

The findings of this research demonstrate that psychological ownership has a double-edged effect on communication satisfaction. First, the findings of this study reveal the downsides of psychological ownership, which are not consistent with its beneficial role. Second, the negative effect of psychological ownership with regard to communication in virtual communities also helps explain communication failure in virtual communities. Finally, despite the downsides of psychological ownership in the context of a virtual community revealed by this study, this factor has an overall beneficial effect.

This paper aims to examine the factors contributing to the helpfulness of online hotel reviews and to measure the impact of manager response on the helpfulness of online hotel reviews.

This investigation used a linear regression model that drew upon 56,284 consumer reviews and 10,797 manager responses from 1,405 hotels on TripAdvisor.com for analysis.

The helpfulness of online hotel reviews is negatively affected by rating and number of sentences in a review, but positively affected by manager response and reviewer experience in terms of reviewer status, years of membership, and number of cities visited. Manager response moderates the influence of reviewer experience on the helpfulness of online hotel reviews.

Using the data from hotels in five major cities in Texas, the results may not be necessarily generalized to other markets, but the important role that manager response plays in online reviews is assessed with big data analysis.

The results suggest hospitality managers should strategically identify opinion leaders among reviewers and proactively influence the helpfulness of the reviews by providing manager response. Additionally, this study makes recommendations to webmasters of social media platforms in terms of advancing the algorithm of featuring the most helpful online reviews.

This study is at the frontier of research to explain how hotel managers can proactively identify opinion leaders among consumers and use manager response to influence the helpfulness of consumer reviews. Additionally, the results also provide new insights to the influence of reviewer demographic background on the helpfulness of online reviews. Finally, this study analyzed a large data set on a scale that was not available in traditional guest survey studies, responding to the call for big data applications in the hospitality industry.

The purposes of this study are to synthesize the current research findings reported in major hospitality and tourism journals and to discuss the knowledge gaps where additional research endeavors are needed.

A systematic review approach was adopted to analyze 67 research articles about online reviews that were published between January 2000 and July 2015 in seven major hospitality and tourism journals.

This study presents a thematic framework of online review research, which was advanced by integrating the interactions among quantitative evaluation features, verbal evaluation features, reputation features and social features of online reviews with important outcomes of consumer decision-making and business performance. The thematic framework helps researchers identify the areas in extant hospitality literature of online reviews and point out possible directions for future studies.

The systematic review approach has a qualitative nature, where relevant literature was interpreted based on the authors’ domain knowledge and expertise.

Practitioners can gain a comprehensive understanding of the dynamic relationships among the key influential factors in online reviews, as presented in the thematic framework of online review research. Accordingly, managers will be able to develop effective strategies to leverage the positive impacts of online reviews to the business outcomes.

This systematic review synthesizes the findings reported in most recent publications (January 2000-July 2015; also including “Online First” articles) in seven major hospitality and tourism journals and develops an integrated research framework, anchoring on four meta-research questions and showing the dynamic relationships among the key players/factors/themes in online review research. This framework provides a visual diagram to practitioners for a better understanding of the relevant literature and assists researchers in developing new research questions for future studies.

The purpose of this study is to document how restaurant’s business-to-consumer communication strategies evolved on Facebook over time and how consumers’ reactions to a variety of Facebook messages changed over time.

This study analyzed 2,463 Facebook messages posted by seven quick-service restaurant chains and three casual-dining restaurant chains in the fourth quarter of 2010, 2012 and 2014. ANOVA and post hoc t-test were used to compare the differences among four media types (photo, status update, video and hyperlink) in terms of their usage by companies and Facebook users’ reactions to these messages (measured by number of “Likes”, number of comments and number of shares).

Over the three periods of time under observation, there is a substantial decrease of status updates by restaurants and a dramatic increase of photo updates. Photo remained as the most “popular” media type, receiving most “Likes”, comments and shares from consumers. Video was not considered “popular” in 2010 but experienced a slight increase in usage and slowly emerged in 2012 and 2014 as another “popular” media, which no longer had statistical difference with photo in number of comments and shares.

Some limitations include an under representable sample and its longitudinal design, but the findings provide additional insight to current literature in social media.

A series of suggestions were advanced from the findings to help hospitality managers better engage Facebook users.

This is probably the first time-series or longitudinal-like analysis in social media research and yields meaningful findings.

The purpose of this study is that on the basis of the competitive edge theory, source mechanism and evaluation approaches of industrial cluster competitiveness, combined with international trends in the automobile industry and the features of Chinese automobile industrial cluster development, an evaluation index system about cluster competitiveness of auto industry is built with comprehensive consideration of factors such as cluster development environment, external scale effect and internal competitiveness from the perspective of value chain of automobile industry.

An evaluation index system for automobile industrial cluster competitiveness was realized by integrating current strengths and future growth capacities with multidimensional, dynamic and comprehensive characteristics, which included 3 second-level, 10 third-level and 16 fourth-level indices. In the light of evaluation methods, a group intelligence optimization algorithm – (cuckoo search) – and traditional methods of complex decision-making system – analytic hierarchy process (AHP) – were combined to propose the cuckoo-AHP evaluation method. It was applied for the calculation and optimization of weight values in an automobile industrial cluster competitiveness evaluation index for the purpose of obtaining better scientific and more reliable results.

The research might further enrich the evaluation theory of automobile industrial cluster competitiveness and also can be useful for showing how traditional evaluation methods can be combined with intelligent algorithms to carry out better automobile industrial cluster competitiveness evaluations. In addition, studies of channels for kick-starting Chinese auto industrial cluster competitiveness are expected to provide references for how to enhance the cluster competitiveness of the Chinese automobile industry.

Changsha and Liuzhou, the Guangxi automobile industrial clusters as the two empirical analysis objects selected for this paper, are geographically adjacent to each other. The automobile industries of the two cities are local pillar industries with the strong support of the local government. Both clusters have their own advantages and weak points with different characteristics of cluster development, and they enjoy a representative significance amongst China’s numerous auto industrial clusters that are taking shape. Comparative analysis of both clusters serves as a good reference for the objective evaluation of the competitiveness of Chinese automobile clusters in terms of their real and practical developments and in respect of the success of reasonable scientific and industrial cluster policies.

Multidimensional, dynamic, integrated evaluation index systems are constructed around automobile industrial cluster competitiveness, which has taken into account developments in current strengths and future growth capacity. The cuckoo-AHP evaluation method has been formed by combining the traditional decision-making method known as AHP with a new meta-heuristic optimization algorithm called “cuckoo search”. Both have been used in evaluations of automobile industrial cluster competitiveness in Liuzhou and Changsha, which will be beneficial for enriching automobile industrial cluster competitiveness evaluation theory and new evaluation methods that will enable better evaluations of automobile industrial cluster competitiveness.

Theory is a kind of condensed human knowledge. This paper is to examine the mechanism of interdisciplinary diffusion of theoretical knowledge by tracing the diffusion of a representative theory, the Technology Acceptance Model (TAM).

Based on the full-scale dataset of Web of Science (WoS), the citations of Davis's original work about TAM were analysed and the interdisciplinary diffusion paths of TAM were delineated, a supervised machine learning method was used to extract theory incidents, and a content analysis was used to categorize the patterns of theory evolution.

It is found that the diffusion of a theory is intertwined with its evolution. In the process, the role that a participating discipline play is related to its knowledge distance from the original disciplines of TAM. With the distance increases, the capacity to support theory development and innovation weakens, while that to assume analytical tools for practical problems increases. During the diffusion, a theory evolves into new extensions in four theoretical construction patterns, elaboration, proliferation, competition and integration.

The study does not only deepen the understanding of the trajectory of a theory but also enriches the research of knowledge diffusion and innovation.

The study elaborates the relationship between theory diffusion and theory development, reveals the roles of the participating disciplines played in theory diffusion and vice versa, interprets four patterns of theory evolution and uses text mining technique to extract theory incidents, which makes up for the shortcomings of citation analysis and content analysis used in previous studies.

To study the consumption pattern, attitude and knowledge of the general population about dietary supplements (DS) in the United Arab Emirates (UAE).

A community-based cross-sectional study was conducted to collect data on knowledge, attitude and consumption pattern related to the use of DS. Participants aged = 18 years were asked to complete a self-administered online questionnaire that included demographic characteristics, health and lifestyle information, consumption patterns, attitudes and knowledge levels regarding the use of DS.

A total of 207 individuals participated in the study, and 117 (56.5%) participants reported using DS products as influenced by the pandemic of coronavirus disease 2019 (COVID-19), of which 63 (53.8%) participants had been using DS for more than one month but less than one year. The majority of the participants was females (64.7%), non-UAE nationals (60.9%) and employed (51.7%). Multivitamins (77.8%) were the most commonly used DS. Use of DS was more prevalent among older participants (n = 78 (61.9%), p = 0.006), non-UAE nationals (n = 79 (62.7%), p = 0.025) and employed (n = 69 (64.5%), p = 0.023). Improving general health (76.1%) and immune booster (47%) were the most frequently identified reasons for using DS, which is relatable to the COVID-19 pandemic. The majority of study participants (72.12%) reported knowing relatively little about the use of DS. About 154 participants (74.4%) did not know that DS products do not treat diseases.

Further studies with a larger sample size need to be conducted to examine the association between gender or chronic disease and the consumption and type of DS products used to fill the gap in the literature and overcome the limitations identified in this study.

This study highlights the need for community education programs and strategies that can raise awareness of the health benefits and risks of using DS. Further studies with a larger sample size need to be conducted to examine the association between gender or chronic disease and the consumption and type of DS products used to fill the gap in the literature and overcome the limitations identified in this study.