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The successful implementation of hospital projects (HPs) tends to confront sundry challenges in the planning and construction (P&C) phases due to their complexity and particularity. Employing key performance indicators (KPIs) facilitates the monitoring of HPs to advance their successful delivery. This study aims to comprehensively investigate the KPIs for hospital planning and construction (HPC).

The KPIs for HPC were identified through a systematic review. Then a comprehensive assessment of these KPIs was performed utilizing a meta-analysis method. In this process, basic statistical analysis, subgroup analysis, sensitive analysis and publication bias analysis were performed.

Results indicate that all 27 KPIs identified from the literature are significant for executing HPs in P&C phases. Also, some unconventional performance indicators are crucial for implementing HPs, such as “Project monitoring effectiveness” and “Industry innovation and synergy,” as their high significance is reflected in this study. Despite the fact that the findings of meta-analysis are more trustworthy than those of individual studies, a high heterogeneity still exists in the findings. It highlights the inherent uncertainty in the construction industry. Hence, this study applied subgroup analysis to explore the underlying factors causing the high level of heterogeneity and used sensitive analysis to assess the robustness of the findings.

There is no consensus among the prior studies on KPIs for HPC specifically and their degree of significance. Additionally, few reviews in this field have focused on the reliability of the results. This study comprehensively assesses the KPIs for HPC and explores the variability and robustness of the results, which provides a multi-dimensional perspective for practitioners and the research community to investigate the performance of HPs during the P&C stages.

International construction joint ventures (ICJVs) will fully realize their potential for success and effectively monitor performance when an adequate and suitable performance benchmark is established. However, existing studies fall short of adequately providing a mutually acceptable benchmark for assessing the performance of ICJVs. This study aims to develop an adequate and suitable performance measurement framework for ICJVs.

A twofold structured questionnaire survey, supplemented by semi-structured interviews, was used to collect data from the practitioners of ICJVs hosted in the developing country of Ghana. The data were analyzed by using descriptive statistics, confirmatory factor analysis (CFA) and a hybrid-fuzzy logic approach.

A list of 30 performance indicators (PIs), defined by project performance, perceived satisfaction, company/partner performance, socio-environmental performance and performance of ICJV management, was validated and proved to be significant. Only 22 out of the 30 PIs, focusing on project efficiency, societal improvement and organizational goals are realized by the ICJV practitioners. Further, suitable determinants and viable quantitative ranges for measuring each PI are established to prevent different interpretations of the meanings of PIs and objectively express the level of success in quantitative terms. The results call for further investigation of the convergence between the practice of and research into some PIs (e.g. socio-environmental performance) and a range of different performance levels (PLs) in a more scientific manner.

This study not only advances the knowledge base and practice of performance measurement in ICJVs but could also assist stakeholders and decision-makers to assess, compare and monitor the performance of different ICJV projects on common grounds objectively.

This study not only comprehensively assessed PIs – what to measure – but also systematically determined suitable determinants – how to measure – for each PI.

In the last decades, various building information modeling–life cycle assessment (BIM-LCA) integration approaches have been developed to assess the environmental impact of the built asset. However, there is a lack of consensus on the optimal BIM-LCA integration approach that provides the most accurate and efficient assessment outcomes. To compare and determine their accuracy and efficiency, this study aimed to investigate four typical BIM-LCA integration solutions, namely, conventional, parametric modeling, plug-in and industry foundation classes (IFC)-based integration.

The four integration approaches were developed and applied using the same building project. A quantitative technique for evaluating the accuracy and efficiency of BIM-LCA integration solutions was used. Four indicators for assessing the performance of BIM-LCA integration were (1) validity of LCA results, (2) accuracy of bill-of-quantity (BOQ) extraction, (3) time for developing life cycle inventories (i.e. developing time) and (4) time for calculating LCA results (i.e. calculation time).

The results show that the plug-in-based approach outperforms others in developing and calculation time, while the conventional one could derive the most accuracy in BOQ extraction and result validity. The parametric modeling approach outperforms the IFC-based method regarding BOQ extraction, developing time and calculation time. Despite this, the IFC-based approach produces LCA outcomes with approximately 1% error, proving its validity.

This paper forms one of the first studies that employ a quantitative and objective method to determine the performance of four typical BIM-LCA integration solutions and reveal the trade-offs between the accuracy and efficiency of the integration approaches. The findings provide practical references for LCA practitioners to select appropriate BIM-LCA integration approaches for evaluating the environmental impact of the built asset during the design phase.

There are many safety hazards in construction workplaces, and inattention to the hazards is the main reason why construction workers failed to identify the hazards. Reasonably allocating attention during hazard identification is critical for construction workers’ safety. However, adverse working environments in job sites may undermine workers’ attention. Previous studies failed to investigate the impacts of environmental factors on attention allocation, which hinders taking appropriate measures to eliminate safety incidents when encountering adverse working environments. This study aims to examine the effects of workplace noise and heat exposure on workers’ attention allocation during construction hazard identification to fill the research gap.

This study applied an experimental study where a within-subject experiment was designed. Fifteen construction workers were invited to perform hazard identification tasks in panoramic virtual reality. They were exposed to three noise levels (60, 85 and 100 dBA) in four thermal conditions (26°C, 50% RH; 33°C, 50% RH; 30°C, 70% RH; 33°C, 70% RH). Their eye movements were recorded to indicate their attention allocation under each condition.

The results show that noise exposure reduced workers’ attention to hazardous areas and the impacts increased with the noise level. Heat exposure also reduced the attention, but it did not increase with the heat stress but with subjects’ thermal discomfort. The attention was impacted more by noise than heat exposure. Noise exposure in the hot climate should be more noteworthy because lower levels of noise would lead to significant changes. These visual characteristics led to poorer identification accuracy.

This study could extend the understanding of the relationship between adverse environmental factors and construction safety. Understanding the intrinsic reasons for workers' failed identification may also provide insights for the industry to enhance construction safety under adverse environments.

In recent decades, interest in digital transformation (DX) within the architecture, engineering, and construction (AEC) industry has significantly increased. Despite the existence of several literature reviews on DX research, there remains a notable lack of systematic quantitative and visual investigations into the structure and evolution of this field. This study aims to address this gap by uncovering the current state, key topics, keywords, and emerging areas in DX research specific to the AEC sector.

Employing a holistic review approach, this study undertook a thorough and systematic analysis of the literature concerning DX in the AEC industry. Utilizing a bibliometric analysis, 3,656 papers were retrieved from the Web of Science spanning the years 1990–2023. A scientometric analysis was then applied to these publications to discern patterns in publication years, geographical distribution, journals, authors, citations, and keywords.

The findings identify China, the USA, and England as the leading contributors in the field of DX in AEC sector. Prominent keywords include “building information modeling”, “design”, “system”, “framework”, “adoption”, “model”, “safety”, “internet of things”, and “innovation”. Emerging areas of interest are “deep learning”, “embodied energy”, and “machine learning”. A cluster analysis of keywords reveals key research themes such as “deep learning”, “smart buildings”, “virtual reality”, “augmented reality”, “smart contracts”, “sustainable development”, “building information modeling”, “big data”, and “3D printing”.

This study is among the earliest to provide a comprehensive scientometric mapping of the DX field. The findings presented here have significant implications for both industry practitioners and the scientific community, offering a thorough overview of the current state, prominent keywords, topics, and emerging areas within DX in the AEC industry. Additionally, this research serves as an invaluable reference and guideline for scholars interested in this subject.

The success of public–private partnership (PPP) projects is highly correlated to the successful management of risks encountered during the operation phase. PPP projects are especially exposed to risk due to the long operation period over which revenues need to be generated to recoup substantial initial investment and operational running costs. Despite the critical impact of risk exposure, limited research has been specifically undertaken on the matter of operational risk management. This study seeks to address this oversight by identifying and evaluating operational risk management strategies for PPPs.

Vulnerability theory is the theoretical lens used, with context drawn from Chinese PPP projects. Based on the data collected from expert interviews and questionnaires, 28 operational risk management strategies are identified. A fuzzy synthetic method is employed to analyze the effectiveness of the 28 strategies.

The findings reveal that providing an exit mechanism clause into the contract, establishing a comprehensive performance evaluation mechanism and developing a clear compensation mechanism are the top three effective strategies. This study also reveals that risk mitigation approaches that reduce vulnerability prove more effective than attempts to reduce external threats. Specifically, strategies aimed at managing contract, political, technical and financial risk are the most effective.

The findings of this study extend current knowledge regarding the risk management of PPP projects. They also offer a reference by which practitioners may select effective operational risk management pathways and thereby, galvanize the sustainable development of PPPs.

In this study, we examine the influence of a firm’s environmental, social, and governance (ESG) performance on analysts’ stock recommendations and earnings forecast accuracy in the Chinese context.

We take a textual analysis approach to analyst research reports issued between 2010 and 2019, and differentiate between two distinct analyst categories: “sustainability analysts,” which refer to those more inclined to incorporate ESG information into their analyses, and “other analysts.”

Our evidence indicates that sustainability analysts tend to be significantly more likely than others to provide positive stock recommendations and demonstrate enhanced accuracy in forecasting earnings for companies with superior ESG performance. Our additional analyses reveal that this finding is particularly prominent for analysts who graduated from institutions emphasizing the protection of the environment, those recognized as star analysts, those affiliated with ESG-oriented brokerages, and forecasts made by analysts in the later part of the sample period. Our findings further indicate that sustainability analysts exhibit a more pronounced negative response when confronted with a negative ESG event.

In general, the evidence from this study reveals the interplay between ESG factors and analyst behavior, offering valuable implications for both financial analysts and sustainable investment strategies.

This study aims to investigate the different effects of physical and social servicescapes on brand love for internet-famous restaurants, focusing on their pathways and strengths of influence.

Structural equation modeling was applied to analyze data from 387 online questionnaires in China.

Results indicate that social servicescape directly influences brand love for internet-famous restaurants, while physical servicescape does not. The effect of physical servicescape on brand love for internet-famous restaurants is mediated by perceived coolness and perceived enjoyment, whereas social servicescape’s influence is mediated solely by perceived enjoyment. Overall, physical servicescape has a stronger impact on brand love for internet-famous restaurants compared to social servicescape.

The findings help internet-famous restaurants create effective physical and social servicescapes to enhance brand love, underscoring that physical servicescape is more crucial than social servicescape for cultivating this love.

This study contributes to the literature by analyzing the heterogeneous pathways and strengths of physical and social servicescapes influencing brand love for internet-famous restaurants, while highlighting the mediating role of perceived coolness and expanding the application scope of cognitive appraisal theory.

This study examines why long-term relationships between expert users and smart voice assistants (SVAs) develop. It postulates that the five dimensions of experience (i.e. sensory, affective, intellectual, behavioural and relational) generate feelings of love for SVAs. The formation of love is examined considering three components: passion, intimacy and commitment. These feelings encourage users to continue employing and to generate long-term relationships with SVAs.

Data from a survey of 403 USA expert users of SVAs provide the input for structural equation modelling.

The results show that three dimensions of experience influence users’ passion towards SVAs: affective, intellectual and behavioural. Moreover, passion can convert the effect of users’ experiences into intimacy and commitment. Finally, intimacy and commitment increase users’ intentions to continue using SVAs.

The findings obtained make three original contributions. First, this study is the first to analyse expert users of SVAs and the post-technology adoption stage. Therefore, it introduces a new case of relational marketing in smart technologies. Second, this study contributes by applying a new theoretical perspective that evaluates the importance of users’ experiences with SVAs. Third, it takes an interpersonal approach to explore user-SVA interactions, revealing that users can develop human-like love feelings for SVAs.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2022-0570

This study aims to examine the influence of reward personalization on financing outcomes in the Industry 5.0 era, where reward-based crowdfunding meets the personalized needs of individuals.

The study utilizes a corpus of 218,822 crowdfunding projects and 1,276,786 reward options on Kickstarter to investigate the effect of reward personalization on investors’ willingness to participate in crowdfunding. The research draws on expectancy theory and employs quantitative and qualitative approaches to measure reward personalization. Quantitatively, the number of reward options is calculated by frequency; whereas text-mining techniques are implemented qualitatively to extract novelty, which serves as a proxy for innovation.

Findings indicate that reward personalization has an inverted U-shaped effect on investors’ willingness to participate, with investors in life-related projects having a stronger need for reward personalization than those interested in art-related projects. The pledge goal and reward text readability have an inverted U-shaped moderating effect on reward personalization from the perspective of reward expectations and reward instrumentality.

This study refines the application of expectancy theory to online financing, providing theoretical insight and practical guidance for crowdfunding platforms and financiers seeking to promote sustainable development through personalized innovation.