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This study aims to investigate the critical success factors of digital transformation in the construction industry and identify whether the respondents' profiles influence their perceptions of critical success factors for digital transformation.

To achieve the objectives, a literature review was first conducted based on technology-organization-environment (TOE) framework. Then a questionnaire survey was carried out. A total of 86 people were surveyed in this study, mainly from the construction industry. At the level of data processing, SPSS was used for analysis. Among the main tests used were the Shapiro–Wilk test, reliability analysis, mean rank analysis, Kruskal–Wallis test and Mann–Whitney U test.

The study identified 15 critical success factors of digital transformation and found the three most important factors of digital transformation. Furthermore, respondents with different years of experience, enterprises with different sizes and different years made no difference in the perception of factors. Respondents' different occupations and types of enterprises created a bias in the perception of factors for digital transformation.

Firstly, the small sample size of the questionnaire limits the reference value of data analysis for certain groups. In addition, this study focuses broadly on construction enterprises without specifically examining different types of enterprises, thus lacking depth in its findings.

This study establishes a connection between TOE theory and the construction industry through an extensive literature review, identifying relevant factors and providing a reference for future research.

The study's results would enrich the research on digital transformation in the construction industry and provide a reference for the digital transformation of construction enterprises.

This study aims to investigate how the public formed their need for information in the early stage of the COVID-19 outbreak. Exploring the formation of information needs can reveal why the public's information needs differ and provide insights on targeted information service during health crises at an essential level.

The data were collected through semi-structured interviews with 46 participants and analyzed using the grounded theory approach. Concepts, sub-categories and categories were developed, and a model was built to examine how the public formed the need for information about the pandemic.

The authors found that participants were stimulated by information asymmetry, severity of the pandemic and regulations to control the pandemic, which triggered their perceptions of information credibility, threat and social approval. After the participants perceived that there was a threat, it activated their basic needs and they actively formed the need for information based on cognitive activities. Moreover, information delivered by different senders resulted in a passive need for information. Participants' individual traits also influenced their perceptions after being stimulated.

Long-term follow-up research is needed to help researchers identify more detailed perspectives and do comparative studies. Besides, this study conducted interviews through WeChat voice calls and telephone calls, and might be limited compared with face-to-face interviews.

The findings of this study provide theoretical contributions to the information needs research and practical implications for information services and public health management.

There is little systematic research on how the public formed information needs in the early stage of the COVID-19 outbreak.

The paper aims to assess the impact of weather-induced shocks on household food consumption in the rural Vietnamese Mekong Delta (VMD) through the case of Long An province and evaluate the effectiveness of widely used coping strategies in mitigating weather-related shock impacts.

The system generalized method of moments (GMM) estimation method is applied to explore information on shock incidence, recovery, and time occurrences. The paper uses a sample of 272 repeated farming households from 5-wave survey data from 2008 to 2016, resulting in 1,360 observations.

The paper confirms the robust negative effect of a natural shock on food consumption. Additionally, using savings proves to be the most potent measure to smooth food consumption. Other favorable coping strategies are “getting assistance from relatives, friends” or “getting assistance from the Government, and non-government organizations (NGOs).” The mitigating effects are also traced in the current analysis.

Using caution when generalizing the results from Long An to the whole VMD is reasonable. The rather limited observations of coping strategies do not allow the authors to analyze any specific strategy.

The proposed approach employs the GMM technique and controls for endogenous coping strategies and thus provides accurate estimates of the effects of weather-related shocks and the mitigation effectiveness in the rural VMD.

Drawing motivation from the greater exposure to uncertainty and condition changes that affect large projects due to their long lifecycle, this paper aims to investigate how the time factor affects the use of governance mechanisms to pursue the success of these projects.

To pursue its aim, the article applies the dichotomization between the hard and soft mechanisms of project governance to the analysis of a historical case study, whose findings are organized over the short, medium and long periods. The case selected is referred to the peculiar water system, made up of tunnels named “bottini,” that was in use in Siena (Italy) as the old aqueduct. Specifically, the study focuses on the project of expansion of this water system that was realized during the 14th century for the construction of the “Bottino maestro di Fontegaia.”

This article highlights the different relevance that, during the lifecycle of large projects, is assumed by hard and soft governance mechanisms, with the former having main relevance in a short and medium period, and the latter usually emerging in the medium period and, subsequently, playing a growing role for the project success in the long period.

The article contributes to the literature on large projects by providing novel insights about how the time factor impacts the governance of these projects. Furthermore, the case study, with its unique history, highlights the relevance of combining effectively the hard and the soft dimensions of project governance to pursue success.

Physical environments, especially the sound environments of ILSs on a university campus, have become increasingly important in satisfying the diverse needs of students. Poor sound environments are widely acknowledged to lead to inefficient and underutilised spaces and to negatively influence students' learning outcomes. This study proposes two hypotheses to explore whether students' sound environment perceptions are related to their individual characteristics and whether students' preferences for the type of ILS are related to their sound environment sensitivities.

An investigation through a questionnaire survey has been conducted on both students' individual characteristics affecting their sound environment perceptions in informal learning spaces (ILSs) of a university campus and their sensitivities to the sound environments in ILSs affecting their preferences for the type of ILSs.

The research findings indicate that students' sound environment perceptions are associated with some of their individual characteristics. In addition, the results show that students' sound environment sensitivities affect their preferences for the type of ILS they occupy.

This study could help architects and managers of university learning spaces to provide better sound environments for students, thereby improving their learning outcomes. The article contributes valuable insights into the correlation between students' individual characteristics, sound environment perceptions and preferences for ILSs. The research findings add to the existing knowledge in this field and offer practical implications for enhancing design and management of university learning environments.

Due to consumption changes in the post-pandemic era, the production safety of agricultural products is affecting global consumers. This paper constructs an evaluation index of the agricultural Internet of things (IOT) traceability system and evaluates it using the dynamic hesitant-fuzzy linguistic term sets (HFLTS)-based DEMATEL method to improve agricultural supply-chain links and improve production quality.

The agricultural IOT traceability index system is constructed using the literature and expert interviews; it comprises 6 first-level indices and 20 second-level indices. The agricultural IOT traceability system is evaluated using the dynamic HFLTS-DEMATEL method.

Producers' awareness of agricultural-production safety (A11) has the most significant impact on production and processing links, while warehouse location and storage capacity (A31) have the largest impact on the circulation link. Inspection authenticity and transparency and quarantine information (A41) have the largest impact on the detection-consumption link. The extent to which the traceability-platform construction is complete (A62) has the largest impact on technical support.

The present paper may be limited to the era of post-pandemic, and it is hard to consider all the indices. Further research can broaden the research context and establish a more comprehensive index system.

The index system constructed in this study will surely help relevant regulatory authorities in China to promote the construction of agricultural IOT traceability system and establish a unified standard, so as to provide a basis for future developers to enter the field. Accordingly, it also can help every subject to identify the key indices of each process in the agricultural-product supply chain and guide relevant departments to conduct targeted information tracking and management. The consumers could also understand the standards of traceable agricultural products and effectively protect their own rights and interests.

The existing literature does not provide an objective, unified standard for measuring a decentralized traceability system or identifying key processes. This study therefore proposes a new evaluation index system and uses a dynamic evaluation method to determine the importance of key indices. This study identifies the most important indices in each process, making it possible to discover, improve, and enhance the quality of agricultural products at a practical level.

This study aims to identify the driving factors that influence blockchain technology adoption in the context of a supply chain (SC), considering three dimensions: technology, transactions and collaboration.

An integrative systematic literature review of previous studies was conducted. Using three main dimensions: technology, transactions and SC collaboration, supported by the unified theory of acceptance and use of technology, transaction cost economics (TCE) and concepts of SC collaboration, the authors categorized factors that contributed to blockchain technology in SC in the extant literature and proposed a theoretical model that covers these three dimensions.

The findings reveal that the information sharing category – related to the SC collaboration dimension – is the category with the greatest number of motivating factors for blockchain adoption in the SC context, followed by performance expectancy and behavioral uncertainty.

The review considers papers published until 2021 obtained from a specific database.

This study focuses on filling the research gap concerning technology adoption as it considers the interconnection formed by two organizations, interorganizational transactions and SC collaboration, using complementary theories to explain the phenomenon.

This study aims to establish an assessment model to measure big data readiness in the public sector, specifically targeting local governments at the provincial and city/regency levels. Additionally, the study aims to gain valuable insights into the readiness of selected local governments in Indonesia by using the established assessment model.

This study uses a mixed-method approach, using focus group discussions (FGDs), surveys and exploratory factor analysis (EFA) to establish the assessment model. The FGDs involve gathering perspectives on readiness variables from experts in academia, government and practice, whereas the survey collects data from a sample of selected local governments using a questionnaire developed based on the variables obtained in FGDs. The EFA is used on survey data to condense the variables into a smaller set of dimensions or factors. Ultimately, the assessment model is applied to evaluate the level of big data readiness among the selected Indonesian local governments.

FGDs identify 32 essential variables for evaluating the readiness of local governments to adopt big data. Subsequently, EFA reduces this number by five and organizes the remaining variables into four factors: big data strategy, policy and collaboration, infrastructure and human resources and data collection and utilization. The application of the assessment model reveals that the overall readiness for big data in the selected local governments is primarily moderate, with those in the Java cluster displaying higher readiness. In addition, the data collection and utilization factor achieves the highest score among the four factors.

This study offers an assessment model for evaluating big data readiness within local governments by combining perspectives from big data experts in academia, government and practice.

This study aims to explore the critical application areas of radio frequency identification (RFID) technology for sustainable buildings.

The quantitative research approach was adopted through a structured questionnaire administered to relevant stakeholders of construction projects. The data collected were analysed with the exploratory factor analysis, relative importance index (RII) and fuzzy synthetic evaluation (FSE).

The study’s results have categorised the crucial areas of application where construction industry stakeholders should focus their attention. These areas are divided into four categories: management technologies, production technologies, sensing technologies and monitoring technologies. The findings from the FSE indicate that monitoring technologies represent the most significant category, whereas management technologies rank as the least significant. Moreover, the RII analysis highlights that tools management stands out as the most important application of RFID, while dispute resolution emerges as the least significant RFID application.

The study establishes the core areas of RFID application and their benefits to sustainable buildings. Consequently, it helps stakeholders (consultants, clients and contractors) to examine the RFID application areas and make informed decision on sustainable construction. Furthermore, it provides systematic proof that can aid the implementation of RFID in developing countries.

The study provides an insight into the possible application areas and benefits of RFID technology in the construction industry of developing countries. It also developed a conceptual frame for the critical application areas of RFID technology in the construction industry of developing countries.

Although knowledge based on business intelligence (BI) is crucial, few studies have explored the core of BI knowledge; this study explores this topic.

The authors collected 1,306 articles and 54,020 references from the Web of Science (WoS) database and performed co-citation analysis to explore the core knowledge of BI; 52 highly cited articles were identified. The authors also performed factor and cluster analyses to organize this core knowledge and compared the results of these analyses.

The factor analysis based on the co-citation matrix revealed seven key factors of the core knowledge of BI: big data analytics, BI benefits and success, organizational capabilities and performance, information technology (IT) acceptance and measurement, information and business analytics, social media text analytics, and the development of BI. The cluster analysis revealed six categories: IT acceptance and measurement, BI success and measurement, organizational capabilities and performance, big data-enabled business value, social media text analytics, and BI system (BIS) and analytics. These results suggest that numerous research topics related to big data are emerging.

The core knowledge of BI revealed in this study can help researchers understand BI, save time, and explore new problems. The study has three limitations that researchers should consider: the time lag of co-citation analysis, the difference between two analytical methods, and the changing nature of research over time. Researchers should consider these limitations in future studies.

This study systematically explores the extent to which scholars of business have researched and understand BI. To the best of the authors’ knowledge, this is one of the first studies to outline the core knowledge of BI and identify emerging opportunities for research in the field.