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During the COVID-19 pandemic, lockdowns were implemented to achieve two goals: (1) to reduce the number of COVID-19 cases and (2) to reduce the number of COVID-19 deaths. In this paper, the authors aim to look at empirical evidence on how effectively lockdowns achieved these goals among small island developing states (SIDS) and for one specific SIDS economy, Guam.

The authors reviewed existing studies to form two hypotheses: that lockdowns reduced cases, and that lockdowns reduced deaths. Defining a lockdown as a positive value for Oxford COVID-19 government response tracker, OxCGRT's stringency index, the authors tested the above hypotheses on 185 countries, 27 SIDS economies and Guam using correlation and regression analyses, and using different measures of the strictness, duration and timing of the lockdown.

The authors found no evidence to support the hypothesis that lockdowns reduced the number of cases based on data for all 185 countries and 27 SIDS economies. While the authors found evidence to support the hypothesis in the case of Guam, the result required an unrealistically and implausibly long time lag of 365 days. As to the second hypothesis that lockdowns reduced the number of deaths, the authors found no evidence to support it for 185 countries, 27 SIDS economies as well as Guam.

From the review of the existing literature, the authors are the first to conduct this type of study among SIDS economies as a group and on Guam.

We examine the association between conditional conservatism in initial public offering (IPO) underpricing and post-issue stock market survival in the U.S.

We adopt an archival approach by collecting data for 1,761 U.S. IPO issuers for the period 1990–2017. Regression analyses are conducted to evaluate the association between conditional conservatism in initial public offerings with underpricing and post-issue stock market survival. We identify firms that went public in the period 1990–2012. These firms are then followed for five years after the IPO to assess their stock market survival.

We find that pre-issue conditional conservatism is significantly associated with less IPO underpricing. We also detect that IPO firms with higher levels of conditional conservative reporting are more likely to survive in the post-IPO stock market in the three-, four-, and five-year periods after the IPO. Our main findings are robust after controlling for other factors in our models, such as IPO cycles, venture capitalists, research and development investment, and pre-IPO accounting performance.

We extend research by demonstrating that conditional conservative reporting practices help firms reduce their indirect costs of raising their initial public capital. Additionally, our research introduces new evidence on the association between pre-IPO conditional conservatism and after-issue stock market survival. Our findings empirically support the International Accounting Standards Board’s (IASB) decision to reintroduce the concept of prudence into the conceptual framework, by showing how conservative reporting can reduce information asymmetry in IPO firms.

From May 2024, Victoria (Australia) will mandatorily raise the minimum house energy rating standards from 6 to 7 stars. However, the latest data shows that only 5.73% of new Victorian houses were designed beyond 7-star. While previous literature indicates the issue’s link to the compliance behaviour of building practitioners in the design phase, the underlying behavioural determinants are rarely explored. This study thus preliminarily examines building practitioners’ compliance behaviour with 7-star Australian house energy ratings and beyond.

Using a widely-applied method to initially examine an under-explored phenomenon, eight expert interviews were conducted with building practitioners, a state-level industry regulator and a leading national building energy policy researcher. The study triangulated the data with government-led research reports.

The experts indicate that most building practitioners involved in mainstream volume projects do not go for 7 stars, mainly due to perceived compliance costs and reliance on standardized designs. In contrast, those who work on custom projects are more willing to go beyond 7-star mostly due to the moral norms for a low-carbon environment. The experts further agree that four behavioural determinants (attitudes towards compliance, subjective norms, perceived behavioural control and personal norms) co-shape building practitioners’ compliance behaviour. Interventions targeting these behavioural determinants are recommended for achieving 7 stars and beyond.

This study demonstrates the behavioural determinants that influence building practitioners’ compliance decisions, and offers insight regarding how far they will go to meet 7 stars. It can facilitate the transition to 7 stars by informing policymakers of customized interventions to trigger behaviour change.

This paper aims to investigate whether the implementation of generative artificial intelligence (GAI) impacts government functionality. The study will analyse GAI’s positive attributes across different dimensions to comprehensively understand its value proposition for public organisations. Furthermore, the paper will outline the strategic interventions required to integrate GAI effectively within the organisational context of government transformation.

This study measures “government functionality” and “GAI implementation” using abstract macro variables as a second-order formative model. It also includes first-order measurable micro-variables to better understand the concept. In addition, the study introduces “organisational context” as a moderating factor to explain the complex dynamics of integrating GAI to improve government functionality. The study proposes a conceptual framework, which was analysed using exploratory data analysis, with primary data collected through questionnaires.

The study finds a positive correlation between the implementation of GAI and improved government functionality. Furthermore, it found that organisational contextualisation significantly moderates this relationship. All the empirical outcomes align with the prescribed statistical thresholds, concluding that the articulated conceptual framework holds significance.

The study has significant implications for managers, researchers and anyone involved in making, implementing or evaluating decisions related to digital government through GAI. However, the study has limitations, including a limited sample size and contextualisation of the Indian public sector.

The study contributes to existing knowledge by showing that implementing GAI positively correlates with improving government functionality. It further highlights the significance of GAI implementation according to the specific organisational context.

Green signaling refers to the notion that environmentally friendly purchases signal consumers' prosociality and willingness to pay more, thus enhancing their social status. This study investigated the green signaling effect among Gen Z and Millennial consumers on social media by adopting costly signaling theory.

A series of experimental studies were conducted to test the hypotheses. Thus, a 2 (organic vs. nonorganic) × 2 (luxury vs. non-luxury) between-subjects design was used in Study 1 (150 participants) and a 2 (organic vs. nonorganic) × 2 (high vs. low brand authenticity) between-subjects design was employed in Study 2 (148 participants).

The results of Studies 1 and 2 confirmed the mediating role of perceived status in the relationship between apparel greenness and purchase intention. However, brand label and authenticity did not have a moderating influence. The overall findings confirmed the green signaling effect of organic apparel in the social media marketing context.

This study contributes to the existing literature by suggesting a cultural capital perspective for promoting green products among Gen Z and Millennial consumers. It also encourages marketers to implement green messaging on social media, highlighting the amount of resources and efforts invested in sustainable production.

This study adopted existing brands to test the hypotheses, using images of female influencers.

In China, information-based construction management (ICM) has not obtained the expected results because of the benefit game between construction enterprises and the government. Promoting ICM is a long-term, complex and dynamic game process. Therefore, an evolutionary game model is established to promote ICM.

MATLAB was used to conduct evolutionary game analysis on the behavioural strategies of two parties. System stability analysis and numerical simulation were conducted. The variables affecting ICM realization were analysed and comprehensively considered. The optimal stability strategy and key variables were obtained.

The results show that the system includes four evolutionarily stable strategies (ESSs) with 10 decisive parameters. Information technology costs, benefits, reputation, the government intervention level, the enterprise ICM level and the degree of reward and punishment determine the ideal ESS. Increasing tangible benefits, enhancing corporate reputation, improving the level of government guidance and reducing intervention costs can promote ICM implementation. Rewards are more effective than punishments, and appropriate rewards should be determined.

First, reducing labour disputes, accidents and environmental pollution brings great social and ecological benefits; hence, the recognition of external benefits and the establishment of a benefit compensation mechanism by the government will be a future focus of research (Jia et al., 2020). Second, this study considered only the government and construction enterprises, and there may be other stakeholders, such as owners and the public, in the ICM adoption process, which needs further analysis (Zhang and Li, 2022). Third, this research is based on the specific context of government intervention in ICM. The selection of parameters and the determination of values were based on the national conditions in China. Therefore, the generalizability of the research results to other countries and other political contexts needs to be further improved (Hardie et al., 2013; Martínez-Román et al., 2017). Fourth, the empirical data were collected from Shandong Province and a pilot project, and the universality of the data remains to be verified. Nevertheless, the data were used only for the initial values of the simulation, which did not affect the simulation path.

(1) This study comprehensively summarized the benefit and cost indexes for the government and enterprises to promote ICM and constructed the payment matrix model. (2) This study determined the theoretical relation that the parameters should meet when system evolves into a certain strategy, and the research findings provide recommendations for overall control for the government and enterprises to synergistically adopt the ICM. (3) The study determined the influence of the main parameters on system evolution path and identified the core parameters, thus providing targeted improvement recommendations for the government and enterprises.

Real-time data-based management can ensure product quality and production safety and improve decision-making and efficiency. For the government, ICM can effectively reduce project quality and safety accidents, labour disputes, supplier mix-ups and environmental pollution, thus reducing the government's management costs and improving social benefits.

(1) Based on the challenges of ICM implementation, the payment matrix is constructed, with the cost and benefit parameters fully considered. (2) This study determines the theoretical relationship that should be met when both parties coordinate their implementation and when enterprises implement independently, and the optimal strategy is specified. (3) Incorporating an actual case, a simulation is conducted to clarify the influence of a single parameter on the evolutionary path of behaviours. (4) A decision-making basis for governments and enterprises to control and improve ICM is provided.

Construction activities generate overwhelming waste that is typically disposed of in landfills, which has significant environmental consequences and hinders national progress. However, with the appropriate competencies, there is an opportunity to identify construction activities that produce recyclable materials, offering a path to a sustainable future. This study aims to assess the competencies for identifying construction activities that produce recyclable materials. To attain that aim, the study seeks to identify the key competencies and assess the index level of the competencies.

A systematic literature review was conducted, and 20 competencies were identified and categorized into knowledge, skills, and abilities. A questionnaire survey was developed based on the competencies and completed by 101 individuals. The collected data were analyzed using normalized mean analysis, confirmatory factor analysis, and fuzzy synthetic evaluation (FSE).

The results revealed that the key competencies are problem-solving skills, communication skills, skills in providing vocational training, and knowledge of the environmental impacts of construction activities. The FSE ranks the constructs in order of skills, knowledge, and abilities. Also, the FSE illustrated that the overall index level is inclined to be important.

This study leads to saving natural resources, using raw materials efficiently, protecting from environmental pollution, and mitigating resource depletion by providing the index level of the competencies.

The findings can guide professionals in effective waste management, policymakers in creating new policies and regulations, and researchers in compiling a list of competencies for identifying construction activities that produce recyclable materials.

This study, a narrative literature review, aims to examine the combined benefits of the active and passive use of social media (SM) for well-being (WB), physical and mental health during the COVID-19 pandemic.

A search strategy has been carried out in the databases: Riss, PubMed, Medline, Scopus and Google Scholar, including all the articles published until 19 October 2023.

SM offers various benefits, including global risk awareness, health information, social connections and support. With the natural increase in physical inactivity due to COVID-19 social restrictions, SM has been identified as an appropriate tool for promoting physical activity (PA) at home to improve health.

It suggests that the combined use of active and passive benefits of SM could potentially play an important role in public health by increasing individuals’ health behaviours. In addition, dissemination, sharing and social interaction of information provided by YouTube can encourage healthy behaviours, contribute to WB, physical and mental health and raise public health awareness.

The findings presented in this study highlight the combined benefits of differentiating the features of SM use. Compared to other SM platforms, YouTube can be used as a useful tool for home-based PA that promotes health by enabling people to remain active and avoid barriers to PA due to social restrictions during the global crisis. In addition, some recommendations from the findings may help protect against potential risks and improve public health outcomes during global crises, such as the COVID-19 pandemic, among the general public using SM.

Using building information modelling (BIM) technology, a conventional structure in this study was converted into a green building to measure its energy usage and CO2 emissions.

Digital images of the existing building conditions were captured using unmanned aerial vehicle (UAV), and were fed into Meshroom to generate the building’s geometry for 3D parametric model development. The model for the existing conventional building was created and converted to an energy model and exported to gbXML in Autodesk Revit for a whole building analysis which was carried out in the Green Building Studio (GBS). In the GBS, the conventional building was retrofitted into a green building to explore their energy consumption and CO2 emission.

By comparing the green building model to the conventional building model, the research found that the green building model saved 25% more energy while emitting 46.8% less CO2.

The study concluded that green building reduces energy consumption, thereby reducing the emission of CO2 into the environment. It is recommended that buildings should be simulated at the design stage to know their energy consumption and carbon emission performance before construction.

Occupant satisfaction, operation cost and environmental safety are essential for sustainable or green buildings. Green buildings increase the standard of living and enhance indoor air quality.

This investigation aided in a pool of information on how to use BIM methodology to retrofit existing conventional buildings into green buildings, showing how green buildings save the environment as compared to conventional buildings.

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.