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With the development of a new generation of digital technologies, digital transformation (DT) has become an inevitable trend for enterprises to achieve development in various industries. Nevertheless, the contradiction between the “fragmented” use of digital technologies and the “systematic” transformation of the industry leads to the underperformance of DT in the construction industry. Whilst previous studies have examined why DT is needed and how separate digital technologies can be used in construction projects, they failed to specify effective tools that can help enterprises identify key resources that facilitate DT from the organisational perspective.

This study established an objective assessment framework for evaluating the digital transformation capability (DTC) of construction enterprises in identifying limitations in their transformation efforts. This study also established a management entropy quantitative model and a comprehensive capability evaluation model of DT to analyse the DT performance of construction enterprises from the internal and external perspectives. Data were collected from 95 listed enterprises in China’s construction industry in 2020 as a case study.

This study concluded that enterprise profitability provides a strong endogenous driving force for DT. Research and development capabilities and DT proficiency of enterprises are the most critical factors in facilitating DT. In addition, China’s construction enterprises' DT was characterised by uneven development and low orderliness. The lack of a unified digital integration platform is key to cracking the dilemma.

This paper systematically identified key DTC in construction enterprises and proposed an objective framework for measuring DTC to enhance the DT performance of these enterprises.

Financial flexibility, investment efficiency and effective corporate governance mechanisms have been issues of concern to stakeholders. Yet, little empirical evidence on the combined moderating effects investment efficiency and corporate governance mechanisms on the nexus between financial flexibility and firm performance. This study aims to address this gap and extend the extant literature by examining the moderating effects of corporate governance and investment efficiency on the nexus between financial flexibility and financial performance.

The empirical study is based on progression analysis using a sample of 13,865 US listed companies selected from BoardEx (WRDS) for the period (2010–2022) with 89,198 firm-year observations.

Findings of this study indicate that financial flexibility improves firm value as well as accounting performance. Furthermore, the results reveal that both investment efficiency and corporate governance moderate the effect of financial flexibility on firm performance. The authors complement and extend the literature on the optimal investment strategies domain by showing that the combined impact of corporate governance mechanisms and investment efficiency strengthens the nexus between financial flexibility and firm performance.

Key limitations of this study due to the characteristics of the sample selection: country-specific context and proxies used by this study.

Findings of this study have managerial and theoretical implications for firms’ boardrooms, institutional and individual investors, regulators, academics and other stakeholders regarding behavioural aspects of investment decision-making.

The authors’ novel contribution to the extant literature is articulated by the conceptual framework underlying this study and by the new evidence regarding exploring the combined effect of corporate governance mechanisms on nexus between financial flexibility and companies’ performance.

This paper aims to design a PD controller for an active suspension system to improve the car’s moving smoothness.

The controller parameters are optimized by an in-loop genetic algorithm (iL-GA). Unlike previous studies that only used conventional GAs to tune coefficients for the controller, the iL-GA designed in this paper provides outstanding efficiency when determining the optimal value range for the system. The optimal value range of parameters is determined by the in-loop algorithm based on criteria related to systematic errors. The optimal values are then calculated by the GA based on this range instead of an uncertain one.

Simulation results show that vehicle body acceleration and displacement values are significantly reduced when using the active suspension system compared to the conventional passive suspension system. The phase difference phenomenon does not occur in the iL-GA situation. In addition, the frequency domain investigation also shows the system’s stability when using iL-GA instead of conventional GA.

To the best of the authors’ knowledge, this is a new application that provides positive effects to the suspension controller. This algorithm can be applied to tune coefficients for direct controllers in the future.

This study addresses the prevailing complexities and limitations in estimating and managing construction overhead costs (COCs) in the existing literature, with the purpose of enhancing the accuracy of cost performance indicators in construction project management.

An innovative approach is proposed, employing the activity-based costing (ABC) accounting method combined with building information modelling (BIM) to assign real overhead costs to project activities. This study, distinguished by its incorporation of a real case study, focuses on an administrative building with a four-story concrete structure. It establishes an automated method for evaluating project cost performance through the detailed analysis of earned value management (EVM) cost indicators derived from ABC results and BIM data.

The results show that the ABC integration improves the accuracy of cost performance indicators by over 9%, revealing the project's true cost index for the first time and demonstrating the substantial value of the approach in construction engineering and management.

The current study highlights a notable gap in the existing literature, addressing the challenges in onsite overhead cost estimation and offering a solution that incorporates the state-of-the-art techniques.

The proposed method has significant implications for project managers and practitioners, enabling better-informed decisions based on precise cost data, ultimately leading to enhanced project outcomes.

This research uniquely combines ABC and BIM, presenting a pioneering solution for the accurate estimation and management of COCs in construction projects, adding significant value to the current body of knowledge in this field.

This study aims to evaluate the efficiency of various techniques for enhancing indoor air quality (IAQ) in construction. It analyzed the alterations in the concentration of indoor air pollutants over time for each product employed in controlling pollution sources and removing it, which included eco-friendly substances and adsorbents. The study will provide more precise and dependable data on the effectiveness of these control methods, ultimately supporting the creation of more efficient and sustainable approaches for managing indoor air pollution in buildings.

The research investigates the impact of eco-friendly materials and adsorbents on improving indoor air quality (IAQ) in Dubai's tall apartment buildings. Field experiments were conducted in six units of The Gate Tower, comparing the IAQ of three units built with “excellent” grade eco-friendly materials with three built with “good” grade materials. Another experiment evaluated two adsorbent products (H and Z) in the Majestic Tower over six months. Results indicate that “excellent” grade materials significantly reduced toluene emissions. Adsorbent product Z showed promising results in pollutant reduction, but there is concern about the long-term behavior of adsorbed chemicals. The study emphasizes further research on household pollutant management.

The research studied the effects of eco-friendly materials and adsorbents on indoor air quality in Dubai's new apartments. It found that apartments using “excellent” eco-friendly materials had significantly better air quality, particularly reduced toluene concentrations, compared to those using “good” materials. However, high formaldehyde (HCHO) emissions were observed from wood products. While certain construction materials led to increased ethylbenzene and xylene levels, adsorbent product Z showed promise in reducing pollutants. Yet, there is a potential concern about the long-term rerelease of these trapped chemicals. The study emphasizes the need for ongoing research in indoor pollutant management.

The research, while extensive, faced limitations in assessing the long-term behavior of adsorbed chemicals, particularly the potential for rereleasing trapped pollutants over time. Despite the study spanning a considerable period, indoor air pollutant concentrations in target households did not stabilize, making it challenging to determine definitive improvement effects and reduction rates among products. Comparisons were primarily relative between target units, and the rapid rise in pollutants during furniture introduction warrants further examination. Consequently, while the research provides essential insights, it underscores the need for more prolonged and comprehensive evaluations to fully understand the materials' and adsorbents' impacts on indoor air quality.

The research underscores the importance of choosing eco-friendly materials in new apartment constructions for better IAQ. Specifically, using “excellent” graded materials can significantly reduce harmful pollutants like toluene. However, the study also highlights that certain construction activities, such as introducing furniture, can rapidly elevate pollutant levels. Moreover, while adsorbents like product Z showed promise in reducing pollutants, there is potential for adsorbed chemicals to be rereleased over time. For practical implementation, prioritizing higher-grade eco-friendly materials and further investigation into furniture emissions and long-term behavior of adsorbents can lead to healthier indoor environments in newly built apartments.

The research offers a unique empirical assessment of eco-friendly materials' impact on indoor air quality within Dubai's rapidly constructed apartment buildings. Through field experiments, it directly compares different material grades, providing concrete data on pollutant levels in newly built environments. Additionally, it explores the efficacy of specific adsorbents, which is of high value to the construction and public health sectors. The findings shed light on how construction choices can influence indoor air pollution, offering valuable insights to builders, policymakers and residents aiming to promote public health and safety in urban living spaces.

Following the imperative for human-centric digital innovation brought by the paradigm of Industry 5.0, the article aims to integrate the dispersed and multi-disciplinary literature on individual risks for workers to define, explain and predict individual risks related to Industry 4.0 technologies.

The paper follows the question, “What is the current knowledge and evidence base concerning risks related to Industry 4.0 technologies, and how can this inform digital innovation management in the manufacturing sector through the lens of the Industry 5.0 paradigm?” and uses the method of systematic literature review to identify and discuss potential risks for individuals associated with digital innovation. N = 51 contributions met the inclusion criteria.

The literature review indicates dominant trends and significant gaps in understanding risks from a human-centric perspective. The paper identifies individual risks, their interplay with different technologies and their antecedents at the social, organizational and individual levels. Despite this, the paper shows how the literature concentrates in studying risks on only a limited number of categories and/or concepts. Moreover, there is a lack of consensus in the theoretical and conceptual frameworks. The paper concludes by illustrating an initial understanding of digital innovation via a human-centered perspective on psychological risks.

Findings yield practical implications. In investing in the adoption, generation or recombination of new digital technologies in organizations, the paper recommends managers ensure to prevent risks at the individual level. Accordingly, the study’s findings can be used as a common starting point for extending the repertoire of managerial practices and interventions and realizing human-centric innovation.

Following the paradigm of Industry 5.0, the paper offers a holistic view of risks that incorporates the central role of the worker as crucial to the success of digital innovation. This human-centric perspective serves to inform the managerial field about important factors in risk management that can result in more effective targeted interventions in risk mitigation approaches. Lastly, it can serve to reinterpret digital innovation management and propose future avenues of research on risk.

Intelligent prediction of node localization in wireless sensor networks (WSNs) is a major concern for researchers. The huge amount of data generated by modern sensor array systems required computationally efficient calibration techniques. This paper aims to improve localization accuracy by identifying obstacles in the optimization process and network scenarios.

The proposed method is used to incorporate distance estimation between nodes and packet transmission hop counts. This estimation is used in the proposed support vector machine (SVM) to find the network path using a time difference of arrival (TDoA)-based SVM. However, if the data set is noisy, SVM is prone to poor optimization, which leads to overlapping of target classes and the pathways through TDoA. The enhanced gray wolf optimization (EGWO) technique is introduced to eliminate overlapping target classes in the SVM.

The performance and efficacy of the model using existing TDoA methodologies are analyzed. The simulation results show that the proposed TDoA-EGWO achieves a higher rate of detection efficiency of 98% and control overhead of 97.8% and a better packet delivery ratio than other traditional methods.

The proposed method is successful in detecting the unknown position of the sensor node with a detection rate greater than that of other methods.

This study examines the psychological factors that directly influence individuals’ intentions to book or arrange reservations through resort hotel websites within the context of the Unified Theory of Acceptance and Use of Technology (UTAUT). Specifically, this study focused on identifying and understanding the key drivers of online purchase intentions, including performance expectancy, effort expectancy, social influence and facilitating conditions within the UTAUT model.

This study used the UTAUT as a theoretical framework. A quantitative research approach was adopted and data were collected through surveys using a five-point Likert scale. The sample consisted of 270 participants, selected through purposive sampling. Data analysis was performed using Smart PLS software.

The results indicate that performance and effort expectancy significantly influence online purchase intentions. However, social influence did not significantly affect online purchase intention. By contrast, facilitating conditions significantly and positively influence online purchase intention.

These findings have practical implications for hotel managers and marketers. Emphasizing performance, effort expectations and facilitating conditions can improve customer perceptions of website usefulness and directly increase their intention to book. The limited impact of social influence suggests the need to focus less on social recommendations or endorsements in online marketing efforts and prioritize individual factors and objective information.

This study contributes to the field of technology and tourism by examining the psychological factors that directly influence users’ intention to purchase from resort hotel websites. This extends the understanding of online purchase intentions in the context of resort tourism and highlights the importance of facilitating conditions. This study also partially confirms the applicability of the UTAUT model in this domain and provides insights for future research on online purchase intentions in resort tourism.

This study aims to analyze the effects of performance expectancy, effort expectancy, social influence and facilitating conditions on the behavioral intention of using mobile health applications, especially during and after the COVID-19 pandemic.

A survey was developed using an online survey platform and distributed to Indonesian consumers for three weeks, and 149 usable responses were obtained. The principal component analysis, linear regression and analysis of variance tests were performed to test the validity and reliability of the measurement model and the hypothesized relationships among constructs.

Surprisingly, unlike previous studies on IT adoption, the findings show that social influence has no significant impact on behavioral intention. Facilitating conditions have a very weak to almost no significant impact on behavioral intention to use mobile health applications.

This research is conducted during pandemic COVID-19 where using mobile health apps is a must. In the future this research can be expanded as comparison study after the pandemic COVID-19 stated.

The result implies that digital technologies adoption intention is strongly affected by performance expectancy and effort expectancy, with performance expectancy as the most significant predictor. Nonetheless, the interaction of performance expectancy, effort expectancy, social influence and facilitating conditions influences behavioral intention significantly. Therefore, social influence and facilitating conditions are still important even with very insignificant effects.

To improve consumers’ behavioral intention to use mobile health applications, application providers should promote mobile health applications as useful telemedicine tools by primarily focusing on the application performance and usage experience.

This study aims to re-examine the growth effects of tourism and role of financial development in this process for countries across the globe for the period 1995–2018.

Considering the asymmetric effect of tourism on economic performance, the author makes use of a recently developed method of moments approach of quantile regression with fixed effects proposed by Machado and Silva (2019).

Model-based on tourist arrival shows a positive effect of tourism on economic performance. Yet, the effect is conditional and shows that relatively low-income economies are benefited more than high-income economies. The results also show that all types of tourism are beneficial for growth, yet business-related tourist arrivals are found to be comparatively more effective than other types of tourism. The evidence also shows that excess tourism does not dampen growth. Finally, the evidence suggests that financial development offers critical absorptive capacity, and tourism-led growth cannot be realised without it.

In contrast to previous studies, the approach effectively deals with non-normality, endogeneity and heterogeneity across issues. Furthermore, this study uniquely tests the role of financial development in channelling tourism-led development.