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Digital transformation is crucial for achieving high-quality development in the construction industry. Assessing the industry's digital maturity is an urgent necessity. The Digital Transformation Maturity Model is a potential tool to systematically evaluate the digital maturity levels of various industries. However, most existing models predominantly focus on sectors such as the Internet and manufacturing, leaving the construction industry comparatively underrepresented. This study aims to address this gap by developing a maturity model tailored specifically for digital transformation within the construction industry.

This study leverages the Capability Maturity Theory and integrates the unique characteristics of the construction industry to construct a comprehensive maturity model for digital transformation. The model comprises five critical dimensions: industry environment, strategy and organization, digital infrastructure, business process and management digitization, and digital performance. These dimensions encompass a total of 25 assessment indexes. To validate the model's feasibility and effectiveness, a digital transformation maturity assessment was conducted within China's construction industry.

The results of the maturity assessment within the Chinese construction industry reveal that it currently operates at the third level of digital maturity (defined level). The industry's maturity score stands at 2.329 out of 5. This outcome indicates that the developed model is accurate and reliable in assessing the level of digital transformation maturity within the construction industry.

This paper contributes both practical and theoretical insights to the field of digital transformation within the construction industry. By creating a tailored maturity model, it addresses a significant gap in existing research and offers a valuable tool for assessing and advancing digital maturity levels within this industry.

The advancements of mobile technologies and devices have greatly facilitated the extension of online services from web to mobile environments. Drawing on the categorization theory, the purpose of this paper is to explore the impact of perceived entitativity on users’ web-mobile service extension behavior. The research model considers how perceived entitativity serves as a category cue to link the category- and piecemeal-based processing and shape users’ adoption of extended mobile services.

An online survey (n=552) was conducted to empirically test the model. The data were analyzed by structural equation modeling approach.

The results offer two major findings. First, performance expectancy, perceived controllability and subjective norm are important antecedents of users’ usage intention. Second, perceived entitativity has three types of effects on usage intention: it exerts a direct and positive influence on usage intention; it indirectly facilitates usage intention through increasing PE and perceived controllability; and it moderates the relationship between subjective norm and usage intention.

This study contributes to the literature by taking into account the interplay of category- and piecemeal-based processing to understand consumers’ web-mobile service extension behavior.

Drawing from the spillover effect literature, the purpose of this paper is to investigate the spillover effect in consumers’ web-mobile payment extension behavior. The authors figure out two categories of factors associated with the spillover effect: relevant schema and schematic fit. Cognitive trust and emotional trust in web payment are used to capture relevant schema, while perceived similarity and perceived business tie are proposed to denote schematic fit in the web-mobile payment extension context.

An online survey (n =552) was conducted to empirically test the model. The data were analyzed by structural equation modeling approach.

The results show that relevant schema and schematic fit factors positively influence perceived value of mobile payment (MP), which facilitates consumers’ behavioral intention of MP.

This study contributes to the extant literature by theoretically identifying the key factors of the spillover effect and empirically investigating its role during the web-mobile service extension process.

Vehicle lightweight design has positive implications for reducing energy consumption and abating greenhouse gas emissions. The traditional trailer axle design mainly focuses on the overall performance of the trailer axle. Only when the local performance does not meet the requirements will local performance optimization be done, such as local heat treatment to improve local strength. Such a design results in an uneven distribution of axle performance and excess performance in some local structures. The purpose of this study is to investigate the weight reduction on the premise of ensuring the structural dimensions of the outer surface of the axle remain unchanged and the reliability of the axle.

The axle is parameterized by computer aided design, and the optimized axle finite element model based on computer aided engineering is established and verified by taking the eight dimensions of the axle cavity structure which affect the performance as parameters. A genetic algorithm is used to optimize the axle cavity structure size and axle weight based on multiobjective optimization, and eight optimized size parameters of axle cavity structure are obtained.

The total weight of the optimized axle of TM1314 is reduced by 10.2 kg, and the weight reduction ratio reaches 10.7%. According to the optimized structural size of the axle, the specimen was trial-manufactured, and the bench tests of stiffness, strength and fatigue life were carried out according to the test requirements of the trailer axle standard (JT/T 475-2002). The test results show that the maximum deformation of the specimen is 2.46 mm, the strength safety factor of the specimen body and the steel plate spring seat are 6.71 and 6.86 and bear the alternating load more than 1.05 × 106 times, which meets the standard of the trailer axle and is better than the original design requirements of the trailer axle.

In this study, the multiobjective optimization model of the axle is established, the response surface is constructed by the Latin hypercube sampling design method and the optimal solution set is obtained by the multiobjective genetic algorithm. It has been verified by bench tests that it can achieve a weight reduction of 10.7% under the premise of the same structure and size of the outer surface of the axle. The lightweight method based on multiobjective optimization proposed in this paper can provide a reference for the lightweight design of other key vehicle components.

Drawing on the control agency theory and the network effect theory, the purpose of this paper is to examine the effect of privacy assurance approaches, network externality and technology complementarity on consumers’ self-disclosure in mobile payment (MP) applications. The authors identify four types of privacy assurance approaches: perceived effectiveness of privacy setting, perceived effectiveness of privacy policy, perceived effectiveness of industry self-regulation and perceived effectiveness of government legislation. The research model considers how these privacy assurance approaches influence privacy concerns and consumers’ self-disclosure in MP applications under boundary conditions of network externality and technology complementarity.

An online survey with 647 sample users was conducted to empirically validate the model. The target respondents were current consumers of a popular MP application. The empirical data were analyzed by a structural equation modeling approach.

The empirical results reveal several major findings. First, privacy assurance approaches can effectively decrease privacy concerns, which ultimately formulates consumers’ self-disclosure in MP applications. Second, network externality and technology complementarity weaken the effect of perceived effectiveness of privacy setting on privacy concerns. Third, network externality and technology complementarity strengthen the relationship between perceived effectiveness of government legislation and privacy concerns, while they have non-significant interaction effect with perceived effectiveness of privacy policy and industry self-regulation on privacy concerns.

MP providers and stakeholders can harness the efficacy of privacy assurance approaches in alleviating privacy concerns and promoting consumers’ self-disclosure in MP applications.

The authors’ work contributes to the information privacy literature by identifying effective privacy assurance approaches in promoting consumers’ self-disclosure in MP applications, and by highlighting boundary conditions of these privacy assurance approaches.

Drawing on the social learning theory, the purpose of this paper is to examine the antecedents and consequences of users’ excessive online social gaming. Specifically, the authors develop a model to propose that observational learning and reinforcement learning mechanisms together determine excessive online social gaming, which further foster adverse consequences.

The model is empirically validated by a longitudinal survey among users of a popular online social game: Arena of Valor. The empirical data are analyzed using component-based structural equation modeling approach.

The empirical results offer two key findings. First, excessive online social gaming is determined by observational learning factors, i.e. social frequency and social norm, and reinforcement learning factors, i.e. perceived enjoyment and perceived escapism. Second, excessive online social gaming leads to three categories of adverse consequences: technology-family conflict, technology-work conflict and technology-person conflict. Meanwhile, technology-family conflict and technology-work conflict further foster technology-person conflict.

This study contributes to the literature by developing a nomological framework of excessive online social gaming and by extending the social learning theory to excessive technology use.

The purpose of this study is to design a new type of cold plate to improve the thermal performance of liquid-cooled thermal management system of lithium-ion batteries.

A cold plate with leaf type channels is proposed to enhance the cooling performance. Effects of the leaf type channel parameters (i.e. channel angle 20°, 40°, 60°, 80°; coolant mass flow rate 0.25 × 10^–3, 0.50 × 10^–3, 0.75 × 10^–3, 1.00 × 10^–3, 1.25 × 10^–3 kg·s⁻¹; channel number 1, 3, 5, 7) on the performance are numerically investigated by using a 3D mathematical model.

Compared to the traditional I type channels, the leaf type channels have better cooling performance. It is found that the battery temperature variation and channel pressure drop are decreased with decreasing channel angle and increasing channel number. In addition, the cooling performance can be improved by increasing the coolant mass flow rate.

This study can provide guidance for the development of novel effective cold plates.

The design of cold plates with leaf type channels can be used in liquid-cooled thermal management system to reduce the battery temperature difference.

The purpose of this study is to improve the thermal management of lithium-ion batteries. The phase change material (PCM) cooling does not require additional equipment to consume energy. To improve the heat dissipation capacity of batteries, fins are added in the PCM to enhance the heat transfer process.

Computational fluid dynamics method is used to study the influence of number of vertical fins and ring fins (i.e. 2, 4, 6 and 8 vertical fins, and 2, 3, 4 and 5 ring fins) and the combination of them on the cooling performance.

The battery maximum temperature can be decreased by the PCM with vertical or ring fins, and it can be further decreased by the combination of them. The PCM with eight vertical fins and five ring fins reduces the battery maximum temperature by 5.21 K. In addition, the temperature and liquid-phase distributions of the battery and PCM are affected by the design of the cooling system.

This work can provide guidelines for the development of new and efficient PCM cooling systems for lithium-ion batteries.

The combination of PCM and fins can be used to reduce the battery maximum temperature and temperature difference.

Most economic growth is concentrated in the eastern and coastal provinces of China, while the western and central provinces have not yet experienced the expected economic growth. This study aims to address the following crucial research questions: Do the central and western provinces achieved potential efficiency in economic growth? Have China’s provinces used their resources effectively in implementing economic growth strategies?

The research design concerns the use of a panel dataset on province-specific economic growth in China over the years to 2000–2020. The methodology used was a stochastic frontier gross domestic product (GDP) model with time-varying technical efficiency over time. The approach uses the existing literature to identify the important variables influencing economic growth at the provincial level to model the stochastic frontier GDP model for empirical analysis.

This study concludes that the central provinces show the highest rate of efficiency in economic growth, though not 100%, followed by the Eastern and Western provinces. By increasing and improving skilled education institutes and intensifying supply chain opportunities through foreign direct investment (FDI), the central provinces achieving 100% growth efficiency may not be ruled out.

The modes of economic governance and policies to improve GDP growth have been rapidly changing from increasing incentives to improving competition. Thus, more unique avenues and expansion of the horizon for impending research on provincial, national and international macroeconomics would emerge that would make current methodologies of the growth analysis outdated.

The empirical analysis highlights the importance of improving skilled education institutes and intensifying supply chain opportunities through FDI for achieving sustained economic growth.

The empirical analysis facilitates finding ways to reduce income inequality across provinces in China.

To the authors' knowledge empirical analysis examining the Chinese province-specific economic growth efficiency explicitly has not been carried out using the recent Chinese panel dataset.

In the growing information systems (IS) literature on metaverse, augmented reality (AR) technology is regarded as a cornerstone of the metaverse which enables interaction services. Interaction has been identified as a core technology characteristic of metaverse shopping environments. Based on previous human–technology interaction research, the authors further explicate interaction to be multimodal sensory. The purpose of this study is thus to better understand the unique nature of interaction in AR technology and highlight the technology's benefits for shopping in metaverse spaces.

An experiment has been conducted to empirically examine the authors' research model. The authors use the structural equation modeling (SEM) approach to analyze the collected data.

This study conceptualizes image, motion and touchscreen interactions as the three dimensions of multimodal sensory interaction, which can reflect visual-, kinesthetic- and haptic-based sensation stimulation. The authors' findings show that multimodal sensory interaction of AR activates consumers' intention to purchase via a psychological process. To delineate this psychological process, the authors use feelings-as-information theory to posit that experiential factors can influence cognitive factors. More specifically, multimodal sensory interaction is shown to increase multisensory experience and spatial presence, which can effectively reduce product uncertainty and information overload. The two outcomes have been considered to be key issues in online shopping environments.

This study is one of the first ones that shed light on the multimodal sensory peculiarity of AR interactions in the extant IS literature. The authors further highlight the benefits of AR in addressing major online shopping concerns about product uncertainty and information overload, which are largely overlooked by prior research. This study uses feelings-as-information theory to explain the impacts of AR interactions, which reveal the essential role of the experiential process in sensory-enabling technologies. This study enriches the existing theoretical frameworks that mostly focus on the cognitive process. The authors' findings about AR interactions provide noteworthy guidelines for the design of metaverse environments and extend the authors' understanding of how the metaverse may bring benefits beyond traditional online shopping settings.