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This study aims to investigate the “digitalization paradox” in manufacturing digital transformation, where significant investments in digital technology may not necessarily lead to increased returns. Specifically, it explores the intricate relationship between digital technology convergence, financial performance, productivity and technological innovation in listed Chinese manufacturing firms, drawing upon theories of digital innovation and knowledge networks.

Using a large panel data from 747 listed firms in China’s manufacturing sector and their 428,927 patents spanning from 2013 to 2022, this research first quantifies manufacturing firm-level digital technology convergence through patent network analysis. Furthermore, this study employs hierarchical regression analysis and the instrumental variable method to investigate the curvilinear relationship between digital technology convergence and financial performance. Furthermore, the moderating role of firms’ productivity and technological innovation is tested.

Three types of firm-level digital technology convergence (DTC) are delineated and quantified: local authority in digital convergence (DegreeDTC), convergence with heterogeneous digital knowledge (BetweenessDTC) and shortest-path convergence with digital technologies (ClosenessDTC, where a higher value signifies a more conservative and shorter path in adopting digital technologies). Network visualization shows that manufacturing firms' DTC has consistently increased over time. Contrary to traditional assumptions, our research reveals a U-shaped relationship between DTC (specifically, DegreeDTC and BetweenessDTC) and financial performance. This relationship is characterized by a negative correlation at lower levels and a positive one at higher levels. The joint effect of firms’ productivity and technological innovation significantly strengthens this relationship. These findings are robust across a series of robustness checks.

Our findings offer practical insights for both managers and policymakers. We recommend a balanced approach to digital innovation management within the technology convergence paradigm. Manufacturing firms can generate economic value by strategically choosing to either shrink or expand their digital technology application areas, thereby reducing uncertainties related to emerging convergent businesses. Additionally, the study underscores the synergistic strategy of combining innovation with productivity. Within the DTC business context, integrating productivity with technological innovation not only enhances cost flexibility but also improves problem-solution matching, ultimately amplifying synergistic benefits.

To the best of our knowledge, this is the first study to apply a digital technology co-occurrence network to unveil nuanced relationships in “DTC – finance performance” within the manufacturing sector. It challenges conventional thinking regarding the common positive effect of digital innovation and technological convergence. This study provides a comprehensive analysis of DTC, financial performance, productivity and technological innovation dynamics, as well as offers managerial implications for managers and policymakers.

• (1)

  We quantify manufacturing firm-level DTC through patent network analysis and find consistent increases over time.

• (2)

  A significant U-shaped relationship between DTC and financial performance, being negative at lower levels and positive at higher levels.

• (3)

  The joint effect of firms’ productivity and technological innovation reinforces this relationship by distributing costs and enhancing synergistic benefits.

• (4)

  We challenge existing literature by uncovering a complex relationship in “DTC – finance performance”, contrary to popular belief of a monotonic effect of digital innovation or technological convergence.

We quantify manufacturing firm-level DTC through patent network analysis and find consistent increases over time.

A significant U-shaped relationship between DTC and financial performance, being negative at lower levels and positive at higher levels.

The joint effect of firms’ productivity and technological innovation reinforces this relationship by distributing costs and enhancing synergistic benefits.

We challenge existing literature by uncovering a complex relationship in “DTC – finance performance”, contrary to popular belief of a monotonic effect of digital innovation or technological convergence.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

Despite the continuous development and application of new digital technologies in the construction industry, there has been little research on digital technology trajectories in the construction industry. The study addresses the issue faced by the construction industry in exploring digital technology trajectories: how to comprehensively identify and analyse digital technology pathways across multiple technology fields in the construction industry.

Firstly, the digital technology patent identification and classification method based on text mining is used to identify digital technology patents and construct a digital technology innovation network. Second, the main path of the digital technology innovation network is identified with the help of SPNP. Then, the subpaths of the digital technology innovation network are identified with the help of the Louvain algorithm and SPNP. Finally, starting from the technology nodes where the main path and subpaths intersect, the technological similarity of the paths is analysed to explore the evolutionary characteristics of the technology trajectories. In light of this, the developed method is applied to the global construction industry patent dataset to analyse the trajectories of digital technologies.

The technological innovation path in the construction industry starts with construction materials and gradually expands to intelligence, automation and digital data processing technology. Equipment and devices with electronic digital data processing capabilities as well as improvements in green building technologies and user experience-enhancing technologies, may be the future of the construction industry. With the increasing demand for green buildings and intelligent buildings, the direction of digital technology innovation in the construction industry is gradually tilted towards these areas. In addition, influenced by geographic and economic factors, there is a spatial clustering effect of digital technology innovation in the construction industry.

Future research should analyse in depth the performance of different countries and regions in digital technology innovation and explore the root causes, motivations and influencing factors behind it, such as the policy environment, the level of the economy and the investment in research and development. Exploring the reasons affecting digital technology innovation can help formulate more targeted policies and promote cooperation and exchange of digital technology innovation in the global construction industry. Meanwhile, to solve the problems of overly broad IPC categorization and the difficulty of accurately describing cross-field innovations, combining IPC co-occurrence networks with patent citation networks is an effective strategy. This strategy can track technologically interrelated patents and provide more specific contents to know the advantages and challenges of the construction industry in the field of digital technology innovation.

The study has practical implications for the construction industry. The identification of digital technology innovation trajectories provides valuable insights for industry firms and research institutes. It helps them understand the current and future directions of digital technology in construction, enabling them to stay at the forefront of technological advancements. The findings highlight the importance of focusing on areas such as solar energy utilisation, green energy, intelligence, automation and data applications. This knowledge can guide firms in developing new building materials, incorporating digital information technologies and enhancing user experiences. The study’s results can inform strategic decision-making, technology adoption and innovation management in the construction sector.

The social implications of this study are significant for various stakeholders. The identification of digital technology innovation trajectories in the construction industry highlights the potential benefits for society. The focus on green energy, intelligent buildings and enhanced user experiences aligns with the increasing demand for sustainability, energy efficiency and comfortable living environments. These technological advancements can contribute to reducing environmental impact, improving quality of life and promoting sustainable development. The findings can inform policymakers, urban planners and architects in shaping regulations, designing sustainable cities and creating buildings that prioritize energy efficiency and user well-being. Ultimately, the study’s social implications aim to foster a more sustainable and livable built environment.

An identification method integrated with SPNP and the Louvain algorithm is developed to map digital technology innovation trajectories in the construction industry. This study helps to reveal the trajectories of digital technology innovation, provides new perspectives, insight and ideas for research in related fields and has great potential for applications in practice to promote the innovation and development of the construction industry.

This study aims to evaluate enterprise technology innovation capability in prefabricated construction (PC) from an input-output perspective, using six integrated enterprises in China as cases.

An evaluation system for enterprise technology innovation capability in PC was constructed, including total input, technology output (TO) and project output. All the evaluation indexes were quantified, and the subject and object indexes weights were determined using the fuzzy cognitive map and information entropy, respectively. The final scores and ranks were evaluated through gray relational analysis (GRA) based on the combined weights.

It was found that enterprise technology innovation capability in PC was low in China, with its unbalanced development in different dimensions and the poorest performance in TO, currently.

This research has developed an evaluation system for technology innovation capability in PC at the enterprise level and scientifically quantified all the indexes, which is a breakthrough over existing studies. The GRA model based on the combined weights proposed in this study can be applied to other comparable fields and regions, with its easy operation.

Conscious of the benefits building information modeling (BIM) has brought about to the architecture, engineering, construction and operations (AECO) industry, the Chinese government has been driving BIM adoption. Nonetheless, its acceptance and proliferation in China remain stagnant. Most relevant literature focuses on BIM diffusion at the industry and organizational levels, but the impact of non-managerial practitioners executing BIM or the traditional drafting approach in day-to-day work tends to be disregarded. This study aims to extend theoretical models pertaining to technology acceptance to understand non-managerial practitioners’ perceptions toward working with BIM in China.

A new BIM acceptance model was proposed based on previous technology acceptance theories. After a pilot study, a survey was conducted with 153 non-managerial practitioners in the Chinese AECO industry.

Among factors impacting non-managerial practitioners’ BIM acceptance in China, performance expectancy and task-technology fit significantly and positively influence behavioral intention to accept BIM, while the impacts from effort expectancy, social influence and facilitating conditions are not essential.

Management strategies, such as improving non-managerial staff’s benefits and sense of BIM usefulness, selecting suitable tools to match with the staff’s tasks and promoting a middle-out approach in parallel with top-down interventions, are proposed for Chinese AECO organizations to enhance BIM acceptance.

Few studies have explored BIM acceptance from the perspective of non-managerial users in the Chinese AECO industry, especially using the theories related to technology acceptance. The BIM acceptance model developed in this study is different from those used in previous global studies in terms of influencing factors.

With the uptake of prefabricated construction (PC) facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation. However, the construction community has paid little attention to PC innovation, especially networked innovation. This study aims to gain deep insights into what impacts the formation and dynamics of a prefabricated construction innovation network (PCIN). With the uptake of PC facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation.

The research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. It first conceptualizes the PCIN based on the quadruple helix model and formulates a corresponding system dynamics model based on causality analysis. After validating the PCIN model using empirical data, simulations are carried out using Vensim PLE software. Finally, this study identifies key factors that promote the formation of PCIN in China through sensitivity analysis.

The results show that PC predicts a continuous increase in practice as of 2030. The tested drivers all positively influence the formation of the PCIN, with market demand and risk sharing having the greatest influence, followed by competitive pressure, profit government support, scientific and technological advancement and collaborative innovation strategy.

The study makes three major contributions. First, it provides a novel angle for a deeper understanding of the PC innovation. Second, it proposes a new approach for probing the formation and dynamics of the PCIN. Finally, it offers a theoretical reference for promoting the formation of innovation networks and the development of PC.

This study aims to establish an evaluation method for cross-regional major infrastructure project (CRMIP) supportability. The focus is to identify evaluation indicators from a complexity perspective and develop an evaluation model using qualitative and quantitative methods. Case studies are carried out to verify the reliability of the evaluation model, thereby providing theoretical and practical guidance for CRMIP operations and maintenance (O&M).

Guided by the idea of complexity management, the evaluation indicators of CRMIP supportability are determined through literature analysis, actual O&M experience and expert interviews. A combination of qualitative and quantitative methods, consisting of sequential relationship analysis, entropy weighting, game theory and cloud model, is developed to determine the indicator weights. Finally, the evaluation model is used to evaluate the supportability of the Hong Kong–Zhuhai–Macao Bridge (HZMB), which tests the rationality of the model and reveals its supportability level.

The results demonstrate that CRMIPs' supportability is influenced by 6 guideline-level and 18 indicator-level indicators, and the priority of the influencing factors includes “organization,” “technology,” “system,” “human resources,” “material system,” and “funding.” As for specific indicators, “organizational objectives,” “organizational structure and synergy mechanism,” and “technical systems and procedures” are critical to CRMIPs' O&M supportability. The results also indicate that the supportability level of the HZMB falls between good and excellent.

Under the guidance of complexity management thinking, this study proposes a supportability evaluation framework based on the combined weights of game theory and the cloud model. This study provides a valuable reference and scientific judgment for the health and safety of CRMIPs' O&M.

This paper aimed to introduce a systematic body of knowledge via a scientometric review, guiding the sustainable transition from conventional construction to prefabricated construction. The construction industry currently faces a challenge to balance sustainable development and the construction of new buildings. In this context, one of the most recent debates is prefabricated construction. As an emerging construction approach, although existing knowledge makes contributions to the implementation of prefabricated construction, there is a lack of a comprehensive and in-depth overview of the critical knowledge themes and gaps.

This study uses the scientometric analysis to review the state-of-the-art knowledge of prefabricated construction. It retrieved data from the Web of Science core collection database. CiteSpace software was used to conduct the analysis and visualization; three analysis methods identify the knowledge hotspots, knowledge domains and knowledge topics. Finally, according to integrating the hidden connections among results, a body of knowledge for prefabricated construction application can be inferred.

The results show that 120 knowledge hotspots, five critical knowledge domains and five prominent knowledge topics are vital for promoting implementation of prefabricated construction. Based on the afore analysis, a body of knowledge for prefabricated construction that can systematically cover a broad knowledge of prefabricated construction-related research and activities are integrated and proposed in this paper.

Body of knowledge systematically covers a broad knowledge of prefabricated construction applications and is vital to guide researchers and practitioners to conduct related research and activities, thereby promoting the sustainable transition to prefabricated construction implementation.

The complicated nature of megaprojects requires appropriate analysis of multiple stakeholders to achieve project objectives and to accommodate stakeholder interests. During the last two decades, many stakeholder theories and empirical studies have sprouted. Although previous studies have contributed to the development of stakeholder theory, it seems that these theoretical advances have not been fully adopted and acknowledged in practices, especially in megaprojects. The purpose of this paper is to explore the evolution of stakeholder analysis and engagement practices adopted in the Australian megaprojects over the last two decades.

Four mega construction projects are described and analysed in this study. Secondary data were first assembled in order to get general knowledge of each case. Interviews were conducted with the project directors. Project documents were collected from the project teams and reviewed. Wherever the project information was unclear, e-mails were sent to the directors and the team members to confirm the details.

Project teams have started to apply snowball rolling and stakeholder attribute assessment methods to analyse stakeholders. However, there is still a way to adopt the “network” analysis perspective because the project teams are reluctant to use complicated tools which need specialists’ assistance. The stakeholder engagement practices have evolved to an extent where the project teams monitor the dynamics of stakeholders’ requirements. Projects teams have identified the importance of continuity to manage stakeholders in these massive projects. However, a structured method selection mechanism for stakeholder engagement has not been developed.

This study will help academics to understand the adoption progress and status of stakeholder management methods.

Owing to multiple superiorities to traditional counterparts, prefabricated construction (PC) has gained increasing attention worldwide. The development of PC projects reflects the effects of both policy supervision and PC practice, which aids the government in reasonably identifying the key issues of PC's promotion and rationally improving the policy deployment. However, existing studies fail to address this aspect, especially lacking quantitative exploration. This study explores the micro mechanism of PC's promotion, from the perspective of developing PC projects.

A tripartite evolutionary game model based on prospect theory of the government, developers and contractors is constructed. After rigorous theoretical deduction, this study adopts Changchun in China as a case city and collects the data using the Delphi technique, policy documents and literature analysis.

Results indicate that contractors are generally willing to implement PC projects and the government chooses to actively supervise PC's promotion. The negative investment behavior of developers is the main obstacle to promote PC in Changchun currently.

The conclusions are applicable to other comparable regions. This study is of value to promote PC with high efficiency and effect.

The tripartite evolutionary game model based on prospect theory proposed in this study is conducive to reveal the essence of PC's promotion. This is an important breakthrough in extant studies, with a broad applicability in the PC domain beyond China.