Textual Analysis of Intelligent Construction Policies from the Perspective of Policy Instruments in Fujian Province, China

Abstract

Intelligent construction represents the future direction of the traditional construction industry, with numerous national policies driving its transformation. This study constructs a three-dimensional analysis framework of policy instruments, stakeholders, and targets. It uses content analysis to examine 47 intelligent construction policy texts from Fujian (2015–2024). This analysis examines Fujian Province’s intelligent construction policies, identifying key areas for improvement. The findings show that regulatory policies are over-utilized, incentives are moderate, and markets and social participation are slightly under-utilized, indicating significant gaps in the use of policy instruments. Futhermore, The main challenges are threefold: First, the disconnect between research and the market hinders the development of practical solutions for the construction industry. Second, the lack of market-oriented talent development and industry alignment has resulted in a skills gap and divergent views, hindering long-term innovation in intelligent construction. Third, inadequate focus on inclusive policies and a strong industrial upgrade system hinders aligning stakeholder interests with technological progress. Furthermore, weak regulatory policies and market competition require targeted enhancements to promote the growth of intelligent construction in Fujian Province, China.

1. Introduction

With the advancement of new-type industrialization and informatization strategies, various industries are accelerating their intelligent transformation [1]. The construction industry, a cornerstone of the national economy, plays a pivotal role in sustaining China’s economic health. However, it faces significant challenges such as low productivity, high resource consumption, and severe environmental pollution due to its fragmented and extensive development model [2]. Data from the McKinsey Global Institute highlights that China’s construction sector is among the least digitized industries globally [3], with construction informatization investment constituting a mere 0.08% of the industry’s total output value, starkly contrasting with the 1% observed in developed European and American countries [4]. Consequently, intelligent construction emerges as a critical pathway for modernizing the construction sector, essential for fostering high-quality development. It facilitates the integration of the construction supply chain [5], leveraging digital and intelligent technologies to optimize design, construction, and operational processes, thereby enhancing construction quality.

Currently, the academic community lacks a unified definition of intelligent construction, with research primarily focusing on the deep integration of modern information technology and the construction industry. American expert Teicholz [6] onceptualizes intelligent construction as the digital transformation of conventional construction processes, primarily facilitated by building information modeling technology to optimize lifecycle management across design, construction, and operational phases. Originating from BIM technology, which was used in major U. S. projects as early as 2007 [7], the concept has evolved significantly. In China, Ding and Lin [8,9] define intelligent construction as a model integrating IoT, big data, AI, and BIM to enable smart design, automated construction, and intelligent operations throughout a project’s lifecycle. Qian [10] argues that it bridges high-tech IT and engineering practice, progressively replacing manual labor with mechanized processes. Wang et al. [11] emphasize that intelligent construction integrates architecture with communication, networking, information, big data, and AI to innovate design and construction, improve operational efficiency, reduce costs, and minimize environmental impact. Zhan [12] further describes intelligent construction optimizes structure, systems, services, and management to meet user needs, delivering a more efficient, convenient, comfortable, reliable, and flexible environment. Current research focuses on BIM’s advanced applications [13,14,15], intelligent construction equipment [16,17], and system integration with data analytics [18,19] to enhance building intelligence, streamline processes, and boost efficiency [20,21,22].

The realization of intelligent construction needs to rely on the deployment and implementation of policies, and the focus of intelligent construction policies varies from country to country. The United States policy promotes industrial upgrading through technical standardization [23,24] and industry–academia collaboration, such as Revit 2020, Navisworks 2020, and other BIM software vendors driving this process [25]. This market-dominated technological ecosystem has shaped a market-driven policy paradigm for intelligent construction. Japan’s intelligent construction policy incentivizes companies to adopt intelligent construction technologies through financial subsidies, tax incentives, and public works incentive point systems [26,27], and the policies are based on positive guidance. For example, the “i-Construction” program provides a 50% equipment subsidy, and the bidding bonus point system directly enhances firms’ competitiveness [28]. The UK’s policy framework is primarily socially participatory, focusing on multi-interest coordination and advancing intelligent construction via transitional arrangements, tripartite platforms, and public engagement mechanisms [29], and the Construction 2025 strategy further fosters sector-wide collaboration [30]. Singapore has adopted BIM standards as part of its compulsory policy to integrate intelligent facilities management into the built environment and drive sustainable digital transformation [31]. The regulatory processes in land development must incorporate BIM technology to facilitate intelligent construction strategies [32]. Although the current policy preferences of countries have been effective in specific areas, over-reliance on a single preference may lead to structural imbalances, and for this reason, many scholars have now begun to study the positive effects of policy instruments on policy.

Policy instruments are the key means for governments to regulate the development of the industry [33], which can help achieve specific policy targets. Currently, many scholars use policy instruments to sort out the policies in the field of digitization and intelligence. Huang [34] conducted a quantitative analysis of China’s intelligent manufacturing policies, revealing inefficiencies in certain policy instruments and highlighting the need to optimize their allocation across the innovation chain. Zhao [35] et al. analyzed industrial policies for intelligent equipment in China, the US, Germany, and Japan, revealing that China employs the most environmental policy instruments and the fewest supply-side policy instruments. Yang Yue et al. [36] applied Rothwell and Zegveld’s 12-policy tool framework to examine China’s blockchain policies, showing that enterprises prioritize technology application, whereas policies emphasize regulation and governance. These findings offer developmental insights for the distributed intelligent healthcare sector. Fang [37] utilized content analysis to construct two analytical frameworks: a three-dimensional “issuing body-policy process-policy type” model and a two-dimensional “policy instrument-policy content” model for examining intelligent community governance policy texts. The findings indicate that China’s community intelligent governance policies lack stability and continuity, with insufficient voluntary and mixed policy instruments, ultimately hindering effective policy implementation. Chen et al. [38] examined the efficacy of current policy instruments in facilitating stakeholder engagement during the construction industry’s digital transformation. Their study demonstrated that China lacks a cohesive, multi-tiered policy framework to support this transition, underscoring the need for enhanced top-level design to foster a conducive development environment. Zhang et al. [39] analyzed 81 national-level policy documents and applied policy tools to assess BIM policy development. The evolution of BIM policies in China is traced, barriers to implementation are highlighted, and strategies to increase BIM adoption throughout the project life cycle are proposed. Zhang [40] constructs a “value-issue-tool” framework to analyze the governance modernization of mega-cities, revealing the overall picture, core concepts, and behavioral paths of digital transformation. Meanwhile, Chen et al. [41] develop a three-dimensional “tool-stage-interest” framework to comprehensively analyze China’s low-carbon building policies and propose targeted recommendations. Yu et al. [42] evaluated intelligent construction development in 24 Chinese pilot cities, highlighting the necessity of needs-based policy tools. They recommend leveraging local government-funded demonstration projects and public programs to advance intelligent construction technology adoption. Chen [43] employs a “theme-instrument-evaluation” framework to examine the digital transformation policies within the construction industry. By developing a system dynamics model, the study simulates various policy scenarios, leading to the formulation of targeted optimization strategies.

Furthermore, Fujian Province plays a vital role in cross-strait exchanges and regional cooperation [44]. Li et al. [45] revealed limited digital adoption in the construction, with survey data indicating that only 10% of projects incorporate digital technologies. Given this low penetration rate, analyzing Fujian Province’s policies has become critical to driving regional industry modernization.

It can be seen that few scholars have utilized policy instruments to study the texts of intelligent construction policies and are often limited to a single policy tool perspective or two-dimensional frameworks, neglecting the systematic examination of policy tool selection and stakeholder implementation effects from the perspective of policy objectives. What is more, existing studies on intelligent construction policy analysis predominantly focus on national-level policies, overlooking the need for local adaptation. Therefore, this study takes Fujian Province as an example and establishes a multidimensional framework to analyze the implementation effects of the policy objectives of intelligent construction.

2. Materials and Methods

This paper utilizes content analysis to quantitatively examine intelligent construction policy texts in Fujian Province through the lens of policy instruments. Grounded in stakeholder theory, it establishes a three-dimensional analytical framework integrating policy instruments, stakeholders, and policy targets to elucidate the formulation logic and developmental trajectory of Fujian’s intelligent construction policies.

2.1. Theory of Policy Instruments

Policy instruments translate policy recommendations into concrete initiatives and are an important means of policy implementation. The selection and development of these tools are directly related to the effectiveness of policy implementation and the achievement of desired targets.

The term “policy instruments” originated in the mid-1950s within Lindblom and Dahl’s work “Politics-Economics Technology Adopted by Modern States”. Subsequently, the Dutch Geelhoed Committee linked policy failures to the absence of suitable policy instruments. The concept rose to prominence in Western academia during the 1980s and expanded significantly by the late 20th century. It remains a fundamental element of contemporary policy research [46]. The classification of policy instruments originated from the foreign school of instrumentalism, which posits that selecting appropriate policy instruments is crucial for the success of government policies. Understanding their attributes and characteristics enables the identification of patterns and the development of a scientific framework. For instance, Rothwell and Zegveld [47] categorized policy instruments into supply-oriented, environment-oriented, and demand-oriented types based on their impact on technological innovation, simplifying complex systems to highlight key policy elements and interactions. Schneider and Ingram [48] classified instruments into authoritative, causal, empowering, symbolic, persuasive, and learning types to guide policy makers in achieving developmental goals. Howlett and Ramesh [49] distinguished policy instruments as authoritative, hybrid, or voluntary based on the degree of government intervention, clarifying the spectrum of policy rigidity. McDonnell and Elmore [50] further categorized instruments into command, incentive, capacity-building, and authority reorganization types according to their intended objectives. These classifications enhance policy precision and effectiveness by systematically organizing instrument functions and applications.

The construction industry has entered the era of intelligentization, prompting this study to innovate upon Howlett and Michael’s methodology by categorizing policy tools into regulatory, market, incentive, and social participation types.

2.2. Content Analysis Method

According to Franzosi, content analysis is a research method that objectively, systematically, and quantitatively describes specific communication content [51]. It involves analyzing authoritative policy texts to convert subjective qualitative issues into quantitative data, helping to identify and correct cognitive biases in policy implementation over time. This method gained prominence through John Naisbitt’s Megatrends, which was praised for capturing the “pulse of the times [52]. “In China, Wang [53] argues that nearly all media content can be analyzed using this approach. With advancements in computer technology, the method has evolved, enabling the processing and mining of multimedia information in networked environments, further solidifying its role as a mature research theory. At present, many scholars have adopted the content analysis method to carry out social science research. Based on the research content of this paper, nine basic steps are summarized, as shown in Figure 1: (1) Determine the research problem: conduct text analysis of policies related to intelligent construction in Fujian Province; (2) Data crawling: complete data crawling through official government websites, data crawling software, and AI tools; (3) Data collection: select the policies on intelligent construction at the provincial level in Fujian Province; (4) Data screening: binding normative documents such as plans, opinions, guidelines, outlines, regulations, circulars, decisions, etc., to the policies of Fujian Province, decisions, etc.; (5) Sample determination: select research samples and choose the intelligent construction policies at the provincial level in Fujian Province as the research samples; (6) Construct analytical framework: construct an analytical framework for the policy texts based on the theory of related policy instruments, and code and frequency statistics for the selected policy texts; (7) Conduct quantitative analysis: conduct data analysis, and objectively evaluate the intelligent construction policies in Fujian Province based on quantitative analysis; (8) Propose policy recommendations: make recommendations based on the results of the study; (9) Revise and Perfect: feed the modifications back to the government to improve the existing policies.

2.3. Data Sources and Text Selection

To ensure the accuracy of the sample data, the search for intelligent construction policy texts adhered to three key principles: First, keyword searches were conducted using terms such as “intelligent construction”, “BIM”, and “building industrialization”. Second, policy texts were sourced exclusively from authoritative entities, including the People’s Government of Fujian Province, the Department of Housing and Urban-Rural Development, and the Xiamen Municipal Government. Third, the study focused on currently effective policy documents, encompassing planning reports, opinions, guidance, and outlines, while excluding speeches and approvals. An initial collection of 352 construction industry policy texts from Fujian Province was refined to 47 valid documents, covering the period from 2015 to 2024.

Table 1. Policy documents related to intelligent construction in Fujian Province and Xiamen.

Number	Policy Name	Year
1	Guiding opinions on promoting the pilot modernization of the construction industry	2015
2	Notice on the issuance of guidelines for promoting the application of building information modeling	2015
3	Fujian building information modeling (BIM) technology application alliance established	2015
4	Fujian “13th Five-Year Plan” digital Fujian special plan	2016
5	Notice on the issuance of the 2016 Fujian Province intelligent manufacturing special action plan	2016
…	…	…
43	Notice on the announcement of the organization of on-site observation activities of intelligent Construction Projects	2024
44	On the issuance of Xiamen city housing and municipal engineering intelligent site evaluation guidelines (for trial implementation) notice	2024
45	Guidance on measures to reduce construction waste at design source	2024
46	Notice on the pilot work of BIM review of construction drawings of building construction projects	2024
47	Notice on encouraging the addition of intelligent construction technology application evaluation in tendering activities	2024

outlines the screening process in detail.

2.4. Three-Dimensional Analysis Framework Construction

The analytical framework of this paper is represented by a three-dimensional orthogonal diagram, where the policy instruments (X-axis) are categorized into regulatory, incentive, market, and socially participatory types. The policy stakeholders (Y-axis) are divided into governments and relevant departments, construction-related enterprises, universities and research related organizations, and the general public, based on their participation in the policy. Finally, the policy targets (Z-axis) are classified according to comprehensive development goals, including technological innovation, industrial upgrade, well developed systems, cultivation of talent, and establishing the concept according to the overall development objective. The schematic diagram is shown in Figure 2.

2.4.1. X-Axis: Policy Instruments

• Regulatory policies: These use government authority to guide national development. They set rules, standards, and laws to directly regulate intelligent construction. This includes government plans, building smart systems for industry upgrades, and setting requirements for construction industry innovation. Regulatory policy instruments include target planning, regulatory control, administrative approval, industrial restructuring, well-established regulatory systems, and expert capacity-building.
• Incentive-oriented policies: These span economic, technological, and social areas. Economically, they boost intelligent construction by diversifying funding, offering fiscal incentives, and refining tax policies. Technologically, they advance R&D and ensure a steady flow of intellectual resources. Socially, they encourage collaboration among businesses, universities, and research bodies through training platforms, fostering sector-wide growth. These policies is further refined into economic incentives, technological incentives, social incentives, and judging incentives, as well as pilot demonstrations and innovation leadership.
• Market-oriented policies: These aim to enhance the government’s guiding role in top-level design, planning, and policy formulation, etc., while fully leveraging the market’s decisive role in resource allocation. These policies strengthen the market position of enterprises, actively explore paths and modes for the synergistic development of intelligent construction and construction industrialization, guide the effective aggregation of various market elements, accelerate the transformation and upgrading of the construction industry, improve quality and efficiency, and comprehensively enhance intelligent construction. These policy instruments are divided into market competition, market credit, market collection, integration of market resources, and market price regulation.
• Social participation-oriented policies: These highlight the government’s role in fostering societal engagement through initiatives like promoting technical seminars and on-site observation events by social organizations and universities. These efforts create an environment that supports the high-quality development of intelligent construction. Additionally, ongoing education and professional training for technical staff can boost their skills. This approach leverages collective wisdom and resources, addressing the limitations of unilateral government actions and enhancing social unity. These policy instruments are divided into three categories: promotional guidance, voluntary action, and continuing education training.

Overall, regulatory policy instruments are more direct and provide strong support for the development of intelligent construction. Market-oriented policies are conducive to playing the decisive role of the market in resource allocation, while incentive-oriented policies can fully mobilize the enthusiasm and initiative of various entities in the market. These two types of policies complement each other, creating a synergistic effect. Social participation-oriented policies can enhance public recognition of intelligent construction, promote industrial collaboration, and create a positive social atmosphere.

2.4.2. Y-Axis: Policy Stakeholders

The advancement of intelligent construction demands collaboration across various sectors. Governments should establish supportive policies and oversight mechanisms. Industry players, including design and construction firms, must utilize 3D models, integrate smart systems, and employ virtual simulations for effective project management. Developers should lead in R&D, leveraging their resources to push technological progress. The public should embrace and support intelligent construction products. Meanwhile, universities and research bodies need to partner with industry leaders to drive innovation and knowledge sharing. Ultimately, the growth of intelligent construction relies on this multifaceted cooperation. This paper divides the stakeholders in the intelligent construction policy into governments and relevant departments, construction-related enterprises, universities and research organizations, and the general public, among which the construction-related enterprises include the construction units, design units, developers, and so on.

2.4.3. Z-Axis: Policy Targets

The core of intelligent construction policies is to drive the industry’s smart transformation across all sectors. These policies both promote and ensure the achievement of intelligent construction goals, which, in turn, guide policy development, creating a close, mutually reinforcing relationship. Current efforts focus on five key aspects: establishing the concept, technological innovation, industrial upgrade, well-developed systems, and cultivation of talent.

Firstly, establishing the concept is the foundational step, as it guides strategic direction at national and regional levels. Only by prioritizing a shared vision of intelligent construction can we systematically inject momentum into subsequent actions. Secondly, technological innovation serves as the core driving force, enabled by this conceptual framework. New technological achievements propel the industry from traditional methods toward modernization and intelligence. Thirdly, industrial upgrading follows as a practical step, creating application spaces and market demand for innovations. As industries evolve, enterprises gain competitive advantages, further stimulating the need for advanced technologies and models. Fourthly, well developed systems sustain progress by fostering a supportive environment through intellectual property protection, infrastructure enhancement, and institutional incentives for researchers and practitioners. Lastly, cultivation of talent is the enduring cornerstone, ensuring the long-term viability of technological innovation and industrial upgrade through high-quality training in intelligent construction practices.

2.5. Policy Text Coding

This study utilized Nvivo 12 Plus software to code 47 policy documents issued between 2015 and 2024. The coding followed a hierarchical structure: Level 1 (policy documents, numbered 1–47 by issuance date), Level 2 (three-dimensional framework: X = 1, Y = 2, Z = 3), Level 3 (further categorization for X-axis; Y and Z marked as 0), and Level 4 (specific content, e.g., 1(3) for the third point in the first paragraph). Levels were separated by ‘-’, resulting in a comprehensive policy coding table, as illustrated in

Table 2. Policy text codes (partial).

	Policy Papers (Level 1)	Dimension (Level 2)	Category (Level 3)	Detailed Entries (Level 4)	Code Content
Policy instruments	Fujian Province construction industry ‘14th Five-Year Plan’ development plan	Regulatory	Industrial restructuring	Optimisation of the industrial layout of components and parts and components	13-2-5-2(5)
	Guiding opinions on promoting the pilot modernisation of the construction industry	Incentive-oriented	Pilot demonstrations and innovation leadership	Summarise and disseminate pilot experiences in a timely manner	1-1-5-5(5)
	Notice of call for intelligent construction machines, equipment and facilities and construction robot technology	Market-oriented	Marketplace	Call for Intelligent Construction Machines, Intelligent Equipment and Facilities, Construction Robots, etc.	41-3-3-1
	Work programme on accelerating the development of intelligent construction in Fujian Province	Social participation-oriented	Publicise and guide	Increase the publicity of outstanding projects and typical cases	24-4-1-6(13)
Policy stakeholders	Suggestions on accelerating the development of the assembly building industry	Governments and relevant departments	Not classified	Increasing targeted policy guidance	14-1-0-2(2)
	Work programme on accelerating the development of intelligent construction in Fujian Province	Construction-related enterprises		Accelerating the cultivation of intelligent construction industry clusters	24-2-0-5(7)
	Notice on vigorous promotion of industrialised construction of municipal works	Universities and research related organisations		Encourage the development of basic common technology and key core technology issues.	29-3-0-5(8)
	Implementation opinions on accelerating the development of new construction industrialisation	The general public		Create an atmosphere of public opinion in which the whole society pays attention to and supports the development of the new type of industrialisation of construction	15-4-0-4(4)
Policy targets	On the issuance of 2024 Xiamen city urban and rural construction quality mention notice	Technological innovation	Not classified	Implementation of ‘system instead of brain’, forward design and artificial intelligence-assisted review of construction drawings	39-1-0-5(39)
	Implementation opinions on accelerating the development of new construction industrialisation	Industrial upgrade		Promote resource sharing, system integration and linkage development upstream and downstream of the industrial chain.	15-2-0-3(2)
	Fujian building information modelling (BIM) technology application alliance established	Well developed system		Promote BIM production, learning, research and use of technology	3-3-0-1
	Implementing the opinions on promoting the sustainable and healthy development of the construction industry	Cultivation of talent		Vigorously promote the school-enterprise joint-industry talent construction	7-4-0-5
	Notice on the issuance of the implementation plan for carbon peak in urban and rural construction in Fujian Province	Establishing the concept		Promoting the application of renewable energy	28-5-0-3(26)

.

To delve deeper into the structural system and evolutionary trajectory of intelligent construction policies in Fujian Province, this study constructs a three-dimensional analytical framework based on policy instruments and employs content analysis to systematically analyze current intelligent construction policies. The aim is to uncover their internal logic and developmental laws, providing a theoretical foundation and practical guidance for further optimizing related policies.

3. Validation and Analysis

3.1. Analysis of the Basic Situation of Intelligent Construction Policies

From 2015 to 2024, both Fujian Province and its intelligent construction pilot city, Xiamen, consistently released relevant policies each year, averaging 4.7 annually and peaking at 16, as shown in Figure 3.

3.1.1. The First Phase

From 2015 to 2019, Fujian Province underwent a formative stage in intelligent construction, driven by policy support. The “Guiding Opinions on Promoting the Pilot Modernization of the Construction Industry” identified Fuzhou, Xiamen, Zhangzhou, Quanzhou, Ningde, and Sanming as pilot cities to explore replicable construction structural systems tailored to local conditions. In 2016, aligning with the national “13th Five-Year Plan”, Fujian released the “13th Five-Year Plan for Digital Fujian” to steer the construction industry toward digitalization and informatization. This phase emphasized pilot projects and industry-wide digital transformation to establish a foundation for intelligent construction, though a comprehensive guidance framework had not yet been fully developed.

3.1.2. The Second Phase

From 2020 to 2021, intelligent construction experienced rapid development, propelled by the Guiding Opinions on “Promoting the synergistic development of intelligent construction and building industrialization”, which established the guiding principles, objectives, and key tasks for nationwide smart building advancement. While Fujian Province did not issue a relevant policy in 2020, likely due to the need to align national guidelines with local conditions, it substantially intensified policy efforts in 2021. The province’s “14th Five-Year Plan” outlined clear development targets, prioritizing BIM technology, prefabrication and assembly, integration and synergy, pilot programs for general engineering contracting, and industrial chain optimization.

3.1.3. The Third Phase

Between 2022 and 2024, intelligent construction in Fujian Province initially declined due to the COVID-19 pandemic but rebounded after restrictions eased in late 2022, reaching peak policy prominence in 2023 and 2024. In 2023, Fujian introduced the “Work program on accelerating the development of intelligent construction”, promoting systemic automation through BIM technology, construction robots, and prefabricated assembly in medium-to-large projects. Xiamen prioritized talent development by creating an expert database and implementing credit incentives and scoring advantages in bids for smart building projects. These initiatives signify Fujian’s shift from conventional construction to a smart building paradigm.

3.2. Reliability and Validity Analysis

In both survey research and content analysis, the accurate measurement of variables is fundamental. After coding in content analysis, evaluating the reliability and validity of the coding is essential. Reliability refers to the consistency of results upon repeated analysis, with high consistency indicating minimal error and strong reliability, thereby bolstering the study’s credibility. Validity measures how well the coding results reflect the true nature of the study, ensuring accurate assessment of measurement quality.

Given the inherent subjectivity in content analysis coding, which depends on the researcher’s knowledge and interpretation, errors are inevitable. Thus, assessing reliability and validity is crucial to minimize errors and enhance study quality. This paper analyzes the framework and category coding of intelligent construction policy texts from Fujian Province and Xiamen City, aiming to ensure high reliability and validity, thereby providing a robust foundation for related research and decision-making.

3.2.1. Reliability Test

This study employs a content analysis method to ensure the research’s coherence and progression, incorporating class analysis and judgement analysis. Class analysis evaluates the rationality of establishing indicator attribution dimensions, while judgement analysis assesses the accuracy of these attributions from the researcher’s subjective perspective. The X-axis divides the different types of policy instruments, scientifically categorizes social groups influencing intelligent construction development on the Y-axis, and bases the Z-axis on policy target areas, specifically utilizing Fujian Province’s intelligent construction policy texts to demonstrate policy tendencies effectively. This classification is deemed highly reliable through category analysis.

Re-measurement reliability, also referred to as test-retest reliability, involves administering the same measurement tool to the same group of subjects at different times under consistent procedures to assess the credibility of the measurement [54]. This method is widely utilized in scenarios requiring substantial manpower and resources to evaluate measurement accuracy. Using SPSS 24.0 software’s reliability test function and the retest reliability method, reliability is determined based on Cronbach’s α coefficient: values above 0.8 indicate high reliability, between 0.7 and 0.8 indicate good reliability, between 0.6 and 0.7 indicate acceptable reliability, and below 0.6 indicate poor reliability. To ensure data accuracy and reliability, the collected data were thoroughly examined, and Pearson’s product–moment correlation formula, as shown in Formula (1), was applied.

$$\gamma ={\displaystyle \frac{\sum \left(\frac{X1X2}{N}-M1M2\right)}{S1\ast S2}}$$

(1)

In this test, X₁ and X₂ in Equation (1) denote the number of policy instruments in two classification probes for the same feature category, M₁ and M₂ represent the mean number of policy instruments for each classification study, S₁ and S₂ are their respective standard deviations, and N is the number of feature category classification items.

In this study, a random sample of 80 units was selected from the 285 coded units in Nvivo, and a test-retest reliability analysis was conducted with a one-month interval, yielding the following statistical results:

• Intelligent construction policy instruments: By analyzing the basic policy instruments of these 80 policy samples, a table of coding statistics was summarized, as detailed in the table below.

Calculated by SPSS, the Cronbach’s alpha coefficient in

Table 3. Sample classification statistics for the X-axis.

X-Axis	The First Time	The Second Time
Target planning	18	17
Regulatory control	3	3
Administrative approval	2	2
Industrial restructuring	4	4
Well-established regulatory system	7	7
Expert capacity-building	3	3
Economic incentive	7	8
Technological incentive	3	3
Social incentive	1	1
Judging incentive	5	5
Pilot demonstrations and innovation leadership	7	7
Market competition	2	2
Market credit	3	3
Market collection	3	3
Integration of market resources	3	3
Market price regulation	1	1
Publicise and guide	2	2
Voluntary action	4	4
Continuing education training	2	2

reaches 0.921, which indicates that the classification of intelligent construction policies based on the basic policy instruments dimension has high reliability; thus, the setting of the category can be considered reasonable.

2.

Intelligent construction policy stakeholders: By categorizing and coding the 80 policy units, we were able to obtain a statistical table, which is detailed in

Table 4. Sample classification statistics for the Y-axis.

Y-Axis	The First Time	The Second Time
Governments and relevant departments	39	38
Construction-related enterprises	23	24
Universities and research related organizations	11	11
The general public	7	7

.

The Cronbach’s alpha coefficient, calculated using SPSS software, for the intelligent construction policy stakeholders in Table 4 is 0. 895, demonstrating high reliability and feasibility in class setting.

3.

Intelligent construction policy targets: By categorizing and coding the 80 policy units, we were able to obtain a statistical table, which is detailed in

Table 5. Sample classification statistics for the Z-axis.

Z-Axis	The First Time	The Second Time
Technological innovation	22	23
Industrial upgrade	14	15
Well developed system	23	22
Cultivation of talent	16	15
Establishing the concept	5	5

.

We found that the Cronbach’s alpha coefficient in Table 5 is 0.926, which indicates that all the targets of our constructed intelligent construction policy are scientifically sound.

As a result of this study, we found that the coding has a very high reliability and can be used for effective coding analysis.

3.2.2. Validity Test

As a critical metric for evaluating the validity of test results, validity reflects the accuracy and reliability of a test or instrument in measuring the target object. However, this assessment process is influenced by multiple factors, including the policy screening process, coding methods, analytical frameworks, and classification strategies of policy instruments, all of which may impact the validity.

In the screening of policy texts, this study adhered to rigorous guidelines and procedures, utilizing authoritative databases such as the official websites of the Fujian Provincial and Xiamen Municipal Governments, the Ministry of Housing and Construction, and the China Knowledge Network (CKN), while also engaging with relevant governmental staff to ensure comprehensive coverage and minimize omissions. Through detailed reading, analysis, and screening of 47 policy provisions, the research framework evolved from a single-dimensional to a multidimensional approach, enhancing the precision, scientific rigor, reliability, and validity of the study. This methodological rigor ensures the representativeness and accuracy of the selected policy samples, thereby strengthening the overall validity of the research.

3.3. Theme Word Analysis

Theme word analysis in

Table 6. Word frequency distribution of intelligent construction policy texts in Fujian Province.

The First Phase		The Second Phase		The Third Phase
Subject line	Word frequency	Subject line	Word frequency	Subject line	Word frequency
Technology	15	Enterprises	25	Building	33
Pilot	13	Architecture	20	Construction	31
Enterprise	12	Technology	15	Intelligent	26
Applications	11	Assembly	12	Pilot	25
Architecture	11	System	10	Engineering	25
Markets	9	Construction	10	Technology	18
Projects	8	Intelligent	10	Enterprises	17
Informatisation	8	Development	9	Intelligence	15
Engineering	7	Applications	8	Applications	12
Innovation	7	Construction	8	Green	12
Funding	7	Design	8	Construction Sites	11
Intelligent	6	Encouragement	7	Encouragement	11
Demonstration	6	Pilot	7	Regulation	11
Services	5	Regulation	7	Safety	11
Regulation	5	Markets	7	Management	11
Manufacturing	5	Incentives	6	Facilities	11
R&D	5	Bidding	6	Municipal	11
Platforms	5	Evaluation	6	Construction	11
Economy	5	Accreditation	6	Assembly	11
Materials	5	Credit	5	Resourcing	10
Credit	4	Mechanism	5	Upgrading	10
System	4	Quality	5	Evaluation	10
Cultivation	4	Construction	5	Funding	10
Experts	4	Integration	5	Design	9
Planning	4	Technology	5	Unit	8
Components	4	Perfection	5	Experts	7
Construction	4	Talent	8	Quality	7
Standards	4	Industry Chain	4	Incentives	7
Experience	4	Training	4	Advocacy	7
Motivation	4	Synergy	4	Retrofitting	6
Industrial	3	Whole process contracting	8	Declaration	6
Approvals	3	Leadership	4	Mechanisms	6
Internet	3	Standards	4	Markets	6
Filing	3	Safety	4	Industrialisation	5
System	3	Survey	3	Credit	5
Green	3	Informatisation	3	Tenders	5
Accreditation	3	Innovation	3	Co-operation	5
Encouragement	3	Exploration	3	Leadership	5
Purchase	3	Industrialisation	3	Explore	4
Subsidies	3	Leading	3	Cycle	4

, derived from coding original policy texts in Fujian Province across three periods (2015–2019, 2020–2021, and 2022–2024), involved removing redundant terms from coded paragraphs. The first stage (2015–2019) emphasized keywords such as technology, pilot, cultivation, and informatization, reflecting initial efforts to promote intelligent construction through government incentives, enterprise pilot projects, and the application of information technology, as exemplified by the “2016 Fujian Province Intelligent Manufacturing Special Action Plan. “The second stage (2020–2021) highlighted terms like assembly, integration, synergy, industry chain, and whole process contracting, marking a shift toward integrating upstream and downstream project stages, fostering talent development, and advancing construction industry intelligence, as seen in the “Notice on the Pilot of Extending the Whole Industry Chain by General Contracting Engineering”. The third stage (2022–2024) focused on safety, quality, green, and resourcing, underscoring a commitment to enhancing building performance, creating livable environments, and refining intelligent construction development through policy incentives, market regulation, and social advocacy.

3.4. Policy Network Analysis

Using semantic network analysis, this study interrelates and integrates high-frequency words from Fujian Province’s intelligent construction policy texts, visualized as a network of blue nodes and black arrows, where line density indicates correlation strength. As shown in Figure 4, generated via ROSTCM 6, core thematic words such as “intelligent”, “technology”, “engineering”, and “enterprise” are centrally positioned and extensively interconnected, highlighting their policy significance. Notably, “technology” serves as a pivotal node, linking directly to “application”, “enterprise”, “project”, and “pilot”, emphasizing the need for enterprises to focus on emerging technologies and innovate across domains in pilot projects to advance intelligent construction. The policy text reflects comprehensive coverage and a systematic, evolving framework.

Through thematic word analysis and semantic network visualization of Fujian Province and Xiamen City’s policy texts, this study identifies key priorities, such as promoting BIM for pre-design to minimize rework and advancing prefabricated construction to reduce environmental impact. The findings guide the intelligent transformation of the construction industry, underscoring the importance of pilot demonstration projects, replicable best practices, and establishing a localized, high-quality intelligent construction system, thereby enhancing intergovernmental and enterprise communication and fostering collaborative learning and innovation dissemination.

4. Discussion

4.1. Quantitative Analysis of the X-Axis of Intelligent Construction Policy Instruments

Table 7. Frequency distribution statistics of the X-axis.

Type of Instrument	Instrument Name	Quantity	Percentage Share of Classification	Overall Percentage
Regulatory	Target planning	68	54.40%	47%
	Regulatory control	9	7.20%
	Administrative approval	5	4%
	Industrial restructuring	12	9.60%
	Well-established regulatory system	22	17.60%
	Expert capacity-building	9	7.20%
Incentive-oriented	Economic incentive	23	29.11%	29.70%
	Technological incentive	11	13.92%
	Social incentive	4	5.06%
	Judging Incentive	18	22.78%
	Pilot demonstration and innovation leadership	23	29.11%
Market-oriented	Market competition	6	15.38%	14.66%
	Market credit	9	23.08%
	Market collection	11	28.20%
	Integration of market resources	11	28.20%
	Market price regulation	2	5.13%
Social participation-oriented	Promotional guidance	5	21.74%	8.64%
	Voluntary action	12	52.17%
	Continuing education training	6	26.09%

reveals that regulatory policy instruments dominate at 47%, highlighting the Fujian Province and Xiamen Municipality governments’ emphasis on mandatory measures like target plans and regulatory systems to ensure orderly intelligent transformation in the construction industry. These policies guide development, standardize practices, and underscore the critical role of regulation in advancing regional construction industry intelligentization.

Incentive-oriented policies make up 29.70%, reflecting the government’s focus on economic incentives, pilot projects, and innovation. Financial support, such as subsidies, tax breaks, and special funds, is prioritized, alongside encouraging pilot projects to develop replicable models for intelligent construction.

Market-oriented policies account for 14.66%, indicating significant untapped potential in Fujian’s construction market. The government aims to optimize market resource allocation while regulating prices to prevent resource waste and ensure efficient resource flow during intelligent transformation.

Social participation-oriented policies are the least utilized at 8.64%, due to low public awareness and engagement. The government should enhance publicity, expand participation channels, and mobilize social forces to fully integrate the industry’s intelligent transformation.

4.2. Quantitative Analysis of the Y-Axis of Intelligent Construction Policy Instruments

Figure 5 illustrates the distribution of policy stakeholders in Fujian Province’s intelligent construction initiative. The government and relevant departments, representing 49% of stakeholders, play a pivotal role by leveraging their authority and resource allocation capabilities. They lead the push for intelligent transformation in the construction sector through strategic planning, financial support, and policy guidance, fostering an environment that is conducive to the adoption of new technologies.

Construction-related companies, comprising 28% of stakeholders, are crucial as implementers of intelligent construction projects. They are involved in decision-making, design, production, construction, and operation, necessitating the optimization of construction processes and enhancement of employee expertise to meet high standards and ensure project quality.

Universities and research related organizations, along with the general public, account for 15% and 8% of stakeholders, respectively. While academic institutions drive innovation through research, current policy support for their activities is insufficient, highlighting the need to boost their research potential. Meanwhile, the public’s role is often overlooked, underscoring the importance of leveraging social influence and communication channels to promote broader acceptance of intelligent and low-carbon construction practices.

4.3. Quantitative Analysis of the Z-Axis of Intelligent Construction Policy Instruments

Figure 6 shows Fujian Province’s intelligent construction policy targets. A well-developed system, accounting for 29%, involves setting technical standards to ensure technology compatibility and optimizing management norms. Starting from project planning, big data and AI-powered intelligent evaluation and supervision mechanisms are used to analyze feasibility and environmental impact, promoting efficient approvals.

Technological innovation, at 27%, focuses on integrating cutting-edge tech into construction. AI algorithms, big data for lifecycle data convergence, and IoT for equipment networking all contribute to system improvements.

Industrial upgrade, also 27%, is about optimizing construction enterprise structures and management. Closer cooperation between upstream and downstream firms and within departments breaks information silos, enhancing efficiency and decision-making.

Cultivation of talent, at 20%, is supported by universities setting up relevant majors, vocational enterprise cooperation for customized training, and employee training systems in large enterprises.

Establishing the concept, 7%, is crucial. Inadequate focus may cause a lack of long-term planning. Thus, the construction industry should conduct more training and exchanges to strengthen this aspect.

4.4. X–Y Axis Analysis of Intelligent Construction Policies

However, analyzing from the perspective of a single policy instrument makes it difficult to provide a comprehensive and complete insight into the selection and application of policy instruments. Since the types of policy instruments are not strictly mutually exclusive, there is some crossover between them, so it is necessary to carry out a joint analysis from multiple perspectives. In view of this, this paper integrates the Y and Z perspectives to analyze the policies related to intelligent construction in Fujian Province from a two-way perspective.

Through the two-dimensional analysis of X–Y in

Table 8. X–Y axis frequency distribution statistics.

Instrument Type	Instrument Name	Governments and Related Departments		Construction-Related Enterprises		Universities and Research Related Organizations		The General Public
		Subtotal	Proportions	Subtotal	Proportions	Subtotal	Proportions	Subtotal	Proportions
Regulatory	Target planning	30	71.43%	15	68.18%	0	0	5	83.33%
	Regulatory control	1	2.38%	0	0	0	0	1	16.67%
	Administrative approval	1	2.38%	0	0	0	0	0	0
	Industrial restructuring	7	16.67%	6	27.27%	0	0	0	0
	Well-established regulatory system	3	7.14%	1	4.55%	1	11.11%	0	0
	Expert capacity-building	0	0	0	0	8	88. 89%	0	0
	Total	42	100%	22	100%	9	100%	6	100%
Incentive-oriented	Economic incentive	3	20%	0	0	0	0	0	0
	Technological incentive	2	13.33%	2	28.57%	5	100%	1	33.33%
	Social incentive	0	0	0	0	0	0	2	66.67%
	Judging incentive	4	26.67%	0	0	0	0	0	0
	Pilot demonstrations and innovation leadership	6	40%	5	71.43%	0	0	0	0
	Total	15	100%	7	100%	5	100%	3	100%
Market-oriented	Market competition	1	7.69%	4	36.36%	0	0	0	0
	Market credit	6	46.15%	1	9.09%	0	0	0	0
	Market collection	0	0	0	0	0	0	0	0
	Integration of market resources	3	23.08%	6	54.55%	0	0	0	0
	Market price regulation	3	23.08%	0	0	0	0	0	0
	Total	13	100%	11	100%	0	0	0	0
Social participation-oriented	Promotional guidance	0	0	0	0	1	14.28%	1	33.33%
	Voluntary action	0	0	0	0	3	42.86%	2	66.67%
	Continuing education training	0	0	1	100%	3	42.86%	0	0
	Total	0	0	1	100%	7	100%	3	100%

, we can deeply understand the distribution of policy instruments among different intelligent construction policy stakeholders so as to better formulate effective policies and provide policy makers with a more comprehensive reference basis.

In the policy system of governments and related departments, regulatory policies dominate at 60%, focusing on target planning and industrial restructuring. These policies address imbalances and inefficiencies in the construction industry through macro-strategic planning, directing resources toward emerging and advantageous intelligent construction sectors to foster industrial upgrades and innovation ecosystems. Incentive-oriented policies make up 21.43%, emphasizing pilot demonstrations, innovation leadership, and evaluation incentives. By establishing fair and transparent evaluation standards and reward systems, they aim to identify forward-thinking and replicable practices. Market-oriented policies account for 18.57%, targeting market credit, resource integration, and price regulation. These policies promote orderly market transactions, honesty, and the rational allocation of resources and prices.

In the policy system of construction-related enterprises, regulatory policies dominate at 53.66%, followed by market-oriented policies account for 26.83%, which focus on resource integration and competition. These policies optimize resources like manpower, machinery, materials, law, and the environment, improving operational efficiency and enabling better resource flow among enterprises. Incentive policies make up 17.07%, encouraging innovation in intelligent construction. Social participation policies, at 2.44%, aim to foster technical exchanges and accelerate the adoption of new technologies and ideas across the industry.

In the policy framework of universities and research organizations, regulatory policies make up 42.86%, focusing on building talent teams and ensuring policies to attract construction professionals. Social participation policies account for 33.33%, while incentive policies represent 23.81%, promoting voluntary actions, continuing education, and technological incentives. These measures help break the closed structure of traditional university classrooms, expand the horizons of construction-related disciplines through competition-driven learning, and foster intelligent construction innovations to better align with industry development needs.

In the public policy system, regulatory policies make up 50%, providing comprehensive guidance to standardize public activities in intelligent construction and ensure orderly progress. Incentive policies and social participation policies each account for 25%. Incentive policies focus on recognizing and rewarding positive public behaviors in intelligent construction, fostering a positive social culture and strengthening social cohesion in the field.

In Fujian Province, the development of intelligent construction relies heavily on regulatory policy instruments as the primary tool, emphasizing policy guidance and standardization to drive industrial transformation. Incentive-oriented policies also play a crucial role, providing strong momentum for stakeholders. Regulatory and incentive policies complement each other, forming a dual-wheeled drive that accelerates the growth of the intelligent construction industry.

4.5. X–Z Axis Analysis of Intelligent Construction Policies

The two-dimensional analysis of X–Z in

Table 9. X–Z axis frequency distribution statistics.

Instrument Type	Instrument Name	Technological Innovation		Industrial Upgrade		Well Developed System		Cultivation of Talent		Establishing the Concept
		Subtotal	Proportions	Subtotal	Proportions	Subtotal	Proportions	Subtotal	Proportions	Subtotal	Proportions
Regulatory	Target planning	12	100%	9	69.23%	12	66.67%	0	0	2	100%
	Regulatory control	0	0	0	0	0	0	0	0	0	0
	Administrative approval	0	0	0	0	0	0	0	0	0	0
	Industrial restructuring	0	0	3	23.08%	0	0	0	0	0	0
	Well-established regulatory system	0	0	1	7.69%	5	27.77%	0	0	0	0
	Expert capacity-building	0	0	0	0	1	5.56%	13	100%	0	0
	Total	12	100%	13	100%	18	100%	13	100%	2	100%
Incentive-oriented	Economic incentive	0	0	0	0	0	0	0	0	0	0
	Technological incentive	7	70%	0	0	2	66.67%	0	0	0	0
	Social incentive	0	0	0	0	0	0	0	0	0	0
	Judging incentive	1	10%	0	0	1	33.33%	1	100%	0	0
	Pilot demonstrations and innovation leadership	2	20%	1	100%	0	0	0	0	0	0
	Total	10	100%	1	100%	3	100%	1	100%	0	0
Market-oriented	Market competition	0	0	0	0	0	0	0	0	0	0
	Market credit	0	0	0	0	1	25%	0	0	0	0
	Market collection	1	100%	0	0	0	0	0	0	0	0
	Integration of market resources	0	0	1	100%	3	75%	0	0	0	0
	Market price regulation	0	0	0	0	0	0	0	0	0	0
	Total	1	100%	1	100%	4	100%	0	0	0	0
Social Participation-Oriented	Promotional guidance	0	0	0	0	1	100%	0	0	0	0
	Voluntary action	1	100%	0	0	0	0	1	25%	4	100%
	Continuing education training	0	0	0	0	0	0	3	75%	0	0
	Total	1	100%	0	0	1	100%	4	100%	4	100%

is used to understand the distribution of policy instruments in the case of different intelligent construction policy targets and to provide policy makers with a reference for policy improvement.

In technological innovation, regulatory policies dominate at 50%, providing a stable institutional environment by setting strategic goals and frameworks for intelligent construction innovation through authority and standardization. Incentive policies make up 41.66%, encouraging R&D enthusiasm via rewards for innovation outcomes and, in turn, fostering a cycle of technological advancement. Market-oriented policies, however, represent only 4%. Despite the market’s ability to identify industry trends, these policies are underutilized in directing resources toward innovative, economically viable projects.

At the industrial upgrade level, regulatory policies dominate at 86.66%, playing a central role in the intelligent construction industry’s transformation. These policies guide development paths, eliminate outdated capacities, and promote resource clustering in advantageous areas, driving industry-wide progress. In contrast, incentive-oriented and market-oriented policies each account for only 6.67%, indicating their limited influence. This reflects the current strategy’s underdeveloped focus on incentivizing industry growth and regulating the construction market, suggesting it is still in the early stages of exploring innovative models to lead industry upgrading.

At the system level, regulatory policies dominate at 69.23%, highlighting the need for macro-level strategic alignment with intelligent construction and strict tripartite oversight to ensure continuous system optimization. Market-oriented policies, at 15.38%, focus on integrating construction resources to enhance resource allocation, promote lifecycle efficiency, drive technological innovation, and scale the industry. Market credibility also fosters a favorable trading environment, supporting system improvement. Incentive policies, at 11.54%, aim to boost enterprise innovation, accelerate new technology adoption and R&D, and drive the ongoing advancement of the intelligent construction system.

At the talent cultivation level, regulatory policies accounted for 72.22%, social participation policies 22.22%, and incentive policies 5.56%. Regulatory policies focus on cultivating high-end professionals to meet the demanding knowledge and management requirements of intelligent construction. Meanwhile, social participation policies promote diverse training programs tailored to market and industry needs, enhancing adaptability in projects and supporting industry growth.

At the level of establishing the concept, social participation accounts for 66.67%, and regulation accounts for 33.33%. Voluntary, community-driven initiatives help embed intelligent construction concepts across society, while government regulation provides supportive coordination and standardization. Together, these efforts combine the strengths of all sectors to advance innovation and practice in intelligent construction.

In summary, technological innovation relies on regulatory and incentive policies to drive progress, while market-oriented policies, though smaller in proportion, play a key role in guiding innovative resource allocation. Industrial upgrade is primarily driven by regulatory policies, but the impact of incentive and market-oriented policies should be strengthened. Regulatory policies provide a foundation for system optimization, market policies enhance resource allocation, and incentive policies speed up technological advancement. Talent development requires regulatory policies to cultivate high-end professionals and social participation policies to improve market adaptability, together building a strong talent base for the industry. Establishing the concept of social participation, supported by regulatory policies, fosters societal integration and innovative practices in intelligent construction. Overall, while the intelligent construction policy system is improving, further optimization of policy synergy is essential for sustainable and healthy development.

5. Results

An analysis of Fujian Province’s intelligent construction policies (2015–2024) shows that regulatory policies dominate, with the government defining goals, plans, and industry boundaries. Incentive and market-oriented policies support enterprise R&D, innovation, and market growth, while market-oriented policies guide resource allocation and capital flow. Social participation policies are underemphasized, reflecting limited public engagement. Enhancing social participation is vital for multi-party governance and sustainable development. Furthermore, a two-dimensional X–Y and X–Z analysis identifies six key issues, with corresponding improvement measures suggested.

5.1. Separation of the Main Stakeholder of Each Policy

Universities and research institutions are often disconnected from society and the market, leading to inefficient transformation of cutting-edge achievements and difficulty in meeting immediate market needs. The government should create an industry–university–research collaboration platform to foster deeper cooperation among these entities, enhancing achievement transformation. Additionally, public awareness of intelligent construction remains low. When developing market-oriented policies, the government should prioritize talent cultivation and the promotion of industry concepts.

5.2. Lack of Multi-Party Guidance from Society in Policymaking

The government, relevant departments, and construction enterprises lack the guidance of social participation in policymaking. The current policies are mainly government-led, ignoring the enthusiasm and creativity of multiple stakeholders. This situation affects the social acceptance of projects and their environmental friendliness. It is essential to introduce relevant policies, such as establishing a public participation mechanism. This can stimulate the enthusiasm of all sectors of society to participate in promoting technological innovation and the efficient use of resources and also enhance the transparency and credibility of policies.

5.3. Lack of Diversified Inputs Due to Insufficient Social Participation

Industrial upgrade and system development face challenges due to limited social participation in policymaking. Without social capital involvement and risk-sharing mechanisms, enterprises find it difficult to bear high costs and build a sustainable industrial ecosystem. Additionally, the absence of diverse societal input makes it hard for standards to balance interests and adapt to technological advancements. To address this, broader participation from all sectors should be encouraged to establish a high-tech intelligent construction hub with Fujian’s unique characteristics.

5.4. Weakness in Fostering a Long-Term Talent Development Mindset

Current policies lack market-oriented guidance for talent cultivation and conceptual development, often prioritizing short-term economic gains over long-term investments. This neglect leads to a shortage of skilled talent and misaligned industry concepts, hindering the intelligent construction industry’s long-term growth and innovation. Without sufficient talent reserves, the industry may struggle to address future challenges. Thus, fostering a long-term talent training mindset is essential.

5.5. Lack of Regulation, Fair Competition, and Efficient Approvals

From the perspective of policy instruments, regulatory policies lack sufficient focus on regulatory control and administrative approval. Despite target planning, weak enforcement and ineffective oversight hinder implementation. Additionally, inefficient approval processes create obstacles, unclear procedures, and challenges in approving innovative projects. Therefore, efforts will be made to enhance regulatory control and streamline the approval process.

5.6. Lack of a Fair Competition and Credit Environment

When analyzing policy targets and instruments, market-oriented policies fall short in fostering competition and credit. The lack of a level playing field disadvantages enterprises and stifles small innovators, while a weak credit system increases trust costs. Insufficient evaluation and constraints also hinder project progress and quality. To address these issues, it is crucial to enhance competition, improve the credit system, stimulate market-driven growth, and advance intelligent construction in Fujian Province.

6. Conclusions

Drawing on extensive scholarly research, this paper classifies intelligent construction policy instruments into four categories. Integrating policy stakeholders and targets, a three-dimensional analytical framework for intelligent construction policies in Fujian Province is developed. The framework’s validity is confirmed through reliability and validity tests, followed by a quantitative analysis of each dimension, yielding three key findings:

6.1. Rational Use of Policy Instruments

Currently, the distribution of policy instruments for intelligent construction in Fujian Province is uneven, marked by a significant lack of social participation tools and suboptimal internal structures in market-oriented and incentive-oriented policies. In the future, it is crucial to sustain the dominance of regulatory policies while enhancing societal participation across diverse sectors. Furthermore, increased investment in market funding and research should be encouraged to optimize resource allocation and achieve balanced policy instrument utilization.

6.2. Balance of Interests of the Stakeholders

Government policies should prioritize the developmental goals of intelligent construction, clearly defining the roles of government, enterprises, universities, and the public in applying policy instruments. Moving forward, policy frameworks should align intelligent construction planning with practical business needs and establish structured timelines. Emphasis should be placed on the contribution of academic research to intelligent construction, facilitating expert participation through funded projects. Additionally, raising public awareness is crucial to ensuring the collective interests of all stakeholders are protected.

6.3. Achieving Policy Targets Requires Balanced Tools and Stakeholder Collaboration

The realization of intelligent construction policy targets depends on the effective use of policy instruments and the collaborative efforts of stakeholders. However, imbalances in policy instruments and insufficient stakeholder cooperation pose dual challenges to achieving these goals. Thus, policy formulation must prioritize the balanced allocation and coordinated application of policy instruments to ensure their effective integration. Concurrently, stakeholders should strengthen communication and collaboration, fostering synergy to collectively drive the advancement of intelligent construction.