Analysis of influencing factors for housing construction technology in Desakota Village and town communities in China

Housing fundamentally shapes social, economic, environmental, and health outcomes. China’s rapid economic growth and urbanization have intensified pressures on land, affordability, and environmental performance, with the construction sector responsible for substantial energy use and carbon emissions. Urban expansion has blurred urban–rural boundaries, accentuating disparities and transforming peri-urban spaces into Desakota regions. While prior work has examined socio-ecological and cultural dynamics of such hybrid spaces, there is a lack of rigorous frameworks to identify the construction technology factors that drive housing development in these contexts. This study addresses that gap by asking: which primary and secondary technological and managerial factors most strongly influence housing construction in China’s Desakota village and town communities? The purpose is to systematically identify, validate, and prioritize these factors to inform sustainable, affordable, and context-sensitive housing development aligned with shifts in ideology, income, urbanization, and policy.

Prior studies link housing affordability and delivery to macroeconomic and sectoral variables including inflation, mortgage finance, material and labor costs, urbanization, and policy regimes. Barriers to sustainable and green technologies adoption encompass cost and risk, government incentives, market and information failures, and managerial capacity (e.g., Ghana, Saudi Arabia). In China and comparable settings, key determinants span economic, technical, political, social, and managerial domains; willingness to pay for green housing is shaped by behavioral and policy factors, while retrofitting is constrained by management, finance, knowledge, and technology. Table 1 synthesizes macroscopic influences into four domains—political (land tenure, regulation, institutional cooperation), economic (financing, mortgage rates, material costs, incomes), cultural (race, family structure, traditions), and natural (climate, land resources). Despite extensive scholarship on urban–rural integration and hybrid spaces, prior work rarely decomposes and ranks the specific technological factors influencing Desakota housing. This study draws on that body of work to curate six primary and twenty secondary factors (Table 2) for empirical assessment.

The research proceeded in two phases: (1) framework development and factor identification; (2) questionnaire survey and statistical analysis. From a rigorous literature review and Desakota sociocultural needs assessment, six primary and twenty secondary influencing factors were identified and refined through an expert panel (civil engineers, architects, project managers, contractors, academics, and knowledgeable students, most with >10 years’ experience). A structured questionnaire using a 5-point Likert scale (1: Not at all likely to 5: Extremely likely) captured respondents’ ratings of each factor’s influence. Field data were collected from more than 60 ongoing construction projects across Desakota towns and villages; 140 questionnaires were distributed and 120 valid responses were received (~86% response rate). Demographics covered age, gender, education, role, experience, and region. Data analysis transformed Likert scores into Relative Importance Index (RII = ΣW/(A×N)) to rank factors; SPSS and Microsoft Excel 2019 were used. Internal consistency was assessed via Cronbach’s alpha: 0.909 for the six primary factors and 0.972 for the twenty secondary factors, indicating excellent reliability. Descriptive statistics (means, standard deviations) were computed for each factor, and results were visualized using stacked bar plots, distribution charts, and ridgelines. Expert feedback was then used to map primary–secondary relationships into a tiered framework.

Sample: 120 valid responses (57.5% male; major age groups: 30–40 years 40.8%, under 30 years 25.8%; education: graduate 52.5%, undergraduate 36.7%; key roles include engineers 19.2%, project managers 18.3%, mechanical engineers 16.7%, architects 10.8%; experience: <5 years 32.5%, >20 years 25.8%; regions: South 24.2%, North 21.7%, East 20.8%, Central 18.3%, West 15%). Reliability: Cronbach’s alpha = 0.909 (primary) and 0.972 (secondary). Primary factor rankings (RII): 1) Architectural and engineering mechanical factors (0.8149); 2) Adaptability and maintainability of the project (0.8112); 3) Construction project surroundings and market conditions (0.8037); 4) Sustainable construction materials and resources (0.7963); 5) Economic benefits of the project (0.7876); 6) Human factors of participants, users (0.7839). Secondary factor rankings (RII): top two tied at 0.823—Cost, financial, and economic evaluation (C17) and Analysis of the impact of the natural environment on buildings (C14). Next: Reasonable maintenance and replacement processes (0.8186), Support from authorities and policies (0.8161), Proper disposal of waste generated (0.8099), Project managers have a management awareness (0.8062), Rational materials supply chain and tendering process (0.8050), Use of resource-friendly materials (0.8037), Relevant ageing and low-carbon policies considered (0.8012). Remaining high-importance factors include: Construction operators are safety-conscious (0.7988), Ease of transport/installation/dismantling (0.7975), Site selection avoiding unfavorable locations (0.7963), Compliance with energy-efficient design codes (0.7950), Favorable conditions for retrofit management (0.7938), Safety of users and site environment (0.7925), Effective regulatory and control measures (0.7913), Cross-industry upstream–downstream cooperation (0.7876), Sound energy-saving retrofit plan (0.7826), Generation of indirect economic effects (0.7789), Good input–output ratio, ROI (0.7764). The analysis establishes a tiered relationship: Tier 1 focuses on architectural/mechanical design and environmental analysis; Tier 2 on project/supply chain management and serviceability; Tier 3 on financial evaluation and economic viability.

The study’s rankings directly address the research question by isolating which technological and managerial factors most influence housing construction in Desakota contexts. The top primary factor—architectural and engineering mechanical factors—together with environmental analysis as the top secondary factor, underscores the centrality of climate-responsive, comfort-oriented design and building systems in semi-urban settings. Tier 2 emphasizes effective project management, materials availability, logistics, safety, regulatory compliance, waste management, and maintainability, aligning site execution with user needs and lifecycle performance. Tier 3 highlights the decisive role of financial assessment, policy support, and ROI in ensuring project feasibility, adoption, and scalability. Compared with prior studies on green housing adoption, affordability, and retrofitting, these results integrate political, economic, cultural, and natural drivers into an actionable framework. The findings suggest that cross-industry cooperation, robust supply chains, and adherence to low-carbon policies can enhance both environmental outcomes and economic benefits. For policymakers and practitioners, the tiered model offers a prioritization roadmap: optimize indoor environmental quality and mechanical systems (Tier 1), reinforce project/supply chain management and maintenance planning (Tier 2), and rigorously evaluate economic feasibility and policy instruments (Tier 3).

The research provides the first structured identification, validation, and prioritization of construction technology factors for housing in China’s Desakota village and town communities. Using RII analysis on 120 responses with excellent reliability, the study found architectural and engineering mechanical factors to be the most influential primary determinant, while cost/financial evaluation and environmental impact analysis were the top secondary determinants. A three-tier framework—(1) design and environmental analysis, (2) project and supply-chain management with lifecycle maintenance, and (3) financial evaluation—offers a comprehensive guide for stakeholder decision-making. Recommendations include: multi-disciplinary collaboration; evidence-based policies that prioritize sustainable, low-carbon practices; supply chain optimization; emphasis on adaptability and maintainability; rigorous financial evaluation; cross-industry cooperation; education and awareness programs; and international comparative studies to generalize the model. Future research should expand sampling, segment rural/urban populations, apply diversified sampling strategies, and deepen qualitative inquiry to refine and validate the framework across regions and countries experiencing semi-urbanization.

The study reports several constraints: focus emphasized a subset of highly ranked influencing factors; a modest sample size (n=120) limits generalizability; reliance on the RII approach suggests the value of complementary sampling and analytical methods (e.g., random/stratified sampling and expanded qualitative inquiry); and lack of separate modeling for rural versus urban sub-populations within Desakotas. Broader cross-national samples are needed to test transferability to other semi-urban contexts. These limitations inform calls for larger, more diverse samples and mixed-methods designs in future work.