Abstract
This study introduces a tailored theoretical readiness model for evaluating the readiness of the architectural construction industry in developing contexts, with Sri Lanka as a case, to adopt construction 3D printing (C3DP) for custom housing. The unique challenges faced by developing countries underscore the need to address localized contexts and technology adoption dynamics, which distinguishes this model from existing readiness frameworks. The development of this model was initiated through a systematic literature review and bibliometric analysis across five key domains: technological, organizational and environmental, psychological, localized context, and adoption dynamics. A theoretical indicator set was constructed and empirically tested through expert validation, reliability analysis, and factor loading. This process resulted in a 19-component index spanning five key domains, in which technology, organization, and environment emerged as positive indicators and psychology and localized context act as negative influences, with adoption dynamics reflecting both dimensions in relation to C3DP uptake. A practical self-assessment tool was developed to allow stakeholders to measure their preparedness and identify critical gaps. The model provides a structured, context-sensitive framework for guiding strategic decision-making in technology adoption within the construction sector. It offers not only a pathway for assessing readiness in Sri Lanka but also a scalable approach adaptable to other developing economies with similar challenges. The study advances digital construction by aligning theoretical insights with real-world application.
1 Introduction
The construction industry has historically demonstrated a slow adoption of innovative technologies, despite the growing recognition of their transformative potential (; ; Wu et al., 2025). A lack of knowledge of the market for the acceptance of new technologies hinders the adoption of and investment in them (). However, the successful implementation of new technologies is increasingly acknowledged as a key determinant of competitiveness and long-term success in construction enterprises (Wang and Zhang, 2023; Zhao et al., 2021). The productivity of the construction sector plays a vital role in driving a nation’s overall economic development (). As a result, establishing effective mechanisms to evaluate and improve technology adoption within the industry has become increasingly urgent, which is recognized as “technology readiness” (TR).
TR has predominantly been discussed in the literature through generalized models, theories, or frameworks that are not specifically designed for the construction industry. Foundational models such as the Technology Acceptance Model (TAM), Technology Readiness Index (TRI), Technology Readiness and Acceptance Model (TRAM), Diffusion of Innovations (DOI) by Rogers, and the Technology–Organization–Environment (TOE) framework by Tornatzky et al. (1990), provide valuable theoretical underpinnings. However, within the construction sector, which is frequently characterized by fragmentation, complexity, and a persistent resistance to change (Wu et al., 2019) a tailored approach to evaluating readiness has been identified as essential. Several studies have explored TR in construction organizations (), individual construction projects (Wu et al., 2018), and as a mechanism for enhancing the sector’s productivity (). A significant body of research has also examined the intersection between Building Information Modeling (BIM) and TR (; ), underlining the sector’s growing need to adopt digital innovations effectively.
More recent contributions, such as the DAWN readiness model () and the organizational readiness model developed by and later tested in the Sri Lankan context by , have attempted to expand these foundations. These models collectively highlight the importance of identifying potential barriers to adoption in order to design more targeted and effective implementation strategies.
Given this inherent complexity of the construction industry, a multifaceted readiness assessment is required (). Based on TOE and Readiness for Workplace Change Management (RWCM), Chen proposed five dimensions: technology, organization, environment, leadership, and workforce. The TRI and TRAM models focus on user acceptance and perceived usefulness. None of the frameworks have identified adoption dynamics as a critical dimension; however, culturally grounded societies require it. This study contributes to this gap by developing a Technology Readiness Index that is tailored to assessing the readiness of construction stakeholders to adopt emerging technologies, particularly in developing countries.
1.1 Study context: construction 3D printing in Sri Lanka
This study situates its inquiry within the context of Sri Lanka, focusing on the prospective adoption of construction 3D printing (C3DP) a novel and rapidly advancing construction technology that remains largely unexplored there, and is hence timely and strategic. In Sri Lanka, where the adoption of such advanced technologies is still nascent, readiness must be assessed through a rigorous, context-sensitive lens. The introduction of any construction innovation requires validation and approval at multiple levels, from internal organizational stakeholders, through regulatory bodies, to end-user acceptance.
While prior studies have largely focused on developed nations with conducive financial, technological, and regulatory environments (; ), the conditions in developing contexts are markedly different. Barriers such as limited access to capital, underdeveloped infrastructure, and gaps in technical expertise may significantly constrain the uptake of innovative construction technologies. Additionally, cultural resistance to change, risk aversion, and weak institutional support mechanisms further complicate the innovation landscape. This study aims to develop a tailored framework that reflects the unique dynamics of developing countries. The primary objective is to identify the critical parameters and indicators that influence technological readiness in the construction sector. The research employs a systematic literature review guided by the PRISMA methodology to ensure a transparent and replicable approach to data extraction and analysis. The review synthesizes insights from major readiness theories—TRI, TRAM, TOE, DOI, and ORM—and cross-references them to derive five domains that are pertinent to assessing construction industry readiness (Silva et al., 2025). The body of knowledge derived from this comparative study and bibliometric analysis produces empirically grounded indicators categorized under these five domains.
A reiterative process involving statistical analysis (; ), followed by expert validation involving architects, engineers, and contractors, was employed to formulate a statistically validated index to assess TR in the construction sector. This model, introduced as the TOPLA (Technology, Organization, Psychology, Local Context, and Adoption Dynamics) framework, will be presented and refined through expert consultation to ensure practical applicability and relevance to the construction field.
Ultimately, this study seeks to formulate a comprehensive framework for assessing TR in the construction industry of developing countries. Such a framework will serve as a valuable tool for policymakers, industry stakeholders, and technology developers striving to promote innovation in construction. By refining and adapting readiness indicators to local conditions, the research contributes meaningfully to the academic discourse on technology adoption and offers practical recommendations for advancing sustainable construction practices in resource-constrained settings.
1.2 Research problem
Current research on technology adoption in the construction sector remains fragmented and lacks a cohesive, integrated, evaluative approach. Existing frameworks address only isolated aspects of technology readiness: TAM and DOI focus on individual-level dimensions, whereas TRI and TOE adopt an organizational lens; no existing framework integrates these with the critical, culturally informed adoption dynamics essential for developing economies. The recently developed ORM, while comprehensive, also lacks this dimension. However, adoption dynamics constitutes a critical element for developing countries (Prakash, 2024), alongside the four established domains of psychological readiness, technological maturity, organizational and environmental factors, and localized context (Silva et al., 2025). The literature further indicates that these five domains have not been consolidated into a single, comprehensive readiness index for the construction sector. Accordingly, this study examines the key indicators within these dimensions and proposes a theoretical readiness index.
1.3 Research objectives
Objective 1: the primary objective is to identify key parameters and indicators that influence technology readiness in the construction sector of developing countries, with a focus on Sri Lanka.
Objective 2: to generate a theoretical readiness model to assess technology readiness in the construction sector of developing countries.
2 Methodology
The methodology comprises four critical steps: 1. revealing; 2. refining; 3. testing; 4. validating ().
Step 1, “revealing,” is a comprehensive literature review to identify two aspects: a) identify a suitable readiness index to use for the construction industry which reveals the five domains to be assessed to achieve successful result, although none of the models cover all the domains in one; b) derive key readiness indicators relevant to the construction sector (; ; ). This review was supplemented by a bibliometric analysis, through which 20 preliminary indicators were shortlisted for further investigation; they were unevenly distributed among the five domains (Figure 1a). Given the limited number of prior studies that focus explicitly on readiness in the construction industry, the identified indicators were considered tentative and required empirical testing. A similar challenge was noted in the development of the Technology Readiness Index (TRI) by , where indicators were initially derived from external studies. Consequently, this research also adopted the iterative scale development process recommended by , which itself is based on the methodological framework proposed by .
FIGURE 1
In, Step 2, “refining” (Figure 1b), a questionnaire-based pilot study was conducted to refine the indicator set. Each of the 20 indicators was assessed using three separate questions measured on a five-point Likert scale. This approach is consistent with the methods applied by , Chen et al. (2022), , and . The pilot study involved 30 AEC respondents (architects, engineers, and contractors) who were experts in the field. Data collection was supported by qualitative feedback from selective responders, enhancing the content validity of the instrument (). Specific methodological improvements were made during this phase, including the clarification of organizational boundaries within the architectural industry, thus recognizing that professionals may operate both independently and within firms. Furthermore, measures were taken to incorporate insights from retired professionals and to ensure that all respondents were adequately informed about the principles and implications of construction 3D printing before participating in the study.
The pilot study data were assessed for reliability and internal consistency as the initial part of Step 3, “testing.” Section (c) in Figure 1 explains the reiterative process of analyzing data using SPSS. Once reliability was checked, exploratory factor analysis was performed to understand underlying clusters. Following the outcome, it was observed that the instrument could have different underlying clustering. Hence, exploratory factor analysis (EFA) was performed. Using the Kaiser criterion, 18 components were selected based on eigenvalues greater than 1.0. Principal component analysis (PCA) with Varimax rotation was applied, and the component matrix was examined to determine how each item (question) loaded onto the extracted components. To ensure the stability and interpretability of the factors, only components with at least three items loading significantly (preferably >0.40) on a single factor were considered viable. This guideline aligns with previous recommendations (; ; ; ). This is marked “Round 01” in the methodology diagram (Figure 1).
In the second round, 13 components were eliminated due to insufficient item loadings or reliability below 0.7. Of the remaining components, five exhibited strong and clean item loadings. Items under these five components were factor analyzed again to find a better grouping. This summarized eight strongly loaded components, all above 0.7. Each retained component was then conceptually reviewed and renamed to reflect the thematic consistency of the underlying items, forming the basis of the new readiness indicators. The EFA process was iterative, involving the addition, deletion, and revision of items to achieve stable factor groupings and a Cronbach’s alpha of at least 0.70 for each resulting indicator. This step ensured both statistical reliability and construct clarity for the readiness index.
For final validation and implementation, the fourth step in the methodology (Figure 1d), the finalized questionnaire was subjected to validation to ensure its construct validity, content coverage, and practical applicability. This was achieved through expert validation: subject matter experts in AEC, including both academics and industry practitioners, reviewed the revised index. Their feedback was used to fine-tune the language, scope, and relevance of each domain and item.
Upon completion of the validation process, the finalized instrument could be distributed among AEC professionals to assess the readiness of the Sri Lankan construction industry to adopt C3DP. This will be the future use of this readiness instrument. A graphical representation of the methodology is presented below for easy reference (Figure 1).
3 Readiness, C3DP, and architectural construction: a literature-informed framework (Step 1: revealing)
Objective 1 of the study, to identify key parameters and indicators influencing technology readiness in the construction sector of developing countries such as Sri Lanka, was covered in “Step 1: revealing” of the methodology. Here the literature review elaborates the three main components of this study: the architectural construction industry, construction 3D printing (C3DP), and the readiness model. This leads to the development of a theoretical readiness model using bibliometric and content analysis.
3.1 Architectural construction industry
in “The Construction Industries in Developing Countries” identifies the crucial role the construction industry plays in a country’s economic growth. emphasizes that a developing government’s close relationship with the construction sector, through investment in new projects, increases its importance. However, a lack of government interest in upgrading the industry can result in slow progress and limited modernization within the construction sector ().
While developed nations prioritize quality of life, developing nations often concentrate on meeting basic shelter needs. Despite this, Sri Lanka, as a developing country, achieved architectural excellence in the 1970s (), creating work admired by other countries. In the aftermath of a civil war that lasted from 1983 to 2009, housing standards have evolved regarding tectonics, materials, comfort, and user perceptions (). While globalization has influenced the development of landmark buildings in Sri Lanka, domestic architecture has largely remained anchored in traditional construction practices. This limitation is largely due to the unsupportive mass production methods embedded in the construction industry.
Furthermore, the scattered nature of the architectural construction industry hinders innovation and conceptualization, often under the guise of cost-saving measures (). This intricate network compromises the deep involvement that clients and architects ideally have during the project creation phase, particularly once the construction stage begins. The “anchor” of architect-driven construction, the master builder, has become outdated, and small-scale residential projects are often caught in a complex web of stakeholders in construction decision-making. Their capacity to embrace innovation becomes critical in assessing the level of readiness.
3.1.1 Industry stakeholders
Stakeholders are individuals or organizations that have, or claim to have, rights, interests, or ownership related to a specific challenge or situation (). Stoelhorst () defined a stakeholder as any group or individual creating and deriving economic value through their interactions with a company. Additionally, describe a stakeholder as an individual or group with a vested interest or share in an undertaking. Overall, stakeholders can be categorized into two main groups: internal and external. identifies five themes of stakeholders based on governing values: financial, social, legal, technical, and functional value. categorized stakeholders based on their direct and indirect involvement in a project. Together, these stakeholders can be identified as follows (Figure 2). For the purpose of assessing the industry, this study considers “technical value” as the main respondent; however, the study has included representation from each of these segments in the theoretical model to ensure equal representation and accuracy. Therefore, the readiness model could be easily adapted for different study scopes in the future.
FIGURE 2
The architectural construction industry in Sri Lanka shares responsibilities and involves stakeholders throughout the project phases. Figure 3 provides an overview of how architects, as the main consultants in residential projects, have engaged with stakeholders (data are represented graphically). The involvement of multiple stakeholders at different stages of a house project requires a tedious circular process, which is carefully managed within the existing architectural construction process in Sri Lanka.
FIGURE 3
3.1.2 Architectural house: design and construction process
To better understand the existing architectural design and construction process, responding architects were specifically questioned in the pilot study. They were presented with four different types of process derived from the literature and were asked to select the architectural house construction process they believed was most prevalent based on their experience. Of the respondents, 56% chose the option that indicated a process that was flexible to suggested changes during the design and development stages (Figure 4).
FIGURE 4
Any new technology proposed for adoption in the industry must first adhere to or adjust to the existing context. As a result, it was noted that a certain degree of flexibility is expected in the house construction process. Adopting a technology with a relatively linear process, such as C3DP, is therefore a strategic move that qualitatively enhances the Readiness Index.
3.2 Construction 3D printing (C3DP)
C3DP, also referred to as “additive manufacturing in construction,” is an emerging digital construction method where building components or entire structures are produced layer by layer using automated robotic or gantry-based systems. Unlike conventional construction, which relies heavily on manual labor and standardized building materials, C3DP enables greater design flexibility, material efficiency, and the potential for reduced construction timelines. Globally, it has been positioned as a disruptive innovation that is capable of addressing housing shortages, sustainability goals, and productivity inefficiencies in the construction sector (Lim et al., 2011; Wang et al., 2018).
In developing countries, C3DP has primarily been adopted through experimental or pilot-scale projects rather than large-scale deployment. In India, for instance, Tvasta and L&T Construction demonstrated the feasibility of 3D-printed housing through low-rise residential prototypes and disaster-relief shelters, highlighting the technology’s potential for speed and affordability (Srivastava et al., 2023; Sesetti et al., 2022). Despite these efforts, progress has been uneven, and uptake remains limited compared to industrialized economies. The adoption of new construction technologies in developing countries is influenced by a complex interplay of diverse factors (; ). Workforce adaptability, professional acceptance, and public trust remain critical, yet resistance to change, rooted in longstanding familiarity with traditional building methods, often creates inertia against innovation ().
For C3DP specifically, infrastructure limitations, shortages of skilled operators, and regulatory ambiguities exacerbate the adoption challenge in much of the Global South. In India, public skepticism toward unconventional building systems has slowed mainstream acceptance, despite demonstrated technical viability (Srivastava et al., 2023). In Malaysia, limited awareness among contractors and client reluctance regarding digital technologies continue to constrain implementation (). These localized challenges echo broader theoretical perspectives which emphasize that technology adoption is not merely a question of capability but is shaped by social systems, organizational practices, and institutional readiness (; ; Tornatzky et al., 1990).
3.3 Readiness models
While several established readiness models exist across disciplines, such as the TRI by and the TOE framework by Tornatzky et al. (1990), the construction industry’s complexity and fragmentation demand tailored evaluation mechanisms, especially for the contexts and practices discussed here earlier. According to , the identification of construction-specific determinants that highlight both the drivers and barriers to technology adoption is necessary.
From a comparative lens, the TOE framework provides a foundation for assessing adoption by examining technological, organizational, and environmental aspects, yet it underrepresents human and cultural dimensions critical in developing countries (; ). Venkatesh et al. (2003) emphasized that behavioral intention, perceived usefulness, and social influence substantially shape organizational readiness. Scholars such as and reinforce that effective adoption frameworks must operate on both systemic and individual levels, particularly in industries like construction where skill diversity and informal practices abound. similarly argue for an integrated model that combines individual behavior and organizational readiness to reflect the real-world dynamics of technology diffusion.
Technological attributes such as relative advantage, complexity, compatibility, and trialability are pivotal to adoption (). These aspects significantly influence decisions, especially when innovations are introduced into traditional settings (Turner, 2007). In construction, lifecycle performance such as durability, cost-effectiveness, and maintainability also play a significant role in resource-constrained contexts (). Organizational readiness, which includes financial resources, internal expertise, and openness to innovation, is equally critical. Organizational culture, particularly leadership commitment and tolerance for uncertainty, greatly influences whether firms engage with emerging technologies (). At the macro level, the literature indicates that environmental and policy conditions significantly affect readiness as well. Governmental incentives such as tax relief or pilot programs (), along with clear regulatory standards, are known to boost adoption. However, in developing nations, weak institutional enforcement, fragmented infrastructure, and bureaucratic hurdles often serve as barriers ().
To address these multidimensional aspects in the construction sector, Silva et al. (2025) propose a refined framework that incorporates five interdependent domains that are essential for developing countries: psychological readiness, technological maturity, organizational and environmental factors, adoption dynamics, and localized context. This holistic model draws on the psychological insights of , the innovation attributes of , and the TOE structure (Tornatzky et al., 1990), while incorporating newer constructs such as adoption timing, social contagion, and resistance pathways (). Combining this model with stakeholder distribution discussed in 3.1.1, Figure 5 illustrates the value structure embedded within the proposed five-domain framework. Stakeholders closest to the domains have a direct or indirect impact on the adoption process. This shows the importance of a focused readiness model to achieve an accurate assessment of the industry to adopt new technologies. As complex as it looks, acceptance is hindered by value systems, making the adoption process slower. This study, therefore, seeks to identify the determinants of readiness, a step forward from this diagram.
FIGURE 5
3.4 Determinants of technology adoption
High initial investment costs, affordability, and long-term financial benefits significantly impact technology adoption in the construction industry (). Developing countries often face budget constraints that limit their ability to acquire advanced construction equipment and materials, making clients’ affordability a key consideration. Additionally, a lack of proper infrastructure, such as access to electricity, internet connectivity, and supply chain networks, can hinder the feasibility of adopting technologies like Building Information Modeling (BIM) and C3DP (), thus emphasizing the importance of infrastructure.
A significant challenge within the construction industry is the lack of sufficient technical expertise among the workforce to effectively operate new construction technologies (). To ensure successful implementation of these technologies, it is essential to establish capacity-building programs and provide technical education, referred to as “knowledge/awareness.” Additionally, construction technologies must comply with building codes, safety regulations, and environmental policies (). In many developing countries, outdated regulations can hinder the approval and integration of new technologies. Therefore, government regulation will also be an important factor to consider.
Traditional construction practices are deeply established in many societies (Silva et al., 2025), which can create resistance to change and hinder technology adoption (Wu et al., 2018). Engaging local communities and demonstrating the benefits of new technologies can help improve acceptance: “social acceptance/cultural bias.” In the technology dimension, access to high-quality construction materials is a key factor in technology adoption (). Technologies that require specialized materials may face logistical challenges in countries with underdeveloped supply chains and issues related to the supportive requirements needed to implement the technology. Therefore, technology and technology adaptability are also important considerations. Not all technologies developed for high-income economies are directly applicable to developing countries (). It is important for construction solutions to be customized to fit local climatic conditions, labor availability, and project needs, such as climate, demography, the environment, and disasters.
To promote sustainable building practices, governments and industry stakeholders should implement incentives and create awareness campaigns. The concept of “industry practice forecast/sustainability/impact” will play a crucial role in the adoption of these practices. Additionally, the availability of government grants, private investment, and international funding significantly influence the pace at which new technologies are adopted (Zhao et al., 2025). Public–private partnerships (PPPs) can provide essential financial support, thereby making considerations of “initial cost: industry/government” vital.
From an intellectual standpoint, the successful adoption of new technologies relies on the collective readiness of architects, engineers, contractors, and policymakers (). Collaboration across the construction value chain is essential for smooth integration and wider industry support. The indicator “professional preparedness and acceptability/stakeholders” will be a key factor in successful adoption.
In summary, the main considerations for effective technology integration into the construction industry are as follows (List 1).
Affordability: client
Infrastructure
Knowledge/awareness
Government and regulations
Social acceptance/cultural bias
Technology
Supportive requirements for technology operation/technology adaptability
Climate/demography/environment/disasters
Industry practice forecast/sustainability/impact
Initial cost: industry/government
Professional preparedness and acceptability/stakeholders
According to the literature, these 11 critical areas must be addressed to ensure effective technology integration into the construction industry. Systematically addressing these factors will facilitate the successful adoption of emerging construction technologies in developing countries, promoting efficiency, cost-effectiveness, and sustainability in the built environment. This study thus comes to the second phase of the literature review (Figure 6).
FIGURE 6
3.5 Bibliometric study: identify indicators
Once the critical areas were identified through the literature, a bibliometric study was next. Becker and Hevner have identified a five-step process for developing readiness models, which underpins the steps in this bibliometric study. It also follows the methodological approach of for determining indicators, Chen et al. (2022) on evaluating existing readiness indexes, and for contextualizing. Table 1 is the summarized version of a methodological approach to readiness model development as per Becker and Hevner. As an extension to the proposed five-domain framework of Silva et al. (2025), two bibliometric studies were performed to extract readiness indicators connected to the construction industry. The first determined the link between architecture, construction, and readiness; it showed a weak link between strengths, indicating significant gap in the selected research area. The second bibliometric study was thus carried out to extract readiness and related indicators for the construction industry based on the 11 critical areas identified in Section 3.4.
TABLE 1
| Research method | Steps | Description |
|---|---|---|
| Bibliometric | I | 1. Definition of context |
| II | 2. Identification of relevant technologies and stakeholders 3. Identification of relevant applications of technology, and barriers | |
| III | 4. Analysis and presentation of results | |
| Survey | IV | 5. Formulation of field research 6. Identification of interested parties 7. Application of field research to interested parties |
| V | 8. Analysis and presentation of results |
Methodological approach to readiness model development as per Becker and Hevner.
Source: Adapted from and ().
The search query was formulated using the Boolean expression (TITLE-ABS-KEY (construction) AND TITLE-ABS-KEY (readiness)) AND (LIMIT-TO (SUBJAREA,“ENGI”) OR LIMIT-TO (SUBJAREA,“MATE”)) AND (LIMIT-TO (EXACTKEYWORD, “Construction Industry”) OR LIMIT-TO (EXACTKEYWORD, “Construction”) OR LIMIT-TO (EXACTKEYWORD, “Architectural Design”) OR LIMIT-TO (EXACTKEYWORD, “Technology Readiness Levels”) OR LIMIT-TO (EXACTKEYWORD, “Readiness”) OR LIMIT-TO (EXACTKEYWORD, “Technology Readiness”) OR LIMIT-TO (EXACTKEYWORD, “Readiness Assessment”) OR LIMIT-TO (EXACTKEYWORD, “Developing Countries”)) AND (LIMIT-TO (LANGUAGE, “English”)). Of the total found, the titles of the papers were carefully read, and irrelevant papers were excluded from the selection as they may contribute to false data analysis. A total of 330 bibliographic items were extracted that were in the domain of architecture and construction. Of these, 103 were available on the database to download. They were coded and analyzed to identify possible indicators, and these were rapidly used to assess the readiness of subjects that were related to the construction industry. Using MAXQDA software, occurrences were analyzed using one command. Terms with high occurrences were grouped based on their relevance to the 11 indicators (List 1) and five domains (Silva et al., 2025). The contents of the terms and phrases or sections identified were read to derive a qualitative and subjective decision. They were clustered as per the definition of the domain.
Content analysis was primarily driven by the TOPAL readiness model and the clusters identified within the framework. Figures 7a,b show the five graphs indicating the occurrences and document volumes in appearance of the searched indicators.
FIGURE 7
The study also had an additional indicator checklist; however, they all indicated the ten occurrences below per item. As a rule, we have considered indicators that have more than ten occurrences and more than one document appearance as significantly important. The preliminary list of readiness indicators gathered from the bibliometric analysis are shown in Figure 8 below.
FIGURE 8
Overall, psychological readiness (PR) was tested under three indicators, technological maturity (TR) under two, organizational and environmental (OE) factors under four, adoption dynamics (AD) under five, and localized context (LC) under five indicators. These were coupled into the existing domains. By this stage the study has followed to Step III of Becker’s and Hevner’s method.
4 Pilot study survey to testing (Step 2: refining)
The second phase of Becker’s and Hevner’s method suggests a field survey to assess the testability and validity of the model. We surveyed 30 experts to scale the 20 indicators. Each indicator was tested under three questions under a 1–5 Likert scale answer system. The profile of the responder group is shown in Figure 9.
FIGURE 9
5 Results and analysis (Step 3: “testing” and Step 4: “validation”)
“Step 3: testing” was identified in the methodology. The data gathered from 60 questions were analyzed to determine reliability using SPSS software with the following benchmarks, Cronbach’s alpha ranging from 0 to 1 higher = better consistency, ≥0.90 – excellent, ≥0.80 – good, ≥0.70 – acceptable and <0.60 – problematic (Table 2).
TABLE 2
| # | Indicator | Initial α | Revised α | Interpretation |
|---|---|---|---|---|
| 1 | Reliability | 0.630 | 0.637 | Weak initially. Improves after removing Q1_1. Can be kept after revision |
| 2 | Observability | 0.487 | — | Problematic. Alpha too low. Likely needs item revision or removal |
| 3 | Client demand | 0.143 | 0.502 | Very poor; major coherence issue. Removing Q3_1 helps slightly but is still below acceptable. Needs reworking |
| 4 | Cost savings | 0.179 | 0.714 | Huge improvement after removing Q4_1. Retain revised set |
| 5 | Government policies | −0.130 | 0.576 | Negative alpha → serious issue. After removing Q5_2, it gets better but still marginal. Reconsider items |
| 6 | Policy support | 0.288 | -- | Very low alpha, even after removal. Likely poorly constructed or too few items |
| 7 | Resource savings | 0.469 | 0.562 | Acceptable with removal of Q7_1. Revised set is marginal but usable |
| 8 | Legal framework | 0.652 | 0.676 | Acceptable even before item removal. Stable |
| 9 | Decision-making process | −0.200 | — | Negative average covariance – highly problematic. Indicates major issues with item correlation or reverse coding |
| 10 | Organizational culture | −0.005 | 0.192 | Very weak internal consistency. Likely conceptual misalignment or poorly worded items |
| 11 | Initial investment | 0.354 | 0.529 | Poor alpha. Can improve slightly by removing Q11_1, but still questionable |
| 12 | Technical expertise | −0.288 | — | Highly problematic – suggests item responses move in opposite directions. Likely needs removal |
| 13 | Energy efficiency | −0.245 | 0.502 | Negative alpha. Removing Q13_3 improves it to an acceptable level but still poor. Needs major revision |
| 14 | Relative advantages | −0.223 | 0.172 | Serious coherence issues. Even with item removal, still too weak |
| 15 | Time savings | 0.015 | 0.459 | No consistency. Items likely not aligned. Should be revised/reconsidered or removed |
| 16 | Versatility | 0.380 | 0.521 | Very weak alpha. May be slightly improved with item Q16_2 trimming, but still low |
| 17 | Resistance to change | −0.107 | 0.167 | Weak |
| 18 | Maintenance | 0.800 | — | Strong internal consistency. Good scale |
| 19 | Complexity | 0.731 | — | Acceptable reliability. No revisions needed |
| 20 | Durability | −0.921 | — | Extremely problematic. An alpha this negative is likely due to item keying error or strong inverse correlation. Should be removed or entirely revised |
Reliability and interpretations for first set of indicators (20 indicators).
The internal consistency shown above was checked using Cronbach’s alpha; as is evident, the outcome was notably insufficient to establish a strong readiness index. According to the series of iterative analysis “paradigm for developing scales” (), this underperformance could be due to incorrect clustering, so running a factor analysis was the next logical step to explore the underlying structure. We thus ran an exploratory factor analysis (EFA) in SPSS for the 20 indicators (based on 60 items). According to the Kaiser criterion, components with eigenvalues >1 must be retained (Table 3).
TABLE 3
| Component | Initial eigenvalues | ||
|---|---|---|---|
| Total | % Of variance | Cumulative % | |
| 1 | 7.750 | 12.917 | 12.917 |
| 2 | 6.682 | 11.136 | 24.053 |
| 3 | 5.754 | 9.590 | 33.644 |
| 4 | 4.273 | 7.121 | 40.765 |
| 5 | 3.589 | 5.982 | 46.747 |
| 6 | 3.277 | 5.461 | 52.209 |
| 7 | 2.955 | 4.925 | 57.134 |
| 8 | 2.807 | 4.678 | 61.811 |
| 9 | 2.678 | 4.464 | 66.275 |
| 10 | 2.461 | 4.102 | 70.377 |
| 11 | 2.166 | 3.610 | 73.987 |
| 12 | 1.986 | 3.309 | 77.296 |
| 13 | 1.715 | 2.859 | 80.155 |
| 14 | 1.485 | 2.475 | 82.630 |
| 15 | 1.339 | 2.232 | 84.861 |
| 16 | 1.277 | 2.128 | 86.989 |
| 17 | 1.233 | 2.055 | 89.045 |
| 18 | 1.074 | 1.790 | 90.835 |
Results from factor analysis to identify underlying clusters.
From our data, 18 components had eigenvalues >1 (Table 3). There are thus 18 underlying factors that explain 90% of the variance in our 60-item scale. To determine if the new grouping is stronger and could explain the full dimension of the index, we constructed a component matrix from principal component analysis (PCA) which indicated how each question loads onto each of the 18 extracted components.
A factor (in this case a NEW domain) should have at least three items loading significantly to be considered stable and interpretable. “Factors defined by fewer than three variables should generally not be interpreted, as they are often unstable” (). “A minimum of three items per factor is usually required to adequately define a latent construct” (). “Variables should load strongly (preferably >0.40) and exclusively on one factor. Factors with fewer than three items should be interpreted with caution” (). in Discovering Statistics Using IBM SPSS also recommends at least three well-loading items per factor which emphasizes reliability, interpretability, and generalizability.
Furthermore, “Items with low item-total correlations or those that reduce Cronbach’s alpha should be dropped.” . So, while Churchill supports the use of Cronbach’s alpha, Nunnally (1978), , and DeVellis (2016) interpret ranges of threshold, along with Hair et al. (2010). This defines ≥0.90 as excellent, 0.80–0.89, as good, 0.70–0.79 as acceptable, 0.60–0.69 as questionable/weak 0.50–0.59, and as <0.50 as unacceptable.
Analysis for strong loadings on components, the reliability of the revised alpha identified four strongly loaded components above 8.0 and four above 7.0. Table 4 provides the summary of the testing for strong loadings on the 18-component analysis.
TABLE 4
| Component | Initial alpha | Suggestion for item removal | Revised alpha |
|---|---|---|---|
| Component 02 | 0.726 | Q17_1 | 0.831 |
| Component 01 | 0.723 | Q12_3 | 0.822 |
| Component 15 | 0.751 | Q7_1 | 0.816 |
| Component 05 | 0.806 | 0.806 | |
| Component 08 | 0.711 | Q20_2 | 0.727 |
| Component 04 | 0.520 | Q4_3 | 0.722 |
| Component 13 | 0.615 | Q20_1 | 0.72 |
| Component 03 | 0.490 | Q14_2 | 0.708 |
Reliability and rationale for eight dimensions (components) suggested by statistical evaluation.
In order to have a meaningful set of indicators which could assess the majority of the dimensions, the full question set of the above eight dimensions was subject to iterative factor analysis until a reliable set of questions loadings (underlying grouping) were met (as mentioned in Figure 1c–Round 2), which gave six new groupings with eigenvalues above 1. This explained 74.8% of the cumulative variance. This iterative process of reliability among newfound groupings gave six components with an alpha above 0.7, which was acceptable. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.501, indicating a borderline acceptable level for factor analysis. Bartlett’s test of sphericity was significant (χ2 (171) = 281.644, p < 0.001), confirming that the correlation matrix was suitable for factor extraction. This was a good foundation and number of clusterings to generate indicators that cover the construction industry’s assessment points (Table 5).
TABLE 5
| Set | Reliability (loading) | Items (summarized) | Suggested indicator | Rationale (summarized) | Polarity |
|---|---|---|---|---|---|
| 1 | 0.817 | Organizational capacity on par with technical skills, availability of software/equipment training facilities | Technology | Measures foundational technological competence (skills, tools, familiarity). Therefore, aligns to assess technological maturity | Positive (+) |
| 2 | 0.797 | Suitability, regulatory confidence, organizational belief in success, positive employee attitudes | Organization and environment | Captures enabling organizational climate and external environment, including regulatory adaptability and employee acceptance. Hence, assesses organizational and environmental factors | Positive (+) |
| 3 | 0.770 | Decision delays, perceived complexity among clients, professionals, workforce | Psychology | Reflects cognitive and emotional barriers, perceived complexity, and behavioral reluctance. Assessed psychological readiness | Negative (−) |
| 4 | 0.837 | Limited material options, cost challenges due to traditional finishes | Adoption dynamics: constraints | Represents practical barriers related to material compatibility and cost expectations. Higher values imply stronger structural barriers | Negative (−) |
| 5 | 0.720 | Reduced human error, faster delivery on client demand | Adoption dynamics: performance | Measures perceived benefits of C3DP in terms of efficiency, trustworthiness, and delivery speed. Reflects DOI “relative advantage” and TAM “perceived usefulness” | Positive (+) |
| 6 | 0.791 | Resistance due to climatic, cultural, aesthetic, and personalized housing norms | Localized context | Evaluates social and physical contexts related to technology adoption. Low compatibility signals misalignment with entrenched building practices | Negative (−) |
Reliability and rationale for second set of indicators (six indicators).
Questions loaded under the above indicators are therefore considered the final set of questions to be included in the index. Figure 10 below is the template of the final TOPAL index.
FIGURE 10
Indicators and questions aligned with the proposed new index are given below in Table 6.
TABLE 6
| Set | Indicator/domain | Component no. | Question |
|---|---|---|---|
| 01 | Technology | T_1 T_2 T_3 T_4 T_5 | Your organization has the capacity to bring in a 3D printer to the Sri Lankan construction industry Your organization has enough technically qualified people to adopt a technology like 3D printing Our industry is familiar with 3D printing and related application areas Availability of equipment and software systems to implement 3D printing technology is satisfactory (CAD, 3D software, Slicer software, capacity to learn machines) Facilities for employee education and training to ensure technology proficiency are satisfactory |
| 02 | Organization and environment | O_1 O_2 O_3 O_4 | Some 3D printers are capable of operating on uneven terrain and in real onsite conditions; you believe such systems will be compatible with Sri Lanka’s geographical context** You are confident that the legal and regulatory environment in Sri Lanka can adapt to emerging construction technologies like 3D printing within the next 5 years Your organization feels confident that construction 3D printing will be successful in Sri Lanka The attitudes of employees toward utilizing 3D printing for construction projects is high |
| 03 | Psychology | P_1 P_2 P_3 P_4 | You have to wait for top management approvals to decide on a new construction method Adopting construction 3D printing is complex for investors/clients Adopting construction 3D printing is complex for industry professionals Adopting construction 3D printing is complex for workforce and related product developers (carpenters, plumbers, electricians etc.) |
| 04 | Adoption dynamics: constraints | Ac_1 Ac_2 | 3D printing offers limited material choice for Sri Lankan clients 3D printing will not save cost as Sri Lankans need traditional finishes at the end |
| 05 | Adoption dynamics: performance | Ap_1 Ap_2 | 3D printing can reduce human error in construction; you believe that will increase the trustworthiness of community and stakeholders 3D printing gives you opportunity to deliver products with less time on client demand |
| 06 | Localized context | L_1 L_2 | Sri Lankan house designs and construction are biased to climate, finances, and other parameters; a new construction technology will not offer any change to that practice Material choice (mainly concrete) and design flexibility of construction 3D printing is not adequate for Sri Lankan personalized house culture |
TOPAL index.
Underlined term: term could be the specific technology.
*Reversed score: not included in this final list.
**Reworded to ensure clarity.
TABLE 7
| Rotated component matrix | ||||||||
|---|---|---|---|---|---|---|---|---|
| New component number | Old component number | Technology | Organization and environment | Psychology | Adoption dynamics: constraints | Adoption dynamics: performance | Localized context | |
| 1 | 2 | 3 | 4 | 5 | 6 | |||
| 1 | Ap_1 | Q1_2 | -- | -- | -- | -- | 0.876 | -- |
| 2 | Ac_1 | Q3_1 | -- | -- | -- | 0.863 | -- | -- |
| 3 | Ap_2 | Q3_2 | -- | -- | -- | -- | 0.679 | -- |
| 4 | Ac_2 | Q4_2 | -- | -- | -- | 0.861 | -- | -- |
| 5 | O_1 | Q7_3 | -- | 0.840 | -- | -- | -- | -- |
| 6 | O_2 | Q8_1 | -- | 0.773 | -- | -- | -- | -- |
| 7 | O_3 | Q9_1 | -- | 0.807 | -- | -- | -- | -- |
| 8 | O_4 | Q10_2 | -- | 0.627 | -- | -- | -- | -- |
| 9 | P_1 | Q10_3 | -- | -- | 0.736 | -- | -- | -- |
| 10 | T_1 | Q11_1 | 0.814 | -- | -- | -- | -- | -- |
| 11 | T_2 | Q12_2 | 0.626 | -- | -- | -- | -- | -- |
| 12 | L_1 | Q14_3 | -- | -- | -- | -- | -- | 0.861 |
| 13 | L_2 | Q16_3 | -- | -- | -- | -- | -- | 0.787 |
| 14 | T_3 | Q18_1 | 0.746 | -- | -- | -- | -- | -- |
| 15 | T_4 | Q18_2 | 0.707 | -- | -- | -- | -- | -- |
| 16 | T_5 | Q18_3 | 0.842 | -- | -- | -- | -- | -- |
| 17 | P_2 | Q19_1 | -- | -- | 0.735 | -- | -- | -- |
| 18 | P_3 | Q19_2 | -- | -- | 0.771 | -- | -- | -- |
| 19 | P_4 | Q19_3 | -- | -- | 0.815 | -- | -- | -- |
| Cronbach’s alpha | 0.817 | 0.797 | 0.770 | 0.837 | 0.720 | 0.791 | ||
Rotated component matrix with group Cronbach’s alphas which point out the strong loading of questions on each indicator.
The Table 7 below shows the rotated component matrix with group Cronbach’s alphas which point out the strong loading of questions on each indicator.
Referring the methods used by Prasuraman’s TRI, newfound indicators are divided into positive enablers and negative drivers. Positive enablers are technology, organization and environment, and adoption dynamics (performance). Negative drivers are psychology, adoption dynamics (constraints), and localized context. Therefore, the proposed adapted formula is
| Readiness score | Readiness level |
|---|---|
| +2.5 to +4 | High |
| +0.5 to +2.4 | Moderate |
| −0.5 to +0.4 | Neutral/transitional |
| −2.4 to −0.6 | Low |
| −4 to −2.5 | Very low (high resistance) |
The process of calculating the readiness score is to calculate the indicator mean score and then apply it in the equation to obtain the readiness score for the tested technology adoption. Score interpretation is as mentioned above.
Deriving this final formula to assess readiness in the construction industry to adopt C3DP was verified with the industry experts. Although it is a simplified version of the initial 60-question index, a collective agreement was obtained that that the model does cover the expected domains. Hence, the theoretical model is now complete to use and test in the field.
6 Conclusion
This study presents a comprehensive and contextually grounded theoretical readiness model which is designed to evaluate the adoption potential of construction 3D printing within the Sri Lankan architectural construction industry—specifically targeting the custom housing sector. Recognizing the complex interplay between the five domains of technology, organizational factors, psychology, localized conditions, and adoption dynamics, the model offers a multidimensional lens through which readiness for emerging construction technologies can be rigorously assessed.
Using a systematic literature review, bibliometric mapping, and empirical validation using a 60-question instrument, the study distilled a robust set of readiness indicators. Factor and reliability analyses revealed six strong readiness dimensions (indicators), three of which act as enablers and three as potential barriers, culminating in a refined 19-question index. These indicators span the five domains and are hence named to match with the domain, collectively forming a practical, scalable, and diagnostic readiness tool. This tool is not only sensitive to the unique socio-technical context of Sri Lanka but is also capable of generating actionable insights for a broad range of stakeholders, including architects, engineers, contractors, and policymakers.
Importantly, the model facilitates self-assessment and comparative analysis among indicators, enabling stakeholders to identify specific readiness gaps and target them strategically. In doing so, it dispels generalized misconceptions about technology adoption and supports evidence-based decision-making.
The implications of this research extend beyond its immediate context. While tailored for Sri Lanka, the model’s structure and methodology offer a replicable framework which can be adapted to similar contexts facing similar economic, infrastructural, institutional, and cultural challenges. Future research may explore its applicability to other digital or new construction technologies, thereby contributing to the development of a unified readiness assessment framework.
Ultimately, this study bridges the gap between theoretical knowledge and practical readiness, offering the construction industry a pathway toward more resilient, efficient, and inclusive innovation adoption. It marks a critical step in aligning technological potential with on-ground preparedness, reinforcing the importance of contextualized assessment in the pursuit of digital transformation.
Statements
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
NS: Conceptualization, Formal Analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review and editing. UR: Supervision, Writing – review and editing. CJ: Formal Analysis, Methodology, Writing – review and editing. RK: Data curation, Software, Writing – review and editing. CU: Supervision, Writing – review and editing.
Funding
The author(s) declare that no financial support was received for the research, and/or publication of this article.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
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Summary
Keywords
3D printing, architecture, construction, readiness model, index
Citation
Silva N, Rajapaksha U, Jayasuriya C, Katugaha R and Udawattha C (2026) A theoretical readiness model for 3D printing technology in the architectural construction industry. Front. Built Environ. 11:1710403. doi: 10.3389/fbuil.2025.1710403
Received
22 September 2025
Revised
19 November 2025
Accepted
24 November 2025
Published
04 February 2026
Volume
11 - 2025
Edited by
Roberto Alonso González-Lezcano, CEU San Pablo University, Spain
Reviewed by
Fakhira Khudzari, Universiti Malaysia Pahang Al-Sultan Abdullah Fakulti Teknologi Kejuruteraan Awam, Malaysia
Senthil Kumaran Ganesan, Copperbelt University, Zambia
Updates
Copyright
© 2026 Silva, Rajapaksha, Jayasuriya, Katugaha and Udawattha.
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*Correspondence: Neesha Silva, ruvindisilva502@gmail.com, 1951MD2023005@fgs.sjp.ac.lk
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