See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/366514860 Success factors of digital technologies (DT) tools adoption for sustainable construction in a developing economy Article  in  Construction Innovation · December 2022 DOI: 10.1108/CI-08-2022-0207 CITATIONS 21 READS 216 4 authors, including: Ayodeji Emmanuel Oke Federal University of Technology 641 PUBLICATIONS   7,024 CITATIONS    SEE PROFILE John Ogbeleakhu Aliu University of Georgia 132 PUBLICATIONS   1,028 CITATIONS    SEE PROFILE Iyanu Simeon Michael Redeemer's University 5 PUBLICATIONS   21 CITATIONS    SEE PROFILE All content following this page was uploaded by Ayodeji Emmanuel Oke on 16 May 2025. The user has requested enhancement of the downloaded file. https://www.researchgate.net/publication/366514860_Success_factors_of_digital_technologies_DT_tools_adoption_for_sustainable_construction_in_a_developing_economy?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_2&_esc=publicationCoverPdf https://www.researchgate.net/publication/366514860_Success_factors_of_digital_technologies_DT_tools_adoption_for_sustainable_construction_in_a_developing_economy?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_3&_esc=publicationCoverPdf https://www.researchgate.net/?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_1&_esc=publicationCoverPdf https://www.researchgate.net/profile/Ayodeji-Oke?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_4&_esc=publicationCoverPdf https://www.researchgate.net/profile/Ayodeji-Oke?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_5&_esc=publicationCoverPdf https://www.researchgate.net/institution/Federal-University-of-Technology?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_6&_esc=publicationCoverPdf https://www.researchgate.net/profile/Ayodeji-Oke?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_7&_esc=publicationCoverPdf https://www.researchgate.net/profile/John-Aliu-3?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_4&_esc=publicationCoverPdf https://www.researchgate.net/profile/John-Aliu-3?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_5&_esc=publicationCoverPdf https://www.researchgate.net/institution/University_of_Georgia?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_6&_esc=publicationCoverPdf https://www.researchgate.net/profile/John-Aliu-3?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_7&_esc=publicationCoverPdf https://www.researchgate.net/profile/Iyanu-Simeon-Michael?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_4&_esc=publicationCoverPdf https://www.researchgate.net/profile/Iyanu-Simeon-Michael?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_5&_esc=publicationCoverPdf https://www.researchgate.net/institution/Redeemers_University?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_6&_esc=publicationCoverPdf https://www.researchgate.net/profile/Iyanu-Simeon-Michael?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_7&_esc=publicationCoverPdf https://www.researchgate.net/profile/Ayodeji-Oke?enrichId=rgreq-ffa75c78e666d132a75efc80917150a9-XXX&enrichSource=Y292ZXJQYWdlOzM2NjUxNDg2MDtBUzoxMTQzMTI4MTQ0MTc4Njc2M0AxNzQ3Mzk1NzgwMjMy&el=1_x_10&_esc=publicationCoverPdf Success factors of digital technologies (DT) tools adoption for sustainable construction in a developing economy Ayodeji Emmanuel Oke Department of Quantity Surveying, Federal University of Technology Akure, Akure, Nigeria and Faculty of Engineering and the Built Environment, cidb Centre of Excellence, University of Johannesburg, Johannesburg, South Africa John Aliu Institute for Resilient Infrastructure Systems, College of Engineering, University of Georgia, Athens, Georgia, USA, and Solomon Onajite and Michael Simeon Department of Quantity Surveying, Federal University of Technology Akure, Akure, Nigeria Abstract Purpose – As the construction sector constantly seeks ways to address ever-growing societal demands, the need to embrace innovation and digital technologies (DTs) cannot be overstated. Therefore, the purpose of this study is to assess the success factors influencing the adoption of DTs to achieve sustainable construction in a developing economy such as Nigeria. Design/methodology/approach – A quantitative research approach was conducted with close-ended questionnaires developed and administered to construction professionals based in Lagos State, Nigeria. Data obtained was analyzed using percentages, frequency, mean item score and exploratory factor analysis. Findings – The findings from the mean scores revealed the leading influential success factors which were education and training, methodology of model constructions, organization development, customer satisfaction and profitability and new revenue. Factor analysis revealed three clusters of success factors which were management needs, constructionmethodology and effective communication. Practical implications – To adequately integrate DTs into construction industry activities and processes, awareness about the technologies must be created and enhanced if already in play. This study posits that the construction sectormust accept and implement this new paradigm of innovation to benefit from this disruptive era. Originality/value – This study serves as a foundation for other related studies that are aimed at advocating the efficacy of DTs in the effective and efficient execution of construction activities. The assessment of the success factors influencing the adoption of DTs would help construction organizations and stakeholders to understand the need to embrace and implement smart technologies into the activities, operations and processes of the construction industry. Keywords Building information modeling, Cyber-physical system, Digital technologies, Sustainable construction, Virtual reality Paper type Research paper 1. Introduction The construction sector is considered one of the leading contributors of revenue to the economies of nations across the world, producing almost $10tn of construction-related goods CI 24,4 950 Received 16August 2022 Revised 15 November 2022 Accepted 1 December 2022 Construction Innovation Vol. 24 No. 4, 2024 pp. 950-964 © EmeraldPublishingLimited 1471-4175 DOI 10.1108/CI-08-2022-0207 The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/1471-4175.htm http://dx.doi.org/10.1108/CI-08-2022-0207 and services on an annual basis (Chen et al., 2021). However, as rapid urbanization and population expansions continue to surge, there is a salient need for the creation and maintenance of more infrastructural facilities to cope with these ever-growing societal demands (Mbala et al., 2018). To seamlessly address these demands, the construction sector has had to seek new ways to evolve to become more effective, more efficient and more productive. Thus, the advent of digital technologies (DTs) has provided the construction sector with innovative and automated ways to improve its activities and processes to achieve improved performance within the industry (Burton et al., 2021). More so, the pressures to reduce cost, improve safety, meet sustainability goals and satisfy clients’ needs have accelerated the need for the construction industry to adopt DTs (Adekunle et al., 2022). The application of DTs in improving the activities, processes and outlook of the construction sector has been well documented. For example, additive manufacturing or three-dimensional (3D) printing has been found to decrease waste from production processes as well as provide design flexibility and constructability (Oke et al., 2022). Also, artificial intelligence can be used to track the real-time performance and interaction of machinery and workers on site which can help in reducing cost overruns, improving safety on site and enhancing the project processes (Abioye et al., 2021). Moreover, building information modeling (BIM) can help to improve decision-making on site (Gamil and Rahman, 2019), while smart wearable technologies can enhance safety practices among construction professionals (Abuwarda et al., 2022). These are just a few examples to highlight how DT is changing the landscape of the construction industry. Thus, implementing DTs in the construction domain has become an important business (Osunsanmi et al., 2019) and has drawn increased attention from stakeholders, policymakers and governments in recent times (Aghimien et al., 2022a, 2022b; Oke et al., 2022). Although previous studies have addressed the barriers to the adoption of DTs (Chen et al., 2021) and their roles in improving the activities of the construction sector (Aghimien et al., 2022a, 2022b), very few studies have evaluated the success factors influencing the adoption of DTs in the context of developing countries, a gap this study intends to address. Therefore, this study assesses the critical success factors influencing the adoption of smart technologies in the execution of construction activities through the lens of the Nigerian construction industry. The understanding of the success factors is relevant to comprehend how the construction sector can experience digital transformation in both the short and the long term. As a theoretical contribution, this study is significant because it fosters the ever- growing discussions on the need for the construction sector to accept and implement DTs in its activities and processes to improve the quality of project delivery. The outcomes of this study will be beneficial to construction stakeholders, top management of construction organizations, clients, decision-makers and even the government to the need for integrating DTs to achieve project goals. This can serve as a steppingstone to encourage the digital transformation of the construction industry in Nigeria and developing economies as a whole. This study also provides a section to explain how the findings of this research will be of importance for policy, practice and subsequent research. This study further makes several key recommendations on how the adoption of DTs can be integrated into the construction industry. 2. Success factors influencing digital technologies in the construction industry There is a vast range of DTs that can be implemented into core construction activities to improve both efficiency and effectiveness of activities and processes. According to Dallasega et al. (2018) and Olanipekun and Sutrisna (2021), these DTs can be divided into four critical components such as automation systems, connectivity, digital access and digital Success factors of digital technologies (DT) 951 data. Automation involves the use of innovative technologies to produce self-organizing systems such as robots for improving worker safety and addressing labour shortages (Melenbrink et al., 2020) and blockchain technology for making executable payments to contractors for the job done (Akinradewo et al., 2022). Connectivity embraces the connections and synchronization of different activities such as the fusing the physical-to-digital-to- physical in construction activities using technologies like Augmented Reality, Virtual Reality and Mixed Reality, cloud computing, Internet of Things and sensors (Olanipekun and Sutrisna, 2021). Through automation systems, digital access is derived which deals with the access to internet connections and networks to carry out activities in real-time. Through the use of data analytics on construction sites, it is possible to generate meaningful insights, make on-the-spot decisions and even future projects (Ngo et al., 2020). In the case of digital data, information is generated and evaluated when used on construction sites. For example, the use of unmanned aerial vehicles and wearable sensors can serve as a collection point of digital data on construction sites (Aghimien et al., 2022a, 2022b). The adoption of DTs is aided by top management support, suitable financial resources, an organic structure and an acceptable culture (Ikuabe et al., 2020). This indicates that the top management teams are critical in deciding whether or not to use DTs, which makes sense given that the top management team could be a solitary proprietor or a small group of people whose judgment is final. In addition, significant financial resources are necessary, as the lack of funds to procure some of these DTs could become a factor influencing their adoption (Saka et al., 2020). The study by Osunsanmi et al. (2019) and Ngo et al. (2020) showed that the expertise and support of top management had a strong positive influence on the adoption of DTs. According to Aghimien et al. (2018), staff training on DTs is critical for their use in the construction industry. These training programs can be tailored to teach users the practical skills they need to use digital tools effectively and efficiently in their respective fields. Software providers can organize coaching sessions at their training facilities, and construction company management can use this training to groom their employees in the usage of necessary DTs. Osunsanmi et al. (2018) recommend construction professionals take advantage of the benefits of embracing construction 4.0 principles by attending seminars, workshops and training on DTs. Ezeokoli et al. (2016) have also articulated some critical success factors of DTs. Some of these success factors are the need to boost profitability and revenue growth, the need to achieve customer satisfaction, the need to improve operational efficiency, the need to attain high technical standards, the need to bolster business agility and improve the productivity of employees. Others were the need to stay competitive and the need to reduce the burden of data storage (Adekunle et al., 2022). Furthermore, Aghimien et al. (2018) highlighted the top three viable solutions to the usage of DTs as government loans for technical development, improving and employing people with technology abilities and adequate employee training. More so, Ogwueleka (2015) advocates for the inclusion of BIM expertise in the existing curricula of higher education institutions for students and the need to establish continuing education courses for practitioners. This also aligns with some of the recommendations posited by Adekunle et al. (2022). The study by Saka et al. (2020) identified some critical success factors that influence the adoption of DTs such as appropriate organizational culture, compatibility, organic structure and organization readiness. They further posit that organizational culture is responsible for some adoption determinants such as the willingness to accept DTs, willingness to be adaptable to the market, the management style of the top-level managers, their learning and growth perspective and their openness to such discussions (Ikuabe et al., 2020). In a similar CI 24,4 952 vein, Adepoju and Aigbavboa (2021), highlighted several critical success factors influencing the adoption of DTs. These include the need to enhance the agility of the organization, the need to improve the productivity of the employee, the need to gain competitiveness and stay relevant, the need to achieve high-quality output and the need to be increasingly dynamic in the ever-changing world of work (Adepoju andAigbavboa, 2021). According to Lojda (2020), the successful implementation of DTs can only be made possible under two critical conditions which are – organization development and employee development. The organization must focus on workforce development strategies and the future development plan of the organization as the fundamental requirement for the success of DTs implementation. Adekunle et al. (2022) suggest that the effective transition to BIM implementation may require employees and stakeholders to be trained in key aspects such as basic operation skills, methodology of model construction, modeling standards and work relationship with other parties. In addition, Saka et al. (2020) add that factors such as client demands also play a pivotal role in influencing the adoption of DTs. Some of the factors identified by Yang et al. (2021) include organizational readiness, organizational culture, willingness and intention to be flexible and adaptable among several others. Table 1 provides a summary of several success factors influencing the adoption of DTs in the construction industry with reference to several existing studies. 3. Research methodology As stated throughout this research, the technologies accompanying the Fourth Industrial Revolution have the tendency to transform the construction industry in a tremendous way (Aghimien et al., 2022a, 2022b). Therefore, this study is aimed at evaluating the success factors influencing the adoption of DTs to achieve sustainable construction in a developing economy such as Nigeria. To meet the objectives of this study, registered professionals who are actively engaging in construction activities and had considerable knowledge of digitalization were eligible to participate in this study. Therefore, the target population for Table 1. Summary of success factors for the adoption of digital technologies Success factors A B C D E F G H Government support (SF1) � � � Management and leadership support (SF2) � � � Education and training (SF3) � � � � � � � Customer satisfaction (SF4) � � Profitability and new revenue growth (SF5) � � � Gained competitiveness (SF6) � � Work relationship with other parties (SF7) � Modeling standards (SF8) � � Employee development (SF9) � � � � Organization development (SF10) � � � Increasing demands and complexities of the modern design projects (SF11) Basic operation skills (SF12) � Methodology of model constructions (SF13) � � Standardization (products and process) (SF14) Standard platforms for integration and communication (SF15) Clear definition and understanding of user’s requirement (SF16) Increase in business agility (SF17) � � Notes: A = Aghimien et al. (2018); B = Ogwueleka (2015); C = Osunsanmi et al. (2018); D = Saka et al. (2020); E = Ezeokoli et al. (2016); F = Yang et al. (2021); G = Adekunle et al. (2022); and H = Olanipekun and Sutrisna (2021) Success factors of digital technologies (DT) 953 this study was construction professionals from both private and public construction organizations in Lagos State, Nigeria such as architects, builders, engineers and quantity surveyors. To obtain data from the respondents, a quantitative research approach was used via a well-structured questionnaire. This approach was deemed suitable because it allows the quantification and generalization of data using statistical analysis (Culka, 2018). A quantitative approach was also adopted for this study because of its capacity to produce an unbiased analysis of mathematical, numerical and statistical data (Creswell and Creswell, 2017). To obtain data from the construction professionals, a convenience sampling technique was adopted. As a non-probability sampling technique, convenience sampling encourages the selection of respondents based on their accessibility, availability and proximity to the researcher (Kumar, 2018). The convenience sampling technique was also adopted for this study because of its ability to obtain data from respondents that meet the criteria for eligibility within a short space of time (Etikan et al., 2016). This sampling technique was also found to be effective in similar studies such as that of Adepoju and Aigbavboa (2021) and Oke and Arowoiya (2021). Because of the ease of data collection and time-saving propensities, a close-ended questionnaire was adopted which was structured into two sections. The first section of the questionnaire examined the background information of the respondents such as academic qualifications, professional qualifications, years of experience and years of operation of an organization. The second section required the respondents to rank the various success factors which were identified from existing studies using a five- point Likert scale. The Likert scale was adopted over other scales because of its high- reliability coefficients and a high likelihood of responses that adequately reflect opinions under focus (Willits et al., 2016). Hence, a five-point Likert scale of “very high,” “high,” “average,” “low” and “very low” was adopted for this questionnaire. The critical success factors for this study were obtained from secondary sources such as academic books, book chapters, journals and conference proceedings that are Scopus-indexed on the subject matter. Databases also explored for some of these documents were Web of Science, ScienceDirect, JSTOR and ERIC. Using keywords and themes during the filter process, the examination of extant literature on DTs yielded 17 variables as detailed in Table 1. Before the questionnaires were distributed to the construction professionals, they were pilot tested on a small representative sample to check their appropriateness and suitability for the study. Data obtained from the pilot study were not included in this study, but some of the feedback was used to improve the overall structure and flow of the questionnaire in achieving the objectives of the study. The pilot testing of the questionnaires helped to improve the validity of the study. A total of 121 questionnaires were administered to the construction professionals, and 98 were retrieved and suitable for analysis, signifying a response rate of 81%. This response rate percentage was deemed suitable for analysis as it goes way above the 30% threshold for construction-related studies which was recommended by Oke et al. (2022). The questionnaires were self-administered to professionals using electronic means like Google Forms and the process spanned over two months. Data obtained was analyzed using the Statistical Package for the Social Sciences (SPSS) version 26. Mean item score (MIS) and standard deviation (SD) were first used to rank the identified success factors influencing the adoption of DTs in descending order before exploratory factor analysis (EFA) was used to regroup these success factors into more manageable and clearly identifiable subscales. The data collection instrument possessed a Cronbach’s alpha value of 0.885, signifying the high reliability of the data collection instrument. This value was considered suitable, as it exceeds the 0.7 thresholds suggested by Tavakol and Dennick (2011). CI 24,4 954 4. Results 4.1 Background information of respondents This section presents a summary of the background data obtained from the targeted respondents considered for this research. Some of the data obtained were their academic qualifications, professional qualifications, years of experience and years of operation of an organization. From Table 2, a majority of the respondents had a bachelor’s degree (52%, N-51), 18.4% (N-18) possessed a Master’s degree, 5.1% (N-5) had a Doctorate, while 24.5% (N-24) had a diploma qualification. This implies that the respondents who participated in this study possessed adequate academic qualifications to provide high-quality input to the research. Table 2 also presented the numbers of qualified professionals whowere certified and registered according to the principles and guidelines of their respective professions. It was revealed that 28.6% (N-28) were professionally registered with Quantity Surveyor Registration Board of Nigeria (QSRBN), 27.6% (N-27) were registered with Council for the Regulation of Engineering in Nigeria (COREN) and 23.5% (N-23) were affiliated with Architects Registration Council of Nigeria (ARCON), while 20.4% (N-20) were registered with Council of Registered Builders of Nigeria (CORBON). This information means that the respondents are in good professional standing with their respective professional bodies to participate in this research. The data collected revealed that most of the organizations are less than 5 years in operation (52, N-51), followed by 6–10 years 26.5% (N-26), 11–15 years 12.2% (N-12), 16–20 years 7.1% (N-7) and 21 years above 2% (N-2.2). Overall, the information gleaned from the background data reveals that the respondents for the study were adequately equipped academically and in terms of years of experience to give reasonable answers to the questions of the research. Therefore, the credibility and the reliability of the study’s results were enhanced. 4.2 Success factors influencing the adoption of digital technologies Afterward, the analysis of the success factors influencing the adoption of DTs was conducted and revealed. The reliability statistics revealed a Cronbach’s alpha value of 0.885, Table 2. Background information of the respondents Category Frequency % Academic qualifications National Diploma 13 13.3 Higher National Diploma 11 11.2 Bachelors 51 52 Masters 18 18.4 PhD 5 5.1 Total 98 100.0 Professional qualifications Architects Registration Council of Nigeria (ARCON) 23 23.5 Council of Registered Builders of Nigeria (CORBON) 20 20.4 Quantity Surveyors Registration Board of Nigeria (QSRBN) 28 28.6 The Council for the Regulation of Engineering in Nigeria (COREN) 27 27.6 Total 98 100.0 Years of experience Less than 5 years 53 54.1 6–10 years 29 29.6 11–15 years 10 10.2 16–20 years 2 2.0 21 years above 4 4.1 Total 98 100.0 Success factors of digital technologies (DT) 955 signifying a very high level of internal consistency for the scale of items of the success factors influencing the adoption of DTs. MIS was used to rank the identified success factors in descending order. For factors with the same MIS, SD was used to determine the most significant factor. This aligns with the suggestion by Field (2005), who posits that when factors have the same mean values, the factor with the lowest standard deviation is given the highest ranking. Table 3 shows that education and training (SF3) is the most influential success factor driving the adoption of DTs (M = 4.48 and SD = 0.899). Methodology of model constructions (SF13) was ranked second (M = 4.34 and SD = 3.211), while organization development (SF10) was ranked third (M = 4.30 and SD = 3.231). The least ranked were increasing demands and complexities of the modern design projects (SF11) (M = 4.04 and SD = 0.994), work relationship with other parties (SF7) (M = 4.03 and SD = 1.116) and government support (SF1) (M = 3.94 and SD = 1.138). Overall, the MIS for the respective success factors all ranked highly with every variable possessing mean scores higher than 3.90. 4.3 Exploratory factor analysis All 17 success factors that were obtained from existing literature were subsequently subjected to further analysis using the SPSS version 26 to carry out EFA. The EFA was used to further regroup these success factors into more manageable and clearly identifiable subscales (Pallant, 2013). To examine the data suitability and appropriateness for EFA, this study adopted both the Kaiser–Meyer–Olkin (KMO) and Bartlett’s test of sphericity. According to Yong and Pearce (2013), the KMO examines the sampling adequacy, and values less than 0.5 are not acceptable. Table 4 shows a KMO value of 0.893 which indicates suitability for principal component analysis. This also indicates that 89.3% of the data obtained was satisfactory for factor analysis. Bartlett’s test of sphericity yielded a high chi- squared value of 1,960.838 at 136 degrees of freedom as shown in Table 4. Table 4 on the principal component analysis revealed the presence of three clusters with eigenvalues exceeding 1, explaining 38.378%, 19.526% and 19.378% of the variance. Afterward, a Varimax rotation was carried out which led to the rotated matrix as shown in Table 3. Mean item score of success factors influencing digital technologies Success factors Mean SD Rank Education and training (SF3) 4.48 0.899 1 Methodology of model constructions (SF13) 4.34 3.211 2 Organization development (SF10) 4.30 3.231 3 Customer satisfaction (SF4) 4.26 0.956 4 Profitability and new revenue growth (SF5) 4.26 3.234 5 Management and leadership support (SF2) 4.24 0.897 6 Gained competitiveness (SF6) 4.22 1.099 7 Employee development (SF9) 4.11 1.054 8 Standard platforms for integration and communication (SF15) 4.10 1.026 9 Increase in business agility (SF17) 4.10 1.040 10 Basic operation skills (SF12) 4.09 1.075 11 Modeling standards (SF8) 4.08 1.002 12 Standardization (products and process) (SF14) 4.08 1.022 12 Clear definition and understanding of user’s requirement (SF16) 4.07 1.038 14 Increasing demands and complexities of the modern design projects (SF11) 4.04 0.994 15 Work relationship with other parties (SF7) 4.03 1.116 16 Government support (SF1) 3.94 1.138 17 CI 24,4 956 Table 5. An inspection of the scree plot revealed a break after the third factor with values above 1 retained as clusters as shown in Figure 1. The first principal cluster account for 36.4% of the total variance explained and nine factors loaded onto this cluster, and they are customer satisfaction (86.0%), work Table 4. Total variance explained for success factors influencing digital technologies Component Initial eigenvalues Total % of variance Cumulative % Total % of variance Cumulative % 1 8.694 51.139 51.139 6.184 36.378 36.378 2 3.052 17.951 69.090 3.319 19.526 55.904 3 1.053 6.192 75.282 3.294 19.378 75.282 4 0.751 4.418 79.700 5 0.634 3.727 83.427 6 0.580 3.409 86.836 7 0.468 2.751 89.587 8 0.365 2.146 91.733 9 0.314 1.849 93.582 10 0.279 1.640 95.222 11 0.224 1.317 96.539 12 0.185 1.085 97.625 13 0.170 1.001 98.626 14 0.118 0.696 99.322 15 0.100 0.591 99.913 16 0.012 0.071 99.985 17 0.003 0.015 100.000 KMO and Bartlett’s Test KMOmeasure of sampling adequacy 0.893 Bartlett’s test of sphericity Approximately Chi-Square 1,960.838 df 136 Significance 0 Table 5. Pattern matrix for success factors influencing digital technologies Success factors Component 1 2 3 Customer satisfaction (SF4) 0.860 Work relationship with other parties (SF7) 0.831 Education and training (SF3) 0.816 Management and leadership support (SF2) 0.810 Employee development (SF9) 0.739 Gained competitiveness (SF6) 0.737 Organization development (SF10) 0.727 Profitability and new revenue growth (SF5) 0.699 Government support (SF1) 0.605 Methodology of model constructions (SF13) 0.985 Standardization (products and process) (SF14) 0.983 Modeling standards (SF8) 0.982 Basic operation skills (SF612) 0.799 Increasing demands and complexities of the modern design projects (SF11) 0.737 Standard platforms for integration and communication (SF15) 0.703 Clear definition and understanding of user’s requirement (SF16) 0.657 Increase in business agility (SF17) 0.582 Success factors of digital technologies (DT) 957 relationship with other parties (83.1%), education and training (81.6%), management and leadership support (81.0%), employee development (73.9%), gained competitiveness (73.7%), organization development (72.7%), profitability and new revenue growth (69.9%) and government support (60.5%). Based on the latent similarity in the rotated variables, this cluster was renamed “management needs.” The second extracted cluster accounted for 19.5% of the total variance explained, and five factors loaded onto this cluster. They are methodology of model constructions (98.5%), standardization (products and process) (98.3%), modeling standards (98.2%), basic operation skills (79.9%) and increasing demands and complexities of the modern design projects (73.7%). Subsequently, this cluster was renamed “construction methodology.” The third extracted cluster accounted for 19.4% of the total variance explained, and three factors loaded onto this cluster. These are standard platforms for integration and communication (70.3%), clear definition and understanding of user’s requirements (65.7%) and an increase in business agility (58.2%). This cluster was subsequently named “effective communication.” 5. Discussion of results The objectives of this study were to identify and assess the success factor of adopting DTs in the construction industry. From the results obtained from the survey, the potential success factors were ranked according to their MIS as shown in Table 3. Thus, it was revealed that with mean values of 4.48, 4.34, 4.30 and 4.26, respectively, education and training, methodology of model constructions, organization development, customer satisfaction and profitability and new revenue growth were the most influential success factors for adopting DTs. The least ranked were increasing demands and complexities of the modern design projects, work relationship with other parties and government support. Further analyses using EFA produced three clusters, and these are discussed next. Figure 1. Scree plot for success factors influencing digital technologies CI 24,4 958 The first cluster is management needs which comprised customer satisfaction, work relationships with other parties, education and training, management and leadership support, employee development, gained competitiveness, organization development, profitability and new revenue growth and government support. These findings align with the studies of Osunsanmi et al. (2018) which suggest that one of the critical reasons why construction organizations adopt DTs is to gain competitiveness because of the rapid technological advancement accompanying this innovative era. With the advent of smart technologies and increased automation, construction organizations are now able to provide professionals with a visual representation of what completed projects will look like. Thus, by adopting technologies into their activities and operations, construction organizations can remain agile and competitive (Aghimien et al., 2022a, 2022b). More so, the integration of technological tools can bolster the decision-making capabilities of construction industry employers which can ultimately lead to improved management systems as submitted by Anshari and Hamdan (2022). To fully leverage these innovative technologies, construction organizations have begun to empower their employees (reskilling and upskilling) so that they can fully harness the potential of DTs (Arowoiya et al., 2020). Furthermore, Aghimien et al. (2018) highlighted the top three viable solutions to the usage of DTs which are government loans for technical development, improving and employing people with technology abilities and adequate employee training. Also, the role of government intervention in the adoption of technologies into construction activities was further highlighted by Bohari et al. (2022). Another key variable that loaded onto this cluster was profitability and new revenue growth. This aligns with the study of Aghimien et al. (2021) which submits that the adoption of smart technologies can lead to increased productivity among construction organizations. The second cluster is construction methodology which comprised model constructions, standardization (products and process), modeling standards, basic operation skills and the increasing demands and complexities of modern design projects. These findings are in tandem with the studies of Hossain and Nadeem (2019) and Ebekozien and Aigbavboa (2021), who opine that one of the factors that influence the adoption of DTs is the need to keep up with the increased automation and smart management of projects that accompany this disruptive era. As construction organizations embrace digitalization and smart technologies, projects are expected to be completed more effectively and efficiently (Aghimien et al., 2021). According to McNamara and Sepasgozar (2021), because of the way digital techniques such as artificial intelligence, BIM and cloud computing are transforming the design and planning processes of engineering designs, construction organizations are compelled to adopt DTs. More so, through the use of 3D simulations, Augmented Reality (AR), Virtual Reality (VR) and Mixed Reality (MR), construction professionals can view a building’s digital twin even before the construction work commences (Opoku et al., 2021). These tools can enable the capturing, analysis and storage of data ahead of the construction process, thus speeding up the activities and resulting in higher performance of the projects (Liu et al., 2021). Unlike the traditional methods of construction, the adoption of digital tools further improves the construction methodology of projects (Dallasega et al., 2021). These can ultimately help to modernize the construction industry, reduce risks, improve sustainability, improve safety and raise standards and productivity (Ebekozien and Aigbavboa, 2021; Aghimien et al., 2022a, 2022b). More so, the image of the construction industry is enhanced with the adoption of these digital tools (McNamara and Sepasgozar, 2021). The third cluster which is effective communication comprised integration and communication, clear definition and understanding of user’s requirements and an increase in business agility. These findings resonate with the study of Ebekozien et al. (2022), who Success factors of digital technologies (DT) 959 suggest that improved and effective communication on construction sites is one of the key reasons why construction organizations adopt technologies in their projects. As smart technologies become more integrated into various stages of the construction processes, Big data will become even more prominent. According to Huang (2021), Big data will allow construction companies to collect, analyze and apply enormous amounts of information to help solve problems while providing critical insights for future on-site activities. Thus, construction organizations can complete projects on time, helping them to remain agile and competitive (Wamba et al., 2020). The presence of AI, robotics, quantum computing and 3D printing among several others are blurring the boundaries between the biological, digital and physical, leading to improved communication as humans communicate with machines to solve construction-related problems (Arowoiya et al., 2020). Furthermore, the role of robotic technologies and automation techniques in delivering a safe construction environment cannot be overemphasized. These smart technologies have the potential to take workers out of harm’s way and high-risk areas, thus improving the activities, operations and processes of the construction industry. 6. Implications of findings As climate change, depletion of natural resources and rapid urbanization continue to rage on, the construction sector must jettison traditional methods in favor of digital tools to achieve sustainable construction. According to Alaloul et al. (2020), DTs and innovation have the tendency to boost the activities and processes of the construction sector and ultimately lead to improved economic growth. Therefore, the adoption of DTs will have far- reaching implications for construction organizations, professionals and society at large. Apart from the economic advantages of DTs such as improving quality, productivity and efficiency, the application of innovative tools can lead to improved communication and real- time collaboration to ensure that construction projects are achieved timeously (Chen et al., 2021). Thus, this study intends to stir up the attention of construction stakeholders, top management, clients, decision-makers and even the government to the need for integrating DTs to achieve project goals. From the findings of this study, one of the critical success factors influencing the adoption of DTs lies in the fact that innovative tools will boost the competitive advantage and agility of the construction sector. Through smart technologies, contractors and project managers can monitor the progress of construction projects in real-time, improve on-site collaborations and communications as well as mitigate risk and reduce cost (Akinradewo et al., 2021). Therefore, construction organizations must be ready to invest in the implementation of DTs to boost time efficiency, improve collaboration between construction team members and reduce cost, which can ultimately lead to better output for the industry. In the broader scheme of things, DTs can build stronger infrastructures, foster economic prosperity and mitigate social inequalities. Thus, this study posits that the construction sector must accept and implement this new paradigm of innovation to benefit from this disruptive era because several studies have shown that the sector is often reluctant to embrace advanced technology. 7. Conclusion and recommendations This study set out to evaluate the success factors influencing the adoption of DTs with a view to achieving sustainable construction in a developing economy. Using a structured questionnaire for data collection, the most influential success factors driving the adoption of DTs are education and training, methodology of model constructions, organization development, customer satisfaction and profitability and new revenue. On the other hand, CI 24,4 960 the least influential factors were work relationships with other parties and government support. Further analysis using EFA revealed three clusters of success factors such as management needs, construction methodology and effective communication. Although the construction industry is often labelled as very “traditional” and “slow” to the adoption of innovative technologies, this study makes a case for the acceptance and implementation of DTs as a tool to achieve sustainable construction. Herein lies one of the major motivations for conducting this study. The findings of this study recommend that to adequately integrate DTs into the construction industry, awareness about the technologies must be created and enhanced if already enforced. Therefore, the awareness of DTs should be encouraged through technological workshops, science fairs, innovative forums, conferences and symposiums for top-level management, construction stakeholders and even clients. Barriers to the acceptance and implementation of innovative technologies within the construction industry must also be challenged, and construction stakeholders and professionals should be encouraged to adopt digital tools to drive the efficiency and effectiveness of construction projects. As earlier stated, the adoption of DTs will go a long way in improving the image of the construction industry considering that the sector has been known to lack innovativeness over the years. Thus, to attract skilled professionals, there is a need for the construction sector to experience digital transformation. The findings of this study also make a case for a more dynamic and responsive workforce to handle these DTs. This implies that higher education systems and institutions must respond to the challenge of developing adequately skilled graduates or professionals to handle these technologies. Therefore, this study recommends that higher education institutions must revisit and revamp their existing curricula to reflect the underpinning technologies accompanying the Fourth Industrial Revolution, to produce tech savvy graduates who will fit seamlessly into the disruptive world of work. More so, this study also recommends that top management stakeholders must seek ways to enhance professionals already in the construction sector by creating programs or crash courses to bring existing employees up to speed in the technological sense (upskilling, multiskilling and reskilling). By putting new technologies in context and offering real-world applications of digital tools during training, employees will be intrigued and compelled to adopt these innovative tools in their day-to-day activities. More so, hands-on- experience with these technologies will reduce the resistance to the adoption of DTs. For developing economies, the cost of acquiring some of the expertise and digital tools may prove demanding. This study, therefore, recommends that governments should subsidize the cost of acquiring digital tools and technologies to enable construction organizations to readily access them. Theoretically, this study expands on the existing knowledge surrounding the implementation of DTs in the construction sector of developing countries, where the consciousness and application of such technologies are still sluggish. Therefore, this study contributes to the database of relevant literature and empirical knowledge that can serve as a reference for future studies on the implementation of DTs. Another significant contribution of this study is that it identifies and evaluates the success factors that can enhance the adoption of DTs in the Nigerian construction industry. Thus, future research and studies can benefit from the findings of this study. As a limitation, this research assessed the success factors driving the adoption of DTs. Future studies could go one step further to address the level of awareness and usage of DTs as well as the areas of application of DTs in construction. Future studies could also be conducted to scrutinize the various barriers to the adoption of DTs as ways to mitigate them. Future studies may also pay attention to the socio-technical aspects of DTs and how they can affect the status-quo in the activities and processes of the construction industry. The authors hope that the outcome Success factors of digital technologies (DT) 961 of this study will spur further discussions around how DTs can not only address the issues facing the construction sector but also how they may address other socio-economic factors and demographic challenges plaguing our societies today. References Abioye, S.O., Oyedele, L.O., Akanbi, L., Ajayi, A., Delgado, J.M.D., Bilal, M., Akinade, O.O. and Ahmed, A. (2021), “Artificial intelligence in the construction industry: a review of present status, opportunities and future challenges”, Journal of Building Engineering, Vol. 44, p. 103299. Abuwarda, Z., Mostafa, K., Oetomo, A., Hegazy, T. and Morita, P. (2022), “Wearable devices: cross benefits from healthcare to construction”,Automation in Construction, Vol. 142, p. 104501. Adekunle, S.A., Aigbavboa, C.O. and Ejohwomu, O.A. (2022), “Understanding the BIM actor role: a study of employer and employee preference and availability in the construction industry”, Engineering, Construction and Architectural Management. Adepoju, O.O. and Aigbavboa, C.O. (2021), “Assessing knowledge and skills gap for construction 4.0 in a developing economy”, Journal of Public Affairs, Vol. 21 No. 3, p. e2264. Aghimien, D., Aigbavboa, C. and Matabane, K. (2021), “Dynamic capabilities for construction organizations in the fourth industrial revolution era”, International Journal of Construction Management, pp. 1-10. [doi: 10.1080/15623599.2021.1940745] Aghimien, D., Aigbavboa, C., Oke, A. and Koloko, N. (2018), “Digitalization in construction industry: construction professionals perspective”, Proceedings of the Fourth Australasia and South-East Asia Structural Engineering and Construction Conference, Brisbane,Australia, pp. 3-5. Aghimien, D., Ikuabe, M., Aghimien, L.M., Aigbavboa, C., Ngcobo, N. and Yankah, J. (2022a), “PLS- SEM assessment of the impediments of robotics and automation deployment for effective construction health and safety”, Journal of Facilities Management. Aghimien, D., Ikuabe, M., Aliu, J., Aigbavboa, C., Oke, A.E. and Edwards, D.J. (2022b), “Empirical scrutiny of the behavioral intention of construction organisations to use unmanned aerial vehicles”, Construction Innovation. Akinradewo, O.I., Aigbavboa, C.O., Edwards, D.J. and Oke, A.E. (2022), “A principal component analysis of barriers to the implementation of blockchain technology in the South African built environment”, Journal of Engineering, Design and Technology. Akinradewo, O., Aigbavboa, C., Oke, A., Edwards, D. and Kasongo, N. (2021), “Key requirements for effective implementation of building information modelling for maintenance management”, International Journal of ConstructionManagement, pp. 1-9. Alaloul, W.S., Liew, M.S., Zawawi, N. and Kennedy, I.B. (2020), “Industrial revolution 4.0 in the construction industry: challenges and opportunities for stakeholders”, Ain Shams Engineering Journal, Vol. 11 No. 1, pp. 225-230. Anshari, M. and Hamdan, M. (2022), “Understanding knowledge management and upskilling in fourth industrial revolution: transformational shift and SECI model”, VINE Journal of Information and KnowledgeManagement Systems. Arowoiya, V.A., Oke, A.E., Aigbavboa, C.O. andAliu, J. (2020), “An appraisal of the adoption internet of things (IoT) elements for sustainable construction”, Journal of Engineering, Design and Technology, Vol. 18 No. 5, pp. 1193-1208. Bohari, A.A.M., Bidin, Z.A. and Khalil, N. (2022), “Government intervention through collaborative approach in promoting the adoption of green procurement for construction projects”, International Journal of Sustainable Construction Engineering and Technology, Vol. 13 No. 2, pp. 68-82. Burton, E., Edwards, D.J., Roberts, C., Chileshe, N. and Lai, J.H. (2021), “Delineating the implications of dispersing teams and teleworking in an Agile UK construction sector”, Sustainability, Vol. 13 No. 17, p. 9981. CI 24,4 962 http://dx.doi.org/10.1080/15623599.2021.1940745 Chen, X., Chang-Richards, A.Y., Pelosi, A., Jia, Y., Shen, X., Siddiqui, M.K. and Yang, N. (2021), “Implementation of technologies in the construction industry: a systematic review”, Engineering, Construction and Architectural Management, Vol. 29 No. 8, pp. 3181-3209. Creswell, J.W. and Creswell, J.D. (2017), Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, Sage publications. Culka, M. (2018), “Quantitative scenario design with Bayesian model averaging: constructing consistent scenarios for quantitative models exemplified for energy economics”, Energy, Sustainability and Society, Vol. 8 No. 1, p. 22. Dallasega, P., Rauch, E. and Linder, C. (2018), “Industry 4.0 as an enabler of proximity for construction supply chains: a systematic literature review”, Computers in Industry, Vol. 99, pp. 205-225. Dallasega, P., Marengo, E. and Revolti, C. (2021), “Strengths and shortcomings of methodologies for production planning and control of construction projects: a systematic literature review and future perspectives”, Production Planning & Control, Vol. 32 No. 4, pp. 257-282. Ebekozien, A. and Aigbavboa, C. (2021), “COVID-19 recovery for the Nigerian construction sites: the role of the fourth industrial revolution technologies”, Sustainable Cities and Society, Vol. 69, p. 102803. Ebekozien, A., Aigbavboa, C., Thwala, W.D., Amadi, G.C., Aigbedion, M. and Ogbaini, I.F. (2022), “A systematic review of green building practices implementation in Africa”, Journal of Facilities Management. Etikan, I., Musa, S.A. and Alkassim, R.S. (2016), “Comparison of convenience sampling and purposive sampling”,American Journal of Theoretical and Applied Statistics, Vol. 5 No. 1, pp. 1-4. Ezeokoli, F.O., Okolie, K.C., Okoye, P.U. and Belonwu, C.C. (2016), “Digital transformation in the Nigeria construction industry: the professionals’ view”, World Journal of Computer Application and Technology, Vol. 4 No. 3, pp. 23-30. Field, A. (2005),Discovering Statistics Using SPSS forWindows, SAGEPublications, p. 816. Gamil, Y. and Rahman, I.A.R. (2019), “Awareness and challenges of building information modelling (BIM) implementation in the Yemen construction industry”, Journal of Engineering, Design and Technology, Vol. 17 No. 5, pp. 1077-1084. Hossain, M.A. and Nadeem, A. (2019), “Towards digitizing the construction industry: state of the art of construction 4.0”, Proceedings of the ISEC (Vol. 10). Huang, Y., Shi, Q., Zuo, J., Pena-Mora, F. and Chen, J. (2021), “Research status and challenges of data-driven construction project management in the big data context”, Advances in Civil Engineering, 2021. Ikuabe, M., Aghimien, D.O., Aigbavboa, C.O. and Oke, A.E. (2020), “Inhibiting factors to the adoption of digital technologies in the South African construction industry”, in Proceedings of the 5th Research Conference of the Nigerian Institute of Quantity Surveyors (RECON 5), pp. 455-461. Kumar, R. (2018), ResearchMethodology: A Step-by-Step Guide for Beginners, Sage. Liu, M., Fang, S., Dong, H. and Xu, C. (2021), “Review of digital twin about concepts, technologies, and industrial applications”, Journal of Manufacturing Systems, Vol. 58, pp. 346-361. Lojda, J., N�emec, O., Nývlt, V. and Ližbetinov�a, L. (2020), “Digitalisation in construction as an educational challenge for universities”, IOP Conference Series: Materials Science and Engineering, Vol. 960 No. 4, p. 042095. McNamara, A.J. and Sepasgozar, S.M. (2021), “Intelligent contract adoption in the construction industry: concept development”,Automation in Construction, Vol. 122, p. 103452. Mbala, M., Aigbavboa, C. and Aliu, J. (2018), “Causes of delay in various construction projects: a literature review”, International Conference on Applied Human Factors and Ergonomics, Springer, Cham, pp. 489-495. Melenbrink, N., Werfel, J. and Menges, A. (2020), “On-site autonomous construction robots: towards unsupervised building”,Automation in Construction, Vol. 119, p. 103312. Ngo, J., Hwang, B.G. and Zhang, C. (2020), “Factor-based big data and predictive analytics capability assessment tool for the construction industry”,Automation in Construction, Vol. 110, p. 103042. Success factors of digital technologies (DT) 963 Ogwueleka, A.C. (2015), “Upgrading from the use of 2D CAD systems to BIM technologies in the construction industry: consequences and merits”, International Journal of Engineering Trends and Technology), Vol. 28 No. 8, pp. 403-411. Oke, A.E. and Arowoiya, V.A. (2021), “An analysis of the application areas of augmented reality technology in the construction industry”, Smart and Sustainable Built Environment, Vol. 11 No. 4, pp. 1081-1098. Oke, A., Atofarati, J. and Bello, S. (2022), “Awareness of 3D printing for sustainable construction in an emerging economy”, Construction Economics and Building, Vol. 22 No. 2, pp. 52-68. Olanipekun, A.O. and Sutrisna, M. (2021), “Facilitating digital transformation in construction – a systematic review of the current state of the art”, Frontiers in Built Environment, Vol. 7, pp. 96. Opoku, D.G.J., Perera, S., Osei-Kyei, R. and Rashidi, M. (2021), “Digital twin application in the construction industry: a literature review”, Journal of Building Engineering, Vol. 40, p. 102726. Osunsanmi, T.O., Aigbavboa, C. and Oke, A. (2018), “Construction 4.0: the future of the construction industry in South Africa”, Int J Civ Environ Eng, Vol. 12 No. 3, pp. 206-212. Osunsanmi, T.O., Oke, A.E. and Aigbavboa, C.O. (2019), “Barriers for the adoption of incorporating RFID with mobile technology for improved safety of construction professionals”, in Construction Industry Development Board Postgraduate Research Conference, Springer, Cham, pp. 297-304. Pallant, J. (2013), SPSS Survival Manual, McGraw-Hill Education (UK). Saka, A.B., Chan, D.W. and Siu, F.M. (2020), “Drivers of sustainable adoption of building information modelling (BIM) in the Nigerian construction small and medium-sized enterprises (SMEs)”, Sustainability, Vol. 12 No. 9, p. 3710. Tavakol, M. and Dennick, R. (2011), “Making sense of Cronbach’s alpha”, International Journal of Medical Education, Vol. 2, p. 53. Wamba, S.F., Dubey, R., Gunasekaran, A. and Akter, S. (2020), “The performance effects of big data analytics and supply chain ambidexterity: the moderating effect of environmental dynamism”, International Journal of Production Economics, Vol. 222, p. 107498. Willits, F.K., Theodori, G.L. and Luloff, A.E. (2016), “Another look at Likert scales”, Journal of Rural Social Sciences, Vol. 31 No. 3, p. 6. Yang, M., Fu, M. and Zhang, Z. (2021), “The adoption of digital technologies in supply chains: drivers, process and impact”,Technological Forecasting and Social Change, Vol. 169, p. 120795. Yong, A.G. and Pearce, S. (2013), “A beginner’s guide to factor analysis: focusing on exploratory factor analysis”,Tutorials in Quantitative Methods for Psychology, Vol. 9 No. 2, pp. 79-94. Further reading Hwang, B.G., Ngo, J. and Teo, J.Z.K. (2022), “Challenges and strategies for the adoption of smart technologies in the construction industry: the case of Singapore”, Journal of Management in Engineering, Vol. 38 No. 1, p. 05021014. Ibem, E.O. and Laryea, S. (2014), “Survey of digital technologies in procurement of construction projects”,Automation in Construction, Vol. 46, pp. 11-21. Corresponding author John Aliu can be contacted at: Ajseries77@gmail.com For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com CI 24,4 964 View publication stats mailto:Ajseries77@gmail.com https://www.researchgate.net/publication/366514860 Success factors of digital technologies (DT) tools adoption for sustainable construction in a developing economy 1. Introduction 2. Success factors influencing digital technologies in the construction industry 3. Research methodology 4. Results 4.1 Background information of respondents 4.2 Success factors influencing the adoption of digital technologies 4.3 Exploratory factor analysis 5. Discussion of results 6. Implications of findings 7. Conclusion and recommendations References