1. Introduction
A widely used design and control tool in the construction industry is information technology (IT), to which building information modeling (BIM) is extensively studied and applied. According to the Smart Market Report of McGraw Hill Construction, the use of BIM grew from 28% in 2007 to 71% in 2012 [1]. In a previous study, 70% of the participants stated that their companies either use or are considering using BIM [2]. In Japan, the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) initiated the use of BIM and civil information modeling and maintenance (CIM) in 2014 [3], and general contractors and MLIT research institutions have been implementing these technologies and carrying out research and software development. BIM is a holistic process through which stakeholders can cooperate to design, construct, and operate facilities in a virtual space [4]. It can be used throughout a project’s lifecycle and increase overall productivity. Successful BIM implementation requires the stakeholders’ collaboration and communication [5,6].
BIM education and research can support automation, productivity, and renewable energy in the architecture, engineering, and construction (AEC) sector, as it safeguards workers and maximizes land and plans for capital usage [7,8,9]. Recent studies have shown that BIM education in AEC disciplines covers both the managerial and technical facets of BIM and interdisciplinary collaborations [10]. In the US and Britain, for instance, such education has long been embraced as a significant component of curricula and has enabled the construction industry to achieve successful outcomes, respectively. Considering the growing interest in and applications of BIM, numerous universities across the globe have incorporated BIM education into their curricula through various initiatives that range from pursuing industry partnerships to developing entirely new curricula pertaining to BIM [9,11]. The construction IT (CIT) curricula of most universities center on software introduction combined with CAD or BIM application case studies. Moreover, BIM competencies are becoming essential to students’ future employment, as evidenced by an early survey of practitioners, among whom, 75% regarded students with CIT skills as having an advantage in the job market over those without such abilities [2].
An important issue for consideration, however, is that the process of BIM incorporation into higher education curricula varies across geographic locations [12]. Additionally, there is a glaring lack of CIT dissemination, inclusion, and application in such curricula, even though introducing students to this innovation is necessary. Problems also occur in balancing the addition of new courses to core disciplines, integrating the virtual world and experience with BIM, teaching its principles, and using its tools [13]. Different challenges have been experienced by countries endeavoring to gain traction in BIM implementation. In Pakistan, for example, significant problems with BIM implementation in higher education are evidenced by the dearth of BIM faculty members with training in creating CIT curricula for universities [13]. New Zealand has engaged in comparable discussions of CIT education [14]. In the UK, the highly creative nature of architectural practice and the challenges associated with promoting interdisciplinary collaboration are the primary barriers to incorporating BIM into, for instance, curricula and teaching environments [15]. In the Japanese context, there is a disconnect between industry and MLIT research development and university CIT education, despite the suggestion in the literature that fostering relationships between industry and higher education is essential for increasing productivity [16].
Against this backdrop, the ultimate goal of CIT in higher education should be the development of students’ attitudes, basic knowledge, and skills with respect to this innovation. To accomplish this goal, the first measure is to consider awareness when introducing a new tool into a curriculum [17]. In this regard, Ahn and Kim [18] investigated South Korean architecture students’ acceptance of BIM. Universities in Cambodia have also conducted studies on BIM awareness among their student bodies [12]. However, it is necessary to establish CIT education programs based on students’ awareness to meet current and future industrial needs. Given this research and practice gap, the objective of this study is (1) to inquire into students’ acceptance of CIT courses at Japanese universities through the creation of a comprehensive technology acceptance model (TAM), and (2) on the basis of the results, to propose an industrial needs-based curricular framework to increase students’ knowledge of and experience with CIT for the development for their careers and the construction industry.
In so doing, this work contributes to (1) an increase in students’ CIT education awareness and acceptance, thereby advancing their future careers; (2) sustainable and efficient infrastructure management with respect to severe natural disasters in Japan, such as floods, stormy rain, and earthquakes, through appealing CIT applications; and (3) attracting young individuals to the construction industry via CIT implementation for sustainable industry development.
The rest of this paper is structured as follows. Section 2 summarizes the literature on BIM education, awareness, and acceptance. Section 3 explains the CIT acceptance model developed in this work, and Section 4 details the research methodology. Section 5 presents the results, and Section 6 discusses the findings along with suggestions relevant to CIT education programs. Section 7 concludes the paper.
2. Literature Review
2.1. BIM Education
BIM provides an opportunity for stakeholders in the AECO industry to identify potential problems in a final product and evaluate possible solutions before initiating construction [19]. On the basis of expectations for the current AECO industry and government requirements, high-educational institutions have investigated how BIM can be incorporated into tertiary education systems [20,21]. BIM in AEC education has been paid attention to in the literature, but little is known about its current status in AEC curricula [22,23].
BIM has become prevalent in the industry, but its adoption in construction education generally lags behind industry implementation [24], which may cause difficulties in the workplace for graduates because of a lack of knowledge regarding this technology. Sufficient BIM training and education are important for successful industry innovation. Although the implementation of this technology has gained significant attention, one of the key issues confronting its use in the construction industry is a lack of individuals with sufficient skills and knowledge to apply it [25].
Relatedly, a few overviews of trends in CIT education have been provided [24,26], but a more recent endeavor is one initiated by NATSPEC, a non-profit organization. NATSPEC updated the state of BIM awareness and adoption in multiple countries such as China, Hong Kong, Singapore, Japan, the United States, the United Kingdom, the Netherlands, Australia, New Zealand, etc. [3]. The study revealed that BIM education occurs around the world and indicated that the use of particular BIM software is the focus of current BIM education. Despite the insights derived from this study, it did not document the status of BIM education and awareness in each country.
BIM training is a relatively new domain, but it has now become a key requirement in AEC education [27]. Because suitable practices in CIT education have yet to develop, courses revolving around this technology focus on software application classes that lack a theoretical basis and teamwork practice. The construction industry values both skills and teamwork as the basis for the improved project development and growth of future employees [28]. This assertion finds support in the work of Yarmohammadi and Ashuri, who emphasized the importance of CIT abilities in collaborations related to construction services and how a highly BIM-competent team leader can considerably influence a construction project’s progress [29]. Wu and Issa regard CIT education as a means of accelerating the CIT learning curve, but they also recognize that the competencies of fresh graduates’ BIM abilities are insufficient to satisfy industrial demands [30]. The authors suggested that BIM education prepares graduates in a way that enables organizations to shape their BIM competencies according to their own needs [31]. The idea that understanding application concepts and BIM processes should be included in BIM curricula is emphasized in [24,32,33], but this coverage has been extended by other researchers to encompass soft skills, such as teamwork, cooperation, communication, negotiation, teamwork, leadership, and conflict management [34,35,36,37,38,39]. Similarly, Barison et al. [26] revealed the professional need for positions in both foundational and functional ways by investigating individual aptitude, qualifications, skills, knowledge, and attitude.
2.2. BIM Awareness and Acceptance
BIM acceptance is driven by the awareness and knowledge of this innovation. Awareness among members affects a group’s attitudes, thereby influencing the use of the technology and the effectiveness of such usage [18]. The lack of awareness among practitioners is commonly cited as a barrier to accelerated BIM adoption in most research on its awareness and acceptance [40]—a problem that can be resolved through BIM education and the provision of a foundation for corresponding curricula. Mamter et al. determined the level of BIM awareness among higher-institution students, which is vital for understanding this technology and its implementation in the construction industry [41]. The findings showed that more than 40% of students have little awareness of BIM. Gu and Maina’s study on awareness of BIM in design firms uncovered industry trends and practice requirements that promote BIM adoption from students’ perspectives in Nigeria [42].
No study has reviewed BIM education in Japanese universities, thus rendering CIT education in Japanese academic institutions unknown to the public. Moreover, previous work has disregarded the relationships between awareness—including perceived usefulness and perceived ease of use—knowledge, experience, and acceptance, which serve as the basis for proposing BIM education programs. To bridge this gap, the current research developed a TAM to investigate the acceptance of BIM courses in Japanese universities and its influencing factors, namely, knowledge, experience, perceived usefulness, and perceived ease of use. Determining students’ acceptance of CIT education is expected to facilitate the creation of programs that efficiently meet industry needs.
3. Student’s BIM Acceptance Model
3.1. Technology Acceptance Model
To explore the factors that motivate individuals to accept or reject the use of new technology, various theoretical models have been tested [43,44,45,46,47]. Drawing on the theory of reasoned action, Davis [48] developed the TAM and explained the relationship between perceived usefulness, perceived ease of use, intentions, and behaviors. The TAM has been applied in various studies and has become a significant tool in studies related to technology and acceptance because of its simplicity, effectiveness, and ability to explain the behavior of users in the adoption of technologies [49]. Its effectiveness over other theoretical models was confirmed in a recent review of the model’s application to examining the acceptance of educational technology [49]. In a TAM, perceived ease of use is regarded as predictive of perceived usefulness. Moreover, perceived usefulness predicts the behavioral intention (the degree to which people refrain from engaging in a specific future behavior) to adopt a system. Finally, the use of a system is explained by behavioral intention [48] (Figure 1). There are limitations to TAM according to previous studies. For example, demographic factors, such as age and education, are not considered external factors; the assumption is that the intention of use always translates into actual behavior [50]. To some extent, this study can lessen this limitation. First, the subjects were university students with similar demographic characteristics. Second, this study did not test actual behavior because a CIT curriculum has not been launched in Japan and is unavailable to students.
3.2. Research Model
With Davis’s TAM as grounding, we analyzed earlier studies to determine the external variables that factor into the extended application of the model. We then developed a CIT acceptance model with two external variables: knowledge of CIT and experience with technology use. These variables were identified on the basis of discussions with enterprises, brainstorming with students, and a pilot study (see Section 4.2), and they were incorporated into our hypotheses for verification.
First, the model maintains that the aforementioned variables affect behavioral intentions. Differences between individuals affect the intention to use digital libraries [51]. Correspondingly, usefulness, ease of use, and acceptance are evaluated differently depending on individuals’ knowledge of CIT. Therefore, the following hypotheses were formulated:
Knowledge has a positive effect on the perceived usefulness of CIT.
Knowledge has a positive effect on the perceived ease of use.
Knowledge has a positive effect on acceptance of CIT.
Second, previous studies have indicated that IT usage experience significantly affects IT acceptance [52,53,54]. Such experience also positively affects perceived ease of use and usefulness [55]. Accordingly, our model proposes that students’ experiences with using CIT and attending IT lectures affect its acceptance. Thus, the following hypotheses were developed:
Experience has a positive effect on perceived usefulness.
Experience has a positive effect on perceived ease of use.
Experience has a positive effect on acceptance.
Third, in the original TAM, perceived usefulness and perceived ease of use influence the behavioral intention of using the technology [47]. According to our discussions with engineers and enterprises and brainstorming with students, the usefulness of CIT in future jobs and the development of the industry and the ease of use of CIT positively affect students’ intention to take CIT courses. Therefore, this leads to the following hypotheses:
The perceived usefulness of CIT has a positive effect on students’ acceptance.
The perceived ease of use of CIT has a positive effect on students’ acceptance.
According to the TAM, students’ acceptance (behavioral intention) of CIT education leads to actual attendance behavior regarding CIT courses. Behavioral intention is considered the most important determinant of actual behavior [56]. However, a CIT curriculum has not been launched in Japan and is unavailable to students, thereby rendering the testing of actual behavior difficult. Therefore, we used behavioral intention (i.e., the acceptance of CIT courses) as a proxy for the adoption of a CIT curriculum. Figure 2 shows the student TAM developed in this work.
4. Materials and Methods
4.1. Participants
Students (N = 136) majoring in civil engineering at national universities in Japan were recruited and administered questionnaires in person. Most samples were from sophomore and junior students because the senior students were not available because of job hunting season, and the freshmen were delayed in coming to the campus because of the constraints of COVID-19; thus, they still had a high school mindset. The sample size is estimated to be around 11% of the parent population of sophomore and junior civil engineering students in national universities. Among the sample, 17.6% were female, and 82.4% were male—a gender imbalance stemming from the small number of female students studying civil engineering. The participants were aged 17 to 22 years.
4.2. Materials and Procedures
During the design of the survey instrument, input was obtained from two panels: one with 19 practitioners and the other comprising 10 students. To ensure the clarity of the survey questions and response categories, a pilot study involving a focus group of 20 civil engineering and architecture students was conducted in the construction management laboratory of Utsunomiya University. The results showed that awareness of CIT differed between the civil engineering and architecture students because of dissimilarities in cultures and the level of CIT adoption (the architecture students adopted CIT at a faster rate). The results of the pilot study were used as a reference in refining the questionnaire and study scope. To help civil engineering adopt CIT, the main research focused on this population. The survey was administered face to face, and each respondent took approximately 10 min to complete it. The survey items are described in Appendix A (Table A1).
4.3. Statistical Analyses
Multiple regression analyses were performed to test the effects of knowledge and experience (the variables) on perceived usefulness and ease of use, respectively, and the effects of knowledge, experience, perceived usefulness, and ease of use on levels of acceptance of CIT courses (Figure 3 and Appendix A Table A2 and Table A3). Factor and regression analyses were conducted to test the influencing factor (knowledge, experience, perceived usefulness, and ease of use) of two kinds of acceptance: willingness-related and importance-related acceptance.
4.4. Measures
4.4.1. Acceptance
Five measures were used to create a holistic assessment of CIT acceptance. First, two questions were asked about the respondents’ willingness to use CIT in university courses and their intention to secure internships wherein construction technology is used. Second, three questions instructed respondents to evaluate how seriously they would like to attend CIT-related courses, including those on programming and applications. The respondents were also asked about how important it is to establish CIT courses at school. All the statements were rated on a five-point Likert scale. Two indices were created for analysis: willingness-related (α = 0.913) and importance-related (α = 0.797) CIT course acceptance.
4.4.2. Perceived Usefulness
Perceived usefulness was assessed using six items presented in random order. The respondents were asked three questions about CIT-related jobs (e.g., willingness to perform a job using CIT; a CIT job as beneficial for career development) (α = 0.899) and another three regarding their opinions on the role of CIT in construction industry development (e.g., CIT as beneficial for saving on manpower and increasing productivity) (α = 0.788).
4.4.3. Perceived Ease of Use
The respondents were instructed to rate four items regarding perceived ease of learning on a five-point Likert scale ranging from 1 (very difficult) to 5 (very easy) (e.g., 3D scanner, drone, 3D design). A reliable index was obtained (α = 0.821).
4.4.4. Knowledge of CIT
Knowledge of CIT includes knowledge of the technologies involved (e.g., drones, laser scanners, 3D modeling, virtual reality (VR), augmented reality, UAV lasers, BIM/CIM, Autodesk Revit, MR technology, i-Construction, ICT, the differences between i-Construction and ICT) and CIT policies (i-Construction policies and regulations implemented by the MLIT) in Japan. Example responses included the following:
“I have never heard of it.” = 1
“I have heard of it.” = 2
“I know the effects of the technology/details of the policies.” = 3
A reliable scale was also obtained (α = 0.803).
4.4.5. Experience with CIT
Two questions were used to assess prior experience with CIT. The respondents were asked to recall whether they had attended or experienced CIT-related lectures within the university and beyond and whether they had experience using CIT, such as drones and VR devices. The responses were combined to develop an index that describes personal experience (1 = cannot remember, 1 = no experience, 2 = with experience) (α = 0.802).
5. Results
5.1. Descriptive Statistics
Table 1 provides an overview of the Pearson correlations, means, and standard deviations of the variables used. All the predictor variables are positively and significantly correlated with acceptance, with the correlations (r values) ranging from 0.251 to 0.563. Perceived usefulness is most strongly correlated with acceptance. Knowledge and experience are significantly correlated with perceived usefulness and ease of use, with r ranging from 0.227 to 0.283 and 0.179 to 0.222, respectively.
5.2. Student Technology Acceptance Model
Using a theory-based approach, we conducted multiple regression analyses to evaluate the extent to which knowledge and experience explain perceived usefulness and ease of use, respectively, and the degree to which perceived usefulness, perceived ease of use, knowledge, and experience explain and predict acceptance (Figure 3). Multiple regression analysis was used instead of SEM because this study is exploratory, for which SEM is less suited. The results showed that both knowledge and experience are significant predictors of perceived usefulness (F(2, 133) = 8.109, p < 0.001, Adj. R2 = 0.095). In other words, increased knowledge of and experience with CIT is associated with the rising perception of this technology as useful. Experience is the only significant predictor (F(2, 133) = 4.790, p < 0.010, Adj. R2 = 0.670), indicating that increased experience affects the perceived ease of use of CIT. Finally, the model was used to delve into the explanatory power of perceived usefulness, perceived ease of use, knowledge, and experience with regard to CIT acceptance. All the predictors, except knowledge, are significant and explain 44.8% of the variance in acceptance (F(4, 131) = 18.996, p < 0.001, Adj. R2 = 0.448).
5.3. Perspectives on Students’ Acceptance of CIT
Factorial analysis was carried out to explore the students’ perspectives on acceptance. The results of the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test of sphericity indicated that the variables are suitable for factorial analysis (KMO = 0.824, Bartlett’s test of sphericity = 319.182, df = 21, p = 0.000). Table 2 presents the factor loadings. The seven acceptance-related items in the questionnaire (Appendix A Table A1) were grouped under willingness-related acceptance (five items) and importance-related acceptance (two items). The average levels of acceptance in the two groups are = 3.813 and = 3.408, respectively.
The subsequent analysis was intended to explore how perceived usefulness, ease of use, knowledge, and experience explain willingness-related (Model A) and importance-related (Model B) acceptance. Figure 3 shows that perceived usefulness substantially contributes to the prediction of acceptance. To gain further insights into the results concerning the kind of usefulness that most strongly influences acceptance, perceived usefulness was classified into perceived usefulness in a personal job and perceived usefulness in industry development. Two separate regression analyses were performed (Table 3). First, with all the other variables controlled for in the regression, only perceived usefulness in future jobs and industry development are significant predictors of both willingness- and importance-related acceptance. Second, interestingly, perceived usefulness in future jobs (β = 0.466 ***) more strongly predicts willingness-related acceptance than perceived usefulness in industry development (β = 0.119 *). However, perceived usefulness in industry development more strongly predicts importance-related acceptance (the importance of learning CIT at school) (β = 0.22 ***) than perceived usefulness in future jobs (β = 0.17 *). Altogether, the factors explain 46.3% of the overall variance in willingness-related acceptance (F(5, 130) = 16.405, p = 0.000, Adj. R2 = 0.463) and 20.8% of the variance in importance-related acceptance (F(5, 130) = 4.225, p = 0.000, Adj. R2 = 0.208).
6. Discussion
6.1. Evidence from the CIT Acceptance Model
The conceptual dimensions addressed in this work were validated empirically: Knowledge and experience significantly predict perceived usefulness, but only experience significantly predicts perceived ease of use. When all predictors are used to predict acceptance, perceived usefulness is the most significant factor for explaining and predicting acceptance. Perceived ease of use and experience are also significant factors, but knowledge nonsignificantly predicts acceptance. All the influencing factors explain and predict 44.8% of the students’ CIT acceptance. We also derived empirical evidence of a two-perspective structure (willingness- vs. importance-related acceptance) and identified the different roles of predictors in this structure.
6.1.1. Knowledge of CIT
In terms of the model’s components, knowledge significantly predicts perceived usefulness, which is also the most influential factor in predicting acceptance, even with all variables controlled for. Therefore, knowledge is fundamental for students to accept CIT. These results are consistent with previous studies on BIM education programs designed to increase students’ knowledge of three BIM aspects: techniques, concepts, and cooperation [57]. Ensuring sufficient knowledge of CIT among students helps them understand the construction industry early on and satisfy its needs [58,59].
6.1.2. Experience with CIT
CIT experiences, such as attending related lectures and using IT tools (e.g., drones, VR devices, 3D modeling), are important in stimulating perceived usefulness, ease of use, and acceptance (Figure 3). In particular, experience significantly predicts perceived usefulness, which leads to acceptance. The importance of CIT experience has also been highlighted in previous studies, which indicated that CIT experience can improve students’ collaboration and communication ability through visualization techniques [60,61], further enhancing students’ interest in using CIT in their future careers. Experience with CIT can also help students better understand the BIM courses offered at universities [62,63].
6.1.3. Perceived Ease of Use
Perceived ease of use, together with perceived usefulness, is a significant predictor of acceptance (shown in Figure 3), consistent with a previous study that reported ease of use as predictive of the behavioral intention to use CIT [64]. However, Table 3 reflects that such significance rapidly dissipates when knowledge and experience are used to predict willingness- and importance-related acceptance. To wit, whether CIT is easy to use does not affect students’ acceptance and attendance of CIT courses. This result suggests that CIT program designs should focus on the technology’s usefulness in students’ careers and the industry’s development instead of the accessibility of content.
6.1.4. Perceived Usefulness
Figure 3 also illustrates how perceived usefulness is the most significant predictor of the acceptance of CIT courses, in line with previous studies reporting that usefulness predicts behavioral intention in the CIT field [64]. Table 3 shows that, with the classification of acceptance into factors related to willingness and importance, perceived usefulness in future jobs and industry development remains significant, whereas the other factors lose their significance. Interestingly, perceived usefulness in future jobs more strongly predicts willingness-related acceptance than perceived usefulness in industry development, pointing to students’ enrollment in CIT courses as stemming mainly from their desire to secure excellent employment. Nevertheless, industry development remains a factor in such decisions. Previous studies have indicated that students have a strong motivation in CIT learning for their future careers [65].
Perceived usefulness in industry development more strongly predicts the acceptance associated with the importance of offering CIT courses than perceived usefulness in future jobs. This finding indicates that Japanese civil engineering students support the provision of CIT courses given their importance to the industry.
6.2. Implications of the Proposed CIT Education Program
Many universities worldwide carry out research on CIT and integrate it into their undergraduate and graduate curricula [66], but these programs have been criticized for ineffectively incorporating BIM into existing or future curricula [67]. The successful integration of CIT into academia requires the establishment of an alliance between academia and industry, along with the creation of frameworks that guide and facilitate this process [66,68,69,70,71,72]. The current work presents important implications for students’ awareness of CIT and the consequent improvement in curriculum acceptance. On the basis of the results, we propose an industrial needs-based curricular framework to increase students’ knowledge of and experience with CIT for the development of their careers and the construction industry (Figure 4).
The proposed collaboration-based framework is designed to meet the needs of the construction industry by focusing on cultivating industry-required abilities. It encompasses the design of training content for on-the-job training programs, with the achievement of this goal rooted in the fundamental knowledge and practical skills developed at university. Cooperative endeavors with IT companies, such as providing software and technical support, are equally crucial to CIT development. This approach nurtures human resources while satisfying the requirements of the construction industry. On this basis, this study puts forward the following concrete suggestions:
Each stage of the construction process, from construction planning to maintenance, requires different abilities associated with CIT applications. In the procurement stage, for instance, digital development in the industry requires practitioners with skills such as communication competence for digital information exchange, digital information management, risk management, and the ability to establish BIM/CIM implementation plans. To satisfy industry requirements, construction companies should organize on-the-job training initiatives. Let us again take procurement as an example. Successful procurement can be advanced by enrollment in courses on basic CIT, CIT technical systems, and digital procurement, all of which are necessary to increase the abilities required in the procurement stage. General contractors currently offer on-the-job-training courses designed by the MLIT, and innovative local construction companies introduce CIT to their employees. However, the alignment between industry and higher education is weak. In other words, university education lags behind industry development. Therefore, this study recommends an education program that is based on industrial needs and industrial on-the-job-training requirements.
This program should include both theoretical and technological experiences grounded in research results. For the construction planning stage, classes on information programming, project information management, Revit, and Autodesk are suggested because these courses correspond to on-the-job training courses in the industry and could satisfy its needs in terms of ability. For the procurement stage, construction economics and construction law courses are proposed in order to cultivate familiarity with the bidding process. With theoretical knowledge as the basis, procurement should be practiced through projects. For the information planning stage, recommended university courses include programming and 3D modeling given the demand of the industry for practitioners who are adept at digital communication and the use of digital tools. To address needs related to the information production phase, universities should offer classes on construction information systems, collaboration, and construction estimation to foster students’ abilities to supervise project processes, collaborate, and communicate over digital avenues. To satisfy demands associated with the maintenance stage, such institutions should provide training on digital security management. Moreover, construction information systems that incorporate Revit and VR/AR should be provided for consistency with on-the-job-training classes, such as on technical inspections and modeling. The proposed program is expected to expose students to experiences that approximate industry conditions or practices and prepare them for future employment. It will also mold future talents who will contribute to the development of the construction industry.
7. Conclusions
This research advanced the first study of a student CIT acceptance model and tested it with the participation of civil engineering students in Japan. Numerous reliable measures were used in the analyses. The results provide robust evidence of the influence of knowledge, experience, perceived usefulness, and perceived ease of use on CIT acceptance. These variables also explain nearly 45% of the variance in such acceptance. Among all variables, perceived usefulness influences students’ acceptance most. This implies that CIT program designs should focus on the technology’s usefulness in students’ careers and the industry’s development instead of the accessibility of content, which provides general direction for creating CIT programs. These findings confirmed an empirical distinction between willingness- and importance-related acceptance and highlighted the willingness of Japanese students to accept CIT courses as being driven primarily by future employment. Considering the development of the construction industry, the students feel that it is important to offer CIT courses as early as possible at university or even in high school. This result implies that the CIT education program should be developed based on industrial needs; thus, students’ market values can be increased after receiving these courses. Finally, a collaboration-based CIT education program was proposed as a means by which to meet industry needs. This CIT education program can fill the gap in education in civil engineering in Japan between industry and universities and assist students in joining the industry smoothly after graduation. Moreover, CIT education programs nurture future talents for the development of the construction industry. This study is expected to contribute to the sustainable development of infrastructure via the enhanced awareness of CIT applications.
8. Limitation and Future Study
However, this study has limitations. First, this study is an exploratory study; the external variables are mainly derived from interviews and brainstorming results. Therefore, more tests and experiments are needed to improve the research hypotheses. Second, the subjects of this survey were civil engineering students; therefore, the sample size is not large, although it did meet the statistical requirement. We would like to increase the sample size in the future. Moreover, comparison studies can be conducted between civil engineering students and architecture students. The current study was conducted in Japan. We would like to conduct an international comparison study in the near future.
Conceptualization, R.W.; Methodology, R.W.; Software, R.W.; Validation, R.W. and T.W.; Formal analysis, R.W.; Investigation, R.W. and T.W.; Resources, T.W.; Data curation, T.W. and M.S.; Writing—original draft, R.W.; Writing—review & editing, M.S. and T.W.; Supervision, T.W. and M.S.; Project administration, M.S.; Funding acquisition, R.W., T.W. and M.S. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Informed consent was obtained from all subjects involved in the study.
Data are contained within the article.
We are grateful to the industry experts who provided us with assistance: Takayuki Matsuura, Koji Oyama (Inoue Co., Ltd.), Yoshinori Kii, Nao Senoue, Makoto Takechi, Ritsuo Kodo, Kenta Mima, Toshinori Nakaoka, Akihiko Kida, Kohei Nishida, Kazuki Shinkai, Yoshikazu Uetani, Yoshito Yamashita, Kiri Suzaki, Yuki Kawanishi, Tomoki Kitachi, Haruo Toyota, Takuya Higashi, Aito Ogata, Yuta Sato, and Haruto Funamoto (Otake Co., Ltd.). We also thank members of the education and training working group of the i-Construction committee (JSCE) for the intensive discussion on CIT education. We are grateful to Shelley Burgin for her constructive comments and English editing of this manuscript. We thank the anonymous English editor’s efforts with this manuscript. Finally, we thank all the respondents for their sincere attitudes toward their participation in the survey.
The authors declare no conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 3. Results from the student TAM (Appendix A). Notes: * p < 0.1, ** p < 0.05, *** p < 0.01.
Descriptive statistics and intercorrelations.
| 1 | 2 | 3 | 4 | 5 | Mean | SD | |
|---|---|---|---|---|---|---|---|
| 1. Acceptance | 1 | 3.647 | 0.663 | ||||
| 2. Perceived usefulness | 0.563 *** | 1 | 4.318 | 0.514 | |||
| 3. Perceived ease of use | 0.350 *** | 0.364 *** | 1 | 3.522 | 0.668 | ||
| 4. Knowledge | 0.251 *** | 0.227 *** | 0.179 ** | 1 | 1.784 | 0.333 | |
| 5. Experience | 0.305 *** | 0.283 *** | 0.222 *** | 0.219 ** | 1 | 2.015 | 0.635 |
Notes: ** p < 0.05, *** p < 0.01. All variables are coded so that higher values reflect more of a construct.
Factor loadings of willingness- and importance-related acceptance.
| Acceptance | Factor Loading | |
|---|---|---|
| Willingness-Related | Importance-Related | |
| Willingness to use IT if there is a related course | 0.799 | –0.011 |
| Willingness to perform internship-related work using IT | 0.733 | –0.341 |
| University required to offer courses on CIT | 0.863 | 0.042 |
| Willingness to acquire knowledge related to CIT | 0.787 | –0.118 |
| Willingness to participate in lectures outside the university | 0.746 | –0.003 |
| CIT to be learned before entry into university | 0.156 | 0.867 |
| Importance of studying CIT at university | 0.419 | 0.567 |
Note: Principal component analysis was used as the method of extraction.
Predictors of willingness- and importance-related acceptance.
| Regression Model Acceptance—Predictors | Willingness-Related |
Importance-Related |
|---|---|---|
| Perceived usefulness in future jobs | 0.466 *** | 0.174 * |
| Perceived usefulness in industry development | 0.119 * | 0.224 *** |
| Perceived ease of use | 0.118 | 0.110 |
| Knowledge | 0.085 | 0.036 |
| Experience | 0.092 | 0.056 |
| N | 136 | 136 |
| Adj. R2 | 0.463 | 0.208 |
| F | 16.405 | 4.255 |
Notes: Entries are standardized beta coefficients; * p < 0.1, *** p < 0.01.
Appendix A
The survey items.
| Item | Mean | S.D. |
|---|---|---|
| Demographic characteristics | ||
| Gender | 1.177 | 0.383 |
| CIT knowledge—please choose the level of CIT knowledge, from 1 to 3 | ||
| Drone | 2.721 | 0.450 |
| LS | 1.904 | 0.687 |
| 3D modeling | 2.037 | 0.682 |
| VR, AR | 2.640 | 0.482 |
| UVA leaser | 1.456 | 0.582 |
| BIM, CIM | 1.507 | 0.621 |
| Autodesk Revit | 1.125 | 0.332 |
| MR technology | 1.478 | 0.620 |
| i-Construction | 1.610 | 0.762 |
| ICT | 2.272 | 0.694 |
| The difference between i-Construction and ICT | 1.010 | 0.295 |
| CIT policies from the MLIT | 1.566 | 0.685 |
| Experience with CIT | ||
| I have experience using CIT | 1.765 | 0.845 |
| I have attended CIT lectures inside or outside the university | 2.265 | 0.819 |
| Usefulness of CIT | ||
| I have a job using CIT | 3.990 | 0.894 |
| I think it is fun to use CIT at work | 3.971 | 0.918 |
| I can have good career development with a job using CIT | 4.184 | 0.836 |
| CIT is important for industry development | 4.566 | 0.641 |
| CIT is significant in saving the labor force | 4.618 | 0.609 |
| CIT can increase productivity in the construction industry | 4.522 | 0.750 |
| Ease of use | ||
| I think it is easy to use a 3D scanner | 3.390 | 0.983 |
| I think it is easy to use a drone | 3.765 | 1.034 |
| I think it is easy to use 3D modeling | 3.169 | 1.099 |
| I don’t think it is easy to learn new CIT | 3.765 | 1.049 |
| Acceptance | ||
| Willingness to use IT if there is a related course | 4.302 | 0.810 |
| Willingness to perform internship-related work using IT | 3.603 | 1.194 |
| A university should offer courses on CIT | 4.044 | 0.885 |
| Willingness to study knowledge related to CIT | 3.743 | 1.068 |
| Willingness to participate in lectures outside university | 3.375 | 1.102 |
| CIT should be learned before university | 2.456 | 0.698 |
| It is important to study CIT at university | 4.360 | 0.786 |
Results of the analysis of external variables and ease of use (dependent variable).
| Perceived Usefulness | Perceived Ease of Use | |||||
|---|---|---|---|---|---|---|
| B | β | t | B | β | t | |
| Knowledge | 0.267 (0.129) | 0.173 ** | 2.065 | 0.275(0.172) | 0.137 | 1.599 |
| Experience | 0.198 (0.068) | 0.245 *** | 2.921 | 0.202 (0.090) | 0.192 ** | 2.235 |
| Adj. R2 | 0.095 | 0.067 | ||||
| F | (2, 133) = 8.109, p < 0.001 | (2, 133) = 4.790 ** | ||||
Notes: ** p < 0.05, *** p < 0.01; Std. errors in parentheses.
Results of the analysis of predictive variables and acceptance (dependent variable).
| Acceptance | |||
|---|---|---|---|
| B | β | t | |
| Perceived usefulness | 0.589 (0.100) | 0.456 *** | 5.896 |
| Perceived ease of use | 0.139 (0.075) | 0.140 * | 1.848 |
| Knowledge | 0.189 (0.145) | 0.095 | 1.307 |
| Experience | 0.129 (0.077) | 0.124 * | 1.673 |
| Adj. R2 | 0.448 | ||
| F | (4, 131) = 18.996 *** | ||
Notes: * p < 0.1, *** p < 0.01; Std. errors in parentheses.
References
1. McGraw Hill. Smart Market Report: The Business Value of BIM in North America, Multi-Year Trend Analysis and User Ratings (2007-2012). Available online: https://damassets.autodesk.net/content/dam/autodesk/www/solutions/building-information-modeling/bim-value/mhc-business-value-of-bim-in-north-america.pdf (accessed on 13 October 2023).
2. Azhar, S. Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry. Leadersh. Manag. Eng.; 2011; 11, pp. 241-252. [DOI: https://dx.doi.org/10.1061/(ASCE)LM.1943-5630.0000127]
3. NATSPEC. BIM Education-Global 2023 Update Report. 2023; Available online: https://bim.natspec.org/bim-rnd/177-natspec-international-bim-education-report (accessed on 10 October 2023).
4. Bolpagni, M.; Burdi, L.; Luigi, A. The Implementation of Building Information Modelling and Lean Construction in Design Firms in Massachusetts. Proceedings of the 25th Annual Conference of the International Group for Lean Construction (IGLC); Heraklion, Greece, 9–12 July 2017; [DOI: https://dx.doi.org/10.24928/2017/0310]
5. Yakami, M.; Singh, V.; Suwal, S. What do students and professionals think of BIM competence?. IFIP Adv. Inf. Commun. Technol.; 2017; 517, pp. 358-368. [DOI: https://dx.doi.org/10.1007/978-3-319-72905-3_32]
6. Moradi, S.; Sormunen, P. Integrating lean construction with BIM and sustainability: A comparative study of challenges, enablers, techniques, and benefits. Constr. Innov.; 2023; ahead-of-print [DOI: https://dx.doi.org/10.1108/CI-02-2023-0023]
7. Cheung, F.K.T.; Rihan, J.; Tah, J.; Duce, D.; Kurul, E. Early stage multi-level cost estimation for schematic BIM models. Autom. Constr.; 2012; 27, pp. 67-77. [DOI: https://dx.doi.org/10.1016/j.autcon.2012.05.008]
8. Li, J.; Li, N.; Afsari, K.; Peng, J.; Wu, Z.; Cui, H. Integration of building information modeling and web service application programming interface for assessing building surroundings in early design Stages. Build. Environ.; 2019; 153, pp. 91-100. [DOI: https://dx.doi.org/10.1016/j.buildenv.2019.02.024]
9. Zhang, J.; Xie, H.; Schmidt, K.; Xia, B.; Li, H.; Skitmore, M. Integrated experiential learning-based framework to facilitate project planning in civil engineering and construction management courses. J. Prof. Issues Eng. Educ. Pract.; 2019; 145, [DOI: https://dx.doi.org/10.1061/(ASCE)EI.1943-5541.0000421]
10. Wang, L.; Huang, M.; Zhang, X.; Jin, R.; Yang, T. Review of BIM adoption in the higher education of AEC disciplines. J. Civ. Eng. Educ.; 2020; 146, [DOI: https://dx.doi.org/10.1061/(ASCE)EI.2643-9115.0000018]
11. Zhang, J.; Schmidt, K.; Li, H. BIM and sustainability education: Incorporating instructional needs into curriculum planning in CEM Programs accredited by ACCE. Sustainability; 2016; 8, 525. [DOI: https://dx.doi.org/10.3390/su8060525]
12. Corrado, R.; Soy, M.; Heang, V. BIM and IFC: Awareness and self-assessed knowledge from the perspective of Cambodian university students. Ain Shams Eng. J.; 2023; 14, 101851. [DOI: https://dx.doi.org/10.1016/j.asej.2022.101851]
13. Li, J.; Afsari, K.; Li, N.; Peng, J.; Wu, Z.; Cui, H. A review for presenting building information modeling education and research in China. J. Clean. Prod.; 2020; 259, 120885. [DOI: https://dx.doi.org/10.1016/j.jclepro.2020.120885]
14. Puolitaival, T.; Booth, T.; Ghaffarianhoseini, A.; Park, K.S. BIM Education-Case New Zealand; EPiC Series in Education Science; EasyChair of Cool Press Ltd.: Manchester, UK, 2017.
15. Kocaturk, T.; Kiviniemi, A. Challenges of Integrating BIM in Architectural Education. Computation and Performance, Proceedings of the 31st International Conference on Education and Research in Computer Aided Architectural Design in Europe, Delft, The Netherlands, 18–20 September 2013; Delft University of Technology: Delft, The Netherlands, 2013; [DOI: https://dx.doi.org/10.52842/conf.ecaade.2013.2.465]
16. Australian Industry Group. Connecting for Productivity: University and Industry Partnerships. 2016; Available online: https://cdn.aigroup.com.au/Reports/2016/Connecting_for_productivity_Oct16.pdf (accessed on 10 October 2023).
17. Ahmed, S. Barriers to implementation of building information modeling (BIM) to the construction industry: A review. J. Civ. Eng. Constr.; 2018; 7, pp. 107-113. [DOI: https://dx.doi.org/10.32732/jcec.2018.7.2.107]
18. Ahn, E.; Kim, M. BIM awareness and acceptance by architecture students in Asia. J. Asian Archit. Build. Eng.; 2016; 15, pp. 419-424. [DOI: https://dx.doi.org/10.3130/jaabe.15.419]
19. McAuley, B.; Behan, A. Delivering energy savings for the supply chain through building information modelling as a result of the Horizon 2020 Energy BIMcert Project. Proceedings of the 5th Annual SEEDS Conference; Ipswich, UK, 11–12 September 2019; pp. 11-12.
20. Becker, T.C.; Jaselskis, E.J.; McDermott, C.P. Implications of Construction Industry Trends on the Educational Requirements for Future Construction Professionals. Proceedings of the Associated Schools of Construction 2011 International Conference; Omaha, NE, USA, 6–9 April 2011; pp. 1-12.
21. Chegu Badrinath, A.; Chang, Y.T.; Hsieh, S.H. A review of tertiary BIM education for advanced engineering communication with visualization. Vis. Eng.; 2016; 4, 9. [DOI: https://dx.doi.org/10.1186/s40327-016-0038-6]
22. Becerik-Gerber, B.; Gerber, D.J.; Ku, K. The pace of technological innovation in architecture, engineering, and construction education: Integrating recent trends into the curricula. J. Inf. Technol. Constr.; 2011; 16, pp. 411-432.
23. Valverde Arrotéia, A.; Paes, D.; Irizarry, J.; Burrattino Melhado, S. A comparative diagnosis of students’ proficiency in BIM in construction-related graduate programs in Brazil and in the United States. Proceedings of the 22th Conference of the Iberoamerican Society of Digital Graphics; São Carlos, Brazil, 7–9 November 2018.
24. Lee, N.; Dossick, C.S.; Foley, S.P. Guideline for building information modeling in construction engineering and management education. J. Prof. Issues Eng. Educ. Pract.; 2013; 139, pp. 266-274. [DOI: https://dx.doi.org/10.1061/(ASCE)EI.1943-5541.0000163]
25. Yi, T.; Yun, S. BIM (building information modeling) education program in KSA: A case study of BIM program at Prince Sultan University. E3S Web Conf.; 2018; 65, 04004. [DOI: https://dx.doi.org/10.1051/e3sconf/20186504004]
26. Barison, M.B.; Santos, E.T. The competencies of BIM specialists: A comparative analysis of the literature review and job ad descriptions. Comput. Civ. Eng.; 2011; pp. 594-602. [DOI: https://dx.doi.org/10.1061/41182(416)73]
27. Yalcinkaya, M.; Singh, V. Patterns and trends in building information modeling (BIM) research: A latent semantic analysis. Autom. Constr.; 2015; 59, pp. 68-80. [DOI: https://dx.doi.org/10.1016/j.autcon.2015.07.012]
28. Ahn, Y.H.; Annie, R.P.; Kwon, H. Key competencies for US construction graduates: Industry perspective. J. Prof. Issues Eng. Educ. Pract.; 2012; 138, pp. 123-130. [DOI: https://dx.doi.org/10.1061/(ASCE)EI.1943-5541.0000089]
29. Yarmohammadi, S.; Ashuri, B. Exploring the approaches in the implementation of BIM-Based MEP coordination in the USA. J. Inf. Technol. Constr. (ITcon); 2015; 20, pp. 347-363.
30. Wu, W.; Issa, R.R.A. BIM education and recruiting: Survey-Based comparative analysis of issues, perceptions, and collaboration opportunities. J. Prof. Issues Eng. Educ. Pract.; 2014; 140, 4013014. [DOI: https://dx.doi.org/10.1061/(ASCE)EI.1943-5541.0000186]
31. Succar, B.; Sher, W.; Williams, A. An integrated approach to BIM competency assessment, acquisition and application. Autom. Constr.; 2013; 35, pp. 174-189. [DOI: https://dx.doi.org/10.1016/j.autcon.2013.05.016]
32. Wu, W.; Issa, R.R.A. Key Issues in Workforce Planning and Adaptation Strategies for BIM Implementation in Construction Industry. Proceedings of the Construction Research Congress 2014: Construction in a Global Network; Atlanta, GA, USA, 19–21 May 2014; pp. 847-856.
33. Murphy, S.V.; Atala, A. 3D bioprinting of tissues and organs. Nat. Biotechnol.; 2014; 32, pp. 773-785. [DOI: https://dx.doi.org/10.1038/nbt.2958]
34. Succar, B.; Sher, W.; Williams, A. Measuring BIM Performance: Five metrics. Archit. Eng. Des. Manag.; 2012; 8, pp. 120-142. [DOI: https://dx.doi.org/10.1080/17452007.2012.659506]
35. Eadie, R.; Comiskey, D.; McKane, M. Teaching BIM in a Multidisciplinary Department. Proceedings of the Education, Science and Innovations; European Polytechnical University: Pernik, Bulgaria, 2014.
36. Davies, K.; McMeel, D.; Wilkinson, S. Soft Skill Requirements in a BIM Project Team. Proceedings of the 32nd CIB W78 Conference 2015; Eindhoven, The Netherlands, 27–29 October 2015.
37. Rahman, R.A.; Alsafouri, S.; Tang, P.; Ayer, S.K. Comparing building information modeling skills of project managers and BIM managers based on social media analysis. Procedia Eng.; 2016; 145, pp. 812-819. [DOI: https://dx.doi.org/10.1016/j.proeng.2016.04.106]
38. Ross, C.M.; Young, S.J.; Sturts, J.R.; Kim, K.T. Personal correlates impacting job satisfaction among campus recreational sport employees. Int. J. Sport Manag. Recreat. Tour.; 2014; 14, pp. 68-96. [DOI: https://dx.doi.org/10.5199/ijsmart-1791-874X-14d]
39. Succar, B.; Sher, W. A Competency Knowledge-Base for BIM Learning. Australas. J. Constr. Econ. Build.-Conf. Ser.; 2014; 2, pp. 1-10. [DOI: https://dx.doi.org/10.5130/ajceb-cs.v2i2.3883]
40. Gamil, Y.; Rahman, I.A.R. Awareness and challenges of building information modelling (BIM) implementation in the Yemen construction industry. J. Eng. Des. Technol.; 2019; 17, pp. 1077-1084. [DOI: https://dx.doi.org/10.1108/JEDT-03-2019-0063]
41. Mamter, S.; Mat Salleh, N.; Mamat, E. Building Information Modeling (BIM) Awareness among Higher Education Institution Students. Proceedings of the 2nd International Conference on Green Technology & Ecosystem for Global Sustainable Development; Rome, Italy, 13–14 November 2014.
42. Maina, J.J. CAD and BIM in architecture education: Awareness, proficiency and advantages from the student perspective. Gazi Univ. J. Sci. Part B Art Humanit. Des. Plan.; 2018; 6, pp. 167-178.
43. Ajzen, I. A theory of planned behavior. Organ Behav Hum Decis Process.; 1991; 50, pp. 179-211. [DOI: https://dx.doi.org/10.1016/0749-5978(91)90020-T]
44. Ajzen, I.; Fishbein, M. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research; Ajzen, I. Addison-Wesley Publishing Company: Boston, MA, USA, 1975.
45. Ajzen, I. From intentions to actions: A theory of planned behavior. Action Control; Kuhl, J.; Beckmann, J. Springer: Berlin/Heidelberg, Germany, 1985; pp. 11-39. [DOI: https://dx.doi.org/10.1007/978-3-642-69746-3_2]
46. Venkatesh, V. Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf. Syst. Res.; 2000; 11, pp. 342-365. [DOI: https://dx.doi.org/10.1287/isre.11.4.342.11872]
47. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q.; 2003; 27, pp. 425-478. [DOI: https://dx.doi.org/10.2307/30036540]
48. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. Manag. Inf. Syst. Q.; 1989; 13, pp. 983-1003. [DOI: https://dx.doi.org/10.2307/249008]
49. Ching-Ter, C.; Hajiyev, J.; Su, C.R. Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Comput. Educ.; 2017; 111, pp. 128-143. [DOI: https://dx.doi.org/10.1016/j.compedu.2017.04.010]
50. Ajibade, P. Techonology Acceptance Model Limitations and Criticisms: Exploring the Practical Applications and Use in Technology-related Studies, Mixedmethod, and Qualitative Researches. Library Philosophy and Practice (e-journal). 2018; Available online: https://digitalcommons.unl.edu/libphilprac/1941/ (accessed on 23 November 2023).
51. Al-Qaysi, N.; Mohamad-Nordin, N.; Al-Emran, M. A systematic review of social media acceptance from the perspective of educational and information systems theories and models. J. Educ. Comput. Res.; 2020; 57, pp. 2085-2109. [DOI: https://dx.doi.org/10.1177/0735633118817879]
52. Hong, W.; Thong, J.Y.L.; Wong, W.M.; Tam, K.Y. Determinants of user acceptance of digital libraries: An empirical examination of individual differences and system characteristics. J. Manag. Inf. Syst.; 2002; 18, pp. 97-124. [DOI: https://dx.doi.org/10.1080/07421222.2002.11045692]
53. Chan, Y.E.; Storey, V.C. The use of spreadsheets in organizations: Determinants and consequences. Inf. Manag.; 1996; 31, pp. 119-134. [DOI: https://dx.doi.org/10.1016/S0378-7206(96)00008-0]
54. Szajna, B. Empirical evaluation of the revised technology acceptance model. Manag. Sci.; 1996; 42, pp. 85-92. [DOI: https://dx.doi.org/10.1287/mnsc.42.1.85]
55. Romm, C.T.; Pliskin, N.; Rifldn, W.D. Diffusion of e-mail: An organisational learning perspective. Inf. Manag.; 1996; 31, pp. 37-46. [DOI: https://dx.doi.org/10.1016/S0378-7206(96)01070-1]
56. Igbaria, M.; Guimaraes, T.; Davis, G.B. Testing the determinants of microcomputer usage via a structural equation model. J. Manag. Inf. Syst.; 1995; 11, pp. 87-114. [DOI: https://dx.doi.org/10.1080/07421222.1995.11518061]
57. Wang, X.; Yuen, K.F.; Wong, Y.D.; Teo, C.C. An Innovation Diffusion Perspective of E-Consumers’ Initial Adoption of Self-Collection Service via Automated Parcel Station. Int. J. Logist. Manag.; 2018; 29, pp. 237-260. [DOI: https://dx.doi.org/10.1108/IJLM-12-2016-0302]
58. Kymmell, W. Building Information Modeling: Planning and Managing Construction Projects with 4D CAD and Simulations (McGraw-Hill Construction Series); McGraw-Hill Education: New York, NY, USA, 2008.
59. Joannides, M.M.; Olbina, S.; Issa, R.R.A. Implementation of building information modeling into accredited programs in architecture and construction education. Int. J. Constr. Educ. Res.; 2012; 8, pp. 83-100. [DOI: https://dx.doi.org/10.1080/15578771.2011.632809]
60. Irizarry, J.; Gheisari, M.; Zolfagharian, S.; Meadati, P. Human Computer Interaction Modes for Construction Education Applications: Experimenting with small format interactive displays. Int. J. Constr. Educ. Res.; 2013; 9, pp. 83-101. [DOI: https://dx.doi.org/10.1080/15578771.2012.720356]
61. Sacks, R.; Barak, R. Teaching building information modeling as an integral part of freshman year civil engineering education. J. Prof. Issues Eng. Educ. Pract.; 2010; 136, pp. 30-38. [DOI: https://dx.doi.org/10.1061/(ASCE)EI.1943-5541.0000003]
62. Richards, E.L.; Clevenger, C.M. Interoperable Learning Leveraging Building Information Modeling (BIM) in Construction Management and Structural Engineering Education. Proceedings of the 47th ASC Annual International Conference; Omaha, NE, USA, 6–9 April 2011.
63. LEED, A.P.; Porter, D. Integrating 3D Models in Construction Management Education: Masonry Interactive Homework. Proceedings of the 47th ASC Annual International Conference; Omaha, NE, USA, 6–9 April 2011.
64. Sharag-Eldin, A.; Nawari, N.O. BIM in AEC Education. Proceedings of the Structures Congress 2010; Orlando, FL, USA, 12–15 May 2010; pp. 1676-1688.
65. Park, E.S.; Park, M.S. Factors of the technology acceptance model for construction IT. Appl. Sci.; 2020; 10, 8299. [DOI: https://dx.doi.org/10.3390/app10228299]
66. Azhar, S.; Sattineni, A.; Hein, M. BIM Undergraduate Capstone Thesis: Student Perceptions and Lessons Learned. Proceedings of the 46th ASC Annual Conference; Boston, MA, USA, 7–10 April 2010; Volume 8.
67. Rodriguez, A.K.S.; Suresh, S.; Heesom, D.; Suresh, R. Building information management (BIM) education in the Dominican Republic: An empirical study. Int. Conf. Sustain. Futures (ICSF); 2017; 6, pp. 57-79.
68. Wu, W.; Issa, R.R.A. BIM Education for New Career Options: An Initial Investigation. Proceedings of the BIM Academic Workshop; Washington, DC, USA, 11 January 2013.
69. Macdonald, J.A. A Framework for Collaborative BIM Education across the AEC Disciplines. Proceedings of the 37th Annual Conference of Australasian University Building Educators Association (AUBEA); Sydney, Australia, 4–6 July 2012; Volume 4.
70. Macdonald, J.A.; Granroth, M. Multidisciplinary AEC Education Utilising BIM/PLIM Tools and Processes. Proceedings of the IFIP International Conference on Product Lifecycle Management; Nantes, France, 6–10 July 2013; Springer: Berlin/Heidelberg, Germany, 2013; pp. 663-674.
71. Macdonald, J.A.; Mills, J.E. The Potential of BIM to Facilitate Collaborative AEC Education. Proceedings of the 2011 ASEE Annual Conference & Exposition; Vancouver, BC, Canada, 26–29 June 2011; pp. 22-1487.
72. Byrne, C. Building Information Modelling in Australia, Lessons from the UK; ISS Institute Inc.: Tokyo, Japan, 2015.
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
1 Independent Researcher, Gold Coast 4227, Australia
2 Faculty of Society and Design, Bond University, 14 University Drive, Robina, QLD 4226, Australia;