Analysis of barriers and drivers for BIM adoption

Research on 'digitalization and collaboration' in the construction industry has been gaining momentum in the recent academic engagements. Despite its existence in many industries (i.e. financial services, retailing, publishing and travelling) for over ten years, it is yet to catch up by the construction market; this is due to several challenges whose existence are more dynamic and contextual than generic to various countries. The problems are defined in many studies across borders, but their impacts varied with countries. This case is equally the same to drivers toward the adoption of BIM. This study analyses barriers and drivers to BIM adoption in the Nigerian construction industry from adopters and nonadopters perspectives as to allow an informed decision in developing a strategy for macro BIM adoption. Primary data fetched from professional stakeholders through an online questionnaire survey were analysed using SPSS software and Microsoft Excel. This investigation reveals the most significant barriers against BIM adoption as Lack of expertise, Lack of standardization and protocols to mention but a few. And, most influential drivers from both adopters and non-adopters as Availability of trained professionals to handle the tools, Proof of cost savings by its adoption, BIM Software affordability, and awareness of the technology among the industry stakeholders. The adopters and non-adopters groups have nearly equal Percentage Disagreement (PD) and Percentage Agreement (PA) for both the barriers and drivers to BIM adoption. Thus, this suggests that the adopters are still at the early stage of BIM adoption, so have nearly the same perceptions with the non-adopters. The study recommends proper consideration of the established barriers and drivers while developing any strategy for effective BIM adoption. Further face-to-face (interview) study is necessary to explore more and in-depth challenges to adoption of BIM in the industry; and as the industry is getting more aware of the BIM, periodic evaluation of the critical barriers and drivers is vital.


Introduction:
Building Information Modelling (BIM) is a digital model representing physical and functional characteristics of building or infrastructure (BIM Industry Working Group 2011). Chartered Institute of Builder (CIOB) described the fundamental idea behind the BIM as to create and share the right information at the right time throughout the design, construction and operation of a building or facility to improve efficiency and decision making. This new paradigm shift in the construction industry is gaining high recognition both in the academic discuss (research) and the industry (application). However, its universal adoption is facing common challenges but yet persistent within the industry and across the world. These challenges are more the same rather than different; although their significance and uniqueness vary with country. On the other hand, the drivers that facilitate its adoption have a similar trend with the barriers.
The BIM is similar to other technologies or innovations; it comes with challenges and barriers while adoption and implementation (McAdam, 2010). Barnes and Davies (2015) revealed the most perceived barriers against BIM adoption by organizations as an issue of readiness, high cost of training, and cost of technology investment (hardware and software). This readiness could be the ability to agreeing to change (i.e. awareness driven) or technology and human resources readiness. The construction industry is widely known to be conventional and resistive to changes (Walasek and Barszcz, 2017); although, this new technological process has come to stay. Eadie et al. (2014) worked on the identification of barriers to BIM adoption and their order of importance, this study reveals so much to the UK BIM adoption strategy and more importantly directing to the most significant barriers to allow adopters pay more attention to them. However, solving one or more barriers without considering the rest may not bring the end to the challenges on adoption (Lindblad, 2013). Studies on barriers and drivers to adoption of BIM revealed many barriers and drivers with differential significance by country (Walasek and Barszcz, 2017;Shaban et al., 2018; Ademci and Gundes, 2018; . A recent study undertaken by  on Kingdom of Saudi Arabian AEC sector where BIM adoption barriers were assessed considering six different categories (personal, technical, business, process, market and organisational barriers). The study holistically revealed personal challenges as the significant barriers to the deployment of BIM. These personal challenges are dominated by a lack of understanding of BIM and its benefits, resistance to change, and lack of BIM education and skills. Similarly, few studies from Nigeria revealed some barriers to BIM adoption (Wang, 2015;Onungwa et al., 2017), but not to common professionals or wide market (macro scale). It is therefore difficult to appraise (at market level) the challenges required to be resolved and drivers to persuade the BIM adoption in the Nigerian construction industry.

International Journal of BIM and Engineering Science (IJBES)
ISSN 2571-1075 20 As an extended conference paper (Hamma-adama and Kouider, 2019), this study attempts to fill a gap of differentiating by order of importance, the common barriers and drivers toward BIM adoption from adopters and non-adopters perspectives within the Nigerian construction market. The investigation was set to be achieved through a critical review of literature where potential barriers and drivers for BIM adoption were identified; then ranked by order of significance, and evaluate the adopters and non-adopters perspectives (i.e. the percentage of disagreement). This will allow an informed decision in the development of a strategy to effective BIM adoption within the Nigerian construction market.

Literature review:
BIM is amongst the most discussed subjects in the Architecture Engineering and Construction (AEC) industry, and perhaps the most discussed area of development in the AEC process. There is a huge development in research and efforts to implement this new innovative process. Hjelseth (2017) compiled five years of publications (2013-2017) from Automation in Construction in the field of BIM; his statistics revealed high (>70%) concentration on interoperable technology perspective than collaborative processes. The study suggests more research on awareness of real understanding and how BIM influences AEC activities. On the other hand, some investigators believed that researchers had concentrated mostly on adoption and non-adopters, investigating the barriers and drivers, development of models and frameworks (Hosseini et al., 2016); albeit there is an irregularity in the adoption as well as the implementation across the globe and different disciplines. In the recent academic discussion, there are several investigations on the social aspect of BIM adoption; such as readiness, awareness, level of adoption, capabilities (stages) as well as barriers and driver toward the adoption and implementation of the BIM (Ademci and Gundes, 2018). Such efforts (by countries and organizations) played a significant role in revolutionizing the BIM adoption process (Mustaffa et al., 2017). Subsequent studies on BIM adoption challenges revealed consistent trend, from Walasek and Barszcz (2017) to Ademci and Gundes (2018), Sun et al. (2017) and Tan et al. (2019). These studies brought about describing, categorizing, and ranking of barriers against the BIM adoption. Wang (2015) study also compiled and ranked some challenges faced by Mechanical, Electrical and Plumber (MEP) firms in Nigeria. The study reveals that lack of technical expertise on BIM tools utilization, lack of awareness of BIM technology as well as high cost of investment on staff training, process change, software and hardware upgrade as the most critical barriers to BIM adoption. While Onungwa et al. (2017) revealed lack of skilled personnel, internet connectivity, the reluctance of other stakeholders to use BIM, lack BIM object libraries, and lack of awareness of the technology as the main barriers against BIM adoption. On common grounds, most studies cited and identified challenges in the lack of trained personnel. They are abreast of the latest development in technology also lamented the BIM knowledge gap where most Architects learn on the job, as no training is mostly offered to them.
In the NBS report (2018), barriers to BIM adoption were reported under two umbrellas, internal (i.e. lack of training, expertise and funds to invest), and external (i.e. lack of BIM demand by the client and lack of large projects that necessitate the BIM deployment). While, the most recent compiled barriers by Ademci and Gundes (2018) were grouped into five categories; these include personal, legal, management, cost, and technical for convenience while carrying out analysis (Sun et al. 2017). Sun et al. (2017) compiled a total of twenty-two BIM adoption barriers; however, that does not necessarily apply to the entire professional fields, organizations, and countries as common. For example, the UK reported 18 barriers in their continuous BIM assessment survey (NBS 2018, p. 35), and these barriers are not exactly as those extracted by Sun et al. (2017) or those by Wang (2015). Though, there are some similarities and common terms across the lists. For example, Khosrowshahi and Arayici (2012) reported many barriers to adopt BIM across the UK, and assert that those barriers are commonly on organizational readiness. Table 1 summarizes the compiled barriers to BIM adoption from across organizations and countries.  The drivers to adopt innovation are merely the facilitators to adopt a new product or process (Saleh, 2015). The facilitators are the enablers as resolving the barriers ease the adoption of innovation; the same way the drivers support the adoption process. Potential drivers mostly fall under empowerment, leadership, and creative culture; and most barriers are interlinked with drivers. In most circumstances, the motivator is achieved by removal of a barrier. For example, resolving the lack of experts or trained personnel on BIM means providing training on BIM. Table 2 below summarizes some potential drivers from previous studies.

Research Methodology:
A literature review was adopted in identifying potential barriers and drivers for BIM adoption. That serves as precedent and baseline to the study; primary data is also involved in this study and was collected within five months period. An online questionnaire survey was used as a tool for data collection. To determine the target population, interested parties were quite insignificant as the study subject awareness appears low (Hamma-adama et al., 2018b). A mixture of purposeful sampling and snowball method was adopted in the sampling and data collection procedure. The purposeful sampling was adopted to allow the researcher selects only the participants who possess the qualities necessary to provide meaningful input and reliable assessment of the study context (Coyne, 1997); and snowball was utilized in generating substantial (in both quality and quantity) responses (Noy, 2008). The purposeful sampling is adopted because; only those who are aware of or have knowledge of BIM are of interest in this study.
A quantitative research approach is adopted. A quantitative research method is used in achieving a wide coverage of the survey with a considerable response rate, bias freeresponse and free from privacy issues (Naoum, 2012). A structured questionnaire survey was used for the primary data collection. The questionnaire was designed mainly on two target enquiries, drivers and barriers to adoption of BIM in the Nigerian construction industry after determination of the respondent's demography. As it is set for a purpose, only those aware of BIM responses are accepted; thus, the system accepts the only target audience.
A reliability test, descriptive statistics and Relative Importance Index (RII) were subsequently deployed in the analysis of data. The reliability test was carried out in ascertaining the internal consistency of the scale of items used in the questionnaire. Descriptive statistics and RII were used in the determination of the most influential items for both adopters and non-adopters.
As for the respondents' profile, categorical data is generated while the main questions involved the use of a five-point Likert rating scale with five as the highest rank and one as the lowest. A five-point Likert rating scale is used with a standard method of ranking using Relative Importance Index (RII).
The relationship defines RII as: Relative Importance Index (RII) = ƩW (0≤index≤1) (Eadie et al., 2013) A x N where: W= element weighting by the respondents using a number between 1 and 5. Considering 1 as the least significant variable, and 5 as the most significant variable; A= highest weight; and Subsequently, the BIM barriers and BIM drivers ranked by the respondents are examined in terms of their interaction with the BIM concept. Some have already adopted the concept, while some are still at the awareness stage. A comparison was carried out using the Rank Agreement Factor (RAF) to determine adopters and non-adopters level of agreement or disagreement to the respective rankings by the group of adopters and nonadopters. The following relationships define RAF: And, maximum RAF (RAFmax) is then evaluated with: Where; Ri,1 is the rank of item i in group 1, Ri,2, is the rank of item i in group 2, N is the total number of items, which is the same for each group, Rj,2 is the rank of item j in group 2, and; j = Ni + 1. Percentage Disagreement (PD) between the two groups is the ratio of RAF to RAFmax, as expressed below: While the Percentage Agreement (PA) between the two ranked groups is the balance of percentage from the PD, which is: PA = 100 -PD A higher RAF value indicates a weaker agreement between the two groups. Thus, the RAF value of zero means a complete agreement between two subject groups. A spider diagram is plotted as in Fig. 3 and 4 to graphically illustrate the ranking variations by the two set groups.

Data collected, results and discussions:
The reliability test result, respondents' demographic information, descriptive statistics on the barriers and the drivers as well as the important relative index are evaluated and presented below.

Reliability test
The reliability test is carried out to ascertain an internal consistency of the scale of items used in the questionnaire as well as the reliability of the questionnaire for further analysis. Thus, Cronbach's Alpha is adopted for the reliability analysis, and the results are compared with George & Malley's (2003) acceptability. Any coefficient of Cronbach's alpha that is greater than 0.6 is considered acceptable, as such, all the items are within acceptable limit with Cronbach's Alpha coefficient of 0.95 (see Table 3 and 4). Moreover, all values >0.7 are considered acceptable according to Pallant (2013); thus, Cronbach's alpha >0.9 indicated a high level of internal consistency of the measured items and mean values they are closely related.   Table 5 presents the details of the respondents participated in the study or survey. The details include their location of practice in Nigeria, years of experience in the industry, size of their organizations, profession, specialization and their highest educational qualification. There are considerably higher respondents from four out the six zones, this happened due to a higher number of researchers' network, and a considerable number of firms and construction works within North-Central and South-West specifically. The predominant respondents are having 5 to 15 years of experience in the industry and mostly (about 80%) came from micro (<10 personnel) and small (10 -50 personnel) firms. In the case of their professions, specialities and educational qualifications, over 60% of them came from Architectural and Civil/Structural engineering backgrounds and working as designers/consultants and contractors. In addition, more than 80% are first degree (B.Sc./B.Tech./B.Eng) and second-degree (MSc/M.Eng.) holders.

BIM awareness and usage
This aspect involves the evaluation of the proportion of those using BIM from those aware but not using the concept. Note that all the respondents are only those aware of BIM; whether they use it or not. Thus, the percentages reflect only within the targeted group (who are aware of BIM). A significant shift can be a notice from the 2017 survey, and this indicated a substantial increase in the awareness and usage within the market (see Fig. 1  below). The proportion of users to awareness increased from 28%:72% to 54%:46% (Fig.  2) based on those aware of BIM.

Barriers to BIM adoption in Nigeria
Subjecting the fourteen generated barriers to BIM adoption in Nigeria into RII (see table 6 below) using the scale of 1-5 (Likert scale), it is realised that, the 1st ninth-ranked barriers are the most significant (RII ≥ 0.70) or mean ≥3.5 in a five-point Likert scale (Badu et al., 2012).
The result, in general, indicated lack of expertise within the organizations, lack of expertise within the project team, lack of standardization and protocols, and lack of client Aware and currently using BIM Just aware of BIM demand as the most influential barriers (1st to 4th) respectively. Moreover, ranked the following as 5th: lack of government policy, lack of additional project finance to support BIM, lack of collaboration among stakeholders and reluctance of team members to share information. These barriers were analyzed further to balance the perceptions by the BIM adopters and the non-adopters. Table 7 presents the two group rankings. From the first glance on radar plot (Fig. 3), adopters ranking was quite simultaneous, indicating a higher level of reality and consistency. At the same time, non-adopters are a sort of zig-zag manner (ranking whether very high or very low). This pattern suggests that while adopting BIM, perception to barriers change as the realities unfold or became dominant. The barriers ranked 1st, 2nd, 3rd and 4th by non-adopters, were ranked 2nd, 6th, 9th and 1st by adopters with quite lower average index, as such what is perceived most influential barriers before adoption tend to change after adoption; such challenges may have been dealt with in the adoption process. On the other hand, they quite agreed over half of the barriers as to their significance or indexes. For instance, High Investment Cost, Lack of infrastructure, and Return on Investment (ROI) issue are scored the same magnitude although they were in different ranks. This situation leads to the determination of PD and PA to allow us to drive exclusive findings. Table 8   Succinctly, nine of the fourteen barriers are significantly crucial to both the adopters and non-adopters; however, the remaining five appeared less important to both groups. These five barriers are resistance at the operational level, high investment cost, lack of infrastructure, return on investment (ROI) issue as well as legal issues around ownership, IP & PI insurance.

Drivers to BIM adoption in Nigeria
Subjecting the ten generated drivers to BIM adoption in Nigeria into RII (see Table 8) using the scale of 1-5 (Likert scale), it was realized that, the 1st seventh-ranked drivers are the most significant (RII ≥ 0.70) or mean ≥3.5 in a five-point Likert scale (Badu et al., 2012). The most influential drivers revealed as availability of trained professionals to handle the tools, proof of cost savings by its adoption, BIM Software affordability and awareness of the technology amongst industry stakeholders (in descending order). Moreover, ranked the following as 5th: clients' interest in the use of BIM in their projects, cooperation and commitment of professional bodies to its implementation, and enabling environment within the industry. These drivers were analyzed further to balance the perceptions by both the adopters and non-adopters. Table 10 presents the two group rankings. From the first glance on radar plot (Fig. 4), the adopters ranking was simultaneous, indicating a higher level of reality and consistency. At the same time, the non-adopters are a sort of zigzag at some points (ranking very high and very low). This suggests that while adopting BIM, perception to driving the adoption changes. The drivers ranked 1st, 2nd, 3rd and 4th by the non-adopters are ranked 1st, 8th, 5th and 2nd by the adopters. Furthermore, with a tiny difference of average RII as such, what is perceived most influential drivers before adoption tend to change after the adoption.
On the other hand, the average RII of 0.68 and 0.67 for the adopters and the non-adopters respectively revealed that the adopters are still at an early stage, so they perceive the drivers' influence the same way with the non-adopters. Notwithstanding, they nearly have the same average RII, the adopters disagree a bit more than they agree with the non-adopters in terms of individual drivers' influence to adopt BIM (Table 10). To demonstrating this scenario, availability of trained professionals to handle the BIM tools, cooperation and commitment of professional bodies to BIM implementation and Government support through legislation are drivers that scored the same and rated the same to moving the adoption further by both the adopters and nonadopters. This finding suggests persistent investment on the drivers to drive the BIM adoption further. Succinctly, all the drivers are of high importance to both the adopters and non-adopters in the exception of three who appear less compared to the rest. These three drivers are cultural change among industry stakeholders, collaborative procurement methods and government support through legislation.

Conclusions:
The urgent need for BIM adoption in construction industry is providing huge opportunities in research and development. However, researches in barriers and drivers to its adoption did not yield fetched universal adoption thus, that leaves a question of inadequacy or misrepresentations. There are several findings on barriers and drivers to BIM adoption from literatures; many of which having different influence over the other. Nigeria is among developing countries where BIM is becoming vibrant; however, BIM adoption in Nigeria remains in its infancy. This piece of research is aim at filling the gap of differentiating by order of importance, the common barriers vis-a-vis to drivers toward

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BIM adoption in the Nigerian construction market. Fourteen barriers and ten drivers were identified from literature, five Likert scale was used for measurement of respondents' perceptions and RII was used to rank the perceptions. The study revealed that barriers ranked from 1st to 9th are highly influential to the adoption of BIM in Nigeria, and the drivers ranked from 1st to 7th are significant to facilitate BIM adoption in Nigeria. Further evaluation was carried out in comparing the perception of those adopted BIM and those that have not. Ranking and scoring of barriers and drivers amongst adopters and nonadopters having nearly 50:50 PD to PA which suggests early adoption stage or low maturity stage. The common and most significant barriers and drivers were established from the two set groups. The common and significant barriers to adopters and nonadopters are: Lack of standardization and protocols, Lack of expertise within the organizations, Industry's Cultural resistance, Lack of additional project finance to support BIM, Lack of client demand, Lack of expertise within the project team, Lack of government policy, Lack of collaboration among stakeholders, and Reluctance of team members to share information. On the other hand, the common and most significant drivers to adopters and non-adopters are: Availability of trained professionals to handle the tools, Proof of cost savings by its adoption, Clients interest in the use of BIM in their projects, Enabling environment within the industry, Awareness of the technology among industry stakeholders, Cooperation and commitment of professional bodies to its implementation, and BIM Software affordability.
The study recommends that, to develop effective BIM adoption framework, the established barriers and drivers should be considered vital. The barriers should be resolved in totality, and drivers should be instigated, motivated and encouraged. Further face-toface (interview) study is necessary to explore more and in-depth challenges of BIM adoption in the industry under study; and as the industry is getting more aware of the BIM, periodic evaluation of the critical barriers and drivers is vital. This study contributes to the body of knowledge in providing an in-depth understanding of barriers and drivers from adopters and non-adopters perspectives, their strengths of influence from the two groups and combined influence to adoption of BIM in the Nigerian construction industry.