Integration of Technology Readiness Index (TRI) Into the Technology Acceptance Model (TAM) for Explaining Behavior in Adoption of BIM

Recently, Building Information Modelling (BIM) technology has attracted much attention in the Architecture, Engineering and Construction (AEC) industry and is becoming globally-recognized standards. The primary objective of this study is to gain better understanding of the drivers and barriers to the adoption of BIM. There is abundance of theories on technology adoption among which this study applies the Technology Acceptance Model (TAM) proposed by Davis (1989) to analyze the adoption and the use of the BIM technology by exploring implications of perceived ease of use, perceived usefulness, attitudes, behavioral intention and actual usage. In addition, this study integrates Technology Readiness Index (TRI) to explain adoption of BIM. With data collected through an online survey, findings of this study will assist in explaining behavior in adoption of BIM that may facilitate realizing the advantages of the BIM technology.


Integration of Technology Readiness Index (TRI) Into the Technology
Acceptance Model (TAM) for Explaining Behavior in Adoption of BIM

Introduction
In recent decades technological advancements has become an integral part of modern life and identify the determinants of adoption of innovations is gaining interests in academia and industry.
Building Information Modelling (BIM) is an intelligent model-based design process that uses reuses and exchanges information with digital documents on a single platform. The implementation of BIM technology has emerged as a mean to escalate productivities in the Architecture, Engineering and Construction (AEC) industry.
While BIM technology and their increasing application are expected to bring some deep changes in AEC industry, it is still unclear how BIM could be used and what the benefits are. (Lee, Yu & Jeong, 2013).To advance these issues, Song et al. (2016)  connections between various components: beliefs, attitudes, intentions and behaviors. TAM is one of the most influential models widely used in the studies of the determinant for user acceptance and usage behavior of information technology because it has been effective in the modeling of acceptance of IT and has received extensive empirical support through the studies predicting the use of information systems.
According to TAM, information system usage behavior is predominately explained by behavioral intention that is formed as a result of conscious decision-making processes. Behavioral intention, in turn, is determined by two belief factors: • Perceived usefulness (PU) -PU is defined as "the degree to which a person believes that using a particular system would enhance his or her job performance" • Perceived ease-of-use (PEOU) -PEOU is demonstrated as "the degree to which a person believes that using a particular system would be free from effort" By manipulating these two factors, system developers can have better control over users' beliefs about the system, and subsequently, their attitudes towards usage, behavioral intention and usage of the system.
Since its inception, TAM has been widely used to scrutinize individual technology acceptance behavior in various types of information systems and been applied extensively for different technologies, under different situations with different control factors and in different contexts which has led to extensions and changes in the original model. For example, Mohammad Abu-Dalbouh (2013) utilized a quantitative approach based on TAM to evaluate the user acceptance of mobile technology application within healthcare industry, Shroff, Deneen, & Ng (2011) examined whether TAM could legitimately be applied in determining the relationship of students' intention to use an e-portfolio system while the result of Park (2009) proved TAM to be a good theoretical tool to understand users' acceptance of e-learning and, in another study, Alsamydai (2019) adapted TAM with additional dimension of quality factors and experience to understand the use of mobile banking services.
However, perception of the technical features of an innovation may not be sufficient to cover all aspects which would potentially affect users' intention to use and actual usage; as such, a more thorough understanding of acceptance and adoption needs to take additional factors into account.

Technology Readiness Index (TRI)
The growth of highly sophisticated technological products has resulted in fundamental transformations in the interaction with users which indicates attention to the readiness of people is needed. A model that considers individual differences is Technology Readiness Index (TRI) which is defined as the individual's general opinion about technology (Parasuraman, 2000) and describes individual's propensity to embrace and use new technologies. Parasuraman and Colby (2001) segment the Technology Readiness construct into four variables: optimism, innovativeness, discomfort and insecurity of which the first two refer to action enablers, while the other two refer to the inhibitors.
Studies of Parasuraman's (2000) technology-readiness concept are widespread and some studies examine the relationships between technology readiness and technology acceptance. Erdoğmus & Esen (2011) revealed that four types of technology readiness have different effects on perceived usefulness and perceived ease of use about e-HRM. Başgöze (2015) integrated technology readiness (TR) into the technology acceptance model (TAM) in the context of consumer adoption of mobile shopping (m-shopping) and theorizes that the impact of TR on mobile shopping intention is mediated by both perceptions of usefulness and ease of use. Buyle et al. (2018) considered the relationship between individual characteristics of decision makers and their intention to use data standards and indicate that respondents who score high on innovativeness have a higher intention to use data standards.

Research Model
In accordance with the research objective and consistent with the related literature, the research model, as shown in Figure 1, is based mainly on the Technology Acceptance Model (TAM) presented by (Davis 1989) with additional considerations of the Technology Readiness Index (TRI) proposed by Parasuraman (2000

Data Analysis
The present study investigates the applicability of using TAM and TRI on two groups: a user group and a non-user group based on their answers to the question "I use BIM frequently?" The survey sample includes 63 respondents of which 26 (41%) use BIM frequently and 37 (59%) are non-users. The following table illustrates the gender difference of users and non-users of BIM. The relationship among the variables was analyzed by means of the chi-square independence test. Note: ** p-value <0 .01, * < 0.05 In this case p < 0.05, so this result indicates gender is linked to the actual usage which is consistent with the prior research that suggests demographic factors such as gender influence acceptance of information technologies (Venkatesh et al., 2012).
To gain better understanding and validate the data, the following table shows the descriptive statistics and correlation analysis of the TAM constructs. The results clearly support the original hypothesis made in the http://aes.ju literature c To study t adopted to    (Turan, Tunc, & Zehir, 2015) and willingness to try out new information technologies is a very important determinant of use decisions (Ngafeeson & Sun, 2015).
It is worth noting the outcomes of the present study support some other studies pertaining to the relationships between the various constructs of TRI and perceived usefulness (PU), perceived ease-of-use (PEOU) and attitudes towards usage (ATU) of TAM. As indicated in the following table, similar outcomes were observed as suggested by Erdoğmuş and Esen (2011) that optimism and innovativeness of the TRI constructs are positively related with usefulness and ease of use of the TAM constructs and in Shih and Fan (2013) that optimism has a significant effect on the attitude dimension of TAM. Note: ** p-value <0 .01, * < 0.05

Conclusion
This study identified the distinctions between the users and non-users of BIM and confirmed that elements of TAM contribute significantly in explaining and predicting the actual usage of BIM. However, optimism, discomfort and insecurity dimensions of TRI have not influenced the adoption while the innovative characteristic of individuals is found to impact the adoption of BIM. Nevertheless, positive relationships were observed between the optimism and innovativeness of TRI and perceived usefulness, perceived ease-of-use and attitudes towards usage of TAM.
Though carefully planned and conducted, this study is not without limitations. Firstly, the generalizability of the findings might be limited due to the sampling procedure, sample size, and diversity in background of respondents. Another limitation of the present study is that it did not attempt to investigate the influences of any external factors. As such, for example, future investigations could build upon the findings of the present study by looking at roles affecting BIM acceptance from an individual and organizational perspective by including some characteristics of the organization, such as, for example, the innovativeness, willingness, technical support and determination of the organization to implement BIM.
Further studies are necessary to better understand user experience with BIM, especially as such research may not only bring economic returns to the users but also contribute to the realization of the advantages of the BIM technology in the Architecture, Engineering and Construction industry. Aspects such as factors affecting individual willingness to use BIM and the role of an organization could be interesting topics for future research.