Ts Dr Mazura Mahdzir
Sr Nik Fatma Arisya Nik Yahya
Pn Rozilah Talib





The “Innovation adoption” perspective is perceived as a process that includes generating, developing, and implementing new ideas or behaviours (Damanpour, 1996). The concept concerns the “managers” or “owners” of professional services organisations in the capability of deciding to adopt new technologies during the “pre-adoption stage” (Klein & Sorra, 1996). 

Although various developers have long introduced the innovation adoption theories, the trends of using innovation adoption theories in construction remain low compared to non-construction industries (Suharti, Soegiono, & Purwati, 2013; Spencer, Buhalis, & Moital, 2012; Peltier, Zhao, & Schibrowsky, 2012;  Awa et al., 2011; Ramdani, Kawalek, & Lorenzo, 2009; Zhu, Kraemer, & Xu, 2006; Zhu & Kraemer, 2005; Gibbs & Kraemer, 2004; Escriba-Esteve, Sanchez-Peinado, & Sanchez-Peinado, 2009; Wiklund & Shepherd, 2003; Thong, 2001; Ta-Tao, Nakatani & Zhou, 2009; Simmons, Armstrong & Durkin, 2008).

The research using innovation adoption theories as a guideline has not been sufficiently studied or tested by construction researchers except from Western countries. For example, Peansupap and Walker (2005) with the diffusion of innovation (IDT/DOI), Adriaanse, Voordijk, and Dewulf (2010) with the combination of a unified theory of acceptance and use of technology (UTAUT), technology acceptance model (TAM), and theory of planned behaviour (TPB), Marcatia, Guidoa, and Peluso (2008) with TPB, Samuelson (2011), Sargent, Hyland, and Sawang (2012) and Davies and Harty (2013) with UTAUT, and Radas and Bozic (2012) with a resource-based view (RBV). 

However, within the Malaysian context, such theories have been adopted mostly by non-construction researchers such as Khong et al. (2009), Junaidah Hashim (2007), and Nor Hazana Abdullah, Eta Wahab, and Alina Shamsuddin (2013). Based on this situation, it is crucial to reduce this gap by examining the strengths and weaknesses and comments on the related theories. The innovation adoption framework related to managers’ decision-making capability can be developed by comparing each theory.



Innovation adoption theories are highly helpful for SME managers to increase their understanding of adoption decisions (Mumtaz, 2012). This is consistent with the research undertaken by Gallivan (2001) and Oliveira and Martins (2010).

Among the prominent innovation adoption theories include; 



                The main reason to limit the discussion to ten innovation adoption theories is that those theories cover two generic factors related to managers, namely behavioural and non-behavioural capability. Also, it represents among the most frequently cited studies in innovation research and is commonly used in assisting managers in making decisions (Nor Hazana Abdullah et al., 2012; Mumtaz, 2012). The study of the strengths and weaknesses of each theory remains an important aspect before selecting the appropriate theory for guidelines. These are compared according to the strengths and weaknesses highlighted in Table 1.1


Table 1.1 Strengths and weaknesses

  Based on the comparison table, two theories remain unsuitable to be applied in the construction of SMEs, namely IDT/DOI, developed by Rogers (1995) and TOE, developed by Child (1972) and Hambrick and Mason (1984). 

        These two theories address different issues. As for TOE, the main purpose of this theory is to determine the factors influencing the adoption of technology from organisational, environmental, and technological contexts (Taalika, 2004; Ta-Tao, Nakatani, and Zhou, 2009). However, the utilisation of factors was inappropriate for SMEs. The factors in TOE theory better explain intra-organisations than other theories (Oliveira & Martins, 2010), but it was missing regarding managerial capability (Wu, 2011; Garaca, 2011) or did not cover human aspects. (Nor Hazana Abdullah, Eta Wahab, and Alina Shamsuddin, 2013). Moreover, it does not consider the unique nature of small organisations (Nor Hazana Abdullah, Eta Wahab, and Alina Shamsuddin, 2013). Hence, the factors were not appropriate for further development of the innovation adoption framework.

        The same issue applies to IDT/DOI. This theory attempts to describe patterns of adoption (Mumtaz, Counsell, and Swift, 2012) from an individual approach. This is a good criterion for developing the innovation adoption framework from an individual point of view. However, the view was limited to their awareness towards technology innovation characteristics or more towards technological factors. This is not suitable for the construction of SMEs, which always emphasises the managers’ involvement in SMEs (Niraj & Goucher, 2013). Another reason is that this theory does not consider the unique nature of small organisations, as stated in Section 1.6.3, like IDT/DOI, despite both theories having been used extensively in the IT field. Based on these limitations, both theories were not recommended for construction SMEs unless some modifications are made prior to utilisation in future years.

     By way of contrast, the remaining theories, as stated the following were identified as appropriate for SMEs;

However, some modifications are needed to suit the construction industry (CI). In this study, the modifications involved factors related to managerial decision-making capability. Some theories were developed to predict the pattern of individual behaviour (TAM2, TAM3, UTAUT, and UET) or organisational capability (RBV) towards technology innovation. Out of these theories, the theories of TRA, TPB, and TAM contain similar issues. TRA theory is a useful and well-known theory developed to determine individual behaviour towards technology (Park, Joy Saplan-Catchapero & Jaegal, 2012; Oye, 2013; Ajzen & Fishbein, 1980). However, the factors involved were limited to behavioural capability. TPB is another individual-based theory developed to predict volitional and non-volitional behaviours (Oye, 2013; Armitage & Conner, 2001), but the innovation adoption factor is behavioural-based. This theory does not consider the non-behavioural capability factors that managers should possess.

 Like TRA and TPB, TAM theory is also concerned with explaining individual behavioural intention to adopt technology adoption (Se-Joon, Thong, and Kar, 2006) based on the simplicity of use and the usefulness of the technology (Dwivedi, Wade, and Schneberger, 2011). However, the listed factors are limited to behavioural capability factors (Wu, 2011). Meanwhile, TAM 2, TAM3, and UTAUT theories also address similar issues with TRA, TPB, and TAM. The factors constructed for those theories are behavioural-based in nature. Thus, the improvement made by the theory developers is more relevant, precise, and powerful.

 TAM 2 adds individual intention to use IT (Venkatesh & Davis, 1996) by considering external factors (Dwivedi, Wade & Schneberger, 2011). Based on the combination of TRA, TAM, and TAM2 (Mumtaz, Counsell, and Swift, 2012), TAM 3 also adds factors related to non-behavioural capability, namely experience. Following this is UTAUT, in which this theory shows an extension of the factors related to non-behavioural factors (Dwivedi, Wade & Schneberger, 2011), namely experience, gender, and age, but these are still insufficient. Each theory remains useful and has been widely used in technology innovation. Nevertheless, within the context of construction SMEs, these theories need further modification because the decision to adopt has been influenced by factors related to managerial capability.

 This situation applies to UET (Nor Hazana Abdullah, Eta Wahab & Alina Shamsuddin, 2013), which has limited non-behavioural capability factors such as age, experience, education, and tenure. Thus, theory extension is needed as all factors related to managerial decision-making capability are suitable for managers from small-size organisations. The organisational-based theory, like RBV, also needs to be modified. Despite it being useful in determining the technology innovation adoption and has been receiving wide application in the human resource field (Williamson et al., 2012), the theory only considers the aspect of organisational resources and has an unclear relationship with managerial capability (Ghobakhloo et al., 2012; Nor Hazana Abdullah, Eta Wahab, and Alina Shamsuddin, 2013).

 From those limitations, it can be concluded that previous theories have limited the factors either in the forms of (i) non-behavioural capability (Enegbuma, Dodo & Ali, 2014), (ii) or behavioural capability as incorporated in TRA, TPB, and TAM, (Park, Joy Saplan-Catchapero & Jaegal, 2012; Mumtaz Abdul Hameed, Counsell & Swift, 2012). Other theories like TAM2, TAM3, and UTAUT include behavioural and non-behavioural capabilities, yet the factors are insufficient. Similar UET covers limited non-behavioural capability (managerial age and experience) and RBV with technological capability factors. Hence, combining both factors with further modifications is needed to remain suitable with CI.

 All related theories have been applied in various fields of study. For example, TRA theory was used widely in education and business fields. TPB also has adopted those theories in health-related studies. Meanwhile, TAM theory and its extension, namely TAM2, TAM3, and UTAUT, are commonly used in IT-related fields (Se-Joon, Thong & Kar, 2006). The context is generic and includes various information systems, such as the Internet and system software. Other theories like TOE, RBV, and UET have also been used extensively in business or human resource fields. Based on this wide application, it can be concluded that innovation adoption theories have been tested extensively by non-construction industries. The characteristics of each theory have met the criteria of the individuals (users) or organisations. The application of innovation adoption theories in non-construction industries is relatively higher than CI.

The empirical studies related to those theories were found in CI but were non-Malaysian based. Examples of the construction researchers are Peansupap and Walker (2005) via the “DOI adoption”, Adriaanse, Voordijk, and Dewulf (2010) via the “combination of UTAUT, TAM, and TPB”, Marcatia, Guidoa, and Peluso (2008) with “TPB”, Samuelson (2011), Sargent, Hyland, and Sawang (2012), and Davies and Harty (2013) via “UTAUT”, and Radas and Bozic (2012) with “RBV”. From the Malaysian context, the empirical and theoretical studies that have used innovation adoption theories as a guideline to adopt new technology are still limited, especially in construction SMEs or small QS organisations (Mazura et al., 2016a and 2016b). The limitation of both studies from construction SMEs has influenced researchers to develop a framework for managers.

 Next, the theoretical and empirical studies involving managerial decision-capability (via innovation adoption theories) during the pre-adoption stage are also scarce (Samuelson & Bjork, 2013), especially among Malaysian construction SMEs. To date, limited studies have been conducted by Khong et al. (2009), Junaidah Hashim (2007), and Nor Hazana Abdullah, Eta Wahab, and Alina Shamsuddin (2013). Meanwhile, the remaining researchers prefer to focus their decision capability to adopt new technology at the post-adoption stage without referring to innovation adoption theories (Zahrizan et al., 2013; Kamaruzzaman et al., 2010; Mastura Jaafar et al., 2007; Zahrizan Zakaria et al., 2012).

 The factors related to managerial decision-making capabilities (DMC) during the pre-adoption decision stage are vital compared to other factors such as technology, process, and management because as a manager, they are the main driver of innovation (Mitropoulos & Tatum, 1999; Ghobadian & Gallear, 1999; Wilson & Stokes, 2006; Sexton et al., 2006). Their DMC will indirectly or directly influence the adoption of technology innovation (Ta-Tao, Nakatani & Zhou, 2009; Ta-Tao, Nakatani & Jason, 2005) or influence the attitude of bottom-down level in utilising the technology that has been adopted (Jong & Hartog, 2003). On the one hand, managerial factors (behavioural and cognition) are more prominent in influencing SMEs adoption of new technology compared to others (Perez, Sanchez, and Carnicer, 2003; Brewer & Runeson, 2009; Qinghua et al., 2016) such as environmental factors (Ramdani, Kawalek, and Lorenzo, 2009) or technological factors (Henderson & Ruikar, 2010).

 The weaknesses of previous theories (insufficiency of the factors and appropriateness of application in CI) in assisting SME managers in understanding their adoption decision from a wider perspective during the pre-adoption stage make it best to consider these two aspects. This notion is consistent with Straub (2009) and Oliveira and Martins (2010), which stressed the importance of combining more than one theory to obtain a better broad-based or a multitude of factors.

 The combination of a multitude of factors is also needed due to the following reasons:

(i)                  the process of technology innovation adoption, which is complex;

(ii)                unique but malleable perceptions raised by individuals regarding technology;

(iii)              where the factors influence the adoption of new technology are not limited to one behavioural aspect (Straub, 2009).



           The ten theories show that the existing behavioural and non-behavioural capability factors are insufficient to help managers understand their decision-making process in adopting new technologies as they are fragmented and generic. As the previous theories state, considering all capability factors in a single framework is crucial (Oliveira & Martins, 2010). For this reason, the two generic factors (behavioural and non-behavioural) should be theoretically and empirically explored to assist SME managers in their decision-making process.



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