Authors 
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.

COMPARATIVE ANALYSIS OF INNOVATION ADOPTION THEORIES FOR MANAGERS

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).

CONCLUSION

           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.

REFERENCES

1.    Adriaanse, A., Voordijk, H., & Dewulf, G. (2010). ‘‘Automation in construction using interorganisational ICT in United States construction projects’’, Automation in Construction, Vol.19 No.1, pp. 73–83.

2.    Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour.

3.    Ajzen, I. (1991). The theory of planned behavior. Organizational behaviour and human decision processes, 50(2), 179-211.

4.    Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta‐analytic review. British journal of social psychology,40(4), 471-499.

5.    Awa, H. O., Eze, S. C., Urieto, J. E., & Inyang, B. J. (2011). Upper echelon theory (UET) is a major determinant of information technology (IT) adoption by SMEs in Nigeria. Journal of Systems and Information Technology, 13(2), 144-162.

6.  Brewer, G., & Runeson, G. (2009). Innovation and attitude: Mapping the profile of ICT decision‐makers in architectural, engineering and construction firms, International Journal of Managing Projects in Business, 2(4), 599 – 610. Retreived August 11, from, http://www,emeraldinsight.com

7.   Bryman, A. (2004). Social Research Methods (2nd ed.): Oxford University Press.

8.    Child, J. (1972). Organizational structure, environment and performance: The role of strategic choice. Sociology, 6(1), 1–22

9.    Damanpour, F. (1996). Organizational complexity and innovation: developing and testing multiple contingency models. Management science, 42(5), 693-716.

10. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13 (3), 319–340

11. Davies, R., & Harty, C. (2013). Implementing ‘Site BIM’: a case study of ICT innovation on a large hospital project. Automation in Construction, 30, 15- 24.Dawling, M. (2008). The SAGE Encyclopedia of     

      Qualitative Research Methods.

12. Dwivedi, Y. K., Wade, M. R., & Schneberger, S. L. (Eds.). (2011). Information Systems Theory: Explaining and Predicting Our Digital Society(Vol. 1). Springer Science & Business Media.

13. Enegbuma, W. I., Dodo, Y. A., & Ali, K. N. (2014). Building Information Modelling Penetration Factors in Malaysia. International Journal of Advances in Applied Sciences, 3(1), 47-56.

14. Escriba‐Esteve, A., Sanchez‐Peinado, L., & Sanchez‐Peinado, E. (2009). The influence of top management teams in the strategic orientation and performance of small and Medium‐sized enterprises.British         Journal of Management, 20(4), 581-597.

15. Fishbein, M., & Ajzen, I. (1975). Belief, attitudes, intention, and behavior. An introduction to theory and research. Massachussets: Addison-Wesley.Ajzen (1985; 1991)

16.  Gallivan, M. J. (2001). Organizational adoption and assimilation of complex  technological innovations: development and application of a new framework.ACM Sigmis Database, 32(3), 51-85

17. Garaca, Z . (2011). “Factors related to the intended use of ERP systems”, Management, Vol. 16.No. 2, pp. 23-42.

18. Ghobakhloo, M., Tang S, H., Mohammad Sadegh Sabouri., & Norzima Zulkifli. (2012). Strategies for Successful Information Technology Adoption in Small and Medium-sized Enterprises,  Information, 3 (1),          36-67. Retrieved August 11, from, http://www.mdpi.com

19.  Ghobadian A., & Gallear, D. (1999). TQM and organisation size, international Journal of Operations and Production Management 17: 121-163

20.  Gibbs, J. L., & Kraemer, K. L. (2004). A cross‐country investigation of the determinants of scope of e‐commerce use: An institutional approach.Electronic markets, 14(2), 124-137.

21. Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers. Academy of management review, 9(2), 193-206

22. Henderson, J. R., & Ruikar, K. (2010). Technology implementation strategies for construction organisations. Engineering, Construction and Architectural Management, 17(3), 309-327.

23. Jong, J. De., & Hartog, D. Den. (2003). Research Report H200303. Leadership as a determinant of innovative behaviour A conceptual framework. Netherlands: Scientific Analysis of Entrepreneurship and     

      SMEs (SCALE)

24. Junaidah Hashim. (2007). Information Communication Technology (ICT) Adoption Among SME Owners in Malaysia. International Journal of Business and Information, 2 (2), 221 – 240. Retrieved August 11,

      from, http://www.knowledgetaiwan.org

25. Kamaruzzaman, S.N., H. Salleh, E.M. Ahmad Zawawi., & Azlan Shah Ali.(2010). Current Use And Needs Of Ict In Malaysian Building Industry: The Industry Perspective. Journal Design and Built, 3,74-84.

26. Khong, S. T., Siong, C.C., Binshan, L., & Uchenna ,C. E. (2009)."Internet-based ICT adoption: evidence from Malaysian SMEs", Industrial Management & Data Systems, Vol. 109 Iss 2 pp. 224 – 244

27. Klein, K. J., & Sorra, J. S. (1996). The challenge of innovation implementation. Academy of management review, 21(4), 1055-1080.

28. Marcati, A., Guido, G., & Peluso, A. M. (2008). The role of SME entrepreneurs’ innovativeness and personality in the adoption of innovations. Research Policy, 37(9), 1579-1590.

29. Mastura Jaafar, T. Ramayah, Abdul Rashid Abdul Aziz & Basri Saad (2007).Technology readiness among managers of Malaysian construction firms . Engineering, Construction and Architectural 

      Management,  14(2), 180- 19. Retreived August 9, from http://www.emeraldinsight.com

30. Mazura  Mahdzir and Sharifah Mazlina Syed Khuzzan (2016a). A Managerial Decision Making Capability Framework for adopting Technology Innovation Within Construction SMEs : Analysis and results,

      Sains Humanika, 8 (4), 1-16.

31. Mazura  Mahdzir and Sharifah Mazlina Syed Khuzzan (2016b). Factors influencing the adoption of new technology within SMEs organisations: Validation of the framework. International Journal of             

      Conceptions on Management and Social Sciences, 4 (4), 1-5.

32. Mitropoulos, P., & Tatum, C.B. (1999). Technology adoption decisions in construction organizations, Journal of Construction Engineering and Management, 125 (5), 330-338 Retreived August 11, from,

      http:www.ascelibrary.org/

33. Mumtaz Abdul Hameed, Counsell, S & Swift, S (2012). A conceptual model for the process of IT innovation adoption in organizations. Journal of Engineering and Technology Management, 29(3), 358-390

34. Niraj Thurairajah., & Goucher., D. (2013). Advantages and Challenges of using BIM: A cost consultant’s perspective. Paper presented at the 49th ASC Annual International Conference Proceedings, California 

     Polytechnic State University, USA. Retrieved August 11, from, http://www. ascpro0.ascweb.org

35. Nor Hazana Abdullah, Alina Shamsuddin, Eta Wahab., & Norhadilah Abdul Hamid.(2012, December). Preliminary qualitative findings on technology adoption of Malaysian SMEs. In Humanities, Science and

     Engineering (CHUSER), 2012 IEEE Colloquium on (pp. 15-20). IEEE.

36. Nor Hazana, Abdullah., Eta Wahab., & Alina Shamsuddin. (2013). Exploring the common technology adoption enablers among Malaysian smes: qualitative findings. Journal of Management and

      Sustainability, 3(4), 78-91. Retrieved August 11, from, http://www. .ccsenet.org

37. Oliveira, T., & Martins, M. F. (2010), ‘‘Literature review of information technology adoption models at firm level,’’ The Electronic Journal Information Systems Evaluation, Vol.14 No.1, pp.110–121. Rogers   

      (1995)

38. Oye, N. D. (2013).Information and Communication Technology Acceptance Framework for Nigerian Public University Academicians (Doctoral dissertation, PhD Thesis).

39. Park, T., Joy Saplan-Catchapero, V., & Jaegal, D. (2012). Predicting Knowledge Sharing Intentions in the Public Sector: Comparing TAM with TPB. International Review of Public Administration, 17(2), 93-120

40. Peansupap, V., & Walker, D. (2005a). Exploratory factors influencing information and communication technology diffusion and adoption within Australian construction organizations: a micro analysis.       

      Construction Innovation: Information, Process, Management, 5(3),135–157

41. Peltier, J. W., Zhao, Y., & Schibrowsky, J. A. (2012). Technology adoption by small businesses: An exploratory study of the interrelationships of owner and environmental factors. International Small Business

      Journal, 30(4), 406–431.

42. Perez, M.P, Sanchez, and A.M., & Carnicer, M.P.D.L. (2003). Top Manager and Institutional Effects on the Adoption of Innovations: The Case of Teleworking 1, 21(1), 58-73. Retreived August 11, from,             

      http://www.tandfonline.com

43. Prahalad, C. K., & Hamel, G. (1994). Strategy as a field of study: Why search for a new paradigm?. Strategic management journal, 15(S2), 5-16

44. Qinghua, H, Ge, W, Lan, L, Qian, S, Jianxun, X & Xianhai, M (2016). Mapping the managerial areas of Building Information Modeling (BIM) using scientometric analysis. International Journal of Project     

     Management, 1-16.

45. Radas, S., & Bozic, L. (2012). Overcoming failure: Abandonments and delays of innovation projects in SMEs. Industry and Innovation, 19(8), 649-669.

46. Ramdani, B., Kawalek, P., & Lorenzo, O. (2009). Predicting SMEs' adoption of enterprise systems. Journal of Enterprise Information Management,22(1/2), 10-24.

47. Samuelson, O. (2011, October). Adoption processes for EDM, EDI and BIM in the Swedish construction industry. In CIB W78-W102 2011 International Conference. Sophia Antipolis, France (pp. 26-28).

48. Samuelson, O., & Björk, B. C. (2013). Adoption processes for EDM, EDI and BIM technologies in the construction industry. Journal of Civil Engineering and Management, 19(sup1), S172-S187.

49. Sargent, K., Hyland, P., & Sawang, S. (2012). Factors influencing the adoption of information technology in a construction business. Construction Economics and Building, 12(2), 86.

50. Se-Joon, H , Thong , J.Y.L., & Kar Y. T. (2006).Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet. Decision support systems,   

      42(3), 1819-1834.

51. Sexton, M, Barrett, P., & Aouad, G. (2006). Motivating small construction companies to adopt new technology. Building Research and Information, 34(1), 11-22. Retrieved August 11, from,  

      http://www.tandfonline.com

52. Simmons G, Armstrong G.A., & Durkin, MR. (2008). A conceptualization of the determinants of small business website adoption. International Small Business Journal 26(3): 351–389.

53. Spencer, A. J., Buhalis, D., & Moital, M. (2012). A hierarchical model of technology adoption for small owner-managed travel firms: An organizational decision-making and leadership perspective. Tourism

     management, 33(5), 1195-1208.

54. Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of educational research, 79(2), 625- 649.

55. Suharti, L., Soegiono, L., & Purwati, Y. (2013). Technology Innovation Adoption Model among SME Tempe Producer (A Study on Tempe Produces in Jawa Tengah, Indonesia).International Journal of Business

     and Management Invention, 3(2), 05-14.

56. Taalika, S. (2004). Assessing the Managerial Influence as a Facilitating Factor in Innovation Adoption Process. In Proceedings of the IAMOT Conference.Washington, DC.

57. Ta-Tao, C., Nakatani., & Jason, C. C. (2005). The Impact of Organizational and Owner's Demographic Characteristics on the Extent of E-Commerce Adoption in SMEs. Spokane: Gonzaga University

58. Ta-Tao, C., Nakatani, K., & Zhou , D. (2009).An exploratory study of the extent of information technology adoption in SMEs: an application of upper echelon theory. Journal of Enterprise Information     

      Management, 22(1/2), 183-196.

59.  Thong, J. Y. (2001). Resource constraints and information systems implementation in Singaporean small businesses. Omega, 29(2), 143-156.

60.  Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990).Processes of technological innovation. Lexington Books.

61.  Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision sciences, 27(3), 451-481

62.  Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.

63.  Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315

64.  Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.and Anderson and Schwager (2004)

65.  Wiklund, J., & Shepherd, D. (2003). Knowledge‐based resources, entrepreneurial orientation, and the performance of small and medium‐sized businesses. Strategic management journal, 24(13), 1307-1314.

66.  Williamson, N. C., Bhadury, J., Dobie, K., Ofori-Boadu, V., Parker Troy, S., & Yeboah, O. (2012). Business coursework and the resource-based view (RBV). International Journal of Wine Business Research, 24(1),      19-32.

67.  Wilson N., & Stokes D. (2006). Managing creativity and innovation. Journal of Small Business and Enterprise Development 12: 366-378

68.  Wu, W.-W. (2011). “Developing an explorative model for SaaS adoption”, Expert Systems with Applications, Vol. 38 No. 12, pp. 15057-15064.

69.  Zahrizan, Nasly Mohamed Ali., Ahmad Tarmizi Haron., Marshall-Ponting., & Zuhairi Abd Hamid. (2013). Exploring the adoption of building information modelling (BIM ) in the malaysian construction industry:

      a qualitative approach. International Journal of Research in Engineering and Technology   (IJRET), 2(8), 384–395

70.  Zahrizan Zakaria, Nasly, Mohamed Ali; Amanda Marshall-Ponting; Ahmad, Tarmizi Haron., & Zuhairi, Abd Hamid. (2012). An Exploratory Study on the Potential of Implementing Building Information Modelling

     (BIM) in Malaysian Construction Industry: Lesson Learnt from Singapore and Hong Kong   Construction Industry (3) International Journal of Civil Engineering and Geo- Environmental http://ijceg.ump.edu.my

     ISSN: 21802742

71.  Zhu, K., & Kraemer, K. L. (2005). Post-adoption variations in usage and value of e-business by organizations: cross-country evidence from the retail industry. Information systems research, 16(1), 61-84.

72. Zhu, K., Kraemer, K. L., & Xu, S. (2006). The process of innovation assimilation by firms in different countries: a technology diffusion perspective on e-business. Management science, 52(10), 1557-1576.