CRITICAL FACTORS FOR OIL PALM PLANTATION WORKERS ACCEPTANCE AND USE OF MECHANIZATION TECHNOVATION TOOLS

Yıl 2017, Cilt: 4 Sayı: 1, 218 - 223, 30.06.2017

Nur Syazwani Mohd Nawi Baba Mb Deros Norani Nordin Mohd Nizam Ab Rahman Ezrin Hani Sukadarin

https://doi.org/10.17261/Pressacademia.2017.537

Öz

Oil palm plantation workers, still rely on manual tools and using
mechanization technovation tools has been big issues as they rejected to use.
Thus, in emphasizing technovation tools in a human activity, this study aims to
examine several factors influencing acceptance and use technovation machine
tools in Malaysia based on the revised Unified Theory of Acceptance and Use of
Technology (UTAUT) model. A total of 126 oil palm plantation workers answer the questionnaire. This model
was analyzed using SPSS technique and conducting reliability test, correlation
analysis and regression analysis. The results reveal that performance
expectancy, facilitating condition and intention to use were supported as
important factors to accept and use of technovation. However, effort expectancy
and social influence have been rejected because not significantly influence
intention to use technovation. The results of study give implications and
suggestions to future researchers and practitioners in order to address
problems regarding technovation acceptance.  

Anahtar Kelimeler

UTAUT, technovation, mechanization tools, oil palm plantation workers, Malaysia

Kaynakça

• Adelson, J. L., & McCoach, D. B. (2010). Measuring the Mathematical Attitudes of Elementary Students: The Effects of a 4-Point or 5-Point Likert-Type Scale. Educational and Psychological Measurement, 70(5), 796–807. http://doi.org/10.1177/0013164410366694
• Alwahaishi, S., & Snásel, V. (2013). Consumers’ Acceptance and Use of Information and Communications Technology: A UTAUT and Flow Based Theoretical Model. Journal of Technology Management & Innovation, 8(2), 9–10. http://doi.org/10.4067/S071827242013000200005
• Azlina Abu Bakar, Fahmi Zaidi Abdul Razak, W. S. W. A. (2013). Assessing the effects of UTAUT and self-determination predictor on students continuance intention to use student portal. World Applied Sciences Journal, 21(10), 1484–1489. http://doi.org/10.5829/idosi.wasj.2013.21.10.2920
• Bagherinejad, J. (2006). Cultivating technological innovations in Middle Eastern countries: Factors affecting firms ’ technological innovation behaviour in Iran. Cross Cultural Management: An International Journal, 13(4), 361–380. http://doi.org/10.1108/13527600610713440
• Bin, A., & Salles-Filhoa, S. (2012). Science , Technology and Innovation Management : Contributions to a Methodological Framework. Journal of Technology Management & Innovation, 7(2), 73–86.
• Cavana, R.Y., Delahaye, B. L. & Sekaran, U. (2000). Applied Research: Qualitative and Quantitative Methods. Sydney: John Wiley & Sons Inc.
• Chi, T., & Yamada, R. (2002). Factors affecting farmers’ adoption of technologies in farming system: A case study in Omon district, Can Tho province, Mekong Delta. Omonrice, 10, 94–100. Retrieved from http://clrri.org/lib/omonrice/10-12.pdf
• Diaconu, M. (2011). Technological Innovation : Concept , Process , Typology and Implications in the Economy. Theoretical and Applied Economics, 563(10), 127–144.
• Elogie, A. A., Ikenwe, I. J., & Idubor, I. (2015). Factors influencing the adoption of smartphones by undergraduate students at Ambrose Alli University, Ekpoma, Nigeria. Information Technologist, 12(August). http://doi.org/http://www.ajol.info/index.php/ict/article/view/121119
• Govindaraju, V. C., Sundram, V. P. K., Kamil, M. H. M., Ibrahim, Z., & Ghapar, F. A. (2005). Science, Technology and Innovation in Malaysia: What Do The Key Indicators Suggest? In IRPA Seminar on (Vol.25, p.27). Putrajaya.
• King, R. N., & Rollins, T. (1995). Factors Influencing The Adoption Decision: An Analysis of Adopters and Nonadopters. Journal of Agricultural Education, 36(4).
• Kung-Teck, W., Osman, R., Pauline Swee Choo, G., & Rahmat, M. K. (2013). Understanding Student Teachers’ Behavioural Intention to Use Technology : Technology Acceptance Model (TAM) Validation and Testing. International Journal of Instruction, 6(1).
• Li, Q., Yang, D., & Chen, X. (2014). Predicting Determinants and Moderating Factors of Mobile Phone Data Flow Service Adoption. 2014 Seventh International Joint Conference on Computational Sciences and Optimization, 390–394. http://doi.org/10.1109/CSO.2014.82
• Liao, Y., Fan, Y., & Xi, Y. (2011). A Technological Innovation Management Based on the Audit. International Business Research, 4(2), 170– 174. http://doi.org/10.5539/ibr.v4n2p170
• Lim, W. M., & Ting, D. H. (2014). E-shopping : An Analysis of the Technology Acceptance Model. Modern Applied Science, 6(4), 49–62. http://doi.org/10.5539/mas.v6n4p49
• Mac Callum, K., Jeffrey, L., & Kinshuk. (2014). Factors Impacting Teachers’ Adoption of Mobile Learning. Journal of Information Technology Education, 13, 141–162. Retrieved from http://jite.org/documents/Vol13/JITEv13ResearchP141-162MacCallum0455.pdf
• Nemoto, M. C. O., Vasconcellos, E. P. G. de, & Nelson, R. (2010). The Adoption of New Technology : Conceptual Model and Application. Journal of Technology Management & Innovation, 5(4).
• Pardamean, B., & Susanto, M. (2012). Assessing User Acceptance toward Blog Technology Using the UTAUT Model. International Journal of Mathematics and Computers in Simulation, 6(27), 203–212.
• Punnoose, A. C. (2012). Determinants of intention to use eLearning based on the technology acceptance model. Journal of Information Technology Education:Research, 11(1), 301–337. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.084877639303&partnerID=tZOtx3y1
• Sargent, K., Hyland, P., & Sawang, S. (2012). Factors Influencing the Adoption of Information Technology in a Construction Business. Australasian Journal of Construction Economics and Building, 12(2), 72–86.
• Shahbaz, M., Saleem, W., Syed, A., Aslam, M., Arshad, J., Farooq, A., … Shaheen, M. (2012). Evaluating the factors responsible for slow rate of technology diffusion in Livestock Sector of South Asia and developing a framework to accelerate this process: A case study using data analysis for Pakistan’s Livestock Sector. Life Science Journal, 9(3), 23–30.
• Straub, E. T. (2009). Understanding Technology Adoption: Theory and Future Directions for Informal Learning. Review of Educational Research, 79(2), 625–649. http://doi.org/10.3102/0034654308325896
• Strong, R., Irby, T., & Dooley, L. (2013). Factors Influencing Agricultural Leadership Students’ Behavioral Intentions: Examining the Potential Use of Mobile Technology in Courses. Journal of Agricultural Education, 54(4), 149–161. http://doi.org/10.5032/jae.2013.04149
• Sun, Y., Wang, N., Guo, X., & Peng, Z. (2013). Understanding the Acceptance of Mobile Health Services: a Comparison and Integration of Alternative Models. Journal of Electronic Commerce Research, 14(2), 183–200.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478.