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Examining the eco-technological knowledge of Smart Green IT adoption behavior: A self-determination perspective

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  • Koo, Chulmo
  • Chung, Namho

Abstract

The purpose of this study is to examine the determinants of Smart Green IT adoption behavior. As opposed to IT adoption behavior, Smart Green IT adoption behavior has several distinct characteristics that are not typically found in general technologies. This study introduces self-determination theory to explain the causal relationship between users' motivations and Smart Green IT adoption behavior. The results show that eco-technological knowledge has a positive and significant impact on intrinsic motivation, integrated regulation, identified regulation, and introjected regulation. Intrinsic motivation has a positive and significant effect on four extrinsic motivations. Although the direct effect of intrinsic motivation on attitude was not significant, it did exert an effect on attitude that was mediated by identified regulation and external regulation. Our findings also show that two extrinsic motivations were significant. Attitude toward Smart Green IT use behavior was the primary predictor of continuous intention to use Smart Green IT. The effects of social influence and external regulations on continuous intention to use Smart Green IT were also significant.

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  • Koo, Chulmo & Chung, Namho, 2014. "Examining the eco-technological knowledge of Smart Green IT adoption behavior: A self-determination perspective," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 140-155.
    • Handle: RePEc:eee:tefoso:v:88:y:2014:i:c:p:140-155
    DOI: 10.1016/j.techfore.2014.06.025
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    Cited by:

    1. Koo, Chulmo & Chung, Namho & Nam, Kichan, 2015. "Assessing the impact of intrinsic and extrinsic motivators on smart green IT device use: Reference group perspectives," International Journal of Information Management, Elsevier, vol. 35(1), pages 64-79.
    2. Kusi-Sarpong, Simonov & Sarkis, Joseph & Wang, Xuping, 2016. "Assessing green supply chain practices in the Ghanaian mining industry: A framework and evaluation," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 325-341.
    3. Yu-Sheng Kao & Kazumitsu Nawata & Chi-Yo Huang, 2019. "An Exploration and Confirmation of the Factors Influencing Adoption of IoT-Based Wearable Fitness Trackers," IJERPH, MDPI, vol. 16(18), pages 1-31, September.
    4. Yujing Zhang & Yilin Luo & Xinjing Zhang & Jing Zhao, 2019. "How Green Human Resource Management Can Promote Green Employee Behavior in China: A Technology Acceptance Model Perspective," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    5. Bigerna, Simona & Micheli, Silvia & Polinori, Paolo, 2021. "New generation acceptability towards durability and repairability of products: Circular economy in the era of the 4th industrial revolution," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    6. Feng, Yuanyue & Yi, Zihui & Yang, Congcong & Chen, Ruoyi & Feng, Ye, 2022. "How do gamification mechanics drive solvers’ Knowledge contribution? A study of collaborative knowledge crowdsourcing," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    7. Garg, Aashish & Goel, Pooja & Sharma, Anuj & Rana, Nripendra P., 2022. "As you sow, so shall you reap: Assessing drivers of socially responsible investment attitude and intention," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    8. Islam Shamimul & Karia Noorliza & Fauzi Firdaus Bin Ahmad & Soliman Mohamed, 2017. "A review on green supply chain aspects and practices," Management & Marketing, Sciendo, vol. 12(1), pages 12-36, March.
    9. Verma, Pranay & Sinha, Neena, 2018. "Integrating perceived economic wellbeing to technology acceptance model: The case of mobile based agricultural extension service," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 207-216.

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