IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

To Evaluate or Not to Evaluate?: A Two-Process Model of Innovation Adoption Decision Making

To Evaluate or Not to Evaluate?: A Two-Process Model of Innovation Adoption Decision Making
View Sample PDF
Author(s): Nan (Tina) Wang (Eastern Illinois University, Charleston, USA)
Copyright: 2018
Volume: 29
Issue: 2
Pages: 20
Source title: Journal of Database Management (JDM)
Editor(s)-in-Chief: Keng Siau (City University of Hong Kong, Hong Kong SAR)
DOI: 10.4018/JDM.2018040103

Purchase

View To Evaluate or Not to Evaluate?: A Two-Process Model of Innovation Adoption Decision Making on the publisher's website for pricing and purchasing information.

Abstract

Using information processing theory (IPT) as the theoretical lens and incorporating various literatures following the IPT lens (e.g., dual-threshold in signal detection), this article develops a two-process model of innovation adoption decision making, accounting for the possibility for potential adopters (at different levels) to make adoption decisions (adopt, do not adopt) with or without an intensive evaluation of the innovation. Specifically, this article proposes that there is an attention process prior to the extensively investigated intensive evaluation process; potential adopters may make adoption decisions (adopt, do not adopt) at the end of the attention process or defer making decisions until after an intensive evaluation is conducted. The impacts of innovation attributes on various influence targets (i.e., relative advantage belief strength, adoption threshold and rejection threshold) during the less examined attention process are also discussed. This article may contribute to the innovation adoption literature and provide practical implications for innovation proponents/detractors regarding how to craft sensegiving messages influencing potential adopters' decision making.

Related Content

Pasi Raatikainen, Samuli Pekkola, Maria Mäkelä. © 2024. 30 pages.
Zhongliang Li, Yaofeng Tu, Zongmin Ma. © 2024. 25 pages.
Zongmin Ma, Daiyi Li, Jiawen Lu, Ruizhe Ma, Li Yan. © 2024. 32 pages.
Lavlin Agrawal, Pavankumar Mulgund, Raj Sharman. © 2024. 37 pages.
Jizi Li, Xiaodie Wang, Justin Z. Zhang, Longyu Li. © 2024. 34 pages.
Amit Singh, Jay Prakash, Gaurav Kumar, Praphula Kumar Jain, Loknath Sai Ambati. © 2024. 25 pages.
Ruizhe Ma, Weiwei Zhou, Zongmin Ma. © 2024. 21 pages.
Body Bottom