Research Report: Richness Versus Parsimony in Modeling Technology Adoption Decisions - Understanding Merchant Adoption of a Smart Card-Based Payment System

By Christopher R. Plouffe, John Hulland, Mark B. Vandenbosch. Information systems research, 2001

740 citations recorded by the source corpus.

Subfields: Information Systems and Management, Management Science and Operations Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/31da33c21f2381ce88db778b190148614f055b89

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

1. Yogesh Kumar Dwivedi
2. Nripendra P. Rana
3. V. Venkatesh
4. Ali Abdallah Alalwan
5. James Y. L. Thong
6. Michael D. Williams
7. Frank K. Y. Chan
8. Timothy Teo

In Management Science and Operations Research

1. Nripendra P. Rana
2. Yogesh Kumar Dwivedi
3. Michael D. Williams
4. James Y. L. Thong
5. Frank K. Y. Chan
6. V. Venkatesh
7. Banita Lal
8. T. Falk

In Sociology and Political Science

1. Yogesh Kumar Dwivedi
2. Nripendra P. Rana
3. V. Venkatesh
4. James Y. L. Thong
5. Ali Abdallah Alalwan
6. Michael D. Williams
7. Frank K. Y. Chan
8. Kar Yan Tam

Citations in the corpus

• IT as enabler of sustainable farming: An empirical analysis of farmers' adoption decision of precision agriculture technology (2012, background): “Following others [1,36,54] the paper draws on the complementarities of technology acceptance and diffusion of innovation perspectives to enrich the PA adoption model.”
• Technology acceptance model: a literature review from 1986 to 2013 (2014, background): “Contextual Factors: contextual factors which could have a moderating effect such as gender and cultural diversity [44, 64, 75] and technology characteristics [68]; and 4.”
• The mediation of external variables in the technology acceptance model (2006, background): “TAM’s strength provides motivation to investigate aspects that have received less attention to date and that could contribute to its continued improvement [48, 59].”
• Determinants and Outcomes of Customers' Use of Self-Service Technology in a Retail Setting (2007, background): “A considerable part of the literature on SSTs examines determinants of SST acceptance (Childers et al. 2001; Curran, Meuter, and Surprenant 2003; Dabholkar 1994, 1996; Dabholkar and Bagozzi 2002; Featherman and Pavlou 2003; Plouffe, Hulland, and Vandenbosch 2001).”
• A review of the predictors, linkages, and biases in IT innovation adoption research (2006, methodology): “…electronic data interchange (e.g. Ramamurthy and Premkumar, 1995), telecommunications technologies (e.g. Grover and Goslar, 1993), DBMS (e.g. Grover and Teng, 1992), smart-card payment systems (e.g. Plouffe et al ., 2001), and computer-aided software engineering tools (e.g. Rai and Howard, 1993).”
• Evaluating the Role of Trust in Consumer Adoption of Mobile Payment Systems: An Empirical Analysis (2010, background): “Current IS studies on payment systems have mostly explored the related field of e-payment systems [Gianluigi, 2006; Plouffe et al., 2001; Varshney, 2002], especially their implementation [e.”