John D. Wells

Information Systems and Management · 2003–2011

A researcher in the Science corpus: 5 papers; trusts 54 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

Whom John D. Wells trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. David Gefen
  2. Detmar W. Straub Jr.
  3. Paul A. Pavlou
  4. Marios Koufaris
  5. Adamantios Diamantopoulos
  6. V. Venkatesh
  7. Jonathan W. Palmer
  8. Philip M. Podsakoff
  9. Eleanor T. Loiacono-Mello
  10. Scott B. MacKenzie

Who trusts John D. Wells overall

  1. Hartmut Hoehle
  2. Tommy K. H. Chan
  3. Ceyda Aysuna Türkyılmaz
  4. Yong Liu
  5. V. Venkatesh
  6. Chunlin Yuan
  7. Christy M. K. Cheung
  8. Anthony Vance
  9. Arne Floh
  10. Hongxiu Li

Papers from John D. Wells's perspective

Ordered by how strongly the researchers John D. Wells trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology
  2. Structural Equation Modeling and Regression: Guidelines for Research Practice (Communications of the Association for Information Systems)
  3. Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Model (International Journal of Electronic Commerce)
  4. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies (Management Sciences)
  5. Trust and TAM in Online Shopping: An Integrated Model (MIS Q.)
  6. A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research
  7. Index Construction with Formative Indicators: An Alternative to Scale Development
  8. Common method biases in behavioral research: a critical review of the literature and recommended remedies. (Journal of Applied Psychology)
  9. Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior (Information systems research)
  10. Accounting for common method variance in cross-sectional research designs. (Journal of Applied Psychology)

Their papers