Cited by 145Technology Adoption and User BehaviourDecision Support System ApplicationsInformation Systems Theories and Implementation

Exploring the influence of perceptual factors in the success of web-based spatial DSS

Jarupathirun, Suprasith, Zahedi, Fatemeh “Mariam”·2007·Decision Support Systems

Integrity signals

Registry

Crossref · 10.1016/j.dss.2005.05.024

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • A positive task-technology-fit (TTF) environment within an organization is positively related with HRM (AI) initiation.

    “Research Proposition 2 (a-d): A positive task -technology-fit (TTF) environment within an organization is positively related with HRM (AI) (a)initiation, (b)adoption, (c)routinization, and (d)extension.”

    From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as Jarupathirun & Zahedi, 2007

  • A positive task-technology-fit (TTF) environment within an organization is positively related with HRM (AI) routinization.

    “Research Proposition 2 (a-d): A positive task -technology-fit (TTF) environment within an organization is positively related with HRM (AI) (a)initiation, (b)adoption, (c)routinization, and (d)extension.”

    From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as Jarupathirun & Zahedi, 2007

  • A positive task-technology-fit (TTF) environment within an organization is positively related with HRM (AI) adoption.

    “Research Proposition 2 (a-d): A positive task -technology-fit (TTF) environment within an organization is positively related with HRM (AI) (a)initiation, (b)adoption, (c)routinization, and (d)extension.”

    From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as Jarupathirun & Zahedi, 2007

  • A positive task-technology-fit (TTF) environment within an organization is positively related with HRM (AI) extension.

    “Research Proposition 2 (a-d): A positive task -technology-fit (TTF) environment within an organization is positively related with HRM (AI) (a)initiation, (b)adoption, (c)routinization, and (d)extension.”

    From AI-augmented HRM: Antecedents, assimilation and multilevel consequences (2023) · cites this paper as Jarupathirun & Zahedi, 2007

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Jarupathirun, S., & Zahedi, F. “.
Affiliations
University of Wisconsin–Milwaukee (US)
Corresponding author
Zahedi, Fatemeh “Mariam”
Venue
Decision Support Systems
Publisher
Elsevier BV
Published
2007-04
Volume
43
Issue
3
Pages
933-951
ISSN
0167-9236 (ISSN-L)
OpenAlex
W2039691505
Cited by
145
References
5
Citations by year
2026: 6 · 2025: 4 · 2024: 10 · 2023: 9 · 2022: 7 · 2021: 8 · 2020: 6 · 2019: 7 · 2018: 8 · 2017: 7 · 2016: 9 · 2015: 9 · 2014: 13 · 2013: 10 · 2012: 3
FWCI
8.48
Type
journal article
Language
en
Open access
closed
Locations
Decision Support Systems (publishedVersion)
Topics
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Decision Support System Applications
  • Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
Keywords
spatial decision support system, web-based decision support, perceived success, task-technology fit, goal setting theory, self-efficacy, spatial visualization, conceptual model, perceived performance, geographic information systems

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review