Internal IT Knowledge and Expertise as Antecedents of ERP System Effectiveness: An Empirical Investigation
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The literature shows that contingency factors such as organizational culture and structure, organization size, top management support, external expertise, and internal support are critical for the effectiveness of Enterprise Research Planning (ERP) systems in adopting organizations. Research on the effect of in-house computer and information technology (IT) knowledge and expertise on the success of such packages is rare. The purpose of this study was to explore the influence of computer/IT skills as antecedents of ERP system effectiveness. Using relevant theoretical foundations, a research model was developed to test eight relevant hypotheses. Data was collected in a cross-sectional field survey of 109 firms in two European countries. The partial least squares (PLS) technique was used for data analysis. The PLS results confirmed six out of the eight hypotheses. The study's conceptualization supported the view that in-house computer/IT skills are indeed pertinent to ERP system success in adopting organizations. The research implications for practice and research conclude this study. Keywords: computer knowledgeEnterprise Resource Planning (ERP)ERP post-implementation successERP system effectivenessIS success evaluationIT professionals' skillsStructural equation modeling ACKNOWLEDGEMENT The author appreciates the efforts of Prof. Birger Rapp, Airi Ifinedo, and Dr. Klas Sundberg in collecting useful data in Finland and Sweden. The author thanks all the participants associated with this research study. Special thanks go to C. M. Ringle, S. Wende, and A. Will for the use of their software, SmartPLS 2.0. The author is grateful to the Editor-in-Chief of JOCEC, Prof. C. Holsapple and three anonymous reviewers for the valuable comments and suggestions that helped to improve the overall quality of an earlier draft of this paper.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it