An empirical study on the influences on the acquisition of enterprise software decisions
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand the decision process of enterprise software acquisition. The research aims to focus on identifying significant influences on enterprise software acquisition decisions. Design/methodology/approach As a research model and theoretical background, the organizational buying model (OBB) is proposed for the acquisition of enterprise systems. Influences on enterprise software acquisition decision processes were found by an empirical study carried out from a practitioner's perspective. The study collected data via a mail survey administered to information systems (IS) professionals involved in the acquisition of enterprise software (ES). The survey questionnaire was developed based on a previous research project and a literature review. Organizational buying behavior (OBB) models in the literature served as the basis for the influences included in the survey instrument. Factor analysis was carried out on the survey data to identify the most significant factors/influences. Findings The following five factors emerged as significant influences on the acquisition decision process of enterprise software: ES strategy and performance; BPR and adaptability; management commitment and user buy‐in; single vendor integrated solution; and consultants, team‐location, and vendor's financing. These factors are discussed and managerial implications are extracted. Conclusions are derived from the study findings and guidelines for further research are suggested. Research limitations/implications The present study provides a starting point for further research in understanding a more comprehensive list of influences on enterprise software acquisition. A bigger sample from more industries is required to examine whether the significance of the influences remains stable. Originality/value Using OBB models has proven to be useful for organizations in making effective decisions on enterprise software acquisition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".