Bibliographic record
Abstract
Purpose The importance of involving subject matter experts (SMEs) in ERP implementations is well established. SMEs' knowledge of business and system processes are critical to conducting gap analyses and configuring enterprise systems. But what happens to SMEs on completion of the implementation phase? Prior qualitative research found that some organizations return SMEs to their old department, which can contribute to knowledge transfer; while other organizations retain the services of SMEs, to assist in ongoing efforts with support and enhancement of the systems. The purpose of this study is to understand post‐implementation organizational choices – when SMEs are retained and returned. The aim is to understand these choices relative to the goals of their project. Theoretically, organizations that return SMEs move toward a distributed or hybrid model, while organizations that retain SMEs employ a centralized functional‐support structure. In accordance with contingency theory, these structural choices should align with an organization's goals and measures of success. Design/methodology/approach This research conceptually builds on prior qualitative research, but is still exploratory in nature. The authors report on findings from an online survey conducted with 65 organizations. The sample included small, medium and large firms. Respondents were key decision‐makers in their organization's ERP initiatives (directors and managers) recruited from two user‐group associations (higher education and health care), primarily from the USA and Canada. Descriptive statistics and t‐tests (when appropriate) were utilized to analyze and report the findings. Findings The hybrid structure (neither completely centralized nor decentralized) was utilized most often (66 percent of the organizations in the sample). The organization's original goals and measures of success did not seem to dictate the final organizational structure, as would be predicted by contingency theory. The authors interpret this as an indication that the choice of structural form is not easily explained based on goals and objectives. They conjecture that devising a structural approach to supporting such a complex inter‐functional system such as ERP requires solving many complex simultaneous organizational problems. Research limitations/implications This research involves a small sample of 65 organizations and is exploratory in nature; hence, it may not be projectable to a larger population. Future research should supplement this study with more industry user groups, expand the sample size, and utilize more advanced statistical methods. Originality/value Previous research has focused on successfully implementing ERP, neglecting post‐implementation design. This study contributes to a growing body of work with regard to post‐implementation design, taking into consideration SMEs and reporting structure, goals, and measures of success utilizing contingency theory as the backdrop.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".