A Model of Organizational Integration, Implementation Effort, and Performance
Bibliographic record
Abstract
The notion of integration is central to the understanding of organizations in general as well as of contemporary phenomena such as e-commerce, virtual organizations, virtual teams, and enterprise resource planning (ERP) implementation. Yet, the concept of integration is ill-defined in the literature, and the impact of achieving high levels of integration is not well understood. The present paper addresses these issues. Drawing on the literature of several fields, this paper proposes the concept of organizational integration (OI), which is defined as the extent to which distinct and interdependent organizational components constitute a unified whole. Six types of OI are identified: two intraorganizational OI (internal-operational, internal-functional) and four interorganizational OI (external-operational-forward, external-operational-backward, external-operational-lateral, and external-functional). This paper then presents a model and develops 14 propositions to predict (1) the effort needed to implement different types of OI, (2) the impact different types of OI will have on organizational performance, and (3) how six factors (interdependence, barriers to OI, mechanisms for achieving OI, environmental turbulence, complexity reduction mechanisms, and organizational configurations) influence the relationship between OI types, implementation effort, and organizational performance. The OI framework and model are then used to develop 14 propositions for ERP implementation research and to explain the findings of recent research on integration.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".