A contingency model for creating value from RFID supply chain network projects in logistics and manufacturing environments
Bibliographic record
Abstract
In the growing literature on RFID and other network technologies, the importance of organizational transformation at the supply chain level has been recognized. However, the literature lacks conceptual model development and salient mechanisms for achieving the level of organizational transformation required for stakeholders to realize the full business benefits from RFID projects. Furthermore, the RFID adoption, use, and impact studies to date largely focus on a single firm setting and on the retail sector. Therefore, this study intends to fill this knowledge gap in the literature, and develop a contingency model for creating value from RFID supply chain projects in logistics and manufacturing environments. For our model development, we draw upon extant diverse literatures; particularly the framework for IT-enabled business transformation (Venkatraman, 1994), and leadership and organizational learning. The framework postulates a positive relationship between the level of organizational transformation effected by the use of information technology (IT) and the level of business benefits realized from IT. The contingency model draws on the framework and explicates five contingency factors influencing value creation from RFID supply chain projects: environmental upheaval; leadership; second-order organizational learning; resources commitment; and organizational transformation. Using the contingency model as a conceptual guide, we also perform an analysis of longitudinal real-world case data from a Canadian third-party logistics service firm’s seven-layer supply chain RFID projects. The case study analysis provides evidence for the imperative of the contingency factors identified in the model for creating value from the RFID projects. Furthermore, it also reveals the differential costs for the focal firm and the up-stream manufacturing as a key barrier to realizing the full RFID benefits at the supply chain level.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".