Strategy, IT and control @ eBay, 1995-2005
Bibliographic record
Abstract
Purpose – This paper aims to adapt Simons’ (1995b) theory of the role of information technology (IT) in shaping and facilitating the levers of control (i.e. the Levers of Control Applied to Information Technology – LOCaIT) as a framework for investigating how eBay’s business strategy was realized through its management control system (MCS) in the first 10 years of the online auction market. Design and method – The qualitative method uses data from public record interviews, teaching cases, books, Securities and Exchange Commission filings and other archival sources to longitudinally trace the realization of eBay’s strategy through its MCS and IT. Findings – Realizing its strategy through the eBay MCS necessitated a diagnostic control system unlike any previously seen. This system created a close-knit online community and enabled buyers and sellers to monitor one another’s performance and trustworthiness. Research limitations and implications – The LOCaIT theory facilitated understanding the core aspects of the realization of eBay’s strategy through its MCS and IT. However, LOCaIT largely omits the strong linkages evident among elements of the MCS, the importance and necessity of building a core IT infrastructure to support eBay’s strategy and the central role of building consumer trust in the realization of this strategy. Practical and social implications – eBay’s MCS is now, perhaps, the world’s most widely imitated model for creating online trust and user interactions (e.g. Yelp, TripAdvisor, Amazon). In addition, eBay’s MCS was “sold” as a consumer product that was instrumental in facilitating consumer trust in the online auction market. Originality/value – Contributions include: tracing the creation, growth and evolution of, perhaps, the world’s largest and most widely imitated MCS, which redefined the boundaries of accounting systems monitoring; and testing the range, usefulness and limitations of Simons’ LOCaIT theory as a lens for understanding eBay’s use of IT in their MCS.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".