Market Positioning by IT Service Vendors Through Imitation
Bibliographic record
Abstract
Information technology (IT) services vendors operate in a highly competitive but also institutional environment that render their service-line offerings mutually observable. This suggests that imitation of rivals’ decisions can be an efficient means for IT vendors when reconfiguring their service-line offerings. To explore how such imitation unfolds in this sector, we estimate a series of logistic regression models of 116 IT vendors’ service-line choices over three time periods. First, from the strategic imitation literature we identify the key imitation “referents,” which is a group of firms or a single firm with specific traits, and we test the relative influence of each referent. All of our analysis includes these referents as predictors of service-line choice. Next, we tested more nuanced models using theoretically guided subsamples as follows. One, based on information systems (IS) literature, we consider the IT vendors as embedded in three distinct “institutional spheres,” each corresponding to a knowledge domain, namely, technical, functional, and vertical industry domains. We separately examine imitation in each subsample corresponding to the three types of service lines. Two, based on strategy literature, we consider that the influence of the imitation referents differs when the choice under consideration is the addition of a new service line versus a withdrawal. Our results across all of these subsamples uncover a nuanced pattern of imitation that sometimes contrasts the full-sample results. The most prominent result is that although imitation is highly salient, the different imitation referents are not universally influential across all knowledge domains and between development versus withdrawal decisions. Specifically, the imitation of similar firms is widespread, whereas the imitation of largest firms or offering popular service-lines, which indicates bandwagon effects, are at play only selectively. This study contributes to the IS literature by laying a basis for a variety of research directions including resource spillovers and vicarious learning in IT sectors.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".