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Record W7020912080

The micro-foundation of innovation in the post-acquisition phase

2023· dissertation· en· W7020912080 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2023
Typedissertation
Languageen
FieldSocial Sciences
TopicDevelopment, Ethics, and Society
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhase (matter)Work (physics)Production (economics)Field (mathematics)Context (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

The literature on the innovation impact of technological acquisition has focused on the deal-specific properties (e.g., knowledge and cultural fit) between an acquirer and its target; the innovation capabilities of the two parties (i.e., acquirer and target); and the managerial capabilities of the acquirer. Although the literature has provided insights into this issue, it has been limited in explaining why technological acquisition often fails to deliver the expected innovation outcomes despite a high level of complementary fit between the two parties. The literature has also not provided an answer to how the knowledge of the two parties is synthesized in the postacquisition phase.To address these problems, I investigate inventors’ innovation behaviors in the postacquisition phase. Inventors, as repositories of technological knowledge and sources of innovation capability, play a vital role in knowledge synthesis processes by collaborating with other inventors. However, inventors’ interests and concerns do not always align with their organizations’ strategic goals (e.g., innovation synergy). Although previous studies have emphasized the integration of the workforce of the two parties for knowledge synthesis processes, their discussions have still focused on which of the target inventors remain and how acquirers retain human capital in the postacquisition phase. This study argues that the role of the human capital of the two parties in a knowledge synthesis process in the post-acquisition phase must be better understood. As a knowledge synthesis mechanism between the two parties in the postacquisition phase, I suggest postacquisition joint inventions in which the inventors of an acquirer and those of its target collaborate and synthesize their knowledge after the acquisition deal. The main research question of this dissertation is as follows: Which inventors (from the acquirer or the target) participate in postacquisition joint inventions, and under what conditions do they do so? The acquirer and target inventors have different stances and perceptions toward acquisition because of the asymmetric relationships between the two parties. I take different theoretical perspectives to investigate the participation behaviors of each side’s inventors.This dissertation consists of three main chapters. In the first chapter, I review the existing research on the innovation aspects of technological acquisition and studies on employees in the acquisition context. In the second chapter, I focus on the innovation behaviors of acquirer inventors. I explore how increased competitive crowding influences their participation in postacquisition joint inventions. In the third chapter, I investigate how job security threat from external and internal factors influences the target inventors’ participation in postacquisition joint inventions. The analysis results show that inventors who are confident in their positions and who are best able to synthesize their knowledge with the acquisition partner’s inventors are least likely to participate in postacquisition joint inventions. This dissertation contributes to the literature on the acquisition, social networks, niche and crowding, human capital, and microfoundation of innovation

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.014
Scholarly communication0.0110.008
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0210.002

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.

Opus teacher head0.040
GPT teacher head0.342
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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