The micro-foundation of innovation in the post-acquisition phase
Notice bibliographique
Résumé
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
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,014 |
| Communication savante | 0,011 | 0,008 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».