An Evaluation of Technological, Organizational and Environmental Determinants of Emerging Technologies Adoption Driving SMEs’ Competitive Advantage
Notice bibliographique
Résumé
This research evaluates the technological, organizational, and environmental determinants of emerging technologies adoption represented by Artificial Intelligence (AI) and Internet of Things (IoT) driving SMEs’ competitive advantage within a resource-based view (RBV) theoretical approach supported by the technological-organizational-environmental (TOE)-framework setting. Current literature on SMEs competitive advantage as outcome of emerging technologies in the technological, organisational, and environmental contexts presents models focused on these contexts individual components. There are no models in the literature to represent the TOE framework as an integrated structure with gradual levels of complexity, allowing for incremental evaluation of the business context in support of decision making towards emerging technologies adoption supporting the firm competitive advantage. This research gap is addressed with the introduction of a new concept, the IT resource-based renewal, underpinned by the RBV, and supported by the TOE framework for providing a holistic understanding of the SMEs strategic renewal decision through information technology. This is achieved through a complex measurement model with four level constructs, leading into a parsimonious structural model that evaluates the relationships between IT resource-based renewal, and emerging technologies adoption driving SMEs competitive advantage. The model confirms the positive association between the IT resource-based renewal and emerging technologies adoption, and between the IT resource-based renewal and SME competitive advantage for the SMEs managers model, with the SME owners model outcomes are found not being supportive towards emerging technologies adoption driving SME competitive advantage. \nAs methodology, PLS-SEM is used for its capabilities of assessing complex paths among model variables. Analysis is done on three models, one for the full sample, with two subsequent ones for owners and managers, respectively, as SME decision makers, with data collected using a web-based survey in Canada, the UK, and the US, that has provided 510 usable answers. This research has a theoretical contribution represented by the introduction of the IT resource-based renewal concept, that integrates the RBV perspective and the TOE framework for supporting organization’s decision on emerging technologies adoption driving SMEs competitive advantage. As practical implications, this thesis provides SMEs with a reference framework on adopting emerging technologies, offering SME managers and owners a comprehensive model of hierarchical factors contributing to SMEs competitive advantage acquired as outcome of AI and IoT adoption. This research makes an original contribution to the enterprise management, information systems adoption, and SME competitive advantage literature, with an empirical approach that verifies a model of emerging technologies adoption determinants driving SMEs competitive advantage.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 0,000 |
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 tête enseignante, 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 ».