Artificial intelligence is reshaping business environments, generating opportunities as well as uncertainty. Research offers mixed views: some firms treat AI as a strategic asset, while others see it as costly and risky. Because competitive advantage is often linked to corporate entrepreneurship (CE), understanding how AI adoption interacts with CE has become an important research area. Yet, despite rising interest in AI and firm performance (FP), its relationship with CE remains underexplored. This Ph.D. thesis addresses this gap through four papers on Italian SMEs, examining what drives AI adoption and how it affects CE and FP in family and non-family firms. The first paper provides a quali-quantitative review of 84 Web of Science articles (1950–2024), identifying six research streams and future directions in AI-CE research. The second paper studies whether market power influences AI adoption. Using a national survey matched with firm-level markups, results show that family firms adopt AI proactively under both positive and negative performance feedback. Under high risk, they continue to invest with positive signals but restrict adoption with negative ones. Non-family firms reduce AI investment when performing well and seldom react to negative feedback, suggesting they view AI as resource-heavy rather than strategic. The third paper examines how AI adoption shapes CE using PCA. Four AI components emerge: product innovation, sustainability, energy efficiency, and consumer insight/patenting. Product-innovation AI strongly enhances CE; sustainability AI has mixed effects; energy-efficiency AI supports product and market strategies. Consumer-insight/patenting AI shows no significant link to CE. The fourth paper explores financial drivers and performance outcomes. Younger and more profitable firms adopt AI more often. After adoption, firms show stronger CE activity; new projects, product changes, and strategic repositioning, along with improvements in sales, employment, productivity, and market value. Overall, the thesis clarifies how AI adoption interacts with CE and FP, offering insights into competitiveness and entrepreneurial transformation.
L’intelligenza artificiale (AI) sta trasformando i contesti aziendali, generando al tempo stesso opportunità e incertezza. La letteratura offre risultati eterogenei: alcune imprese considerano l’AI una risorsa strategica, mentre altre la percepiscono come costosa e rischiosa. Poiché il vantaggio competitivo è spesso collegato all’imprenditorialità aziendale (corporate entrepreneurship, CE), comprendere come l’adozione dell’AI si intrecci con la CE rappresenta un ambito di ricerca rilevante. Tuttavia, nonostante il crescente interesse per AI e performance aziendale (FP), la relazione tra AI e CE rimane poco indagata. Questa tesi di dottorato colma tale lacuna attraverso quattro saggi sulle PMI italiane, analizzando i fattori che guidano l’adozione dell’AI e i suoi effetti su CE e FP nelle imprese familiari e non familiari. Il primo saggio offre una revisione quali-quantitativa di 84 articoli Web of Science (1950–2024), individuando sei filoni di ricerca e prospettive future sul tema AI-CE. Il secondo saggio esamina se il potere di mercato influenzi l’adozione dell’AI. Basandosi su un’indagine nazionale integrata con mark-up aziendali, emerge che le imprese familiari adottano l’AI in modo proattivo sia con feedback di performance positivi sia negativi. In condizioni di rischio elevato, continuano a investire con segnali positivi ma limitano l’adozione con segnali negativi. Le imprese non familiari, invece, riducono gli investimenti in AI durante fasi favorevoli e reagiscono raramente a feedback negativi, suggerendo una percezione dell’AI come risorsa onerosa più che strategica. Il terzo saggio analizza, tramite PCA, come l’AI influenzi la CE. Emergono quattro componenti: innovazione di prodotto, sostenibilità, efficienza energetica e insight/patenting. L’AI per l’innovazione di prodotto rafforza la CE; la sostenibilità mostra effetti eterogenei; l’efficienza energetica sostiene strategie di prodotto e mercato; l’AI per insight e brevetti non presenta associazioni significative con la CE. Il quarto saggio indaga i fattori finanziari e gli esiti di performance. Le imprese più giovani e profittevoli adottano più facilmente l’AI. Dopo l’adozione, crescono le attività di CE—nuovi progetti, modifiche dell’offerta, riposizionamenti strategici—insieme a miglioramenti in vendite, occupazione, produttività e valore di mercato. Nel complesso, la tesi chiarisce come l’adozione dell’AI si intrecci con CE e FP, offrendo nuove evidenze sulla competitività e sulla trasformazione imprenditoriale.
Essays on Artificial Intelligence and Firm Performance. The Italian Evidence / Baqir, M.. - (2026 Mar 25).
Essays on Artificial Intelligence and Firm Performance. The Italian Evidence
BAQIR, MUHAMMAD
2026-03-25
Abstract
Artificial intelligence is reshaping business environments, generating opportunities as well as uncertainty. Research offers mixed views: some firms treat AI as a strategic asset, while others see it as costly and risky. Because competitive advantage is often linked to corporate entrepreneurship (CE), understanding how AI adoption interacts with CE has become an important research area. Yet, despite rising interest in AI and firm performance (FP), its relationship with CE remains underexplored. This Ph.D. thesis addresses this gap through four papers on Italian SMEs, examining what drives AI adoption and how it affects CE and FP in family and non-family firms. The first paper provides a quali-quantitative review of 84 Web of Science articles (1950–2024), identifying six research streams and future directions in AI-CE research. The second paper studies whether market power influences AI adoption. Using a national survey matched with firm-level markups, results show that family firms adopt AI proactively under both positive and negative performance feedback. Under high risk, they continue to invest with positive signals but restrict adoption with negative ones. Non-family firms reduce AI investment when performing well and seldom react to negative feedback, suggesting they view AI as resource-heavy rather than strategic. The third paper examines how AI adoption shapes CE using PCA. Four AI components emerge: product innovation, sustainability, energy efficiency, and consumer insight/patenting. Product-innovation AI strongly enhances CE; sustainability AI has mixed effects; energy-efficiency AI supports product and market strategies. Consumer-insight/patenting AI shows no significant link to CE. The fourth paper explores financial drivers and performance outcomes. Younger and more profitable firms adopt AI more often. After adoption, firms show stronger CE activity; new projects, product changes, and strategic repositioning, along with improvements in sales, employment, productivity, and market value. Overall, the thesis clarifies how AI adoption interacts with CE and FP, offering insights into competitiveness and entrepreneurial transformation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


