Premium
A Self‐Tuning Model for Smart Manufacturing SMEs: Effects on Digital Innovation
Author(s) -
Del Giudice Manlio,
Scuotto Veronica,
Papa Armando,
Tarba Shlomo Y.,
Bresciani Stefano,
Warkentin Merrill
Publication year - 2021
Publication title -
journal of product innovation management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.646
H-Index - 144
eISSN - 1540-5885
pISSN - 0737-6782
DOI - 10.1111/jpim.12560
Subject(s) - ambidexterity , agile software development , business , digital transformation , multinational corporation , industrial organization , adaptation (eye) , sample (material) , order (exchange) , structural equation modeling , knowledge management , process management , computer science , chemistry , physics , software engineering , finance , chromatography , machine learning , world wide web , optics
With the changing way people live, communicate, and work, enterprises are striving to shift their existing business model into a “self‐tuning” one. Enterprises are becoming more agile, adaptive, and ambidextrous in order to boost innovation in the current digital transformation era. Nowadays, “digital innovation” is closely associated with Industry 4.0 enablers and smart enterprises. Prior research has shown that while multinational enterprises—across many sectors—have already embraced the aforementioned advancements, their adoption by small and‐medium‐sized enterprises (SMEs) has so far taken place mainly in the manufacturing sector. Thus, based on a sample of 280 self‐tuned smart manufacturing SMEs and having utilized the structural equation modeling (SEM), this study was aimed to investigate how digital innovation is influenced by the three pillars of self‐tuning models—agility, adaptation, and ambidexterity. Our paper has focussed on the digital systems in which SMEs, spurred by networking and open innovation solutions, operate and innovate in response to external triggers, displaying a balance between exploration and exploitation, and a strong agile capacity.