Tech and grow! Unraveling the interplay between industry 4.0 technologies and supply chain performance: marketing strategy alignment as a moderator

Shafique Ur Rehman (), Stefano Bresciani (), Qingyu Zhang () and Bernardo Bertoldi ()
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Shafique Ur Rehman: Shenzhen University
Stefano Bresciani: University of Torino, Corso Unione Sovietica
Qingyu Zhang: Shenzhen University
Bernardo Bertoldi: University of Torino, Corso Unione Sovietica

International Entrepreneurship and Management Journal, 2024, vol. 20, issue 2, No 30, 1347-1376

Abstract: Abstract This research observes the relationship between industry 4.0 technologies (i.e., Internet of Things or IoT, machine learning, blockchain technology, and big data) and supply chain performance (SCP) with the mediating role of technological intensity in manufacturing firms using a resource-based view (RBV). Marketing strategy alignment is used as a moderator between technological intensity and SCP. The PLS-SEM is used, and 340 responses are used for the final analysis, collected using a simple random sampling technique. Multiple regressions were used to test hypotheses. IoT, blockchain technology, and technological intensity improve SCP. In contrast, machine learning and big data do not directly influence SCP. IoT, machine learning, big data, and blockchain technology significantly determine technological intensity. Besides, technological intensity significantly mediates industry 4.0 technologies and SCP. Finally, marketing strategy alignment strengthens the relationship between technological intensity and SCP. This research recommends that management implement industry 4.0 technologies, technological intensity, and marketing strategy alignment to improve SCP. This initial research examines industry 4.0 technologies, technological intensity, and marketing strategy alignment influence on SCP by using RBV.

Keywords: Industry 4.0 technologies; Technological intensity; Marketing strategy alignment; Supply chain performance; Resource-based view (search for similar items in EconPapers)
JEL-codes: L1 L10 M1 O33 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11365-024-00957-7

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