Examining the impact of deep learning technology capability on manufacturing firms: moderating roles of technology turbulence and top management support

Sheshadri Chatterjee (), Ranjan Chaudhuri (), Demetris Vrontis () and Thanos Papadopoulos ()
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Sheshadri Chatterjee: Indian Institute of Technology Kharagpur
Ranjan Chaudhuri: National Institute of Industrial Engineering (NITIE)
Demetris Vrontis: University of Nicosia
Thanos Papadopoulos: University of Kent

Annals of Operations Research, 2024, vol. 339, issue 1, No 7, 163-183

Abstract: Abstract Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms’ productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm’s predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems.

Keywords: Deep learning; Data science; Predictive maintenance; Anomaly detection; Smart manufacturing system (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-021-04505-2

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