Can intelligent manufacturing empower manufacturing? – an empirical study considering ambidextrous capabilities

Abstract

Purpose Intelligent manufacturing has attracted extensive attention from national strategy, academic research and enterprises' practices. The purpose of this study is to investigate the influence of intelligent manufacturing on performance in manufacturing firms. Moreover, how intelligent manufacturing technology affects enterprise performance, this study provided a practice that can be replicated by other businesses. Design/methodology/approach This study uses text mining to collect the intelligence level of Chinese listed companies. It uses quantitative analysis to test the proposed model based on samples of 2,091 manufacturers. Findings Intelligent manufacturing has positive effect on short-term performance and long-term performance. Intelligent manufacturing can empower firms with ambidextrous capabilities, including exploit capability and explore capability. Exploit capability has positive effects on short-term performance and long-term performance. Explore capability has negative effects on short-term performance, but has positive effects on long-term performance. Originality/value On the theoretical side, it enriches the research framework between intelligent manufacturing and enterprise performance. This study explains the preconditions and results of ambidextrous capabilities. Moreover, based on the practice-based view (PBV), this study proposes that technologies can be used as strategies, filling a gap in the existing research on strategic management. On the practical side, how to quantify the intelligent manufacturing level of enterprises provides a certain reference. Also, this study provides an easy to imitate practice that can serve as a model for under-performing enterprises.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 30 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.