Quantitative survey reveals that eco-sustainability enhances additive manufacturing adoption in emerging economies, suggesting significant performance gains for manufacturers.
Purpose This study explores how ecological sustainability (eco-sustainability) motivations influence the adoption of additive manufacturing (AM), smart manufacturing transformation and sustainable manufacturing performance (SMP) in the manufacturing industry. It aims to bridge the gap in understanding the mechanisms that link sustainability motivations to technology adoption and performance outcomes in emerging economies. Design/methodology/approach A time-lagged survey design using a quantitative method was employed to collect data in two stages from a stratified random sample of manufacturing organizations (n = 347). A structured investigation was conducted using an online survey questionnaire among top-level professionals. SmartPLS4 was used to perform hypothesis testing and gather management insights through partial least squares structural equation modeling and importance-performance map analysis (IPMA). Findings The results indicate that eco-sustainability motivations – eco-efficiency, eco-effectiveness, eco-responsiveness and eco-legitimacy – significantly promote the adoption of AM. The adoption of AM has a positive impact on both exploitative and explorative transformations in smart manufacturing, which in turn enhances the overall SMP. Organizational inertia negatively moderates these relationships. IPMA highlights the crucial role of AM, but its relatively low performance underscores areas needing strategic improvement. Managers should integrate sustainability motivation with AM and address organizational inertia to improve sustainable manufacturing. Originality/value This study empirically verifies the dual effect of AM adoption on sustainability motivation in emerging economies, expanding the view through the lens of natural resources and ambidexterity theory. It offers new insights into organizational inertia and provides practical advice for manufacturing managers.
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Zhang et al. (2025) studied this question.
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