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Volume 5, Issue 3, 2026

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The sustainability of traditional cultural products relies on the capacity of producers to transform consumers' preferences into product design and marketing approaches. In the batik micro, small, and medium enterprises (MSME) context, Generation Z (Gen Z) is one of the key emerging market segments whose preferences depend not only on cultural meanings but also on symbolic value, aesthetics, usability, and relevance of the product. Despite the fact that the Theory of Planned Behaviour (TPB) is extensively applied in explaining purchase intention, only few researchers have attempted to apply this theory to help make product design and management decisions in heritage-based MSMEs. Thus, this study is intended to investigate the impact of symbolic value and aesthetic value on Gen Z’s purchase intention towards traditional batik using an extended TPB model. A quantitative research approach was chosen and 208 participants from Gen Z were surveyed in three cities, namely Yogyakarta, Palembang, and Makassar. Structural equation modelling (SEM) was applied to assess the relationships. The results reveal that symbolic value has significant positive effects on attitude (Estimate = 3.077, $p$ = 0.018), subjective norms (Estimate = 2.046, $p$ = 0.017), and perceived behavioural control (Estimate = 1.677, $p$ $<$ 0.001). On the other hand, aesthetic value don’t significantly affect subjective norms (Estimate = -1.348, $p$ = 0.104) and perceived behavioural control (Estimate = -0.994, $p$ = 0.011), but it have a significant negative effect on attitude (Estimate = -2.312, $p$ = 0.068). Attitude and behavioural control significantly influence purchase intention, whereas subjective norms do not. Purchase intention has a significant positive effect on behaviour. The model demonstrates explanatory power, with $R^2$ values of 0.72 for purchase intention and 0.70 for attitude. These findings contribute to engineering management by showing how SEM-based consumer insights as a decision-support can guide batik MSMEs in product design, product-line segmentation, pricing accessibility, and youth-oriented market adoption..

Abstract

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The fourth industrial revolution, or Industry 4.0, is fundamentally transforming manufacturing through the integration of cyber-physical systems, the Internet of Things (IoT), big data analytics, artificial intelligence, and intelligent automation. Despite its potential benefits, digital transformation remains challenging because it requires substantial investment, workforce capability development, and organizational change. Existing Industry 4.0 maturity models inadequately address systematic criteria weighting and uncertainty in digital maturity assessment, limiting their ability to provide comprehensive and decision-oriented evaluations. This study develops a seven-dimensional Industry 4.0 digital maturity framework by integrating the Analytic Hierarchy Process (AHP) and the Fuzzy Inference System (FIS). AHP is employed to derive expert-based priority weights among maturity dimensions, while FIS accommodates uncertainty and subjectivity in qualitative assessments through fuzzy reasoning. The research methodology comprises model conceptualization, criteria weighting using AHP, maturity evaluation using FIS, and validation through a case study of an automotive manufacturing company. The findings indicate that the Strategy, Culture and Expertise, and Organization and Change Management dimensions receive the highest priority weights, while Intelligent Manufacturing achieves the highest maturity score. The case organization obtained an overall maturity index of 0.73, corresponding to Stage 4, which indicates a high level of digitalization. The proposed AHP–FIS framework provides a structured, adaptive, and data-driven approach for evaluating Industry 4.0 maturity and offers decision support for prioritizing digital transformation initiatives and planning continuous improvement. The findings demonstrate the practical feasibility of the framework within the investigated automotive manufacturing context and provide methodological insights for future development of Industry 4.0 maturity assessment models.
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