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..

Digital systems increasingly require real-time mechanisms that can detect interaction risks and regulate interface responses under variable user behaviour. However, behavioural sensing, probabilistic error prediction, intervention control, and user experience (UX) evaluation are rarely integrated within a single experimentally validated system. This study investigates a systems engineering framework for predicting user errors and governing adaptive UX interventions. A four-week controlled crossover experiment was conducted with 84 users stratified equally by interface experience. The experiment comprised 168 sessions, 1,008 task instances, and 161,616 validated interaction events. Logistic regression, XGBoost, recurrent neural network, and transformer models were evaluated through participant-isolated nested cross-validation. The transformer achieved the strongest predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.941 (95% confidence interval (CI): 0.937–0.945), an F1-score of 0.889, and a Brier score of 0.110. Model-triggered intervention reduced the mean task-level error rate from 0.280 ± 0.059 to 0.160 ± 0.050 and shortened task completion time from 145.2 ± 13.1 s to 117.8 ± 12.0 s. The intervention also improved the System Usability Scale (SUS), User Experience Questionnaire (UEQ), and Net Promoter Score (NPS) by 16.1, 0.23, and 21.43 points, respectively, while reducing the NASA Task Load Index (NASA-TLX) by 11.8 points. Mean end-to-end system latency was 65.4 ms, with a 95th-percentile latency of 93.8 ms. Decision-cost analysis identified an error probability of 0.70 as the preferred intervention threshold within the tested sensitivity corridor. The results indicate that user-error prediction can be incorporated into a closed-loop monitoring and control architecture without disrupting real-time interaction. The framework provides an experimentally grounded basis for managing predictive interventions in adaptive digital systems.

Immersive technologies are increasingly used by cultural institutions to create context-sensitive visitor experiences, yet conventional media branding pipelines rely largely on predefined visual assets and provide limited support for real-time adaptation. This study investigates how generative artificial intelligence (AI) can be integrated into an adaptive systems architecture while preserving institutional visual identity. A mixed-method design was employed, comprising an analysis of immersive branding pipelines, case studies of five cultural institutions, the development of two prototype application scenarios, and an evaluation by nine experts. The proposed architecture connected contextual data acquisition, generative processing, constraint validation, immersive rendering, and user feedback within a closed-loop workflow. A Validator module was introduced to examine generated outputs against predefined color and geometric constraints and to initiate regeneration or fallback procedures when violations were detected. The case analysis produced a mean adaptivity score of 4.2 out of 10 for the existing implementations. Expert evaluation of the proposed architecture yielded mean scores of 4.78 for personalization, 4.56 for visual identity flexibility, and 3.89 for brand consistency. Generation latency ranged from 1.2 to 1.8 s in the augmented reality (AR) scenario and from 2.5 to 4.0 s in the virtual reality (VR) scenario. The findings indicate that generative AI can be incorporated into a feedback-controlled branding pipeline without removing deterministic control over core visual elements. The proposed architecture provides a systems engineering basis for coordinating content generation, identity validation, and immersive delivery, while the observed latency and limited evaluation sample identify priorities for edge deployment and larger-scale experimental validation.

Energy storage systems (ESS) play a central role in renewable energy integration, grid reliability, and the transition toward low-carbon energy systems. In Malaysia, however, the indicators used to evaluate ESS remain fragmented, limiting comparison across technologies and weakening the evidence available for investment, policy, and sustainable supply chain decisions. This study investigates how ESS performance has been evaluated in the Malaysian energy transition and develops a structured framework for linking engineering performance with sustainable supply chain management (SSCM). A systematic review of 40 eligible studies was conducted using bibliometric mapping and thematic analysis. The reported indicators were identified, coded, and classified into technical, economic, operational, and policy/environmental dimensions. The results showed that capacity and sizing were the most frequently reported indicators, followed by renewable energy integration and system reliability or availability. Battery-based systems dominated the reviewed literature, particularly in photovoltaic (PV)-coupled applications, whereas long-duration storage, grid-scale services, lifecycle assessment, and end-of-life considerations received limited attention. Although levelized cost of energy (LCOE) and net present cost (NPC) were commonly reported, none of the retained studies explicitly evaluated the levelized cost of storage (LCOS). The findings indicate that current assessment practices remain concentrated on project-level technical and financial performance and provide insufficient support for evaluating material sourcing, lifecycle impacts, regulatory conditions, and supply chain resilience. The proposed framework connects ESS performance evaluation with technology selection, investment appraisal, supplier assessment, environmental management, and policy planning. It provides a systematic basis for developing national performance benchmarks and supports more consistent ESS decision-making in Malaysia and other Association of Southeast Asian Nations (ASEAN) energy systems.