Ding-Bang Luh

Artificial intelligence has become an important driver of transformation in supply chain management (SCM). Existing research has focused primarily on consumer-facing contexts and localized applications such as forecasting, inventory, and logistics. Comparatively less attention has been given to how leading manufacturing firms implement AI to support SCM processes in real-world production contexts. To address this gap, this study adopts a case study approach to examine Foxconn’s BOMate platform as an example of AI-enabled supply chain management in advanced electronics manufacturing. The current case in study focuses on the AI-driven platform design principles which integrate experts’ rules, big data and AI modeling to augment procurement decision-making. The findings illustrate that BOMate contributes to SCM in several important ways. First, it augments human decision-making by combining expert rules, accumulated sourcing knowledge, and AI-based processing within a human-AI collaborative framework. Second, it enhances sourcing efficiency by accelerating component retrieval, substitute identification, and decision support. More broadly, the case suggests that AI in Foxconn’s supply chain is not limited to localized automation, but supports system-level coordination across procurement, production, and supply chain operations. This study contributes to the literature by providing empirical insight into AI-enabled SCM in a leading manufacturing firm and highlights the strategic importance of AI in improving supply chain efficiency, resilience, and technological transformation.

Ugly–cute memes, which blend visual incongruity with cuteness, have become a prominent form of nonconventional online esthetics on social media, yet little is known about who prefers them and why. The present research examined how perfectionistic discrepancy and stress-related states relate to ugly–cute meme preference across two studies. In Study 1, 356 Chinese adults completed the Discrepancy subscale of the Almost Perfect Scale–Revised, together with measures of perceived stress, state anxiety, and preference for a pretested ugly–cute meme. The mediation model yielded a negative direct association between perfectionistic discrepancy and ugly–cute meme preference, as well as positive indirect associations through state anxiety and through the serial path from perceived stress to state anxiety. Study 2 used a randomized recall-based stress induction with 393 valid participants. Compared with participants in the neutral-recall control condition, those in the stress-recall condition reported higher state anxiety, rated ugly–cute images more favorably, and were more likely to choose an ugly–cute image in the forced-choice task. The estimated indirect effect of the stress manipulation on ugly–cute image preference through state anxiety was statistically significant. Overall, stress-related states appear to increase the appeal of ugly–cute content. Perfectionistic discrepancy, in turn, relates to this preference through opposing direct and indirect routes.

Anthropomorphism is a widely used marketing strategy, yet less is known about how baby-schema anthropomorphic cues embedded in eco-friendly logos function as compact visual identity cues to promote consumers’ green purchase intention through positive emotional attribution. Drawing on baby-schema theory and mental-state attribution, we examine the impact of anthropomorphic eco-friendly logos on green purchase intention, the mediating roles of perceived love and perceived hope, their sequential pathway, and the moderating effect of environmental attitude. A within-subjects study was conducted with 299 valid participants in China, using established and adapted scale items for data collection. Our results demonstrated that anthropomorphic eco-friendly logos significantly enhanced green purchase intention. Perceived love and perceived hope each mediated this relationship, and the sequential pathway from perceived love to perceived hope was also significant. Moreover, environmental attitude positively moderated the link between anthropomorphic logos and perceived love, with a stronger effect among consumers with higher pro-environmental attitudes. These findings highlight a positive emotional attribution mechanism through which anthropomorphic eco-friendly logo cues promote green consumption and clarify the boundary role of environmental attitude.

Background: Although smart products improve older adults’ quality of life, their acceptance and usage of smart assistive devices remain relatively low, and the influencing factors remain unclear. Methods: This study takes canes, which are commonly used by older adults, as the research object. To explore older adults’ acceptance of smart canes and the influencing factors, we constructed an integrated framework based on the extended Technology Acceptance Model (TAM), which incorporates multiple variables: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude (ATT), Social Influence (SI), Safety Trust (ST), Self-Efficacy (SE), and Behavioral Intention (BI). A questionnaire survey was conducted to collect 232 valid responses, and structural equation modeling (SEM) was employed for data analysis. Results: The results indicate that factors including PU, PEOU, and SI have significant impacts on older adults’ ATT towards using smart canes, their SE, and BI. Among these factors, ATT and SE play a mediating role between PU, PEOU, SI, and BI in using smart canes. Conclusions: The practical implications of the current results are discussed with the aim of providing empirical evidence for the development and application of smart assistive devices for older adults.

The inherent spatial inaccessibility of cliff inscriptions and the dilemma between preservation and public access have made digital dissemination essential for their legacy. Using the digital design of the “Shimen Thirteen Inscriptions” in Hanzhong as a case study, this research integrates the four-dimensional experience economy model with the Analytic Hierarchy Process (AHP) to construct a mixed evaluation framework combining expert rational weighting and user perceptual scoring. Weightings were determined by seven experts, and user experience data was collected from 198 questionnaires. The priority for platform optimization was then identified via a “weight × gap” matrix. The results show the following: (1) In digital settings, the experience structure is significantly reordered, with interactivity (44.4%) and immersion (29.8%) taking the lead. (2) Overall platform satisfaction was good (4.05 out of 5.00), but diversity of operations and depth of knowledge emerged as the main shortcomings. (3) A staged optimization scheme of “progressive interaction + hierarchical knowledge delivery” is proposed, which can enhance sustainable dissemination effectiveness without increasing technological barriers. This study proposes a sustainability-oriented strategy prototype for the digital communication of cliff inscriptions, develops second-level constructs for design and measurement support, and employs AHP-based expert weighting to prioritize strategy elements and derive design pathways. The platform functions as a research prototype for academic inquiry and methodological demonstration, without involvement in operational KPI loops or full system deployment. The contribution lies in offering a replicable user experience evaluation grid and a closed-loop optimization process, rather than advancing 3D/AR/VR techniques per se.

With the increase in the global aging population, the demand for elderly-friendly game products is growing rapidly. To address existing limitations, particularly in user demand extraction and design parameter setting, this study proposed a design framework integrating the BTM–AHP–AD–TOPSIS methods. The goal was to accurately identify the core needs of elderly users and translate them into effective design solutions. User reviews of elderly-friendly game products were collected from e-commerce platforms using Python 3.8-based web scraping. The Biterm Topic Model (BTM) was employed to extract user needs from review texts. These needs were prioritized using the Analytic Hierarchy Process (AHP) and translated into specific design parameters through Axiomatic Design (AD). Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to comprehensively evaluate multiple design schemes and select the optimal solution. The results demonstrate that the proposed design path offers a holistic method for progressing from need extraction to design evaluation. It effectively overcomes previous limitations, including inefficient need extraction, limited scope, unclear need weighting, and unreasonable design parameters. This method enhances user acceptance and satisfaction while establishing rigorous design processes and scientific evaluation standards, making it well suited for developing elderly-friendly products.

Post-stroke finger dysfunction severely impacts patients’ daily living abilities and quality of life. Traditional rehabilitation assessment methods face challenges such as high subjectivity, insufficient precision, and difficulty in capturing subtle changes. These challenges are particularly pronounced in small-sample data scenarios, where the accuracy and robustness of assessment models are limited. This study proposes an intelligent rehabilitation assessment method tailored for small-sample scenarios, combining the rehabilitation matching value (RMV) with machine learning to address the challenges of rehabilitation assessment in such contexts. A rehabilitation matching value calculation model is constructed based on existing data, and interpolation methods are employed to expand the small-sample dataset. Machine learning models are then utilized for validation. Experimental results demonstrate that the proposed method effectively captures subtle changes in finger function, significantly improving the sensitivity and accuracy of rehabilitation assessments. This provides a scientific basis for the development of personalized rehabilitation training plans. Compared to traditional methods, the proposed approach exhibits significant advantages in flexibility, practicality, and adaptability to small-sample scenarios.

In the era of artificial intelligence (AI), generative AI tools like ChatGPT 4.5 have greatly improved the ease of obtaining answers to questions, thereby diminishing the importance of memorizing declarative knowledge while increasing the significance of procedural knowledge required in the problem-solving process. However, conventional assessment approaches such as paper-based assessments still focus on assessing declarative knowledge; these are difficult to adapt to the challenges of the AI era. This study aims to explore an innovative approach for the assessment of procedural knowledge, with a specific emphasis on gamification. This study employs a comprehensive approach involving an experimental research method, a case study, and a questionnaire survey. A total of 151 undergraduate students were recruited and randomly assigned to an experimental group and a control group for the experiment. We compared performance outcomes between a gamification-based assessment and paper-based assessment. The results confirmed the effectiveness of the gamification-based assessment, demonstrating its superiority over the paper-based assessment in assessing procedural knowledge. The findings of the paper are not only applicable to assess knowledge in emergency situations such as fire safety but can also be applied to the assessment of procedural knowledge across various academic subjects within educational institutions.