The diffusion of contactless payment technologies has become a critical component of digital transformation strategies aimed at enhancing SME competitiveness in developing economies. Among these technologies, Quick Response (QR) Code Payment offers a low-cost and infrastructure-light solution, yet its adoption among SMEs remains uneven. This study investigates the determinants of QR Code Payment adoption and its subsequent effects on SMEs’ sustainability performance. Anchored in the Technology Acceptance Model (TAM) and the Resource-Based View (RBV), the proposed framework incorporates perceived usefulness, perceived ease of use, digital literacy, QR Code Payment adoption, and sustainability performance as core constructs. Integrating TAM and RBV is essential because belief-based perceptions translate into actual adoption only when supported by adequate organizational resources and capabilities, making adoption decisions the product of an interaction between what users believe and what the firm is able to execute. Survey data from 326 SMEs in the Special Region of Yogyakarta, Indonesia, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that perceived usefulness and digital literacy significantly drive QR Code Payment adoption, whereas perceived ease of use does not, suggesting that performance-oriented beliefs and capability endowments outweigh perceptions of simplicity in shaping adoption behavior. Furthermore, QR Code Payment adoption positively influences economic, social, and environmental aspects of sustainability performance. These findings highlight the strategic value of digital payment integration for advancing SME sustainability and underscore the need to strengthen digital capabilities to accelerate technological uptake. The study extends the literature by jointly applying TAM and RBV to elucidate how belief structures and firm-level capabilities interact to shape adoption outcomes and their performance implications within resource-constrained contexts. For ecosystem coordinators, aligning merchant education with simple analytics dashboards can help SMEs turn payment data into insights—underscoring the need for policy support from government, financial institutions, and payment providers to ensure QR payment adoption translates into real performance gains.
This research addresses the rapid advancements and exponential growth in academic research on the use of augmented reality (AR) and the metaverse in e-commerce. Through comprehensive bibliometric analysis, the research evaluates the performance of publications and citation metrics, uncovers influential works and collaborative networks, and explores thematic trends and researcher sentiments in this domain. Data from Scopus was analyzed using tools such as R, RStudio, BiblioShiny, VOSviewer, Tableau, and Python. In addition, sentiment analysis was conducted via Hugging Face’s DistilBERT model. The research findings highlight key themes, including the integration of AR and the metaverse in retail, online shopping, and mobile commerce, emphasizing the role of immersive technologies in transforming consumer experiences. This study identifies emerging trends and gaps, providing a roadmap for future research and strategic implementation. Sentiment analysis reveals a balanced outlook among researchers, with both enthusiasm for technological advancements and concerns over implementation challenges. The research offers valuable insights for researchers, publications, and the e-commerce industry, guiding informed decision-making, fostering innovation, and enhancing consumer experiences in the evolving landscape of AR and the metaverse in e-commerce.
The rapid growth of wealth-tech platforms has intensified the importance of digital trust, particularly among Generation Z investors who rely heavily on social media–driven information sources when making investment-related decisions. While prior studies have examined influencer marketing, electronic word-of-mouth (e-WOM), and social media engagement in fintech contexts, empirical research that integrates these persuasion mechanisms into a unified trust-based model of wealth-tech adoption intention remains limited. Drawing on Source Credibility Theory, Trust Transfer Theory, and digital engagement frameworks, this study proposes and tests an integrative model in which influencer credibility, e-WOM, and social media engagement simultaneously influence wealth-tech adoption Intention through the mediating role of digital trust. Using survey data collected from 255 Generation Z actual users of wealth-tech platforms in Indonesia, this study employs Structural Equation Modelling (SEM) to simultaneously test the measurement model and the proposed trust-based structural relationships, including the mediating role of digital trust. A purposive sampling approach was adopted to ensure respondents possessed direct experience with wealth-tech applications, thereby enhancing construct validity in this specialized digital investment context. The results indicate that influencer credibility, e-WOM, and social media engagement each exert a significant positive effect on digital trust, which in turn strongly influences wealth-tech adoption Intention. Digital trust is found to play a critical mediating role, reinforcing its central importance in investment-oriented digital platforms characterized by heightened perceived risk. This study contributes to the literature by extending digital trust and fintech adoption research in three ways: (1) by integrating multiple digital persuasion mechanisms into a single trust-centered framework, (2) by empirically validating digital trust as a key mediating mechanism in a wealth-tech investment context, and (3) by providing contextual insights from an emerging market characterized by rapid digital adoption and persistent trust challenges. Practically, the findings offer guidance for wealth-tech platforms and digital marketers in designing trust-enhancing strategies targeting Generation Z investors.
Studies on ChatGPT within the context of online consumer reviews (OCRs) have emerged as part of the broader exploration of generative AI across multiple disciplines. However, to date, no research has systematically examined the current research focus or other key aspects related to the application of ChatGPT in OCRs. To address this gap, this study conducts a systematic literature review to identify dominant research focus areas, highlight existing research gaps, and propose directions for future research. Guided by the PRISMA 2020 protocol and employing a thematic analysis approach, 22 relevant studies were analysed, revealing three overarching themes: (1) ChatGPT for review analytics, (2) ChatGPT for review modeling and evaluation, and (3) ChatGPT for review management. The findings indicate that current research primarily emphasizes ChatGPT’s potential as an analytical tool for OCR datasets, enabling the extraction of valuable and actionable insights for both marketers and researchers. In addition, the review identifies growing concern regarding fake reviews and highlights the emerging use of ChatGPT-generated synthetic reviews as datasets for developing fake review detection models, offering a practical alternative for studies facing challenges in obtaining high-quality training data. Finally, findings related to the third theme demonstrate ChatGPT’s utility in supporting managerial responses to customer reviews, providing insights into its role in enhancing customer relationship management. Overall, this review suggests that research on ChatGPT in OCRs remains at an early stage but offers significant insights and opportunities for future investigation in this emerging field.
This study examines how generative artificial intelligence (AI) is transforming public governance, shifting from process automation to policy intelligence. By comparing China and the United States, the research analyses how different governance logics such as state-led centralisation and decentralised innovation shape AI adoption in public administration. A qualitative comparative case study was conducted using information from government reports, such as the US blueprint of AI bill of rights, think tank publications, and scholarly literature. The analysis applied thematic coding to trace trajectories of AI adoption, institutional roles, governance challenges, and strategic framings, interpreted through the frameworks of Digital Governance and Adaptive Governance. Both the countries have distinct ways to integrate AI in public governance. China has organised AI integration into government portals, legal framework, and intelligent cities with high-capacity state coordination and integrated implementation mechanisms. The United States has unstructured but creative uses, and integration occurs at the agency level, ethical protection, and labour reform. Ethical issues vary by context, and while privacy and data-governance risks are on the agenda in China, bias and accountability are on the agenda in the United States. The article contributes to knowledge by drawing on the comparatively less explored paradigm of policy intelligence and presenting a comparative model that brings together structural integration and adaptive flexibility and their implications for international digital governance.
The study explores the link between digital leadership and cloud intelligence in the context of ethical artificial intelligence (EAI) in relation to three telecom operators in Jordan: Orange, Zain, and Umniah. A total of 424 e-questionnaires were also sent to managers (senior and junior) and staff. The results were processed using SmartPLS4 in PLS-SEM. These results demonstrate that we can develop improved cloud technology solutions to enhance our ethical AI capabilities. This ethical AI facilitates the mediating process in the link between digital leadership and business innovation. Findings lead telecom companies to be much more responsible and ethical in their responsiveness and trust-building with the help of AI-induced cloud intelligence. Finally, the results will summarize theoretical and empirical evidence about responsibility dimensions in AI innovation and data-intensive telecom companies. Along with other important variables such as innovation and integrity, the study emphasizes that telecom managers in today’s digital leadership era are expected to think ahead to ensure that both technological advancement and corporate social responsibility not only develop but genuinely prosper.
The Industrial Era 4.0 has seen industries start shifting towards implementing Decision Support System (DSS) in the manufacturing sector. Technological advancements have made it possible for the development of DSS to be based on Artificial Intelligence (AI) using past data generated by industry, especially in the furniture manufacturing industry. The furniture manufacturing industry is now faced with the challenge of Extreme Programming (XP) model complexity that hinders production and inventory management. The manufacturing industry finds it difficult to comprehend which industries to produce based on the current market trends. This research, therefore, seeks to comprehend how an AI-based DSS system can learn furniture model production trends. Based on such problems, this research can potentially assist in designing an AI-based DSS employing the Autoregressive Integrated Moving Average (ARIMA) model from the XP system development paradigm. This research is segmented into five phases, i.e., problem identification, decision model design, data collection and processing, system development and integration, and implementation. The delivery of this research is a list of best-selling furniture fads from market analysis generated through DSS. These findings are useful in the development of DSS, especially in AI to make predictions of furniture model trends.
This paper presents the design, development, and implementation of an offline chatbot system specialized in answering food safety-related questions, relying entirely on Vietnamese legal documents. The system employs Retrieval-Augmented Generation (RAG) to ensure accurate and contextually relevant responses without internet dependency, a critical feature for low-connectivity environments. Key highlights include robust Vietnamese language support, a flexible vector database using Chroma for seamless legal content updates, and the integration of Qwen2.5:7B-Instruct-Q4_0 as the local language model, selected after comparative testing against DeepSeek-R1, Gemma3:1B, and Mistral. Embeddings are generated using BAAI/bge-small-en-v1.5. By processing Vietnamese queries and retrieving from a localized knowledge base, the chatbot delivers reliable guidance to stakeholders such as food producers, traders, and consumers. Evaluations demonstrate high accuracy in Vietnamese Q&A, stable offline operation, and adaptability to evolving regulations, with discussions on limitations and future enhancements.
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