Omnichannel retailers increasingly manage promotional coupons across interconnected email, mobile, web, point-of-sale (POS), and partner systems. When offer definitions, eligibility rules, and redemption logic are distributed among independently operated channel systems, inconsistencies in promotional execution, fragmented audit trails, and synchronization delays can emerge as the channel environment expands. This study investigates this architectural fragmentation and develops a centralized coupon lifecycle model for consistent promotional management across heterogeneous retail channels. A conceptual architecture was developed by drawing on enterprise application architecture, microservices design principles, and event-driven integration patterns. The resulting model separated coupon management into two coordinated layers: a declarative content layer for centralized offer definition and an orchestration layer for real-time eligibility evaluation, conflict resolution, and cross-channel execution. The architectural analysis showed that this separation established a common authority for promotional decision-making and reduced the structural conditions associated with duplicated rules, inconsistent channel execution, and fragmented decision records. The proposed architecture also provided a unified decision-logging structure that can support cross-channel auditability and provide coherent behavioral data for subsequent personalization models. These findings indicate that coupon lifecycle management is more effectively treated as an enterprise digital architecture problem than as a collection of channel-specific promotional functions. The proposed model provides a reusable architectural framework for retailers seeking to strengthen promotional governance, maintain execution consistency as channel complexity grows, and establish a structured data foundation for technology-enabled personalization.
Rapidly changing consumer preferences, consumption occasions, service experiences, and promotional practices require coffee businesses to continuously identify and evaluate emerging innovation opportunities. However, converting large volumes of unstructured social media discourse into actionable knowledge remains a major challenge, particularly for businesses with limited market research capabilities. This study investigates how social listening can support technology-enabled market sensing and product innovation in the coffee industry. Four Twitter/X corpora collected during 2023 were analyzed, comprising a global coffee corpus of 172,017 tweets and three brand-specific corpora relating to Kopi Kenangan, Starbucks, and Kopi Janji Jiwa. A Biterm Topic Model (BTM) was applied to identify latent consumer preference structures, while topic prevalence and sentiment-derived satisfaction were integrated through importance–satisfaction mapping to distinguish market-supported innovation opportunities. The global corpus yielded 13 preference topics, five of which represented opportunities associated with work-related benefits, coffee-shop experiences, drinking enjoyment, caffeine and sleep, and morning consumption. The analysis identified three opportunity topics among eight preference topics for Kopi Kenangan, five among 12 topics for Starbucks, and ten among 15 topics for Kopi Janji Jiwa. Cross-brand comparison showed that innovation opportunities extended beyond beverage attributes to include service experience, complementary products, pricing, promotions, social influence, customer segments, and collaboration channels. The findings demonstrate that social listening can function as a decision-oriented innovation mechanism rather than merely a descriptive monitoring tool. The proposed framework advances data-driven innovation research by linking digital consumer discourse to structured opportunity identification and provides a practical approach to market sensing, innovation prioritization, and low-cost experimentation for resource-constrained coffee businesses.
Digital transformation increasingly depends on enterprise integration and infrastructure platforms that connect supply chains, workforces, service providers, and business partners. However, these platforms are still commonly assessed through internal measures such as system availability, defect rates, deployment consistency, and operating costs, leaving their wider contribution to organizational resilience insufficiently examined. This study investigates how integration and infrastructure governance practices support resilience during large-scale enterprise transformation. A multiple-case study was conducted across five transformation programs in manufacturing, food and agriculture, and financial services. Governance practices and documented program outcomes were examined through structured evidence mapping across four dimensions: supply continuity, operational dependability, transition disruption, and business continuity across extended stakeholder networks. The analysis found that standardized integration patterns, lifecycle controls, and Integration Center of Excellence (CoE) governance were associated with fewer integration defects and sustained reliability across business-critical operations. Phase-gate migration governance, dependency mapping, validation cycles, and rollback provisions supported cloud migrations and provider transitions without reported service degradation. Hybrid cloud architecture, disaster recovery (DR) design, and multi-vendor coordination also maintained service continuity across partner and customer networks, while platform modernization produced substantial cost savings and improved operational visibility. The findings indicate that enterprise integration and infrastructure governance function as organizational capabilities rather than secondary technical controls. The study presents a practice-derived framework that connects governance mechanisms with resilience outcomes and supports investment decisions concerning digital transformation in operationally critical industries.
Social media provides a rich source of consumer-generated data that can support product innovation decisions. However, firms still face difficulties in transforming unstructured online discussions into measurable insights that are linked to market success. This study proposes a data-driven social media framework for product innovation by integrating text mining, Principal Component Analysis (PCA), and Principal Component Regression (PCR). The framework identifies dominant consumer attribute discussions, reduces correlated attributes into interpretable latent components, and tests their relationship with standardized sales indicators. The study uses two competitive smartphone products, Apple iPhone 8 and Samsung Galaxy Note 7, as empirical cases. Text mining results show that consumers discussed visual, ecosystem, connectivity, battery, charging, camera, and platform-related attributes across both products. PCA results indicate that a small number of principal components can explain most of the variance in consumer discussions. For the iPhone 8, the first two components explain 79% of the total variance, while for the Galaxy Note 7, they explain 81%. The regression results show strong links between selected social media-derived components and market success. For the iPhone 8, Principal Component (PC) 1 has the strongest relationship with standardized sales, with an R² of 0.956. For the Galaxy Note 7, PC7 shows the strongest relationship, with an R² of 0.889, while other components show both positive and negative relationships. These findings show that social media discussions can provide early signals of consumer needs, product risks, and market response. The proposed framework offers a systematic tool for attribute prioritization, launch monitoring, and evidence-based product development.
This study examines the emerging dark side of Artificial Intelligence (AI) in marketing by addressing how AI-driven personalization shapes consumer perceptions of intrusiveness, privacy, and trust. While AI enhances personalization and customer experience, it simultaneously raises concerns about surveillance and loss of autonomy, creating a fundamental tension referred to as the Intrusiveness Paradox. To investigate this issue, a systematic literature review (SLR) was conducted following established review protocols, analyzing 56 peer-reviewed journal articles published between 2022 and 2026. The study combines bibliometric mapping with a structured synthesis framework to identify dominant themes, theoretical foundations, research contexts, and methodological patterns. The findings reveal three primary research streams: emotional and psychological drivers such as perceived creepiness and human-like system design; the trade-off between privacy concerns and trust in data-driven personalization; and the impact of these factors on marketing outcomes including customer experience and brand attitudes. The results show that increased personalization and anthropomorphic design often intensify perceptions of surveillance, reduce trust, and trigger resistance among consumers. Despite rapid growth in this field, literature remains fragmented and heavily reliant on short-term and experimental approaches, with limited attention to longitudinal and real-world contexts. The study concludes that the negative consequences of AI are not isolated effects but interconnected responses reflecting a deeper tension between personalization and autonomy. By integrating these perspectives, the study contributes a unified conceptual understanding of the Intrusiveness Paradox and highlights the importance of transparent, ethical, and balanced AI design. These insights provide guidance for both researchers and practitioners seeking to develop AI systems that enhance value while preserving consumer trust and autonomy.
Under the implementation of the Healthy China strategy, the scientific and technological innovation performance of regional public hospitals should be evaluated not only by research outputs but also by the coordinated performance of innovation efficiency, resource allocation equity, and innovation value creation. Building upon previously established multi-objective linear programming optimization results and using the same sample of regional public hospitals, the driving factors and synergistic mechanisms underlying innovation performance optimization were further investigated. The results indicated that the proportion of highly qualified medical professionals, research funding intensity, and technology transfer capability constituted the principal driving forces for improving scientific and technological innovation performance. Significant synergistic relationships were identified among innovation efficiency, resource allocation equity, and innovation value. Furthermore, optimization of the indicator weight structure was shown to substantially enhance overall innovation performance while maintaining structural balance and resource allocation efficiency under constrained conditions. Accordingly, an integrated implementation framework was proposed in which dynamic adjustment of performance indicator weights, precision allocation of innovation resources, and collaborative promotion of technology transfer and research commercialization were emphasized to achieve sustainable improvements in innovation performance. The proposed framework provides a theoretical basis and practical policy guidance for optimizing innovation governance, improving scientific resource allocation, and strengthening the sustainable innovation capacity of regional public hospitals. The findings also contribute to the development of a systematic performance optimization framework for public healthcare institutions operating under multi-objective decision-making environments.
Under the objective of establishing a manufacturing powerhouse, promoting deep integration of intelligent and green manufacturing has become a key initiative for achieving transformation and upgrading of the manufacturing industry. Exploring the internal logic of digital finance for executing such an integration is of paramount importance for the high-quality development of China’s economy. While provincial panel data from 2011 to 2023 were collected as research samples, this paper employed a two-way fixed effects model to systematically and empirically examine the impact of digital finance on the proposed integration. The results demonstrated that digital finance formed a positive synergistic mechanism with environmental regulations and foreign direct investment to amplify its effect in propelling the integration of intelligent and green manufacturing. The transformation of scientific achievements and technological innovations also serves as the strategic propelling force in the integration process. This study provided empirical evidence and policy references for leveraging digital financial tools, improving the multi-policy synergy system, and accelerating the integration of intelligent and green manufacturing to achieve the “dual carbon” goals.
The advent of artificial intelligence chatbots such as ChatGPT has revolutionized the field of education by offering convenient information accessibility, although accompanied by worrying concerns about the cultivation of critical thinking skills. Nevertheless, there is a lack of extensive research regarding the extent to which learners can develop critical thinking skills in a certain discipline through the utilization of ChatGPT. This research aims to evaluate the capability of ChatGPT to demonstrate critical thinking in its responses, particularly in the domain of cybersecurity. Its objective is to conduct a complete assessment of the analytical capacities of ChatGPT, considering its growing integration into educational settings. In this connection, ChatGPT was presented with a series of inquiries with increasing levels of complexity within the intricate realm of cybersecurity. The responses were subjected to analysis using Lee’s Model of Thinking Levels, which involved categorizing them into “recall”, “rationalization”, or “reflectivity”. The findings suggested that ChatGPT exhibited a prominent level of critical thinking skills, especially in the authentic contexts.
The development of research, innovation, and entrepreneurship (RIE) competencies has been positioned as a strategic priority within Saudi Arabia’s Vision 2030; however, a persistent discrepancy between awareness and active engagement remains insufficiently characterised. In this study, the levels of RIE awareness, perceptions, and experiential participation among university students in Saudi Arabia, with particular reference to the Eastern region, were systematically examined, and their statistical associations with competency development were evaluated. A cross-sectional survey design was employed, in which data were collected from 301 students during April–May 2025 using a validated 24-item, five-point Likert-scale instrument encompassing five constructs: RIE awareness, influencing factors, perceptions and attitudes, educational experiences, and sustainability orientation. High internal consistency was demonstrated (Cronbach’s α = 0.89–0.93), and construct validity was assessed through exploratory factor analysis (EFA). Descriptive statistics indicated that RIE awareness was moderately high (M = 3.54, SD = 1.00), whereas a pronounced participation gap was observed: although 56.6% of respondents reported involvement in research activities, substantially lower engagement was recorded in innovation and entrepreneurship initiatives (24.9%) and start-up activities (19.2%). Perceived importance of RIE for future career development was high (M = 4.13), yet awareness of entrepreneurial mindset constructs remained comparatively limited (M = 3.15). Significant positive correlations were identified among the principal constructs (Spearman’s ρ = 0.666–0.902, p < 0.001), although potential inflation effects attributable to shared measurement items were noted and critically considered. Ordinal logistic regression analysis revealed that participation in research projects and exposure to structured educational experiences constituted the most robust predictors of RIE competency development, surpassing attitudinal variables in explanatory power. These findings suggest that favourable perceptions alone are insufficient to foster competency acquisition in the absence of sustained experiential engagement. It is therefore implied that higher education institutions should prioritise the integration of practice-oriented RIE programmes, strengthen mentorship quality, and enhance transparency in resource accessibility, with policy interventions oriented towards capability development rather than motivational reinforcement. The study provides an empirically grounded baseline for assessing RIE competencies in emerging higher education contexts and offers a transferable measurement framework applicable to Gulf and comparable innovation-driven economies.
Deep neural network-based English handwriting recognition has revolutionised the security or verification system of today; nevertheless, there are certain risks that are inevitable. This paper examines the status of the present technologies in the handwriting recognition systems, and more particularly, by the various deep neural network architectures. It also evaluated common cybersecurity risks such as data poisoning, model inversion, and adversarial attacks, which can be devastating to such systems, as well as common privacy and ethical issues. The potential regulatory compliance and mitigation measures that can be taken to avert these risks and hurdles are also addressed in detail, with requisite emphasis being made on the future outlook of a more secure handwriting recognition system.
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.
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.
Submit to JoRIT
Review for JoRIT
Propose a Special Issue 
Open Access
News 
Journal Metrics
Journal Overview
Associated Journals