The tourism industry is accelerating towards the integration of immersion and intelligence, but the existing service system is difficult to give consideration to real-time interaction and emotional depth. Based on the five-dimensional interactive framework of perception-response-immersion-emotion-memory, this paper constructs a closed-loop design scheme of virtual digital people in the cultural travel scene. Firstly, multi-sensors are used to collect tourists' voices, gestures, and position streams in parallel to form high-frequency multi-modal input. Secondly, an adaptive weight decision pipeline is designed to align and dynamically weight heterogeneous features. Thirdly, the Long Short-Term Memory-Attention prediction model is proposed to visualize the interaction effect by three-dimensional thermal map. Finally, comparative experiments are used to verify the experience improvement of the proposed method. The research provides a systematic design paradigm and quantitative evaluation ideas for digital people-driven cultural travel services.
This study adopts a conceptual and design-oriented approach to investigate blockchain integration into accounting informatization for improving transparency, reliability, and intelligence in enterprise financial services. A blockchain-based big data model is developed using distributed ledger, consensus, and encryption mechanisms to support secure and consistent financial data sharing. A personalized feedback system empowered by smart contracts and data analytics delivers real-time customized accounting information for service-oriented decision-making. Exploratory independence and reliability assessments examine data consistency and system robustness. Preliminary evidence indicates that the proposed framework tends to reduce data deviations, support decision efficiency, and improve user satisfaction over conventional systems. This study contributes to the service-oriented transformation of accounting informatization and offers a design framework for intelligent data-driven financial management systems enabled by blockchain.
This study adopts a conceptual and design-oriented approach to investigate blockchain integration into accounting informatization for improving transparency, reliability, and intelligence in enterprise financial services. A blockchain-based big data model is developed using distributed ledger, consensus, and encryption mechanisms to support secure and consistent financial data sharing. A personalized feedback system empowered by smart contracts and data analytics delivers real-time customized accounting information for service-oriented decision-making. Exploratory independence and reliability assessments examine data consistency and system robustness. Preliminary evidence indicates that the proposed framework tends to reduce data deviations, support decision efficiency, and improve user satisfaction over conventional systems. This study contributes to the service-oriented transformation of accounting informatization and offers a design framework for intelligent data-driven financial management systems enabled by blockchain.
Digital transformation in the service sector is often hampered by a disconnect between technological investment and performance gains. This study addresses this core information resource management (IRM) challenge by introducing a "Dual-Cycle Orchestration" framework. Grounded in the Resource-Based View and Dynamic Capabilities Theory, the framework posits that operational efficiency stems from synergizing the internal reconstruction of digital resources with the external acquisition of complementary resources. Analyzing panel data from Chinese A-share listed companies (2012-2024) through text mining and econometric models, the authors find that internal digital intensity boosts efficiency, an effect significantly amplified by mature regional digital infrastructure. Notably, the transformation path and sensitivity to infrastructure differ between manufacturing and service industries. The findings offer actionable IRM guidance for service enterprises and strategically aligning digital resource integration with external ecosystems and industry-specific contexts.
E-commerce increasingly involves adopting advanced technologies and digital transformation in the retail sector, notably including awareness of and intentions to adopt emerging innovations such as the metaverse. This study explores intentions to adopt metaverse technology for e-commerce by examining the impact of the key constructs of the UTAUT model on both hedonic and utilitarian value. The study was conducted in the context of e-commerce consumers in Palestine. A structured questionnaire was used to gather data from 344 shoppers. The study's results revealed that effort expectancy, social influence, and facilitating conditions had a positive impact on both hedonic and utilitarian value. Hedonic value had a greater impact than utilitarian value on intentions to adopt the metaverse in e-commerce. This finding underscores the selective influence of techno-stress on shoppers' adoption of the metaverse.
E-commerce increasingly involves adopting advanced technologies and digital transformation in the retail sector, notably including awareness of and intentions to adopt emerging innovations such as the metaverse. This study explores intentions to adopt metaverse technology for e-commerce by examining the impact of the key constructs of the UTAUT model on both hedonic and utilitarian value. The study was conducted in the context of e-commerce consumers in Palestine. A structured questionnaire was used to gather data from 344 shoppers. The study’s results revealed that effort expectancy, social influence, and facilitating conditions had a positive impact on both hedonic and utilitarian value. Hedonic value had a greater impact than utilitarian value on intentions to adopt the metaverse in e-commerce. This finding underscores the selective influence of techno-stress on shoppers’ adoption of the metaverse.
Amid the logistics industry’s digital transformation, vocational logistics training bases face “high investment but low utilization” and inefficient resource sharing services. This study proposes a service-centric solution integrated with information systems, featuring cloud-edge-end architecture and digital twin technology for real-time resource perception, standardized data fusion, and “virtual-real complementary” teaching services. It adopts multi-objective programming for dynamic resource scheduling and a Six Sigma-based closed-loop service system—including DMAIC-model quality monitoring and a contribution-driven incentive mechanism. Empirical analysis of 500 equipment sharing records shows the solution reduces critical service defects by over 65%, boosts resource turnover rate to 34.2%, and elevates user satisfaction to 4.7/5. This research achieves Pareto improvement of regional logistics training resources and provides a feasible reference for information system application in the vocational education service sector.
This study explores the sustainability bottlenecks such as “data islands”, lack of user feedback mechanisms, and service homogeneity that are common when artificial intelligence is applied in community service information systems. This study conducts empirical analysis based on the 2023 operation data of community service platforms in three typical Chinese cities and usage logs from over 1,200 residents. A “multi-loop branched closed loop mechanism” collaborative optimization mechanism was proposed and piloted, and the service performance was significantly improved through institutionalized data fusion architecture, dynamic classification of service nodes, and human-machine collaborative feedback closed-loop (user satisfaction increased by 15.7%, and the average response time was shortened by 34.1%). The approach is not only applicable to smart community scenarios, but its core logic-embedding technology into institutionalized collaborative processes to enable sustainable smart services-provides a migratable methodological contribution to a wider range of service information systems.
Digital transformation in the service sector is often hampered by a disconnect between technological investment and performance gains. This study addresses this core information resource management (IRM) challenge by introducing a “Dual-Cycle Orchestration” framework. Grounded in the Resource-Based View and Dynamic Capabilities Theory, the framework posits that operational efficiency stems from synergizing the internal reconstruction of digital resources with the external acquisition of complementary resources. Analyzing panel data from Chinese A-share listed companies (2012-2024) through text mining and econometric models, the authors find that internal digital intensity boosts efficiency, an effect significantly amplified by mature regional digital infrastructure. Notably, the transformation path and sensitivity to infrastructure differ between manufacturing and service industries. The findings offer actionable IRM guidance for service enterprises and strategically aligning digital resource integration with external ecosystems and industry-specific contexts.
This study explores how artificial intelligence literacy and emotional feedback (affective tone, responsiveness, and emotional appropriateness) impact citizens’ intention to continue using digital human agents in government services. Based on survey data from 300 citizens, the study integrates the CASA paradigm and social presence theory. The findings indicate that emotional appropriateness and affective tone fully mediate the effect of artificial intelligence literacy on citizens’ intention to continue using digital human agents, while responsiveness has no significant effect. These results investigate the important role of emotional resonance and professionalism in promoting ongoing engagement with digital government services.
Amid information saturation and aesthetic pluralism, artistic design services grapple with inefficient manual workflows and imbalanced creative diversity-semantic fidelity. To address these and advance information system integration in design, this study proposes a two-stage multi-task generative AI framework for artistic design, integrating latent space remapping, hierarchical cross-modal attention distillation, and dynamic resource scheduling. Evaluated on a 30,000-sample dataset, the framework outperforms baselines: 45% lower FID than GAN-based models, 15% higher CLIP-Score for text-image alignment, over 4.3/5 professional designer satisfaction, and 1.2 iterations/second inference on a single 3080Ti GPU. It resolves existing generative AI flaws and advances human-AI collaboration in design services, laying technical groundwork for workflow innovation, design education support, and brand development.
AI digital humans are increasingly used as intelligent self-service interfaces in global service sectors such as customer support, tourism, and digital government. However, their effectiveness is often limited by poor cultural adaptability, leading to miscommunication and reduced user trust. This study proposes a cross-cultural intelligent adaptation model that integrates context awareness, cultural filtering, and user feedback to enhance service interactions across cultural boundaries. Empirical results from multicultural user tests show significant improvements in communication accuracy and customer satisfaction. The findings offer practical guidance for service organizations deploying culturally sensitive AI agents to improve cross-border customer experience.
Post-pandemic tourist hotels confront simultaneous demand contraction and cost escalation. Drawing on value-chain theory and service-sector information systems (IS) literature, the authors propose a data-driven cost-control framework that links front-desk, housekeeping, food-and-beverage, and back-office workflows. A multi-input/multi-output panel was first constructed from listed-hotel annual reports and on-site surveys; cost-efficiency and driver weights were then simultaneously estimated by Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP). Relative efficiency scores reveal that 32% of properties operate under increasing returns to scale, while cross-departmental collaboration contributes 1.7-times more to total-factor productivity than isolated technological investment. Hidden resource waste-undetectable under traditional accounting-is quantified and traced to three inter-process hand-offs, providing IS-based improvement paths.
Under digital transformation, Financial Shared Service Centers (FSSCs) are evolving from administrative units to strategic enablers. Drawing on case studies from manufacturing and internet firms (2015-2022), this paper identifies three key limitations: process inflexibility in complex scenarios, disconnected financial-business data flows, and delayed risk response. To address these, the authors propose a framework-"hierarchical processes, intelligent tools, and closed-loop risk management." It automates routine tasks via RPA and OCR, reserves expert teams for complex cases, and embeds machine learning and big data analytics into workflows for real-time monitoring. Results show nearly 50% reduction in processing time, 70% fewer annual risk incidents, and over 25% higher cost savings. When processes and technologies are stably integrated, FSSCs enhance not only efficiency but also strategic decision support. This study offers a practical pathway for digital transformation in enterprise service systems, highlighting how information systems align operational automation with strategic agility.
This study examines Shanghai-and Shenzhen-listed service enterprises from 2016 to 2024, constructing a three-dimensional synergistic framework of digital resources-process reengineering-value enhancement. Combining event studies, two-stage data envelope analysis-Malmquist analysis, and Likert scale surveys, it reveals the mechanisms underlying digital transformation and business model optimization in service enterprises. Findings indicate that when digital capabilities, organizational learning, and customer engagement form a closed-loop system, business models evolve diversely toward platformization, subscription-based models, and solution-oriented approaches. Industry digital maturity acts as an intermediary factor, while governance structures and human capital serve as key moderating variables. Dynamic efficiency evolution exhibits a technology-first, process-second pattern, and highly mature enterprises that adopt subscription models more readily capture policy dividends. This study provides decision-making references for digital strategy and business model optimization in service enterprises.
This study examines Shanghai- and Shenzhen-listed service enterprises from 2016 to 2024, constructing a three-dimensional synergistic framework of digital resources–process reengineering–value enhancement. Combining event studies, two-stage data envelope analysis–Malmquist analysis, and Likert scale surveys, it reveals the mechanisms underlying digital transformation and business model optimization in service enterprises. Findings indicate that when digital capabilities, organizational learning, and customer engagement form a closed-loop system, business models evolve diversely toward platformization, subscription-based models, and solution-oriented approaches. Industry digital maturity acts as an intermediary factor, while governance structures and human capital serve as key moderating variables. Dynamic efficiency evolution exhibits a technology-first, process-second pattern, and highly mature enterprises that adopt subscription models more readily capture policy dividends. This study provides decision-making references for digital strategy and business model optimization in service enterprises.
With smart tourism shifting from “search-price comparison” to “search-and-order,” platforms must respond within milliseconds to dynamic contexts like weather and traffic. Using 2.4 million user logs and 20,000 questionnaires, the authors propose a three-level architecture: (1) a multi-granularity Transformer-Encoder unifies long-term interests and short-term intent via spatiotemporal attention;(2) a gradient-aligned distillation layer compresses high-dimensional sparse context into 512 dimensions, achieving 42ms latency with 97% entropy retention; and (3) a Pareto-aware Contextual Bandit dynamically balances CTR, conversion, and merchant fairness. Experiments show that the new framework is improved by 12.4% and 9.7% on NDCG@10 and MAP@20, respectively. The 14-day online A/B test shows that CTR is improved by 15.3%, the order conversion rate is improved by 9.8%. The recall rate of cold start attractions can still be maintained at 0.43. This study provides a low-cost and portable paradigm and lays the foundation for real-time context modeling of smart tourism.
Post-pandemic tourist hotels confront simultaneous demand contraction and cost escalation. Drawing on value-chain theory and service-sector information systems (IS) literature, the authors propose a data-driven cost-control framework that links front-desk, housekeeping, food-and-beverage, and back-office workflows. A multi-input/multi-output panel was first constructed from listed-hotel annual reports and on-site surveys; cost-efficiency and driver weights were then simultaneously estimated by Data Envelopment Analysis (DEA) and Analytic Hierarchy Process (AHP). Relative efficiency scores reveal that 32% of properties operate under increasing returns to scale, while cross-departmental collaboration contributes 1.7-times more to total-factor productivity than isolated technological investment. Hidden resource waste—undetectable under traditional accounting—is quantified and traced to three inter-process hand-offs, providing IS-based improvement paths.
Under digital transformation, Financial Shared Service Centers (FSSCs) are evolving from administrative units to strategic enablers. Drawing on case studies from manufacturing and internet firms (2015–2022), this paper identifies three key limitations: process inflexibility in complex scenarios, disconnected financial-business data flows, and delayed risk response. To address these, the authors propose a framework—“hierarchical processes, intelligent tools, and closed-loop risk management.” It automates routine tasks via RPA and OCR, reserves expert teams for complex cases, and embeds machine learning and big data analytics into workflows for real-time monitoring. Results show nearly 50% reduction in processing time, 70% fewer annual risk incidents, and over 25% higher cost savings. When processes and technologies are stably integrated, FSSCs enhance not only efficiency but also strategic decision support. This study offers a practical pathway for digital transformation in enterprise service systems, highlighting how information systems align operational automation with strategic agility.
Sentiment analysis is vital for actionable insights in service sectors, but Korean sentiment analysis faces challenges from the language’s agglutinative morphology, honorifics, code-mixing, and limited interpretable models. This study proposes an “interpretable-visible-migratable” deep learning framework for multi-domain Korean text in service scenarios, integrating subword preprocessing with feedback, a dual-channel BiGRU-Att for joint sentiment polarity-intensity optimization, and a multi-layer explainability system. A 30,000-sentence FAIR-compliant Korean dataset is also released. Experiments show 90.9% polarity accuracy, 0.38 intensity MAE, and 8.6/10 transparency satisfaction, balancing performance and interpretability to enhance sentiment analysis applicability in service sector information systems.
