Challenges and Opportunities of Integrating Service Science with Data Science and Artificial Intelligence

Topic Information

Dear Colleagues,

The global economy and numerous organizations are evolving to become more service-oriented. More research has recently been conducted on the provision of services, particularly with a cross-disciplinary approach. Beyond the service-oriented architecture (SOA), scientific knowledge from various domains is essential to achieve service excellence for the increasingly complex requirements of a rapidly evolving global environment, including computer science (CS), data science (DS), artificial intelligence (AI), industrial and systems engineering, management sciences, marketing sciences, as well as culture transformation and integration methods based on beliefs, assumptions, principles, and values among different organizations and humanities. In particular, with the current facilitation of big data and massive data integration opportunities, data science has fueled AI with the necessary sources of information as the basis for reasoning, while AI has provided the analysis techniques for DS to excel. Much of such cross-disciplinary knowledge has been widely applied for the excellence of service provision, which is currently one of the biggest global business sectors that emphasizes segmentation, customization, and personalization.

This Topic intentionally seeks scientists, engineers, educators, industry people, policymakers, decision-makers, and others with insight, vision, and an understanding of the significant challenges and opportunities involved. We also aim to help communicate and disseminate relevant recent research across disciplines, cultures, communities, and applications. This Topic welcomes an array of approaches and epistemologies, including qualitative, quantitative, and mixed methods, as well as established methodologies such as action, participatory, evaluation, design, and development. Topics of interest include, but are not limited to, the following:

• Big data and social media analytics for service science;
• Intelligent analytics for business, marketing, logistics, organizations, and customer services;
• Socio-data analytics, bibliometrics, ontology, and linked data;
• Sustainability issues with data science and service science;
• Data science applications in education, social sciences, humanities, and other emerging domains;
• Intelligent analytics and knowledge engineering;
• Recommendation systems, information retrieval, and reputation/rating systems;
• Data-driven technology innovation and service design;
• Digitalization for intelligent analytics;
• Machine learning, neural networks, deep learning, and large language models for service excellence;
• Data Science for the Internet of Things, blockchain, the cloud, service computing, and other emerging service paradigms;
• Adoption, diffusion, applications, innovations, management, and governance of data science and service science;
• Security, privacy, reliability, education, information integrity, system development, adoption, and policies in data science and service science;
• Large Language Models (LLM) approaches to big data processing and analytics.

Dr. Dickson K. W. Chiu
Dr. Stuart So
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	6.5	7.3	2020	20.4 Days	CHF 1800	Submit
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
Electronics electronics	2.9	7.0	2012	14.8 Days	CHF 2400	Submit
Information information	4.3	8.2	2010	18.7 Days	CHF 1800	Submit
Systems systems	3.8	5.4	2013	19.8 Days	CHF 2400	Submit
Technologies technologies	5.2	6.7	2013	17 Days	CHF 1800	Submit

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Published Papers (7 papers)

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23 pages, 1645 KB  

Open AccessArticle

Toward an Effective Organizational Adaptation in Multi-Agent Systems: A Model Based on Markov Decision Processes

by Narimane Sahel, Varun Gupta, Toufik Marir, Maroua Bouzid and Chetna Gupta

Systems 2026, 14(7), 741; https://doi.org/10.3390/systems14070741 - 26 Jun 2026

Viewed by 285

Abstract

Coordinating agents in dynamic and uncertain environments remains a fundamental challenge in multi-agent systems (MAS) research, particularly in contexts where the composition of agent organizations directly affects overall system performance. While significant effort has focused on task allocation and individual agent planning, predicting [...] Read more.

Coordinating agents in dynamic and uncertain environments remains a fundamental challenge in multi-agent systems (MAS) research, particularly in contexts where the composition of agent organizations directly affects overall system performance. While significant effort has focused on task allocation and individual agent planning, predicting the systemic impact of organizational changes and selecting optimal organizational structures under uncertainty remain less explored in MAS. This paper addresses this challenge by introducing a decision-making framework that models structural reorganization as a Markov Decision Process (MDP), where actions represent organizational structures rather than individual agent behaviors, and organizational selection is guided by the anticipated impact on the overall system state. The proposed model captures the stochastic dynamics of multi-agent intervention and diverse agent capabilities through a probabilistic transition function, while a reward function guides the selection of coalition structures that maximize operational effectiveness. The framework is solved using value iteration and evaluated on the RoboCup Rescue simulation platform. Results show that the derived optimal policy identifies, at each decision step, an appropriate coalition structure that reduces system degradation while efficiently utilizing available agents. Full article

(This article belongs to the Topic Challenges and Opportunities of Integrating Service Science with Data Science and Artificial Intelligence)

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29 pages, 424 KB  

Open AccessArticle

Optimizing University Administrative Services with Generative AI: Evidence from Email Inquiry Reduction and Assistant Performance

by Antonio Julio López-Galisteo

Information 2026, 17(6), 587; https://doi.org/10.3390/info17060587 - 12 Jun 2026

Viewed by 318

Abstract

The integration of Generative Artificial Intelligence (GenAI) in higher education has opened new possibilities for optimizing administrative and academic services, particularly in contexts characterized by high-demand communication processes. Within the framework of service science, this study addresses the challenge of efficiently managing high [...] Read more.

The integration of Generative Artificial Intelligence (GenAI) in higher education has opened new possibilities for optimizing administrative and academic services, particularly in contexts characterized by high-demand communication processes. Within the framework of service science, this study addresses the challenge of efficiently managing high volumes of email inquiries in a university master’s program, aiming to improve service quality and operational efficiency. The study examines the implementation of GenAI-based assistants, specifically NotebookLM and custom Gem AI assistants, trained in regulatory, curricular, and historical data from the University Master’s in Teacher Training at Rey Juan Carlos University. A mixed analytical approach is adopted, combining elements of data science to quantify efficiency gains and service science to analyze organizational and service-related transformations. The implementation of GenAI assistants contributes to improved response times, enhanced accuracy of information provided, and a reduction in administrative workload. The results suggest that GenAI can support the scalability and quality of academic administrative services when integrated within a structured service framework. However, its effective adoption requires careful consideration of ethical, organizational, and governance dimensions to ensure sustainable and responsible implementation. Full article

(This article belongs to the Topic Challenges and Opportunities of Integrating Service Science with Data Science and Artificial Intelligence)

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35 pages, 3091 KB  

Open AccessArticle

Modeling Healthcare Accessibility with Endogenous Search Ranges: A Huff-Based Multi-Source Data Approach

by Weijie Chen, Yifei Mao, Tunan Xu, Yibing Wang, Zhengfeng Huang, Markos Papageorgiou and Pengjun Zheng

Systems 2026, 14(5), 571; https://doi.org/10.3390/systems14050571 - 17 May 2026

Viewed by 326

Abstract

This study proposes a Behavior-Calibrated Endogenous Choice 2SFCA (BCEC-2SFCA) framework for assessing spatial accessibility to tertiary hospitals. Using large-scale taxi trajectory data from Ningbo, China, we empirically calibrate the Huff model parameters ($\alpha =1.1758, \beta =2.9608$) [...] Read more.

This study proposes a Behavior-Calibrated Endogenous Choice 2SFCA (BCEC-2SFCA) framework for assessing spatial accessibility to tertiary hospitals. Using large-scale taxi trajectory data from Ningbo, China, we empirically calibrate the Huff model parameters ($\alpha =1.1758, \beta =2.9608$) based on observed hospital choices and construct travel time and distance matrices from observed trips. Unlike existing Huff-based FCA approaches that assume parameter values, BCEC-2SFCA jointly estimates the attractiveness elasticity and distance-decay coefficient directly from local healthcare travel behavior and integrates these calibrated probabilities into a 2SFCA structure where hospital catchments are endogenously generated rather than exogenously imposed. Compared with conventional Gaussian 2SFCA, the BCEC-2SFCA model produces a continuously varying and behaviorally plausible accessibility surface and better replicates the relative order of hospital attractiveness ($\rho =0.527, p<0.05$), although its RMSE is slightly higher (0.02700 vs. 0.02211) while MAPE is clearly lower (32.17% vs. 42.12%). Robustness checks using all 22 hospitals confirm stable estimates, and subgroup analyses show consistent advantages across hospital scales. The framework is specifically designed for high-order medical services with strong inter-facility competition—such as tertiary hospitals—and its applicability to proximity-based services is limited. Full article

(This article belongs to the Topic Challenges and Opportunities of Integrating Service Science with Data Science and Artificial Intelligence)

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33 pages, 2117 KB  

Open AccessArticle

Digital Transformation and AI Readiness in Public Knowledge Ecosystems: Assessing Digital Maturity in European Public Libraries

by Ioana Cornelia Cristina Crihană and Josef Rebenda

Technologies 2026, 14(5), 304; https://doi.org/10.3390/technologies14050304 - 15 May 2026

Viewed by 643

Abstract

This paper discusses how digital transformation takes place in public knowledge institutions by examining public libraries as socio-technical service ecosystems, and conceptualizes digital maturity. Based on Service-Dominant Logic and the socio-technical systems theory, this study explores digital maturity as a natural product of [...] Read more.

This paper discusses how digital transformation takes place in public knowledge institutions by examining public libraries as socio-technical service ecosystems, and conceptualizes digital maturity. Based on Service-Dominant Logic and the socio-technical systems theory, this study explores digital maturity as a natural product of convergence in technological infrastructures, professional expertise, governance mechanisms, and community involvement. The data analysis is conducted on a structured 48-item questionnaire which, at its turn, is based on a sample of 101 members of library staff in public libraries in Romania. The Romanian dataset is contextualized by using a national comparative dataset comprising 363 respondents from France. We employ a mixed method of descriptive and inferential statistical analyses and thematic coding in order to investigate institutional adaptability, AI readiness, and service development trends. The results reveal the continuing movement from collection-centered models toward hybrid physical–digital service platforms and differences in digital maturity and overall strategic planning among institutions. The results demonstrate that digital maturity is sensitive to the organized coordination and the planning capability in institutions rather than to isolated technological adoption. Drawing from this evidence, the study proposes an analytical framework and a tempered analytical lens for interpreting digital transformation processes in public knowledge ecosystems, forming a solid foundation for more general investigations of institutional adaptation to digitally mediated environments. Full article

(This article belongs to the Topic Challenges and Opportunities of Integrating Service Science with Data Science and Artificial Intelligence)

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25 pages, 4156 KB  

Open AccessArticle

Building Trustworthy Digital Archival Services: A Deep Semantic Auditing Approach Based on SHAP Interpretability

by Lihang Feng, Zhengyang Cao, Lili Sun, Yongshi Jin, Jiantao Shi and Dong Wang

Electronics 2026, 15(6), 1147; https://doi.org/10.3390/electronics15061147 - 10 Mar 2026

Viewed by 418

Abstract

In the context of the cross-disciplinary integration of data science and archival management, archival openness auditing stands as a critical process for public information access but faces challenges in processing long texts with sparse core information. To address this, this paper proposes an [...] Read more.

In the context of the cross-disciplinary integration of data science and archival management, archival openness auditing stands as a critical process for public information access but faces challenges in processing long texts with sparse core information. To address this, this paper proposes an Assisted Archival Auditing Model (ALC-MCFN) based on deep semantic understanding and decision transparency. The model aims to leverage intelligent analytics to optimize the decision-making process of archival openness. Regarding deep semantic understanding, a semantic-aware dynamic truncation mechanism is first employed to effectively remove redundancy while preserving key logical structures. Subsequently, by fusing global, local, and logical semantic features extracted by BERT, TextCNN, and TextGCN, the model overcomes the limitations of single-view feature representation. Furthermore, to address the “black box” issue of deep learning in compliance auditing, the SHAP method is introduced to provide post hoc interpretability. By visualizing the contribution of key textual features to the auditing results, the model enhances the transparency and trustworthiness of decision-making. Experimental results demonstrate that ALC-MCFN outperforms mainstream baseline models, with a 77.21% F1-score on the self-built archival domain OParchives dataset (1.15 percentage points higher than the BERT baseline), providing robust data science support for risk control and efficiency improvement in intelligent archival management. Full article

(This article belongs to the Topic Challenges and Opportunities of Integrating Service Science with Data Science and Artificial Intelligence)

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24 pages, 1009 KB  

Open AccessArticle

HiSem-RAG: A Hierarchical Semantic-Driven Retrieval-Augmented Generation Method

by Dongju Yang and Junming Wang

Appl. Sci. 2026, 16(2), 903; https://doi.org/10.3390/app16020903 - 15 Jan 2026

Viewed by 2401

Abstract

Traditional retrieval-augmented generation (RAG) methods struggle with hierarchical documents, often causing semantic fragmentation, structural loss, and inefficient retrieval due to fixed strategies. To address these challenges, this paper proposes HiSem-RAG, a hierarchical semantic-driven RAG method. It comprises three key modules: (1) hierarchical semantic [...] Read more.

Traditional retrieval-augmented generation (RAG) methods struggle with hierarchical documents, often causing semantic fragmentation, structural loss, and inefficient retrieval due to fixed strategies. To address these challenges, this paper proposes HiSem-RAG, a hierarchical semantic-driven RAG method. It comprises three key modules: (1) hierarchical semantic indexing, which preserves boundaries and relationships between sections and paragraphs to reconstruct document context; (2) a bidirectional semantic enhancement mechanism that incorporates titles and summaries to facilitate two-way information flow; and (3) a distribution-aware adaptive threshold strategy that dynamically adjusts retrieval scope based on similarity distributions, balancing accuracy with computational efficiency. On the domain-specific EleQA dataset, HiSem-RAG achieves 82.00% accuracy, outperforming HyDE and RAPTOR by 5.04% and 3.98%, respectively, with reduced computational costs. On the LongQA dataset, it attains a ROUGE-L score of 0.599 and a BERT_F1 score of 0.839. Ablation studies confirm the complementarity of these modules, particularly in long-document scenarios. Full article

(This article belongs to the Topic Challenges and Opportunities of Integrating Service Science with Data Science and Artificial Intelligence)

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25 pages, 5787 KB  

Open AccessArticle

Digital Exposure and Emotional Response: Public Discourse on Mandatory IP Location Disclosure in Chinese Social Media

by Yuehan Lu, Zerong Xie, Dickson K. W. Chiu and Eleanna Kafeza

Systems 2025, 13(11), 975; https://doi.org/10.3390/systems13110975 - 1 Nov 2025

Cited by 1 | Viewed by 3493

Abstract

This study examines the evolving use of social software to combat online disinformation by investigating Weibo users’ attitudes toward IP location disclosure as a measure of transparency and trustworthiness. We analyzed 49,579 posts (April 2022 to May 2023) from Weibo users about IP [...] Read more.

This study examines the evolving use of social software to combat online disinformation by investigating Weibo users’ attitudes toward IP location disclosure as a measure of transparency and trustworthiness. We analyzed 49,579 posts (April 2022 to May 2023) from Weibo users about IP location disclosure, categorized the topics using LDA topic modeling within the frameworks of communication privacy management, the networked public sphere, and digital democracy, and conducted sentiment analysis. We constructed separate semantic networks for positive and negative terms to examine co-occurrence patterns. The results show that Weibo users are generally negative about this policy, as IP location may reveal personally identifiable information about individuals involved in discussions of online social/political events. Mandatory transparency, while intended to enhance accountability, functions as a mandatory visibility regime that reshapes privacy boundaries and undermines inclusive deliberation. The findings contribute to the exploration of the impact of government-mandatory information privacy disclosure policies on the implementation of platform functionality, as well as changes in user sentiment, information behavior, and components of social media discourse. Full article

(This article belongs to the Topic Challenges and Opportunities of Integrating Service Science with Data Science and Artificial Intelligence)

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