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How Chatbots’ Anthropomorphism Affects User Satisfaction: The Mediating Role of Perceived Warmth and Competence 1

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22nd Wuhan International Conference on E-business, WHICEB 2023

作者： Zheng, Tianqi Duan, Xingyu Zhang, Kang Yang, Xiangcheng Jiang, Yi China University of Geosciences Hubei Wuhan430078 China

ISBN: (数字)9783031323027

ISBN: (纸本)9783031323010

Chatbots are widely employed in various areas as an important product of artificial intelligence techniques because they can simulate human conversations and satisfy business demands in various circumstances. However, users do not always appear to be satisfied with chatbot anthropomorphism. This study proposes a dual pathway by which the anthropomorphism of chatbots affects user satisfaction from a theoretical perspective of task technology fit, and explores the key boundary conditions of individual characteristics and task characteristics in shaping user satisfaction. To test our study hypotheses, we conducted a social survey and a laboratory experiment. We evaluated the parallel mediation effect of perceived warmth and perceived competence via an online survey. In addition, users with high social phobia can enhance the effect of anthropomorphism on perceived warmth. In the laboratory study, we used 2 (anthropomorphic: high anthropomorphic vs. low anthropomorphic) x 2 (task creativity: high creative vs. low creative) between subjects who were asked to buy clothes via an e-commerce platform chatbot designed specifically for this study. They then completed an online survey to evaluate their experience. The results showed that the anthropomorphic chatbot induced higher perceptual abilities when performing creative tasks. The current study not only contributes to the literature on AI user satisfaction but also provides directions for the application of task-technology fit theory to human-computer interaction. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human computer interaction

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Categorizing UX Aspects for Voice User Interfaces Using the Kano Model 1

引用

18th International Conference on Web information Systems and Technologies, WEBIST 2022

作者： Kölln, Kristina Klein, Andreas M. Deutschländer, Jana Winter, Dominique Rauschenberger, Maria Faculty of Technology University of Applied Sciences Emden/Leer Emden Germany Department of Computer Languages and Systems University of Seville Seville Spain University of Siegen Siegen Germany

ISBN: (数字)9783031430886

ISBN: (纸本)9783031430879

Although voice user interfaces (VUIs) are widely available, they currently face challenges such as low adoption rates and user concerns. Users assess products through user experience (UX) aspects. Thus, knowing UX aspects for VUIs and their prioritization will improve UX and reduce challenges. In this study, we use a user-centered mixed-methods approach to identify and prioritize UX aspects of VUIs. Thereby, we identified 32 VUI UX aspects from the perspective of intensive users. We then applied the Kano model to categorize these UX aspects for which we analyzed N= 195 VUI users. One thing we found was that 21 VUI UX aspects are distinctively prioritized, such as privacy, data security, and ad-free as must-be, and simplicity, comprehension, and error-free as one-dimensional. These findings can help VUI developers to prioritize specific UX aspects according to their target group’s needs, enabling them to create VUIs that benefit and excite their users. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： User interfaces

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DBSpark: A System for Natural Language to SPARQL Translation 17th

DBSpark: A System for Natural Language to SPARQL Translation

引用

17th International Conference on Research Challenges in information Sciences, RCIS 2023

作者： Cornei, Laura-Maria Trandabat, Diana Faculty of Computer Science Alexandru Ioan Cuza University Iaşi Romania

ISBN: (纸本)9783031330797

Knowledge bases offer clear advantages when compared to traditional databases, mainly due to semantic connections and automated reasoning over large datasets. However, limited knowledge of the specialized knowledge base query language (SPARQL) makes it difficult for most users to freely access these resources. To solve this issue, we propose a question-answering system able to translate natural language questions into SPARQL queries. The presented method is a rule-based approach that integrates information regarding dependency and constituency parsing, WordNet and named entity recognition to capture the structural and semantic representation of the question. The proposed solution is able to handle a wide variety of question types (list, count, yes/no, wh-questions, questions involving rankings, ordinals, and/or superlatives). Moreover, all involved steps except the phrase mapping phase (in which properties and entities from the ontological model are mapped to words from the natural language question) are knowledge base independent. Tests performed over the QALD-9 question-answering dataset using the DBpedia knowledge base have shown that our system obtains state-of-the-art results and a very good time-performance balance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantic Web

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Comparative Study of Unsupervised Keyword Extraction Methods for Job Recommendation in an Industrial Environment 17th

Comparative Study of Unsupervised Keyword Extraction Method...

引用

17th International Conference on Research Challenges in information Sciences, RCIS 2023

作者： Audeh, Bissan Sutter, Maia Largeron, Christine INASOFT Company 2507 avenue de l’Europe Rillieux-La-Pape69140 France UJM-Saint-Etienne CNRS Institut d’Optique Graduate School Laboratoire Hubert Curien UMR 5516 Saint-Etienne42023 France

ISBN: (纸本)9783031330797

Automatic keyword extraction has important applications in various fields such as information retrieval, text mining and automatic text summarization. Different models of keyword extraction exist in the literature. In most cases, these models are designed for English-language documents, including scientific journals, news articles, or web pages. In this work, we evaluate state-of-the-art unsupervised approaches for extracting keywords from French-language Curricula Vitae (CVs) and job offers. The goal is to use these keywords to match a candidate and a job offer as part of a job recommendation system. Our evaluation showed that statistical baselines obtain good results with an interesting processing time in an industrial context. It also allowed us to highlight, on the one hand, biases related to pre-trained word embedding models on corpora of a different nature than CVs and job offers, and on the other hand, the difficulties of annotation within the framework of job search platforms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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Grammar-Based Question Classification Using Ensemble Learning Algorithms 1

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18th International Conference on Web information Systems and Technologies, WEBIST 2022

作者： Mohasseb, Alaa Kanavos, Andreas School of Computing University of Portsmouth Portsmouth United Kingdom Department of Informatics Ionian University Corfu Greece

ISBN: (数字)9783031430886

ISBN: (纸本)9783031430879

Question Classification is one of the important applications of information retrieval, as it plays a crucial role in improving the performance of question-answering systems. Differentiating between factoid and non-factoid questions is a particularly difficult task. Different methods have been suggested to improve the identification and classification of factoid questions. Most of these methods rely on semantic features and bag-of-words. This research paper explores the utilization of a Grammar-based framework for Questions Categorization and Classification (GQCC) to identify question types. This framework incorporates features such as grammatical features, domain-specific features, and patterns. These features leverage the question structure. By employing Ensemble Learning models, experimental findings demonstrate that the integration of question grammatical features with Ensemble Learning models contributes to achieving a good level of accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Examining the Relative Importance of Factors Influencing Perinatal Anxiety in Different Perinatal Periods During the Covid-19 Pandemic: A Random Forest-Based Approach 1

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22nd Wuhan International Conference on E-business, WHICEB 2023

作者： Gan, Xiaobing Guo, Ting Wang, Yang Zhu, Zhenzhen Zhou, Yongjie College of Management Shenzhen University Shenzhen China Shenzhen Kangning Hospital Shenzhen China

ISBN: (数字)9783031323027

ISBN: (纸本)9783031323010

Perinatal anxiety disorders are associated with adverse outcomes for mothers and children. However, most studies investigating perinatal anxiety, especially during the Covid-19 pandemic, have focused on pregnancy symptoms at a certain time. This study aimed to describe the prevalence of anxiety in four perinatal periods and to determine the relative importance of factors influencing anxiety in different perinatal periods during the Covid-19 pandemic. This cross-sectional study recruited 2030 women from February 28 to April 26, 2020, in Wuhan, Beijing, and Lanzhou, China. A random forest model was used to identify factors affecting anxiety during four perinatal periods, and the Shapley Additive explanation (SHAP) method was used to further interpret the results. The results show that during the Covid-19 pandemic, the prevalence of anxiety in the four trimesters of pregnancy was 21.5%, 19.2%, 21.6%, and 27.2%, respectively. Depression, insomnia, and worries or fears during childbirth are all important factors affecting maternal anxiety, among which depression is the most important factor. However, the relative importance of factors in different stages of pregnancy is different, so precise intervention should be implemented in time to alleviate the occurrence of maternal anxiety. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Additives

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A Framework to Measure Corporate Regulatory Exposure 1

引用

11th International Workshop on Enterprise Applications, Markets and Services in the Finance Industry, FinanceCom 2022

作者： Koch, Jascha-Alexander Gomber, Peter University of Siegen Siegen Germany Goethe University Frankfurt Frankfurt am Main Germany

ISBN: (数字)9783031316715

ISBN: (纸本)9783031316708

The amendment of existing and the passing of new regulations keep the corpus of regulation changing and growing dynamically. Against this background, companies face increasing costs to comply with existing and upcoming regulation. However, the high amount of regulatory texts makes it difficult for companies to identify which regulations apply to them. While regulatory technology, so-called RegTech, enables companies to comply with regulatory requirements or serves supervisory authorities to check compliance, there are no tools that enable companies to efficiently determine the relevance of a regulation in an automated manner. Therefore, this paper develops a decision support framework that makes use of techniques from natural language processing. We apply our approach to the Code of Federal Regulations in the U.S and discuss the results. As a key practical implication, our framework enables companies to retrieve regulations that speak to their business activities and may require compliance actions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision support systems

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Reinforcement Learning for Scriptless Testing: An Empirical Investigation of Reward Functions 17th

Reinforcement Learning for Scriptless Testing: An Empirical...

引用

17th International Conference on Research Challenges in information Sciences, RCIS 2023

作者： Rodríguez-Valdés, Olivia Vos, Tanja E. J. Marín, Beatriz Aho, Pekka Open Universiteit Heerlen Netherlands Universitat Politècnica de València València Spain

ISBN: (纸本)9783031330797

Testing web applications through the GUI can be complex and time-consuming, as it involves checking the functionality of the system under test (SUT) from the user’s perspective. Random testing can improve test efficiency by automating the process, but achieving good exploration is difficult because it requires uniform distribution over a large search space while also taking into account the dynamic content commonly found in web applications. Reinforcement learning can improve the efficiency of random testing by guiding the generation of test sequences. This is achieved by assigning rewards to specific actions and using them to determine which actions are most likely to lead to a desired outcome. While rewards based on the difference between consecutive states are commonly used in modern tools, they can lead to the Jumping Between States (JBS) problem, where large rewards are generated without significantly increasing exploration. We propose a solution to the JBS problem by combining rewards based on the change of state and a metric to estimate the level of exploration reached in the next state based on the frequency of actions executed. Our results show that this multi-faceted approach increases the exploration efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Leveraging Exogeneous Data for the Predictive Monitoring of IT Service Management Processes 17th

Leveraging Exogeneous Data for the Predictive Monitoring of ...

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17th International Conference on Research Challenges in information Sciences, RCIS 2023

作者： Hennig, Marc C. University of Applied Sciences Munich Lothstr. 64 Munich80335 Germany

ISBN: (纸本)9783031330797

Accurate prediction of process execution time in IT Service Management (ITSM) is essential for service providers to meet service-level agreements (SLA). However, traditional pre dictive process monitoring methods struggle with processes delivering complex process artifacts, where event log data is insufficient to understand the flow of instances. To overcome this challenge, exogenous predictive process monitoring is proposed, utilizing exogenous data sources available in IT organizations to improve the accuracy of ITSM process predictions. This approach leverages a wide range of exogenous data sources, such as the service knowledge management system, to enhance the predictions and decision-making process. The resulting planning and decision support system, incorporating exogenous data, improves SLA compliance through better resource allocation and decisions throughout the ITSM process instance lifecycles. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision support systems

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Towards an API Marketplace for an e-Invoicing Ecosystem 1

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11th International Workshop on Enterprise Applications, Markets and Services in the Finance Industry, FinanceCom 2022

作者： Manchanda, Chinmay Hussain, Walayat Rabhi, Latif Rabhi, Fethi School of Computer Science and Engineering UNSW Sydney Australia Peter Faber Business School Australian Catholic University North Sydney Australia Ebusiness Software Services Sydney Australia

ISBN: (数字)9783031316715

ISBN: (纸本)9783031316708

Driven by a large number of very diverse and fast-evolving regulations, the adoption of e-invoicing is creating many challenges for solution providers, such as dealing with compliance requirements, cross-border issues, heterogeneity of standards and constant changes. Existing solutions do not represent a cost-effective and vendor-independent alternative to existing legacy systems, ERPs and databases. The proposed solution is based on leveraging cloud computing concepts, Software-as-a-Service concept, API economy, and business Process Modelling (BPM) concepts. It allows solution providers to choose SaaS components and customise their offerings according to customers needs. Given an ecosystem of APIs available via a marketplace, it becomes possible to rapidly compose and build new applications via BPM technologies. The paper describes an implementation of this concept realised using several e-invoicing APIs being composed using the WASP workflow system. Some preliminary results regarding the feasibility of the proposed approach in a simple buyer-seller scenario are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ecosystems

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