Pub Date : 2025-06-01DOI: 10.1016/j.dim.2024.100082
Carter Cousineau , Rozita Dara , Ataharul Chowdhury
As organizations continue to embrace the use of artificial intelligence (AI) systems, it is crucial to ensure that these AI systems can be trusted. However, there is still a significant gap between research on trustworthy AI and its implementation in real-world applications. To address this issue, we sought to explore the perspectives of AI developers and the challenges they face in creating trustworthy AI systems. This exploratory study involved conducting interviews with 19 AI developers. We identified key challenges faced by AI developers due to the immature state of trustworthy AI, inconsistent global regulatory landscape, a lack of standardized definitions of key concepts, limited tools and standards for practical implementation in organizations. This paper provides recommendations for organizations to invest in trustworthy AI processes and practices, this includes building a foundation for trustworthy AI specific to their organization, adopting an organizational approach to trustworthy AI culture, and providing proper data infrastructures to support AI developers in creating trustworthy AI systems. By investing in trustworthy AI practices, organizations can prepare for evolving regulations and ensure that their AI systems are reliable and trustworthy.
{"title":"Trustworthy AI: AI developers’ lens to implementation challenges and opportunities","authors":"Carter Cousineau , Rozita Dara , Ataharul Chowdhury","doi":"10.1016/j.dim.2024.100082","DOIUrl":"10.1016/j.dim.2024.100082","url":null,"abstract":"<div><div>As organizations continue to embrace the use of artificial intelligence (AI) systems, it is crucial to ensure that these AI systems can be trusted. However, there is still a significant gap between research on trustworthy AI and its implementation in real-world applications. To address this issue, we sought to explore the perspectives of AI developers and the challenges they face in creating trustworthy AI systems. This exploratory study involved conducting interviews with 19 AI developers. We identified key challenges faced by AI developers due to the immature state of trustworthy AI, inconsistent global regulatory landscape, a lack of standardized definitions of key concepts, limited tools and standards for practical implementation in organizations. This paper provides recommendations for organizations to invest in trustworthy AI processes and practices, this includes building a foundation for trustworthy AI specific to their organization, adopting an organizational approach to trustworthy AI culture, and providing proper data infrastructures to support AI developers in creating trustworthy AI systems. By investing in trustworthy AI practices, organizations can prepare for evolving regulations and ensure that their AI systems are reliable and trustworthy.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 2","pages":"Article 100082"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.dim.2024.100074
Jing Chen , Hongli Chen , Shubin Zhou , Quan Lu
With the proliferation of online health resources, mobile health information search has become the new norm, in which the task difficulty perception in search affects the user's search experience. This study aimed to investigate the relationship between visual behavior that reflects users' cognitive processing and changes in perceived task difficulty, thereby predicting such changes.
This study was conducted through a controlled experiment. 46 participants were recruited to complete four tasks. Visual behavior data were collected through eye-tracking technology, and changes in task difficulty perception were measured through pre-task and post-task questionnaires. The mobile health information search process is divided into three search activities: querying, browsing, and viewing activities. Predictors were inspected from the overall session and individual search activity levels using the Mann-Whitney U test, and then K-Nearest Neighbor, Extreme-Trees, Naive Bayesian, Support Vector Machine, Logistic Regression algorithms were used to predict and evaluate prediction effects.
The results showed significant differences in participants' fixation and saccade behaviors between increases and decreases in task difficulty, both at the overall session and individual search activity level. The logistic regression algorithm demonstrated the highest predictive performance, Furthermore, visual behavioral indicators for the browsing activity proved to perform better relative to the other search activities.
This study highlights the importance of visual behavioral indicators as reliable predictors of changes in users' perceived task difficulty in mobile health information search. It can help health information providers and administrators to provide timely and targeted assistance and implement effective guidance strategies.
{"title":"Predicting changes in task difficulty perception based on visual behavior in mobile health information search","authors":"Jing Chen , Hongli Chen , Shubin Zhou , Quan Lu","doi":"10.1016/j.dim.2024.100074","DOIUrl":"10.1016/j.dim.2024.100074","url":null,"abstract":"<div><div>With the proliferation of online health resources, mobile health information search has become the new norm, in which the task difficulty perception in search affects the user's search experience. This study aimed to investigate the relationship between visual behavior that reflects users' cognitive processing and changes in perceived task difficulty, thereby predicting such changes.</div><div>This study was conducted through a controlled experiment. 46 participants were recruited to complete four tasks. Visual behavior data were collected through eye-tracking technology, and changes in task difficulty perception were measured through pre-task and post-task questionnaires. The mobile health information search process is divided into three search activities: querying, browsing, and viewing activities. Predictors were inspected from the overall session and individual search activity levels using the Mann-Whitney <em>U</em> test, and then K-Nearest Neighbor, Extreme-Trees, Naive Bayesian, Support Vector Machine, Logistic Regression algorithms were used to predict and evaluate prediction effects.</div><div>The results showed significant differences in participants' fixation and saccade behaviors between increases and decreases in task difficulty, both at the overall session and individual search activity level. The logistic regression algorithm demonstrated the highest predictive performance, Furthermore, visual behavioral indicators for the browsing activity proved to perform better relative to the other search activities.</div><div>This study highlights the importance of visual behavioral indicators as reliable predictors of changes in users' perceived task difficulty in mobile health information search. It can help health information providers and administrators to provide timely and targeted assistance and implement effective guidance strategies.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 2","pages":"Article 100074"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141391065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.dim.2024.100075
Xiling Cui , Xuan Yang , Jifan Ren , Paul Benjamin Lowry , Timon Chih-ting Du
This study aims to investigate how to leverage knowledge sharing (KS) to boost team creativity among information technology (IT) professionals. We examine the effects of intrinsic and intangible extrinsic rewards on in-role and extra-role KS, which increases team creativity. We use data collected from 322 employees in 80 teams from organizations in the IT industry to test the research model and confirm the important roles of KS and motivational rewards. The two types of KS show different patterns in terms of their antecedents and outcomes. Specifically, in-role KS does not affect team creativity directly, while extra-role KS does. Intrinsic rewards significantly affect both in-role and extra-role KS, and the effect on the latter is greater. Image rewards have a greater effect on in-role KS than on extra-role KS. In addition, the two forms of intangible extrinsic rewards exhibit internalization. The study pioneers in addressing a pressing research gap by investigating and comparing the effects of the two types of KS—in-role and extra-role KS—on team creativity.
{"title":"Enhancing team creativity among information technology professionals through knowledge sharing and motivational rewards: A self-determination perspective","authors":"Xiling Cui , Xuan Yang , Jifan Ren , Paul Benjamin Lowry , Timon Chih-ting Du","doi":"10.1016/j.dim.2024.100075","DOIUrl":"10.1016/j.dim.2024.100075","url":null,"abstract":"<div><div>This study aims to investigate how to leverage knowledge sharing (KS) to boost team creativity among information technology (IT) professionals. We examine the effects of intrinsic and intangible extrinsic rewards on in-role and extra-role KS, which increases team creativity. We use data collected from 322 employees in 80 teams from organizations in the IT industry to test the research model and confirm the important roles of KS and motivational rewards. The two types of KS show different patterns in terms of their antecedents and outcomes. Specifically, in-role KS does not affect team creativity directly, while extra-role KS does. Intrinsic rewards significantly affect both in-role and extra-role KS, and the effect on the latter is greater. Image rewards have a greater effect on in-role KS than on extra-role KS. In addition, the two forms of intangible extrinsic rewards exhibit internalization. The study pioneers in addressing a pressing research gap by investigating and comparing the effects of the two types of KS—in-role and extra-role KS—on team creativity.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 2","pages":"Article 100075"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141408578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.dim.2024.100081
Fan Jin, Pengyi Zhang
Books are an important carrier of culture. Library catalogs across different countries can reflect intercultural communication. This paper aims to explore the situation and differences of China-themed books in three ASEAN countries: Malaysia, the Philippines and Thailand. These countries are representative of the intercultural relations between China and ASEAN members. Descriptive analysis is used to analyze the series of books, focusing on the time and subjects of the book resources. The paper also compares the distribution of books about the three countries in the National Library of China. The results show that Malaysia and Thailand have more comprehensive and in-depth cultural collection of China-themed book resources, while the Philippines includes more books related to China's development and social realities. This study is helpful to resource and collection development related to particular countries and regions and the intercultural communication between countries.
{"title":"A comparative analysis of China-themed books in three ASEAN countries: Implications for resource development and intercultural communication","authors":"Fan Jin, Pengyi Zhang","doi":"10.1016/j.dim.2024.100081","DOIUrl":"10.1016/j.dim.2024.100081","url":null,"abstract":"<div><div>Books are an important carrier of culture. Library catalogs across different countries can reflect intercultural communication. This paper aims to explore the situation and differences of China-themed books in three ASEAN countries: Malaysia, the Philippines and Thailand. These countries are representative of the intercultural relations between China and ASEAN members. Descriptive analysis is used to analyze the series of books, focusing on the time and subjects of the book resources. The paper also compares the distribution of books about the three countries in the National Library of China. The results show that Malaysia and Thailand have more comprehensive and in-depth cultural collection of China-themed book resources, while the Philippines includes more books related to China's development and social realities. This study is helpful to resource and collection development related to particular countries and regions and the intercultural communication between countries.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 2","pages":"Article 100081"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Throughout history, cities have represented enduring symbols of human civilization and progress. Today, we are witnessing a technological revolution fueled by the rapid advancement of Information and Communication Technologies (ICT). This transformation has dramatically improved data analysis capabilities through the integration of the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, and other cutting-edge innovations. As key contributors to urban development, researchers must adopt effective methodologies to thoroughly explore the concept of smart cities. Furthermore, it is essential to raise awareness among stakeholders about the inevitable adoption of IoT technologies and their associated benefits. This paper aims to review various methodologies used to collect critical data, prioritize key urban challenges, and assess the performance of urban services. By comparing the findings of the surveyed studies, insights are drawn, and potential directions for future research are outlined.
{"title":"Smart cities services and solutions: A systematic review","authors":"Walid Miloud Dahmane , Samir Ouchani , Hafida Bouarfa","doi":"10.1016/j.dim.2024.100087","DOIUrl":"10.1016/j.dim.2024.100087","url":null,"abstract":"<div><div>Throughout history, cities have represented enduring symbols of human civilization and progress. Today, we are witnessing a technological revolution fueled by the rapid advancement of Information and Communication Technologies (ICT). This transformation has dramatically improved data analysis capabilities through the integration of the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, and other cutting-edge innovations. As key contributors to urban development, researchers must adopt effective methodologies to thoroughly explore the concept of smart cities. Furthermore, it is essential to raise awareness among stakeholders about the inevitable adoption of IoT technologies and their associated benefits. This paper aims to review various methodologies used to collect critical data, prioritize key urban challenges, and assess the performance of urban services. By comparing the findings of the surveyed studies, insights are drawn, and potential directions for future research are outlined.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 2","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.dim.2024.100084
Yunfang Luo , Xiling Cui , Qiang Liu , Qiang Zhou , Yingxuan Zhang
Exaggeration is a specific way in which companies potentially overstate certain aspects of their actual environmental performance, strategically disclosing positive information about their environmental performance. This research aims to identify instances of exaggerated information within environmental, social, and governance (ESG) reports by employing machine learning techniques. We crawled 594 ESG reports and employed a variety of machine learning algorithms to identify instances of exaggeration. Through the cross-validation, we found that random forest exhibits the best performance in predicting exaggeration and ridge regression demonstrates superior performance in predicting the exaggeration scores. A significant contribution of our study is the development of an exaggerated thesaurus tailored specifically to this domain. Ultimately, our study lays a foundation for further investigations into addressing the impact of exaggerated information in ESG reporting.
{"title":"Identifying exaggeration in ESG reports using machine learning techniques","authors":"Yunfang Luo , Xiling Cui , Qiang Liu , Qiang Zhou , Yingxuan Zhang","doi":"10.1016/j.dim.2024.100084","DOIUrl":"10.1016/j.dim.2024.100084","url":null,"abstract":"<div><div>Exaggeration is a specific way in which companies potentially overstate certain aspects of their actual environmental performance, strategically disclosing positive information about their environmental performance. This research aims to identify instances of exaggerated information within environmental, social, and governance (ESG) reports by employing machine learning techniques. We crawled 594 ESG reports and employed a variety of machine learning algorithms to identify instances of exaggeration. Through the cross-validation, we found that random forest exhibits the best performance in predicting exaggeration and ridge regression demonstrates superior performance in predicting the exaggeration scores. A significant contribution of our study is the development of an exaggerated thesaurus tailored specifically to this domain. Ultimately, our study lays a foundation for further investigations into addressing the impact of exaggerated information in ESG reporting.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 2","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144204864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mining sentiment information from the textual content of peer review comments offers valuable insights into the scientific evaluation process. However, previous studies are often constrained by coarse-grained analysis and the lack of differentiation across review rounds. Notably, the dynamic shifts in reviewers' focus and sentiment tendencies throughout multiple review stages remain underexplored. To address this gap, the present study investigates the distribution and evolution of aspect-level sentiments and examines their correlation with the number of review rounds. We begin by segmenting the multi-round review comments of 11,063 accepted papers from Nature Communications and identifying fine-grained review aspect clusters. A manually annotated corpus of approximately 5000 review sentences is then constructed. Using this dataset, we train a series of deep learning-based aspect sentiment classification models. Among them, the LCF-BERT-CDM model achieves the best performance, with a Macro-F1 score of 82.65 %. Subsequent statistical analysis reveals a consistent trend: as the number of review rounds increases, the proportion of positive sentiments rises, while negative sentiments decline. Correlation analysis further indicates that aspect sentiment scores are negatively associated with the total number of review rounds. Key aspects exhibiting stronger correlations include “experiments”, “research significance” and “result analysis”.
{"title":"Aspect-based sentiment evolution and its correlation with review rounds in multi-round peer reviews: A deep learning approach","authors":"Ruxue Han , Haomin Zhou , Jiangtao Zhong , Chengzhi Zhang","doi":"10.1016/j.dim.2025.100105","DOIUrl":"10.1016/j.dim.2025.100105","url":null,"abstract":"<div><div>Mining sentiment information from the textual content of peer review comments offers valuable insights into the scientific evaluation process. However, previous studies are often constrained by coarse-grained analysis and the lack of differentiation across review rounds. Notably, the dynamic shifts in reviewers' focus and sentiment tendencies throughout multiple review stages remain underexplored. To address this gap, the present study investigates the distribution and evolution of aspect-level sentiments and examines their correlation with the number of review rounds. We begin by segmenting the multi-round review comments of 11,063 accepted papers from Nature Communications and identifying fine-grained review aspect clusters. A manually annotated corpus of approximately 5000 review sentences is then constructed. Using this dataset, we train a series of deep learning-based aspect sentiment classification models. Among them, the LCF-BERT-CDM model achieves the best performance, with a Macro-F<sub>1</sub> score of 82.65 %. Subsequent statistical analysis reveals a consistent trend: as the number of review rounds increases, the proportion of positive sentiments rises, while negative sentiments decline. Correlation analysis further indicates that aspect sentiment scores are negatively associated with the total number of review rounds. Key aspects exhibiting stronger correlations include “experiments”, “research significance” and “result analysis”.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 1","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-20DOI: 10.1016/j.dim.2025.100104
Mohammed Jawwadul Islam , Mohammad Fahad Al Rafi , Pranto Podder , Aysha Siddika , Moumy Kabir , Euna Mehnaz Khan , Najmul Islam , Saddam Mukta
Following an irregular bedtime routine and having different amounts of sleep each night might increase a person's risk of obesity, cardiovascular problems, high blood pressure, insulin levels, and other metabolic problems. Similarly, in recent times, social media platforms have gained popularity among users for sharing their interests, thoughts, and opinions. Through social media activities, researchers have been able to mine the text data generated on these platforms to investigate and understand users' behaviors and habits. In this paper, we examine a total of 2,468,697 tweets to identify users' irregular sleeping patterns (ISP) using psycholinguistic and contextual features from their tweets. We conduct a linguistic analysis to understand the factors influencing users' psychological behavior and word use patterns, and find a correlation with their irregular sleeping patterns. We observe that users who have irregular sleeping patterns use anger, anxiety, death, and future categories of words in their tweets largely. In contrast, users with irregular sleeping patterns tend to use positive emotions, family, and other categories of words in their tweets. Building upon our findings, we develop a hybrid prediction model that predicts users' irregular sleeping patterns from psycholinguistic features with an accuracy of 91%. We examine the application of social media data for the early identification of irregular sleep patterns and their related mental and psychological concerns while investigating design prospects for future health technologies to enhance the monitoring and support of healthy sleep behavior.
{"title":"Irregular sleep pattern identification and analysis from social media dataset using hybrid deep learning based attention mechanism","authors":"Mohammed Jawwadul Islam , Mohammad Fahad Al Rafi , Pranto Podder , Aysha Siddika , Moumy Kabir , Euna Mehnaz Khan , Najmul Islam , Saddam Mukta","doi":"10.1016/j.dim.2025.100104","DOIUrl":"10.1016/j.dim.2025.100104","url":null,"abstract":"<div><div>Following an irregular bedtime routine and having different amounts of sleep each night might increase a person's risk of obesity, cardiovascular problems, high blood pressure, insulin levels, and other metabolic problems. Similarly, in recent times, social media platforms have gained popularity among users for sharing their interests, thoughts, and opinions. Through social media activities, researchers have been able to mine the text data generated on these platforms to investigate and understand users' behaviors and habits. In this paper, we examine a total of 2,468,697 tweets to identify users' irregular sleeping patterns (ISP) using psycholinguistic and contextual features from their tweets. We conduct a linguistic analysis to understand the factors influencing users' psychological behavior and word use patterns, and find a correlation with their irregular sleeping patterns. We observe that users who have irregular sleeping patterns use anger, anxiety, death, and future categories of words in their tweets largely. In contrast, users with irregular sleeping patterns tend to use positive emotions, family, and other categories of words in their tweets. Building upon our findings, we develop a hybrid prediction model that predicts users' irregular sleeping patterns from psycholinguistic features with an accuracy of 91%. We examine the application of social media data for the early identification of irregular sleep patterns and their related mental and psychological concerns while investigating design prospects for future health technologies to enhance the monitoring and support of healthy sleep behavior.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 1","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-13DOI: 10.1016/j.dim.2025.100103
Broto Widya Hartanto, Subagyo, I.G.B. Budi Dharma
This study introduces a hybrid method combining topic modeling and an opportunity model to generate novel ideas for automobile product design improvements. Furthermore, it proposes a novel unsupervised topic labeling procedure to address the limitations in current topic modeling interpretations, which are often not fully unsupervised. The procedure comprised automatic generation of labels that directly support opportunity modeling and facilitate product design development. To achieve the stated objectives, data was collected from user comments on YouTube car reviews and analyzed using various algorithms and part-of-speech rules, finding that Non-Negative Matrix Factorization with noun-adjective combinations proved most effective in generating comprehensible topic labels and capturing emotional expressions. The results revealed six underserved labels, one served right, and two overserved categories for new vehicle design improvements, providing valuable insights into user experiences. The insights provided in this context are expected to contribute to the potential improvement of vehicle attribute designs, thereby enhancing the efficiency of the entire design process.
{"title":"Unsupervised topic labeling and opportunity model of social media data for enhancing automotive product design processes","authors":"Broto Widya Hartanto, Subagyo, I.G.B. Budi Dharma","doi":"10.1016/j.dim.2025.100103","DOIUrl":"10.1016/j.dim.2025.100103","url":null,"abstract":"<div><div>This study introduces a hybrid method combining topic modeling and an opportunity model to generate novel ideas for automobile product design improvements. Furthermore, it proposes a novel unsupervised topic labeling procedure to address the limitations in current topic modeling interpretations, which are often not fully unsupervised. The procedure comprised automatic generation of labels that directly support opportunity modeling and facilitate product design development. To achieve the stated objectives, data was collected from user comments on YouTube car reviews and analyzed using various algorithms and part-of-speech rules, finding that Non-Negative Matrix Factorization with noun-adjective combinations proved most effective in generating comprehensible topic labels and capturing emotional expressions. The results revealed six underserved labels, one served right, and two overserved categories for new vehicle design improvements, providing valuable insights into user experiences. The insights provided in this context are expected to contribute to the potential improvement of vehicle attribute designs, thereby enhancing the efficiency of the entire design process.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 4","pages":"Article 100103"},"PeriodicalIF":0.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-29DOI: 10.1016/j.dim.2025.100102
Wenlong Liu , Yashuo Yuan , Yi Jiang , Jian Mou
Shared accommodation has seen robust growth with the advancement of the platform economy. With the growing participation of non-professional service deliverers (i.e., property owners) in this field, service quality and customer satisfaction are currently facing challenges. Leveraging the SERVQUAL model, this research investigates the factors that determine customers' continuance intention (CI) to reuse this type of accommodation. A mixed-method approach is used, in which text analysis is first conducted to identify the dimensions constituting the reformed SERVQUAL model in the accommodation sharing context. Thereafter, a survey-based empirical analysis is carried out to examine the impact of the SERVQUAL dimensions. In the text analysis, 29,787 online reviews on accommodation sharing services from Ctrip.com were collected. After word segmenting and high-frequency word coding using the Jieba package of Python and NVivo 12 plus, eight dimensions of SERVQUAL for accommodation sharing services were extracted: necessities, complementarity, reliability, empathy, assurance, responsiveness, authenticity, and similarity. In the empirical study, the results based on 588 valid samples show that all dimensions identified in this research have either direct or indirect impacts on consumers’ CI. The research findings hold great theoretical and practical significance.
{"title":"Reforming the SERVQUAL model for accommodation sharing services: A mixed-method approach","authors":"Wenlong Liu , Yashuo Yuan , Yi Jiang , Jian Mou","doi":"10.1016/j.dim.2025.100102","DOIUrl":"10.1016/j.dim.2025.100102","url":null,"abstract":"<div><div>Shared accommodation has seen robust growth with the advancement of the platform economy. With the growing participation of non-professional service deliverers (i.e., property owners) in this field, service quality and customer satisfaction are currently facing challenges. Leveraging the SERVQUAL model, this research investigates the factors that determine customers' continuance intention (CI) to reuse this type of accommodation. A mixed-method approach is used, in which text analysis is first conducted to identify the dimensions constituting the reformed SERVQUAL model in the accommodation sharing context. Thereafter, a survey-based empirical analysis is carried out to examine the impact of the SERVQUAL dimensions. In the text analysis, 29,787 online reviews on accommodation sharing services from <span><span>Ctrip.com</span><svg><path></path></svg></span> were collected. After word segmenting and high-frequency word coding using the Jieba package of Python and NVivo 12 plus, eight dimensions of SERVQUAL for accommodation sharing services were extracted: necessities, complementarity, reliability, empathy, assurance, responsiveness, authenticity, and similarity. In the empirical study, the results based on 588 valid samples show that all dimensions identified in this research have either direct or indirect impacts on consumers’ CI. The research findings hold great theoretical and practical significance.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"9 4","pages":"Article 100102"},"PeriodicalIF":0.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145521276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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