Data and information management最新文献

{"title":"How does the perceived ubiquity of social media influence employees’ broad and deep socialization-based social media usage and digital well-being?","authors":"Xuan Yang ,&nbsp;Libo Ivy Liu ,&nbsp;Xiling Cui","doi":"10.1016/j.dim.2025.100118","DOIUrl":"10.1016/j.dim.2025.100118","url":null,"abstract":"<div><div>Social media is closely integrated into our daily life and wellbeing in today's digital era, making it crucial to understand social media impacts on individuals' digital wellbeing. This study examined how social media usage (SMU) affects individuals' digital well-being. We developed a research model to examine the effects of four features of social media ubiquity (continuity, searchability, immediacy and portability) on both broad and deep socialization-based social media usage (SMU), which further affects individuals' digital well-being. We collected data from 600 employees in a two-wave survey setting to test this model. We found that continuity and searchability positively influenced both broad and deep socialization-based SMU. Immediacy was shown to positively affect deep SMU, while portability enhances broad SMU. Broad socialization-based SMU had a positive impact on digital well-being, but deep socialization-based usage did not. These findings highlight the distinct roles that different features of social media ubiquity play in shaping social media usage and well-being. This research contributes to the theoretical understanding of social media ubiquity and its nuanced effects on digital well-being. Furthermore, it offers practical insights for designing social media platforms that promote well-being outcomes.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 2","pages":"Article 100118"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790096","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}

{"title":"A topic-enhanced network via contrastive learning for abstractive text summarization","authors":"Chuanming Yu ,&nbsp;Dianyuan Zhang ,&nbsp;Xiping Hao ,&nbsp;Xueqing Fu ,&nbsp;Jie Shen ,&nbsp;Lu An","doi":"10.1016/j.dim.2025.100114","DOIUrl":"10.1016/j.dim.2025.100114","url":null,"abstract":"<div><div>Abstractive text summarization has arisen as a notable research task and has garnered considerable attention. Despite the advancements made, existing methods still struggle to effectively address the issue of exposure bias, resulting in a disparity between training and inference. In addition, most contrastive-learning-based models neglect the importance of global semantics, such as the potential topic information. To address these problems, this paper proposes a novel topic-enhanced sequence-to-sequence network via contrastive learning (TESC) model. In contrast to most current research, this paper utilizes a combination of topic modeling and contrastive learning to lessen the exposure bias problem and improve the quality of the generated summaries. In addition, this paper employs hard negative sampling by selecting negative samples close to the positive one. Exposure bias refers to the discrepancy in automatic summarization models where training relies on ground-truth data while inference depends on self-generated sequences, leading to error accumulation and degraded summary quality. This paper performed rigorous experiments on four datasets, namely CNN/DailyMail, XSum, Reddit-TIFU, and SAMSum. The results from our experiments provide evidence of the efficacy and applicability of the TESC approach. The research sheds light on the role of topic consistency and the effectiveness of hard negative sampling in leveraging contrastive learning for enhancing the performance of current models.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 2","pages":"Article 100114"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420313","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}

{"title":"Impact Score: Optimizing the timeliness and accuracy of journal impact assessment","authors":"Jing Li ,&nbsp;Xue Yang ,&nbsp;Xiaoli Lu ,&nbsp;Dengsheng Wu","doi":"10.1016/j.dim.2025.100121","DOIUrl":"10.1016/j.dim.2025.100121","url":null,"abstract":"<div><div>In the era of electronic publishing, the speed of journal publication, dissemination, and citation has significantly increased. However, traditional journal evaluation metrics fail to capture this immediate impact of journal dissemination. Considering the issues of journal evaluation metrics in the context of the digital publishing era, this paper introduces an improved metric for journal evaluation, named Impact Score (IS). It integrates the number of citations a journal receives in its publication year within the framework of Journal Impact Factor (JIF) to measure journal impact in a more timely and comprehensive manner. The IS for a journal in a given year (Y) is calculated as the total number of citations received by the journal's items published from Y-2 to Y, divided by the total number of the citable items published during the same period. This paper systematically calculates the IS of 10,736 journals indexed in the <em>Web of Science</em> (WoS) database, analyses the performance differences of IS across different disciplines and journals, and further explores the correlation between IS and journal dissemination speed-related indicators (such as Citation Half-Life). Empirical results indicate that IS exhibits a high positive correlation with both JIF and the Immediacy Index (II). Journals with high citation counts and II scores in their publication year achieve higher rankings in the IS system. IS effectively identifies journals with rapid knowledge dissemination characteristics while maintaining the stability of the traditional JIF evaluation framework, thereby providing a more sensitive and comprehensive measurement tool for journal evaluation in the electronic publishing environment.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 2","pages":"Article 100121"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883392","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}

{"title":"Key factors and configuration paths of digital innovation in manufacturing enterprises under the TOE framework","authors":"Jingmei Ma,&nbsp;Jie Wu,&nbsp;Zhiqing Li","doi":"10.1016/j.dim.2025.100116","DOIUrl":"10.1016/j.dim.2025.100116","url":null,"abstract":"<div><div>Digital innovation is crucial for manufacturing enterprises to enhance competitiveness and achieve leapfrog development. This paper examines Chinese A-share listed manufacturing enterprises from 2012 to 2022, analyzing the effects of individual factors and multi-factor linkages on digital innovation through the TOE framework. It integrates the CatBoost and SHAP machine learning algorithms with a multi-period QCA. The findings are as follows: (1) The CatBoost regression model demonstrates strong explanatory capacity. The SHAP analysis identifies six key determinants of manufacturing digital innovation: market competition, R&amp;D investment, firm size, workforce size, absorptive capacity, and R&amp;D personnel. (2) The multi-period QCA reveals distinct configurational pathways across different stages of digital transformation. In the initial exploration phase, seven configurations emerge, primarily characterized by technology-driven and technology-environment synergies. In the high-speed development phase, eight configurations are identified, highlighting technology-organization and organization-environment synergies. In the acceleration phase, ten configurations are found, illustrating the interaction among technology-driven, technology-organization, technology-environment, and organization-environment factors. (3) Technological and organizational factors remain core conditions for high digital innovation throughout all periods. As digitalization progresses, environmental factors play an increasingly important role.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 2","pages":"Article 100116"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468519","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}

{"title":"Exploring Chinese user's online dis-identification: An integration of technology-organization-environment obstacles and person–environment misfits","authors":"Xi Chen ,&nbsp;Cheng Chen ,&nbsp;Jian Mou ,&nbsp;Xiangwen Cai","doi":"10.1016/j.dim.2025.100119","DOIUrl":"10.1016/j.dim.2025.100119","url":null,"abstract":"<div><div>Online dis-identification is when users intentionally distance themselves or disassociate from an online platform. This study explores Chinese user's online dis-identification from the standpoint of person–environment (P–E) misfits resulting from technological obstacles due to online privacy issues. Two-stage research was conducted using a mixed-methods approach. Semi-structured interviews with 50 participants were conducted to identify the characteristics of technology-organization-environment (TOE) obstacles and P–E misfits. A conceptual model was developed, and a structural equation model (SEM) was used, drawing on survey data from 1142 former Weibo users who had discontinued their usage, to test the proposed hypotheses. Except for role conflict, which did not significantly affect reduced platform usage, five TOE obstacle factors had a significant impact on the three P–E misfits. Privacy concerns significantly influenced reduced platform use and refusal to disclose personal information. The three P–E misfits contributed significantly to online dis-identification. This study provides an explanatory theoretical framework, trigger factors and process for understanding user's online dis-identification.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 2","pages":"Article 100119"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736604","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}

{"title":"Uncovering the landscape of sustainable innovation funding through structural topic modeling","authors":"Satpreet Kaur,&nbsp;Rajeev Kumar Panda","doi":"10.1016/j.dim.2025.100117","DOIUrl":"10.1016/j.dim.2025.100117","url":null,"abstract":"<div><div>Sustainable innovation acts as a catalyst to address social and environmental challenges while generating economic benefits for the firms. However, the firms aiming to instigate this transformation face challenges in acquiring funds. As the realm of sustainable innovation continues to expand, a robust understanding of its funding mechanisms is necessary. The study uses an unsupervised machine-learning approach to build a precise and comprehensive knowledge of the art. The research paper employs the structural topic modeling framework, a quantitative technique that utilizes advanced statistical methods to derive semantic knowledge from extensive textual data. The study delineates prominent patterns in the domain, indicating an integrated framework that links key components of sustainable innovation and finance while emphasizing the role of green credit policy interventions. The findings from structural topic modeling identify ten distinct topics and propose multiple research prospects for forthcoming investigations on sustainable innovation funding mechanisms. The research acts as a vital tool for investors, policymakers, and entrepreneurs in optimizing resource allocation, designing targeted policies, and aligning business strategies to attract sustainable funding. From a methodological standpoint, this research leverages structural topic modeling as an innovative approach to literature review, thereby enabling an in-depth analysis of a broader range of research outputs and generating more valuable insights than conventional methodologies.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 2","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145684594","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}

{"title":"Enhancing fake news detection through estimating user tendencies to spread fake news","authors":"Ahmad Hashemi ,&nbsp;Mohammad Reza Moosavi ,&nbsp;Wei Shi ,&nbsp;Anastasia Giachanou","doi":"10.1016/j.dim.2025.100115","DOIUrl":"10.1016/j.dim.2025.100115","url":null,"abstract":"<div><div>The growing influence of social media on how people consume information has reshaped the landscape of public communication. Alongside its benefits, this shift has led to the faster spread of fake news, reducing public trust and influencing people’s perception of events. Gaining insight into how fake news propagates and understanding the roles different users play in its dissemination are essential steps toward effective detection. In this research, we investigate how predicting users’ sharing behaviors can improve fake news detection (FND). We introduce a regression-based approach to estimate a user’s Tendency to Spread Fake News (TSFN) by leveraging linguistic features derived from their online posts. To train and evaluate the model, we present two new datasets, each comprising 5000 users. Subsequently, we employ the trained TSFN estimator models for the detection of fake news, presenting a two-step FND system. In the first step, for a given news item, the system estimates the TSFN scores of its spreaders using the trained estimators. Then, leveraging these scores, the system determines the authenticity of the news item. By further combining news content features, the system achieves improved performance. Experimental results indicate that the proposed framework performs reliably even in the early stages of news dissemination. Moreover, we explore how emotional signals contribute to distinguishing between fake and real news and to identifying fake news spreaders, offering valuable insights into the models’ decisions.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 2","pages":"Article 100115"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145371183","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}

{"title":"Incorporating moral motivation in automatic summary generation of literary fiction","authors":"Chong Jiang ,&nbsp;Weiwei Jin ,&nbsp;Xiaoguang Wang ,&nbsp;Liang Zhao","doi":"10.1016/j.dim.2025.100120","DOIUrl":"10.1016/j.dim.2025.100120","url":null,"abstract":"<div><div>Automatic summary generation aims to condense knowledge and improve users' information retrieval and learning efficiency across various fields. In digital reading, the goal of attracting users' attention and guiding in-depth reading has led to a change in the function of summarization. The moralizing potential of literary fiction represents a unique feature that draws readers' attention and facilitates the retention of moral information. Yet, prior research has predominantly emphasized information summarization, thereby neglecting these underlying moral-cognitive mechanisms. This study introduces the concept of psychological moral motivation and constructs the MoralTextRank model, using 50 literary fiction works to generate summaries containing moral information. Evaluation indexes were designed, and tests were conducted with 120 participants to assess reading effects. The results show that summaries containing moral information significantly attract users' reading attention compared to both neutral and low-morality summaries, particularly among male users (<span><math><mrow><msub><mi>t</mi><mrow><mi>l</mi><mi>o</mi><mi>w</mi></mrow></msub><mo>=</mo><mo>−</mo><mn>3.03</mn></mrow></math></span>, <span><math><mrow><mi>p</mi><mo>=</mo><mn>0.0034</mn></mrow></math></span>; <span><math><mrow><msub><mi>t</mi><mrow><mi>h</mi><mi>i</mi><mi>g</mi><mi>h</mi></mrow></msub><mo>=</mo><mo>−</mo><mn>2.9</mn></mrow></math></span>, <span><math><mrow><mi>p</mi><mo>=</mo><mn>0.0049</mn></mrow></math></span>). Specifically, male participants showed 8.2 % and 15.2 % more engagement with high-morality summaries than with low- and no-morality ones, respectively. This paper argues that domain-specific needs significantly influence the purpose and design of summary generation. Integrating moral information into literary fiction summaries can effectively capture readers’ attention and enrich digital reading experiences. In turn, this practice can enhance the efficiency of attention allocation within the digital content environment. This research aims to optimize information technology design and processes by integrating socio-cultural factors, thereby enriching its socio-cultural connotations.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 2","pages":"Article 100120"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839449","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}

{"title":"Multiple engagement by an individual on a social media post is rare: Insight from an analysis of 3.5 million Instagram user accounts and 29 user interviews","authors":"Kholoud Khalil Aldous ,&nbsp;Sercan Şengün ,&nbsp;Joni Salminen ,&nbsp;Ali Farooq ,&nbsp;Soon-Gyo Jung ,&nbsp;Bernard J. Jansen","doi":"10.1016/j.dim.2025.100113","DOIUrl":"10.1016/j.dim.2025.100113","url":null,"abstract":"<div><div>This research examines how often and why an individual user engages with a social media post, such as reacting, sharing, commenting, or tagging, multiple times versus only once, referred to as Multiple Engagement Behavior (MEB) or Single Engagement Behavior (SEB), through two studies. The first study quantitatively analyzes 345 million interactions on 231,554 Instagram posts from 43 organizations with a combined 3,527,289 user accounts to identify the frequency of the MEB of Reacting and Commenting. Findings show that MEB occurred more than 2.1 million times, but it comprises only 0.63 % of the combined engagement, indicating that SEB is the most common. The second study qualitatively analyzes 29 social media user interviews to investigate drivers and barriers to MEB, showing that users prioritize preserving the anonymity of others and have little incentive for multiple public interactions in most situations. When they do engage in MEB, it often occurs privately, such as by direct messaging to avoid publicness. A key takeaway is that public social media post counts serve as a reasonable proxy for people counts, as platforms often withhold these people counts from the public, an impactful insight for design, legal, and marketing.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 1","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529205","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}

{"title":"Unveiling the collective behaviors of large language model-based autonomous agents in an online community: A social network analysis perspective","authors":"Huiru Chen ,&nbsp;Zhenhua Wang ,&nbsp;Ming Ren","doi":"10.1016/j.dim.2025.100107","DOIUrl":"10.1016/j.dim.2025.100107","url":null,"abstract":"<div><div>As Large language models (LLMs) continue to advance, the autonomous agents built upon them—LLM-based Autonomous Agents (LLMAAs) —are becoming more capable and widely used. While existing research has primarily focused on the capabilities of individual AI agents or their collaboration with humans, less is known about the emergent behaviors that arise when LLMAAs interact with each other at scale. This study addresses this gap by examining the collective behavior of LLMAAs in Chirper, a social simulation platform exclusively inhabited by AI agents. Drawing on theories from social network analysis and machine behavior, we investigate whether LLMAAs exhibit social dynamics commonly found in human communities, such as clustering, influential hubs, and homophily. Our findings reveal that LLMAAs form structured interaction networks that share key properties with human social systems, including power-law degree distributions and interaction homophily, though without exhibiting typical small-world characteristics. These insights represent an early step toward understanding the collective behavior of autonomous AI agents. They contribute to the emerging field of AI sociality and help inform the design of future multi-agent systems for engineering and social science applications.</div></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"10 1","pages":"Article 100107"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145529207","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}