Data and information management最新文献

Impact Score: Optimizing the timeliness and accuracy of journal impact assessment 影响评分：优化期刊影响评估的及时性和准确性

Data and information management

Pub Date : 2025-12-29 DOI: 10.1016/j.dim.2025.100121

Jing Li , Xue Yang , Xiaoli Lu , Dengsheng Wu

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 Web of Science (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.

在电子出版时代，期刊的出版、传播和被引速度显著提高。然而，传统的期刊评价指标无法捕捉到期刊传播的这种直接影响。针对数字出版时代期刊评价指标存在的问题，提出了一种改进的期刊评价指标——影响评分（Impact Score， IS）。在期刊影响因子（journal Impact Factor， JIF）的框架内，将期刊在出版年度的被引次数进行整合，从而更及时、全面地衡量期刊的影响力。期刊在给定年份(Y)的IS计算方法为该期刊在Y-2至Y期间发表的论文被引用的总次数除以同一时期发表的可引用论文的总次数。本文系统计算了Web of Science （WoS）数据库收录的10736种期刊的IS，分析了不同学科、不同期刊的IS性能差异，并进一步探讨了IS与期刊传播速度相关指标（如引文半衰期）的相关性。实证结果表明，IS与JIF和即时性指数（II）均呈高度正相关。在其出版年度中，高引用次数和高II分数的期刊在IS系统中的排名更高。IS在保持传统JIF评价框架稳定性的同时，有效地识别出具有知识快速传播特征的期刊，从而为电子出版环境下的期刊评价提供更灵敏、更全面的测量工具。

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引用次数: 0

Incorporating moral motivation in automatic summary generation of literary fiction 文学小说自动总结生成中的道德动机

Data and information management

Pub Date : 2025-12-27 DOI: 10.1016/j.dim.2025.100120

Chong Jiang , Weiwei Jin , Xiaoguang Wang , Liang Zhao

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 (

t_{l o w} = - 3.03

,

p = 0.0034

;

t_{h i g h} = - 2.9

,

p = 0.0049

). 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.

摘要自动生成旨在压缩知识，提高用户跨领域的信息检索和学习效率。在数字阅读中，为了吸引用户的注意力，引导深度阅读，摘要的功能发生了变化。文学小说的道德化潜力表现出一种独特的特征，即吸引读者的注意力，促进道德信息的保留。然而，先前的研究主要强调信息总结，从而忽视了这些潜在的道德认知机制。本研究引入心理道德动机的概念，构建MoralTextRank模型，使用50部文学小说作品生成包含道德信息的摘要。设计评价指标，对120名被试进行阅读效果评价。结果表明，与中性和低道德的摘要相比，包含道德信息的摘要显著吸引了用户的阅读注意力，尤其是在男性用户中（flow = - 3.03, p=0.0034; thigh= - 2.9, p=0.0049）。具体来说，男性参与者对高道德总结的参与度分别比低道德总结和无道德总结高8.2%和15.2%。本文认为，特定领域的需求对摘要生成的目的和设计有很大的影响。将道德信息整合到文学小说摘要中，可以有效地吸引读者的注意力，丰富数字阅读体验。反过来，这种做法可以提高数字内容环境中注意力分配的效率。本研究旨在整合社会文化因素，优化信息技术设计与流程，丰富信息技术的社会文化内涵。

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引用次数: 0

How does the perceived ubiquity of social media influence employees’ broad and deep socialization-based social media usage and digital well-being? 感知到的无处不在的社交媒体如何影响员工广泛而深入的基于社交的社交媒体使用和数字幸福感？

Data and information management

Pub Date : 2025-12-18 DOI: 10.1016/j.dim.2025.100118

Xuan Yang , Libo Ivy Liu , Xiling Cui

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.

在当今的数字时代，社交媒体与我们的日常生活和福祉密切相关，因此了解社交媒体对个人数字福祉的影响至关重要。这项研究调查了社交媒体使用（SMU）如何影响个人的数字幸福感。我们开发了一个研究模型来检验社交媒体无处不在的四个特征（连续性、可搜索性、即时性和便携性）对基于社交的广泛和深度社交媒体使用（SMU）的影响，这进一步影响了个人的数字幸福感。我们从600名员工中收集了两波调查设置的数据来测试这个模型。我们发现，连续性和可搜索性对基于广泛和深度社会化的SMU都有积极影响。即时性对深度SMU有积极影响，而可移植性对广度SMU有积极影响。基于广泛社交的SMU对数字幸福感有积极影响，但基于深度社交的使用没有积极影响。这些发现强调了社交媒体无处不在的不同特征在塑造社交媒体使用和幸福感方面所起的不同作用。这项研究有助于从理论上理解社交媒体的普遍性及其对数字幸福感的细微影响。此外，它还为设计促进福祉的社交媒体平台提供了实际见解。

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引用次数: 0

Exploring Chinese user's online dis-identification: An integration of technology-organization-environment obstacles and person–environment misfits 中国用户网络失认：技术-组织-环境障碍与人-环境不匹配的整合

Data and information management

Pub Date : 2025-12-13 DOI: 10.1016/j.dim.2025.100119

Xi Chen , Cheng Chen , Jian Mou , Xiangwen Cai

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.

在线不认同是指用户故意与某个在线平台保持距离或断绝联系。本研究从网络隐私问题带来的技术障碍导致的人-环境不适应的角度探讨了中国用户的网络失认。采用混合方法进行了两阶段的研究。对50名参与者进行了半结构化访谈，以确定技术-组织-环境（TOE）障碍和P-E不匹配的特征。本文建立了一个概念模型，并利用结构方程模型（SEM），利用对1142名停止使用微博的前微博用户的调查数据来检验提出的假设。除了角色冲突对平台使用率降低没有显著影响外，五个TOE障碍因素对三个P-E不匹配有显著影响。隐私问题显著影响了平台使用的减少和拒绝披露个人信息。三个P-E错配对在线失认有显著贡献。本研究为理解用户网络失认提供了一个解释性的理论框架、触发因素和过程。

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引用次数: 0

Uncovering the landscape of sustainable innovation funding through structural topic modeling 通过结构主题建模揭示可持续创新资助的格局

Data and information management

Pub Date : 2025-12-02 DOI: 10.1016/j.dim.2025.100117

Satpreet Kaur, Rajeev Kumar Panda

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.

可持续创新作为解决社会和环境挑战的催化剂，同时为企业创造经济效益。然而，旨在推动这一转型的公司在筹集资金方面面临挑战。随着可持续创新领域的不断扩大，对其资助机制的深入了解是必要的。这项研究使用了一种无监督的机器学习方法来建立一个精确而全面的艺术知识。本文采用结构化主题建模框架，这是一种利用先进统计方法从大量文本数据中获得语义知识的定量技术。该研究描述了该领域的突出模式，指出了一个将可持续创新和金融的关键组成部分联系起来的综合框架，同时强调了绿色信贷政策干预的作用。结构主题模型的研究结果确定了10个不同的主题，并为未来可持续创新资助机制的研究提出了多种研究前景。该研究为投资者、政策制定者和企业家优化资源配置、设计有针对性的政策和调整企业战略以吸引可持续资金提供了重要工具。从方法论的角度来看，本研究利用结构主题建模作为一种创新的文献综述方法，从而能够对更广泛的研究成果进行深入分析，并产生比传统方法更有价值的见解。

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引用次数: 0

Key factors and configuration paths of digital innovation in manufacturing enterprises under the TOE framework TOE框架下制造企业数字化创新的关键因素与配置路径

Data and information management

Pub Date : 2025-11-06 DOI: 10.1016/j.dim.2025.100116

Jingmei Ma, Jie Wu, Zhiqing Li

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&D investment, firm size, workforce size, absorptive capacity, and R&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.

数字化创新是制造业企业提升竞争力、实现跨越式发展的关键。本文以2012 - 2022年中国a股制造业上市企业为研究对象，通过TOE框架分析了个体因素和多因素关联对数字创新的影响。它将CatBoost和SHAP机器学习算法与多周期QCA集成在一起。结果表明：(1)CatBoost回归模型具有较强的解释能力。SHAP分析确定了制造业数字化创新的六个关键决定因素：市场竞争、研发投资、企业规模、劳动力规模、吸收能力和研发人员。(2)多周期QCA揭示了数字化转型不同阶段的不同配置路径。在最初的勘探阶段，出现了7种配置，主要特征是技术驱动和技术环境协同作用。在高速开发阶段，确定了八种配置，突出了技术-组织和组织-环境的协同作用。在加速阶段，发现了技术驱动因素、技术组织因素、技术环境因素和组织环境因素之间相互作用的十种配置。(3)在所有时期，技术和组织因素都是高度数字化创新的核心条件。随着数字化的发展，环境因素的作用越来越重要。

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引用次数: 0

A topic-enhanced network via contrastive learning for abstractive text summarization 基于对比学习的主题增强网络抽象文本摘要

Data and information management

Pub Date : 2025-10-29 DOI: 10.1016/j.dim.2025.100114

Chuanming Yu , Dianyuan Zhang , Xiping Hao , Xueqing Fu , Jie Shen , Lu An

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.

摘要摘要已成为一项引人注目的研究课题，引起了人们的广泛关注。尽管取得了进步，现有的方法仍然难以有效地解决暴露偏差的问题，导致训练和推理之间的差距。此外，大多数基于对比学习的模型忽略了全局语义的重要性，例如潜在的主题信息。为了解决这些问题，本文提出了一种基于对比学习（TESC）模型的主题增强序列到序列网络。与目前大多数研究相比，本文利用主题建模和对比学习相结合的方法来减少暴露偏差问题，提高生成摘要的质量。此外，本文采用硬负抽样，选取接近正样本的负样本。暴露偏差是指自动总结模型中训练依赖于ground-truth数据而推理依赖于自生成序列的差异，导致错误积累和总结质量下降。本文在CNN/DailyMail、XSum、Reddit-TIFU和SAMSum四个数据集上进行了严格的实验。实验结果证明了TESC方法的有效性和适用性。该研究揭示了主题一致性和硬负抽样在利用对比学习提高当前模型性能方面的作用。

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引用次数: 0

Enhancing fake news detection through estimating user tendencies to spread fake news 通过估计用户传播假新闻的倾向，加强假新闻检测

Data and information management

Pub Date : 2025-10-28 DOI: 10.1016/j.dim.2025.100115

Ahmad Hashemi , Mohammad Reza Moosavi , Wei Shi , Anastasia Giachanou

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.

社交媒体对人们如何消费信息的影响越来越大，重塑了公共传播的格局。除了带来的好处，这种转变还导致假新闻传播速度加快，降低了公众的信任，影响了人们对事件的看法。深入了解假新闻的传播方式，了解不同用户在传播过程中扮演的角色，是有效检测假新闻的重要步骤。在本研究中，我们探讨了如何预测用户的分享行为来提高假新闻检测（FND）。我们引入了一种基于回归的方法，通过利用来自他们在线帖子的语言特征来估计用户传播假新闻的倾向（TSFN）。为了训练和评估模型，我们提出了两个新的数据集，每个数据集包含5000个用户。随后，我们使用训练好的TSFN估计器模型来检测假新闻，提出了一个两步FND系统。在第一步，对于给定的新闻，系统使用训练好的估计器估计其传播者的TSFN分数。然后，利用这些分数，系统确定新闻项目的真实性。通过进一步结合新闻内容特点，提高了系统的性能。实验结果表明，即使在新闻传播的早期阶段，该框架也表现可靠。此外，我们探讨了情绪信号如何有助于区分假新闻和真实新闻以及识别假新闻传播者，为模型的决策提供了有价值的见解。

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引用次数: 0

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 一个人在社交媒体上多次参与是罕见的：对350万Instagram用户账户和29个用户采访的分析得出的见解

Data and information management

Pub Date : 2025-10-08 DOI: 10.1016/j.dim.2025.100113

Kholoud Khalil Aldous , Sercan Şengün , Joni Salminen , Ali Farooq , Soon-Gyo Jung , Bernard J. Jansen

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.

本研究通过两项研究考察了个人用户参与社交媒体帖子的频率和原因，如反应、分享、评论或标记，多次而不是一次，被称为多次参与行为（MEB）或单一参与行为（SEB）。第一项研究定量分析了来自43个组织的231554条Instagram帖子的3.45亿次互动，共有3527289个用户账户，以确定反应和评论的MEB频率。研究结果表明，MEB发生了210多万次，但仅占总参与的0.63%，表明SEB是最常见的。第二项研究定性分析了29个社交媒体用户访谈，以调查MEB的驱动因素和障碍，表明用户优先考虑保持他人的匿名性，并且在大多数情况下很少有动力进行多次公共互动。当他们参与MEB时，通常是私下进行的，比如通过直接发送消息来避免公开。一个关键的结论是，公开的社交媒体帖子数量可以作为人数数量的合理代表，因为平台通常会向公众隐瞒这些人数数量，这是对设计、法律和营销的有效洞察。

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引用次数: 0

The influence of multidisciplinary mega-journals on the Journal Impact Factor: Discipline, country/region, category, JIF quartile, and journal 多学科大型期刊对期刊影响因子的影响：学科、国家/地区、类别、JIF四分位数和期刊

Data and information management

Pub Date : 2025-10-07 DOI: 10.1016/j.dim.2025.100112

Jing Li , Dengsheng Wu , Xinxin Chen

This study examines the significant impact of mega-journals (MJs) on the scholarly evaluation system, particularly their citation contributions and the increased impact factor scores of influenced entities. By analyzing seven MJs across multiple dimensions, including discipline, country/region, Web of Science (WoS) category, and JIF quartile, we used the Generalized Impact Factor (GIF) as a proxy for the Journal Impact Factor (JIF) and developed the Contribution to Impact Factor (CIF) metric to quantify MJs' contributions. Our findings indicate that MJs can increase the GIF of scholarly entities by up to 7.79 %, even in countries/regions with millions of citations. Notably, Computer Science, Information Systems received 14.21 % of its citations from MJs, the highest among 241 fields studied. These results highlight the potential for MJs to inflate citation-based metrics, posing challenges to the academic evaluation system. We recommend adaptive strategies to mitigate JIF inflation or the development of alternative metrics to ensure a fair evaluation system.

本研究考察了大型期刊（MJs）对学术评估系统的重大影响，特别是它们的引用贡献和受影响实体的影响因子得分的增加。通过对学科、国家/地区、Web of Science （WoS）类别和期刊影响因子四分位数等7个维度的期刊影响因子进行分析，采用广义影响因子（GIF）代替期刊影响因子（JIF），并建立影响因子贡献（CIF）指标来量化期刊影响因子的贡献。我们的研究结果表明，即使在引用量高达数百万的国家/地区，MJs也能使学术实体的GIF增加7.79%。值得注意的是，计算机科学、信息系统领域有14.21%的引用来自于MJs，在241个研究领域中是最高的。这些结果突出了MJs夸大基于引用的指标的潜力，对学术评估系统提出了挑战。我们建议采用适应性策略来缓解JIF通胀或开发替代指标，以确保公平的评估体系。

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引用次数: 0