Data and information management最新文献_第2页

An evaluation method of academic output that considers productivity differences 考虑生产率差异的学术成果评估方法

Data and information management Pub Date : 2024-09-01 DOI: 10.1016/j.dim.2023.100062

{"title":"An evaluation method of academic output that considers productivity differences","authors":"","doi":"10.1016/j.dim.2023.100062","DOIUrl":"10.1016/j.dim.2023.100062","url":null,"abstract":"<div><p>There are productivity differences among academic fields. Researchers who work in academic fields that have low productivity are pressured to publish more, and this policy may cause researchers to publish more in journals that have lenient standards and publish articles that are not necessarily valuable for their academic field. The problem is not solved by normalizing journals’ impact factors by the subjects because the normalized impact factors do not reflect the difficulty of publication in that subject. In this paper, we propose an evaluation method –Reference Group Similarity Index-that addresses the productivity differences issue. The method uses the publications of a reference group of departments that are believed to have the right publication incentives. Then, other departments are evaluated to the degree that their publications are similar to that of the reference group. We apply the method to the top 50 economics departments according to USNews rankings and show that the department rankings that we get from the Reference Group Similarity Index are largely consistent with the USNews Rankings.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"8 3","pages":"Article 100062"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2543925123000360/pdfft?md5=3830333946bad3c804ea62905bfdac95&pid=1-s2.0-S2543925123000360-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138985866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A literature review of artificial intelligence research in business and management using machine learning and ChatGPT 利用机器学习和 ChatGPT 进行商业和管理领域人工智能研究的文献综述

Data and information management Pub Date : 2024-09-01 DOI: 10.1016/j.dim.2024.100076

Nazmiye Guler, Samuel N. Kirshner, Richard Vidgen

{"title":"A literature review of artificial intelligence research in business and management using machine learning and ChatGPT","authors":"Nazmiye Guler, Samuel N. Kirshner, Richard Vidgen","doi":"10.1016/j.dim.2024.100076","DOIUrl":"10.1016/j.dim.2024.100076","url":null,"abstract":"<div><p>This paper investigates applying AI models and topic modelling techniques to enhance computational literature reviews in business, management, and information systems. The study highlights the significance of impactful journals and emphasises the need for interdisciplinary and transdisciplinary research, especially in addressing AI's ethical and regulatory challenges. We demonstrate the effectiveness of combining machine learning and ChatGPT in the literature review process. Machine learning is used to identify research topics, and ChatGPT assists researchers in labelling the topics, generating content, and improving the efficiency of academic writing. By leveraging topic modelling techniques and ChatGPT, we uncover and label topics within the literature, shedding light on the thematic structure and content of the research field, allowing researchers to uncover meaningful insights, identify research gaps, and highlight rapidly expanding research areas. Additionally, we contribute to the literature review process by introducing a methodology that identifies impactful papers, helping to bridge the gap between computational literature reviews and traditional literature reviews.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"8 3","pages":"Article 100076"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2543925124000123/pdfft?md5=2959ac9dd5a9d4cb769f8ea9a9c1a550&pid=1-s2.0-S2543925124000123-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142149109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting changes in task difficulty perception based on visual behavior in mobile health information search 根据移动健康信息搜索中的视觉行为预测任务难度感知的变化

Data and information management Pub Date : 2024-06-01 DOI: 10.1016/j.dim.2024.100074

Jing Chen, Hong-Lin Chen, Shubin Zhou, Quan Lu

引用次数: 0

Enhancing team creativity among information technology professionals through knowledge sharing and motivational rewards: A self-determination perspective 通过知识共享和激励奖励提高信息技术专业人员的团队创造力：自我决定的视角

Data and information management Pub Date : 2024-06-01 DOI: 10.1016/j.dim.2024.100075

Xiling Cui, Xuan Yang, Jifan Ren, Paul Benjamin Lowry, Timon Chih-ting Du

引用次数: 0

Special issue: Systematic review and meta-analysis in information management research 特刊：信息管理研究中的系统综述和荟萃分析

Data and information management Pub Date : 2024-03-13 DOI: 10.1016/j.dim.2024.100069

Jian Mou, Jason Cohen

引用次数: 0

Generative AI: A systematic review using topic modelling techniques 生成式人工智能：使用主题建模技术的系统综述

Data and information management Pub Date : 2024-02-15 DOI: 10.1016/j.dim.2024.100066

Priyanka Gupta , Bosheng Ding , Chong Guan , Ding Ding

{"title":"Generative AI: A systematic review using topic modelling techniques","authors":"Priyanka Gupta , Bosheng Ding , Chong Guan , Ding Ding","doi":"10.1016/j.dim.2024.100066","DOIUrl":"10.1016/j.dim.2024.100066","url":null,"abstract":"<div><p>Generative artificial intelligence (GAI) is a rapidly growing field with a wide range of applications. In this paper, a thorough examination of the research landscape in GAI is presented, encompassing a comprehensive overview of the prevailing themes and topics within the field. The study analyzes a corpus of 1319 records from Scopus spanning from 1985 to 2023 and comprises journal articles, books, book chapters, conference papers, and selected working papers.</p><p>The analysis revealed seven distinct clusters of topics in GAI research: image processing and content analysis, content generation, emerging use cases, engineering, cognitive inference and planning, data privacy and security, and Generative Pre-Trained Transformer (GPT) academic applications. The paper discusses the findings of the analysis and identifies some of the key challenges and opportunities in GAI research.</p><p>The paper concludes by calling for further research in GAI, particularly in the areas of explainability, robustness, cross-modal and multi-modal generation, and interactive co-creation. The paper also highlights the importance of addressing the challenges of data privacy and security in GAI and responsible use of GAI.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"8 2","pages":"Article 100066"},"PeriodicalIF":0.0,"publicationDate":"2024-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2543925124000020/pdfft?md5=dbdf97fdc7e10603e5fe8ff706294d18&pid=1-s2.0-S2543925124000020-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139825717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative AI: A systematic review using topic modelling techniques 生成式人工智能：使用主题建模技术的系统综述

Data and information management Pub Date : 2024-02-01 DOI: 10.1016/j.dim.2024.100066

Priyanka Gupta, Bosheng Ding, Chong Guan, Ding Ding

引用次数: 0

Special issue: Systematic review and meta-analysis in information management research - Editorial 特刊：信息管理研究中的系统综述和荟萃分析 - 社论

Data and information management Pub Date : 2024-01-11 DOI: 10.1016/j.dim.2024.100065

Jian Mou, Jason Cohen

引用次数: 0

The impact of artificial intelligence on organisational cyber security: An outcome of a systematic literature review 人工智能对组织网络安全的影响：系统文献综述成果

Data and information management Pub Date : 2023-12-25 DOI: 10.1016/j.dim.2023.100063

Irshaad Jada, Thembekile O. Mayayise

{"title":"The impact of artificial intelligence on organisational cyber security: An outcome of a systematic literature review","authors":"Irshaad Jada, Thembekile O. Mayayise","doi":"10.1016/j.dim.2023.100063","DOIUrl":"10.1016/j.dim.2023.100063","url":null,"abstract":"<div><p>As digital transformation continues to advance, organisations are becoming increasingly aware of the benefits that modern technologies offer. However, with greater technology adoption comes a higher risk of cyber security threats and attacks. Therefore, there is a need for more advanced measures to protect against constantly evolving threats. One potential solution is the use of Artificial Intelligence (AI). The aim of this research paper was to conduct a systematic literature review (SLR) to assess the impact of AI-based technologies on organisational cyber security and determine their effectiveness compared to traditional cyber security approaches. The PRISMA flow diagram was used to guide the review process. Peer-reviewed articles from 2018 to 2023 were included from EBSCO Host, Google Scholar, Science Direct, ProQuest & SCOPUS and 73 remaining articles were synthesised.</p><p>The results revealed that AI can impact cybersecurity throughout it's entire life cycle, yielding benefits like automation, threat intelligence, and improved cyber defense. Nevertheless, it also brings challenges like adversarial attacks and the need for high-quality data, which could lead to the inefficiency of AI. These results affirm the positive influence of AI on cybersecurity, enhancing effectiveness and resilience. These findings provide a solid foundation for further research in the field of organisational cybersecurity. These results can help organisations make informed decisions on AI implementations by offering an impartial view of its impacts.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"8 2","pages":"Article 100063"},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2543925123000372/pdfft?md5=80d99e19ddfeeda09861bf3bc7ea0505&pid=1-s2.0-S2543925123000372-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139191526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic literature review and analysis of try-on technology: Virtual fitting rooms 试穿技术的系统文献回顾与分析：虚拟试衣间

Data and information management Pub Date : 2023-12-12 DOI: 10.1016/j.dim.2023.100060

Raheela Batool , Jian Mou

{"title":"A systematic literature review and analysis of try-on technology: Virtual fitting rooms","authors":"Raheela Batool , Jian Mou","doi":"10.1016/j.dim.2023.100060","DOIUrl":"10.1016/j.dim.2023.100060","url":null,"abstract":"<div><p>To enhance customer satisfaction and transform negative perceptions of online apparel shopping, fashion brands are increasingly adopting virtual fitting rooms. Despite clothing being the most frequently purchased item online, customers often struggle to find garments that fit their size and skin tone. Consequently, the clothing industry experiences a higher return rate compared to other e-commerce sectors. To address this issue, fashion brands prioritize the implementation of try-on technology, aiming to improve product visibility and provide sensory input. However, existing studies have yielded conflicting findings. This research aims to resolve this issue by organizing and categorizing the relevant literature. To collect related publications, scholarly databases such as Scopus, Emerald, Springer, Wiley, Science Direct, ProQuest, and IEEE Xplore were extensively searched. The investigation employed a systematic literature review strategy, with the search conducted from January 2005 to February 2023. Ultimately, eighty publications meeting the selection criteria were chosen for further review. The study classifies the literature into subfields based on publication year and region, thoroughly exploring various aspects of TOT, including theories, influencing factors, moderating, or mediating variables, outcomes, and notable findings. Based on the evaluation results, a conceptual model and research gap for TOT is proposed to guide future research in this domain, providing valuable insights for both the management and academic research communities.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"8 2","pages":"Article 100060"},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2543925123000347/pdfft?md5=55d479f9f9ddbd6a41d3a83b0e4ceedf&pid=1-s2.0-S2543925123000347-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139024737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0