MIS QUARTERLY管理信息系统季刊
MIS QUARTERLY(英文缩写 MIS QUART),ISSN 0276-7783,中文译名:管理信息系统季刊 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 8.513 | Q1 |
| 2022 | 7.300 | Q1 |
| 2023 | 7.000 | Q1 |
| 2024 | 6.000 | Q1 |
| 2025 | 7.300 | Q1 |
MIS QUARTERLY 最新收录文献
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1. VALIDITY IN DESIGN SCIENCE.
1. 设计科学中的有效性PMID:日期:2025-12Researchers must ensure that the claims about the knowledge produced by their work are valid. However, validity is neither well-understood nor consistently established in design science, which involves the development and evaluation of artifacts (models, methods, instantiations, and theories) to solve problems. As a result, it is challenging to demonstrate and communicate the validity of knowledge claims about artifacts. This paper defines validity in design science and derives the Design Science Validity Framework and a process model for applying it. The framework comprises three high-level claim and validity types-criterion, causal, and context-as well as validity subtypes. The framework guides researchers in integrating validity considerations into projects employing design science and contributes to the growing body of research on design science methodology. It also provides a systematic way to articulate and validate the knowledge claims of design science projects. We apply the framework to examples from existing research and then use it to demonstrate the validity of knowledge claims about the framework itself.
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2. AI-Augmented Content Validation in Behavioral Research: Development and Evaluation of the RATER System.
PMID:日期:2025-06-23Content validation is an essential aspect of the scale development process that ensures that measurement instruments capture their intended constructs. However, researchers rarely undertake this core step in behavioral research because it requires costly data collection and specialized expertise. We present RATER (Replicable Approach to Expert Ratings), a free web-based system (www.contval.org) that can help the broader research community (scientists, reviewers, students) gain quick and reliable insights into the content validity of measurement instruments. Guided by psychometric measurement theory, RATER evaluates whether a scale's items correspond to their intended construct, remain distinct from other constructs, and adequately represent all aspects of the construct's content domain. The system employs two unique artificial intelligence models, RATER and RATER, which leverage psychometric scales from 2,443 journal articles spanning eight disciplines and two state-of-the-art large language model architectures (i.e., BERT and GPT). A set of six complementary studies confirms the RATER system's accuracy, reliability, and usefulness. We find RATER can augment the scale development and validation process, increasing the validity of findings in behavioral research.
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3. Digression and Value Concatenation to Enable Privacy-Preserving Regression.
PMID:日期:2014-09Regression techniques can be used not only for legitimate data analysis, but also to infer private information about individuals. In this paper, we demonstrate that regression trees, a popular data-analysis and data-mining technique, can be used to effectively reveal individuals' sensitive data. This problem, which we call a "regression attack," has not been addressed in the data privacy literature, and existing privacy-preserving techniques are not appropriate in coping with this problem. We propose a new approach to counter regression attacks. To protect against privacy disclosure, our approach introduces a novel measure, called , which assesses the sensitive value disclosure risk in the process of building a regression tree model. Specifically, we develop an algorithm that uses the measure for pruning the tree to limit disclosure of sensitive data. We also propose a dynamic value-concatenation method for anonymizing data, which better preserves data utility than a user-defined generalization scheme commonly used in existing approaches. Our approach can be used for anonymizing both numeric and categorical data. An experimental study is conducted using real-world financial, economic and healthcare data. The results of the experiments demonstrate that the proposed approach is very effective in protecting data privacy while preserving data quality for research and analysis.