评估意图-行为联系中的遗漏变量偏差:缓解策略和研究意义

IF 20.1 1区 管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE
Anand Jeyaraj
{"title":"评估意图-行为联系中的遗漏变量偏差:缓解策略和研究意义","authors":"Anand Jeyaraj","doi":"10.1016/j.ijinfomgt.2024.102809","DOIUrl":null,"url":null,"abstract":"<div><p>Omitting relevant variables in research models is a significant challenge in academic research involving non-experimental research methods. Omitted variables may bias the empirical findings and lead to erroneous conclusions about relationships between factors underlying information systems phenomena. Using data coded from 128 samples reported in 105 prior studies, this study applies meta-regression methods to quantify the extent to which omitted variables bias the reported effect sizes between behavioral intention and system use. Since a direct examination of omitted variables is not possible, this study quantifies four measures: independent variables common to both behavioral intention and system use, moderators for the relationship between behavioral intention and system use, moderators for relationships between other independent variables and system use, and control variables for system use. Meta-regression results show that the effect size for the relationship between behavioral intention and system use decreases when independent variables common to behavioral intention and system use or moderators for the relationship between behavioral intention and system use are included in research models. This implies that the non-inclusion of relevant variables distorts the effect size for the relationship between behavioral intention and system use resulting in a biased understanding of the relationships. It is crucial for research models to include relevant variables such that the omitted variables bias can be effectively handled. Several mitigation strategies and research implications are described.</p></div>","PeriodicalId":48422,"journal":{"name":"International Journal of Information Management","volume":"78 ","pages":"Article 102809"},"PeriodicalIF":20.1000,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing omitted variables bias in intention-behavior linkages: Mitigation strategies and research implications\",\"authors\":\"Anand Jeyaraj\",\"doi\":\"10.1016/j.ijinfomgt.2024.102809\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Omitting relevant variables in research models is a significant challenge in academic research involving non-experimental research methods. Omitted variables may bias the empirical findings and lead to erroneous conclusions about relationships between factors underlying information systems phenomena. Using data coded from 128 samples reported in 105 prior studies, this study applies meta-regression methods to quantify the extent to which omitted variables bias the reported effect sizes between behavioral intention and system use. Since a direct examination of omitted variables is not possible, this study quantifies four measures: independent variables common to both behavioral intention and system use, moderators for the relationship between behavioral intention and system use, moderators for relationships between other independent variables and system use, and control variables for system use. Meta-regression results show that the effect size for the relationship between behavioral intention and system use decreases when independent variables common to behavioral intention and system use or moderators for the relationship between behavioral intention and system use are included in research models. This implies that the non-inclusion of relevant variables distorts the effect size for the relationship between behavioral intention and system use resulting in a biased understanding of the relationships. It is crucial for research models to include relevant variables such that the omitted variables bias can be effectively handled. Several mitigation strategies and research implications are described.</p></div>\",\"PeriodicalId\":48422,\"journal\":{\"name\":\"International Journal of Information Management\",\"volume\":\"78 \",\"pages\":\"Article 102809\"},\"PeriodicalIF\":20.1000,\"publicationDate\":\"2024-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Information Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0268401224000574\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Information Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0268401224000574","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 0

摘要

在涉及非实验研究方法的学术研究中,忽略研究模型中的相关变量是一项重大挑战。遗漏变量可能会使实证研究结果产生偏差,并导致对信息系统现象的基本因素之间的关系得出错误的结论。本研究使用 105 项先前研究中报告的 128 个样本的编码数据,采用元回归方法量化遗漏变量对报告的行为意向和系统使用之间效应大小的偏差程度。由于不可能对遗漏变量进行直接检验,本研究量化了四个衡量指标:行为意向和系统使用的共同自变量、行为意向和系统使用之间关系的调节变量、其他自变量和系统使用之间关系的调节变量以及系统使用的控制变量。元回归结果显示,当研究模型中包含行为意向和系统使用共同的自变量或行为意向和系统使用之间关系的调节变量时,行为意向和系统使用之间关系的效应大小会减小。这意味着,不包含相关变量会扭曲行为意向与系统使用之间关系的效应大小,导致对两者关系的理解出现偏差。研究模型必须包含相关变量,这样才能有效处理遗漏变量偏差。本文介绍了几种缓解策略和研究意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing omitted variables bias in intention-behavior linkages: Mitigation strategies and research implications

Omitting relevant variables in research models is a significant challenge in academic research involving non-experimental research methods. Omitted variables may bias the empirical findings and lead to erroneous conclusions about relationships between factors underlying information systems phenomena. Using data coded from 128 samples reported in 105 prior studies, this study applies meta-regression methods to quantify the extent to which omitted variables bias the reported effect sizes between behavioral intention and system use. Since a direct examination of omitted variables is not possible, this study quantifies four measures: independent variables common to both behavioral intention and system use, moderators for the relationship between behavioral intention and system use, moderators for relationships between other independent variables and system use, and control variables for system use. Meta-regression results show that the effect size for the relationship between behavioral intention and system use decreases when independent variables common to behavioral intention and system use or moderators for the relationship between behavioral intention and system use are included in research models. This implies that the non-inclusion of relevant variables distorts the effect size for the relationship between behavioral intention and system use resulting in a biased understanding of the relationships. It is crucial for research models to include relevant variables such that the omitted variables bias can be effectively handled. Several mitigation strategies and research implications are described.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
International Journal of Information Management
International Journal of Information Management INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
53.10
自引率
6.20%
发文量
111
审稿时长
24 days
期刊介绍: The International Journal of Information Management (IJIM) is a distinguished, international, and peer-reviewed journal dedicated to providing its readers with top-notch analysis and discussions within the evolving field of information management. Key features of the journal include: Comprehensive Coverage: IJIM keeps readers informed with major papers, reports, and reviews. Topical Relevance: The journal remains current and relevant through Viewpoint articles and regular features like Research Notes, Case Studies, and a Reviews section, ensuring readers are updated on contemporary issues. Focus on Quality: IJIM prioritizes high-quality papers that address contemporary issues in information management.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信