Susanne Jana Adler , Pratyush Nidhi Sharma , Lăcrămioara Radomir
{"title":"Toward open science in PLS-SEM: Assessing the state of the art and future perspectives","authors":"Susanne Jana Adler , Pratyush Nidhi Sharma , Lăcrămioara Radomir","doi":"10.1016/j.jbusres.2023.114291","DOIUrl":null,"url":null,"abstract":"<div><p>Driven by the high-profile failures to reproduce and replicate published findings, there have been increasing demands to adopt open science practices across scientific disciplines in order to enhance research transparency. Critics have highlighted the use of underpowered studies and researchers’ analytical degrees of freedom as factors contributing to these issues. Despite methodological advances and updated guidelines, similar concerns persist regarding studies utilizing partial least squares structural equation modeling (PLS-SEM). Open science practices can help alleviate these concerns by facilitating transparency in PLS-SEM-based studies. However, the current level of adherence to these practices remains unknown. In this article, we conduct a comprehensive literature review of leading marketing journals to assess the extent to which open science practices are implemented in PLS-SEM-based studies. Based on the observed lack of adoption, we propose a PLS-SEM-specific preregistration template that researchers can use to foster transparency in their analyses, thereby bolstering confidence in their findings.</p></div>","PeriodicalId":15123,"journal":{"name":"Journal of Business Research","volume":"169 ","pages":"Article 114291"},"PeriodicalIF":10.5000,"publicationDate":"2023-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Business Research","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0148296323006501","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 1
Abstract
Driven by the high-profile failures to reproduce and replicate published findings, there have been increasing demands to adopt open science practices across scientific disciplines in order to enhance research transparency. Critics have highlighted the use of underpowered studies and researchers’ analytical degrees of freedom as factors contributing to these issues. Despite methodological advances and updated guidelines, similar concerns persist regarding studies utilizing partial least squares structural equation modeling (PLS-SEM). Open science practices can help alleviate these concerns by facilitating transparency in PLS-SEM-based studies. However, the current level of adherence to these practices remains unknown. In this article, we conduct a comprehensive literature review of leading marketing journals to assess the extent to which open science practices are implemented in PLS-SEM-based studies. Based on the observed lack of adoption, we propose a PLS-SEM-specific preregistration template that researchers can use to foster transparency in their analyses, thereby bolstering confidence in their findings.
期刊介绍:
The Journal of Business Research aims to publish research that is rigorous, relevant, and potentially impactful. It examines a wide variety of business decision contexts, processes, and activities, developing insights that are meaningful for theory, practice, and/or society at large. The research is intended to generate meaningful debates in academia and practice, that are thought provoking and have the potential to make a difference to conceptual thinking and/or practice. The Journal is published for a broad range of stakeholders, including scholars, researchers, executives, and policy makers. It aids the application of its research to practical situations and theoretical findings to the reality of the business world as well as to society. The Journal is abstracted and indexed in several databases, including Social Sciences Citation Index, ANBAR, Current Contents, Management Contents, Management Literature in Brief, PsycINFO, Information Service, RePEc, Academic Journal Guide, ABI/Inform, INSPEC, etc.