Pub Date : 2021-01-01DOI: 10.1177/1094428119854153
Chris Kaibel, Torsten Biemann
In experiments, researchers commonly allocate subjects randomly and equally to the different treatment conditions before the experiment starts. While this approach is intuitive, it means that new information gathered during the experiment is not utilized until after the experiment has ended. Based on methodological approaches from other scientific disciplines such as computer science and medicine, we suggest machine learning algorithms for subject allocation in experiments. Specifically, we discuss a Bayesian multi-armed bandit algorithm for randomized controlled trials and use Monte Carlo simulations to compare its efficiency with randomized controlled trials that have a fixed and balanced subject allocation. Our findings indicate that a randomized allocation based on Bayesian multi-armed bandits is more efficient and ethical in most settings. We develop recommendations for researchers and discuss the limitations of our approach.
{"title":"Rethinking the Gold Standard With Multi-armed Bandits: Machine Learning Allocation Algorithms for Experiments","authors":"Chris Kaibel, Torsten Biemann","doi":"10.1177/1094428119854153","DOIUrl":"https://doi.org/10.1177/1094428119854153","url":null,"abstract":"In experiments, researchers commonly allocate subjects randomly and equally to the different treatment conditions before the experiment starts. While this approach is intuitive, it means that new information gathered during the experiment is not utilized until after the experiment has ended. Based on methodological approaches from other scientific disciplines such as computer science and medicine, we suggest machine learning algorithms for subject allocation in experiments. Specifically, we discuss a Bayesian multi-armed bandit algorithm for randomized controlled trials and use Monte Carlo simulations to compare its efficiency with randomized controlled trials that have a fixed and balanced subject allocation. Our findings indicate that a randomized allocation based on Bayesian multi-armed bandits is more efficient and ethical in most settings. We develop recommendations for researchers and discuss the limitations of our approach.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"24 1","pages":"78 - 103"},"PeriodicalIF":9.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1094428119854153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41983757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1177/1094428119872531
J. Cortina, Hannah M. Markell-Goldstein, Jennifer P. Green, Yingyi Chang
Latent variable models and interaction effects have both been common in the organizational sciences for some time. Methods for incorporating interactions into latent variable models have existed since at least Kenny and Judd, and a great many articles and books have developed these methods further. In the present article, we present an empirical review of the methods that organizational science investigators use to test their interaction hypotheses. We show that it is very common for investigators to use fully latent methods to test additive portions of their models, but to abandon such methods when testing the multiplicative portions of their models. By contrast, investigators whose models do not contain interactions tend to stick with fully latent methods throughout. As there is little rational basis for this pattern, it is likely due to continued discomfort regarding the proper application of existing fully latent methods. Thus, we end by offering R code that implements some of the more sophisticated fully latent approaches, and by offering a sequence of decisions that investigators can follow in order to choose the best analytic approach.
{"title":"How Are We Testing Interactions in Latent Variable Models? Surging Forward or Fighting Shy?","authors":"J. Cortina, Hannah M. Markell-Goldstein, Jennifer P. Green, Yingyi Chang","doi":"10.1177/1094428119872531","DOIUrl":"https://doi.org/10.1177/1094428119872531","url":null,"abstract":"Latent variable models and interaction effects have both been common in the organizational sciences for some time. Methods for incorporating interactions into latent variable models have existed since at least Kenny and Judd, and a great many articles and books have developed these methods further. In the present article, we present an empirical review of the methods that organizational science investigators use to test their interaction hypotheses. We show that it is very common for investigators to use fully latent methods to test additive portions of their models, but to abandon such methods when testing the multiplicative portions of their models. By contrast, investigators whose models do not contain interactions tend to stick with fully latent methods throughout. As there is little rational basis for this pattern, it is likely due to continued discomfort regarding the proper application of existing fully latent methods. Thus, we end by offering R code that implements some of the more sophisticated fully latent approaches, and by offering a sequence of decisions that investigators can follow in order to choose the best analytic approach.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"24 1","pages":"26 - 54"},"PeriodicalIF":9.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1094428119872531","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42487641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-30DOI: 10.1177/1094428120980047
Sebnem Cilesiz, Thomas Greckhamer
Trends toward convergence on common methodologies and standardized templates restrict the diversity of qualitative methods in organizational research. Considering that graduate education is a critical process in the socialization of researchers into the norms and dominant practices of their discipline, graduate students’ socialization into research methodologies is vital for understanding methodological convergence. The purpose of our study was to understand how graduate students’ socialization shapes their methodological and paradigmatic preferences. Showcasing methodological bricolage as an alternative to qualitative templates, we constructed a research design that combined thematic, discourse, and narrative analyses to investigate graduate students’ reflections throughout a qualitative methods course introducing alternative research paradigms. Our findings highlight the role of institutional, disciplinary, and personal influences as well as identity work in researchers’ socialization and trace alternative trajectories by which socialization and methodological identity construction processes may unfold. We offer a sketch of methodological socialization and suggest that its understanding should be central to nurturing paradigmatic and methodological plurality in qualitative research. We conclude with implications for future research and for research methods training.
{"title":"Methodological Socialization and Identity: A Bricolage Study of Pathways Toward Qualitative Research in Doctoral Education","authors":"Sebnem Cilesiz, Thomas Greckhamer","doi":"10.1177/1094428120980047","DOIUrl":"https://doi.org/10.1177/1094428120980047","url":null,"abstract":"Trends toward convergence on common methodologies and standardized templates restrict the diversity of qualitative methods in organizational research. Considering that graduate education is a critical process in the socialization of researchers into the norms and dominant practices of their discipline, graduate students’ socialization into research methodologies is vital for understanding methodological convergence. The purpose of our study was to understand how graduate students’ socialization shapes their methodological and paradigmatic preferences. Showcasing methodological bricolage as an alternative to qualitative templates, we constructed a research design that combined thematic, discourse, and narrative analyses to investigate graduate students’ reflections throughout a qualitative methods course introducing alternative research paradigms. Our findings highlight the role of institutional, disciplinary, and personal influences as well as identity work in researchers’ socialization and trace alternative trajectories by which socialization and methodological identity construction processes may unfold. We offer a sketch of methodological socialization and suggest that its understanding should be central to nurturing paradigmatic and methodological plurality in qualitative research. We conclude with implications for future research and for research methods training.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"25 1","pages":"337 - 370"},"PeriodicalIF":9.5,"publicationDate":"2020-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1094428120980047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41650099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-16DOI: 10.1177/1094428120982699
{"title":"Corrigendum to Nonlinear Transformations in Organizational Research: Possible Problems and Potential Solutions","authors":"","doi":"10.1177/1094428120982699","DOIUrl":"https://doi.org/10.1177/1094428120982699","url":null,"abstract":"","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"24 1","pages":"484 - 484"},"PeriodicalIF":9.5,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1094428120982699","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45185625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-08DOI: 10.1177/1094428120969905
Marc H. Anderson, Russell Lemken
Citation context analysis is a detailed and rigorous form of literature review that goes beyond traditional narrative and systematic reviews to better understand the impact of seminal works and influential authors. We discuss the types of questions citation context analyses can answer and provide a set of guidelines for how to effectively conduct them. Citation context analysis holds promise for enabling a more systematic assessment of how theories are used, empirically tested, and critiqued by subsequent citing authors. This has implications for both theory development and testing, and for the improvement of citation practices within the field of organizational studies and the social and physical sciences more broadly.
{"title":"Citation Context Analysis as a Method for Conducting Rigorous and Impactful Literature Reviews","authors":"Marc H. Anderson, Russell Lemken","doi":"10.1177/1094428120969905","DOIUrl":"https://doi.org/10.1177/1094428120969905","url":null,"abstract":"Citation context analysis is a detailed and rigorous form of literature review that goes beyond traditional narrative and systematic reviews to better understand the impact of seminal works and influential authors. We discuss the types of questions citation context analyses can answer and provide a set of guidelines for how to effectively conduct them. Citation context analysis holds promise for enabling a more systematic assessment of how theories are used, empirically tested, and critiqued by subsequent citing authors. This has implications for both theory development and testing, and for the improvement of citation practices within the field of organizational studies and the social and physical sciences more broadly.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"26 1","pages":"77 - 106"},"PeriodicalIF":9.5,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1094428120969905","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43865033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-28DOI: 10.1177/1094428120967716
Jacqueline Mees-Buss, Catherine Welch, R. Piekkari
Researchers are exposed to multiple interpretive challenges in the journey from field data to theoretical understanding. A common response to these challenges is to turn to the guidance of templates such as the Gioia methodology—currently a preferred template for interpretive management research. Given its popularity, we examine how this methodology approaches the interpretive process of fieldwork. We find that the inductive route to theory that it offers does not address the challenges of interpretation. As an alternative, we propose a return to the epistemological tradition of hermeneutics. We argue that fieldwork informed by a hermeneutic orientation is able to generate credible and novel theory by confronting the challenges of interpretation head on. This process cannot be represented by the orderly steps of a template. We argue that a return to a hermeneutic orientation opens the way to more plausible and insightful theories based on interpretive rather than procedural rigor, and we offer a set of heuristics to guide both researchers and reviewers along this path.
{"title":"From Templates to Heuristics: How and Why to Move Beyond the Gioia Methodology","authors":"Jacqueline Mees-Buss, Catherine Welch, R. Piekkari","doi":"10.1177/1094428120967716","DOIUrl":"https://doi.org/10.1177/1094428120967716","url":null,"abstract":"Researchers are exposed to multiple interpretive challenges in the journey from field data to theoretical understanding. A common response to these challenges is to turn to the guidance of templates such as the Gioia methodology—currently a preferred template for interpretive management research. Given its popularity, we examine how this methodology approaches the interpretive process of fieldwork. We find that the inductive route to theory that it offers does not address the challenges of interpretation. As an alternative, we propose a return to the epistemological tradition of hermeneutics. We argue that fieldwork informed by a hermeneutic orientation is able to generate credible and novel theory by confronting the challenges of interpretation head on. This process cannot be represented by the orderly steps of a template. We argue that a return to a hermeneutic orientation opens the way to more plausible and insightful theories based on interpretive rather than procedural rigor, and we offer a set of heuristics to guide both researchers and reviewers along this path.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"25 1","pages":"405 - 429"},"PeriodicalIF":9.5,"publicationDate":"2020-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1094428120967716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48192384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-23DOI: 10.1177/1094428120971683
Louis Hickman, Stuti Thapa, L. Tay, Mengyang Cao, P. Srinivasan
Recent advances in text mining have provided new methods for capitalizing on the voluminous natural language text data created by organizations, their employees, and their customers. Although often overlooked, decisions made during text preprocessing affect whether the content and/or style of language are captured, the statistical power of subsequent analyses, and the validity of insights derived from text mining. Past methodological articles have described the general process of obtaining and analyzing text data, but recommendations for preprocessing text data were inconsistent. Furthermore, primary studies use and report different preprocessing techniques. To address this, we conduct two complementary reviews of computational linguistics and organizational text mining research to provide empirically grounded text preprocessing decision-making recommendations that account for the type of text mining conducted (i.e., open or closed vocabulary), the research question under investigation, and the data set’s characteristics (i.e., corpus size and average document length). Notably, deviations from these recommendations will be appropriate and, at times, necessary due to the unique characteristics of one’s text data. We also provide recommendations for reporting text mining to promote transparency and reproducibility.
{"title":"Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations","authors":"Louis Hickman, Stuti Thapa, L. Tay, Mengyang Cao, P. Srinivasan","doi":"10.1177/1094428120971683","DOIUrl":"https://doi.org/10.1177/1094428120971683","url":null,"abstract":"Recent advances in text mining have provided new methods for capitalizing on the voluminous natural language text data created by organizations, their employees, and their customers. Although often overlooked, decisions made during text preprocessing affect whether the content and/or style of language are captured, the statistical power of subsequent analyses, and the validity of insights derived from text mining. Past methodological articles have described the general process of obtaining and analyzing text data, but recommendations for preprocessing text data were inconsistent. Furthermore, primary studies use and report different preprocessing techniques. To address this, we conduct two complementary reviews of computational linguistics and organizational text mining research to provide empirically grounded text preprocessing decision-making recommendations that account for the type of text mining conducted (i.e., open or closed vocabulary), the research question under investigation, and the data set’s characteristics (i.e., corpus size and average document length). Notably, deviations from these recommendations will be appropriate and, at times, necessary due to the unique characteristics of one’s text data. We also provide recommendations for reporting text mining to promote transparency and reproducibility.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"25 1","pages":"114 - 146"},"PeriodicalIF":9.5,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1094428120971683","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47377036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-23DOI: 10.1177/1094428120968614
Mikko Rönkkö, Eunseong Cho
Discriminant validity was originally presented as a set of empirical criteria that can be assessed from multitrait-multimethod (MTMM) matrices. Because datasets used by applied researchers rarely lend themselves to MTMM analysis, the need to assess discriminant validity in empirical research has led to the introduction of numerous techniques, some of which have been introduced in an ad hoc manner and without rigorous methodological support. We review various definitions of and techniques for assessing discriminant validity and provide a generalized definition of discriminant validity based on the correlation between two measures after measurement error has been considered. We then review techniques that have been proposed for discriminant validity assessment, demonstrating some problems and equivalencies of these techniques that have gone unnoticed by prior research. After conducting Monte Carlo simulations that compare the techniques, we present techniques called CICFA(sys) and χ 2 (sys) that applied researchers can use to assess discriminant validity.
{"title":"An Updated Guideline for Assessing Discriminant Validity","authors":"Mikko Rönkkö, Eunseong Cho","doi":"10.1177/1094428120968614","DOIUrl":"https://doi.org/10.1177/1094428120968614","url":null,"abstract":"Discriminant validity was originally presented as a set of empirical criteria that can be assessed from multitrait-multimethod (MTMM) matrices. Because datasets used by applied researchers rarely lend themselves to MTMM analysis, the need to assess discriminant validity in empirical research has led to the introduction of numerous techniques, some of which have been introduced in an ad hoc manner and without rigorous methodological support. We review various definitions of and techniques for assessing discriminant validity and provide a generalized definition of discriminant validity based on the correlation between two measures after measurement error has been considered. We then review techniques that have been proposed for discriminant validity assessment, demonstrating some problems and equivalencies of these techniques that have gone unnoticed by prior research. After conducting Monte Carlo simulations that compare the techniques, we present techniques called CICFA(sys) and χ 2 (sys) that applied researchers can use to assess discriminant validity.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"25 1","pages":"6 - 14"},"PeriodicalIF":9.5,"publicationDate":"2020-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1094428120968614","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47529510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.1177/1094428120969884
M. Chelli, A. Cunliffe
We examine an underaddressed issue in organizational research, the nature of the politicization of knowledge and its consequences for conducting research. Drawing on an illustrative case from a PhD research study and the underutilized theory of politicization, we go beyond previous work on politics in organization and management research to offer three contributions. First, we develop a process model underscoring the potentially emergent and interwoven nature of the politicization of research. In particular, we suggest politicization be seen as a trajectory of moments of difference in which researchers may or may not be aware of the potential political significance. Second, we offer four analytical resources to help researchers make sense around why politicization may occur: disputes over the “ownership” of knowledge, clashes of representational logics, ideological differences, and identity struggles. Third, we argue that politicization can be a catalyst, rather than an obstacle, for knowledge production and propose ways of anticipating and negotiating differences. Our aim is to raise awareness of the importance of understanding and anticipating the politicized situations researchers may encounter in their work.
{"title":"Anticipating and Addressing the Politicization of Research","authors":"M. Chelli, A. Cunliffe","doi":"10.1177/1094428120969884","DOIUrl":"https://doi.org/10.1177/1094428120969884","url":null,"abstract":"We examine an underaddressed issue in organizational research, the nature of the politicization of knowledge and its consequences for conducting research. Drawing on an illustrative case from a PhD research study and the underutilized theory of politicization, we go beyond previous work on politics in organization and management research to offer three contributions. First, we develop a process model underscoring the potentially emergent and interwoven nature of the politicization of research. In particular, we suggest politicization be seen as a trajectory of moments of difference in which researchers may or may not be aware of the potential political significance. Second, we offer four analytical resources to help researchers make sense around why politicization may occur: disputes over the “ownership” of knowledge, clashes of representational logics, ideological differences, and identity struggles. Third, we argue that politicization can be a catalyst, rather than an obstacle, for knowledge production and propose ways of anticipating and negotiating differences. Our aim is to raise awareness of the importance of understanding and anticipating the politicized situations researchers may encounter in their work.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"25 1","pages":"88 - 113"},"PeriodicalIF":9.5,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1094428120969884","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48212280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-23DOI: 10.1177/1094428120965706
Garima Sharma, P. Bansal
Systematic reviews of academic research have not impacted management practice as much as many researchers had hoped. Part of the reason is that researchers and managers differ significantly in their knowledge systems—in both what they know and how they know it. Researchers can overcome some of these challenges by including managers as knowledge partners in the research endeavor; however, doing so is rife with challenges. This article seeks to answer, how can researchers and managers navigate the tensions related to differences in their knowledge systems to create more impactful systematic reviews? To answer this question, we embarked on a data-guided journey of the experience of the Network for Business Sustainability, which had undertaken 15 systematic reviews that involved researchers and managers. We interviewed previous participants of the projects, observed different systematic review processes, and collected archival data to learn more about researcher-manager collaborations in the systematic review process. This article offers guidance to researchers in imbricating academic with practical knowledge in the systematic review process.
学术研究的系统评论并没有像许多研究人员所希望的那样对管理实践产生影响。部分原因是研究人员和管理人员的知识体系存在显著差异——无论是他们知道什么，还是他们如何知道。研究人员可以通过将管理者作为研究工作中的知识伙伴来克服其中的一些挑战;然而，这样做充满了挑战。本文试图回答，研究人员和管理人员如何驾驭与他们的知识体系差异相关的紧张关系，以创造更有影响力的系统评价?为了回答这个问题，我们开始了一段以数据为指导的商业可持续发展网络(Network for Business Sustainability)的经验之旅，该网络进行了15次系统审查，涉及研究人员和管理人员。我们采访了以前的项目参与者，观察了不同的系统评审过程，并收集了档案数据，以了解更多关于研究人员-管理者在系统评审过程中的合作。本文为研究人员在系统评价过程中如何将学术知识与实践知识相结合提供了指导。
{"title":"Partnering Up: Including Managers as Research Partners in Systematic Reviews","authors":"Garima Sharma, P. Bansal","doi":"10.1177/1094428120965706","DOIUrl":"https://doi.org/10.1177/1094428120965706","url":null,"abstract":"Systematic reviews of academic research have not impacted management practice as much as many researchers had hoped. Part of the reason is that researchers and managers differ significantly in their knowledge systems—in both what they know and how they know it. Researchers can overcome some of these challenges by including managers as knowledge partners in the research endeavor; however, doing so is rife with challenges. This article seeks to answer, how can researchers and managers navigate the tensions related to differences in their knowledge systems to create more impactful systematic reviews? To answer this question, we embarked on a data-guided journey of the experience of the Network for Business Sustainability, which had undertaken 15 systematic reviews that involved researchers and managers. We interviewed previous participants of the projects, observed different systematic review processes, and collected archival data to learn more about researcher-manager collaborations in the systematic review process. This article offers guidance to researchers in imbricating academic with practical knowledge in the systematic review process.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"26 1","pages":"262 - 291"},"PeriodicalIF":9.5,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1094428120965706","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65407828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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