Pub Date : 2024-05-02DOI: 10.1177/10944281241246772
Wen Wei Loh, Dongning Ren
Understanding the experiences of vulnerable workers is an important scientific pursuit. For example, research interest is often in quantifying the impacts of adverse exposures such as discrimination, exclusion, harassment, or job insecurity, among others. However, routine approaches have only focused on the average treatment effect, which encapsulates the impact of an exposure (e.g., discrimination) applied to the entire study population—including those who were not exposed. In this paper, we propose using a more refined causal quantity uniquely suited to address such causal queries: The effect of treatment on the treated (ETT) from the causal inference literature. We explain why the ETT is a more pertinent causal estimand for investigating the experiences of vulnerable workers by highlighting three appealing features: Better interpretability, greater accuracy, and enhanced robustness to violations of empirically untestable causal assumptions. We further describe how to estimate the ETT by introducing and comparing two estimators. Both estimators are conferred with a so-called doubly robust property. We hope the current proposal empowers organizational scholars in their crucial endeavors dedicated to understanding the vulnerable workforce.
了解弱势工人的经历是一项重要的科学追求。例如，研究兴趣往往在于量化歧视、排斥、骚扰或工作不稳定等不利暴露的影响。然而，常规方法只关注平均处理效果，即某一暴露（如歧视）对整个研究人群（包括未暴露人群）的影响。在本文中，我们建议使用一种更精细的因果量，它非常适合解决此类因果问题：因果推断文献中的治疗对被治疗者的影响（ETT）。我们通过强调三个吸引人的特点来解释为什么 ETT 是调查弱势工人经历的更相关的因果估计量：更好的可解释性、更高的准确性以及对违反经验上无法检验的因果假设的稳健性。通过介绍和比较两种估计方法，我们进一步介绍了如何估计 ETT。这两个估计器都具有所谓的双重稳健性。我们希望当前的建议能够增强组织学者的能力，使他们能够致力于了解弱势劳动力的重要工作。
{"title":"Enhancing Causal Pursuits in Organizational Science: Targeting the Effect of Treatment on the Treated in Research on Vulnerable Populations","authors":"Wen Wei Loh, Dongning Ren","doi":"10.1177/10944281241246772","DOIUrl":"https://doi.org/10.1177/10944281241246772","url":null,"abstract":"Understanding the experiences of vulnerable workers is an important scientific pursuit. For example, research interest is often in quantifying the impacts of adverse exposures such as discrimination, exclusion, harassment, or job insecurity, among others. However, routine approaches have only focused on the average treatment effect, which encapsulates the impact of an exposure (e.g., discrimination) applied to the entire study population—including those who were not exposed. In this paper, we propose using a more refined causal quantity uniquely suited to address such causal queries: The effect of treatment on the treated (ETT) from the causal inference literature. We explain why the ETT is a more pertinent causal estimand for investigating the experiences of vulnerable workers by highlighting three appealing features: Better interpretability, greater accuracy, and enhanced robustness to violations of empirically untestable causal assumptions. We further describe how to estimate the ETT by introducing and comparing two estimators. Both estimators are conferred with a so-called doubly robust property. We hope the current proposal empowers organizational scholars in their crucial endeavors dedicated to understanding the vulnerable workforce.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"51 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826389","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 : 2024-04-22DOI: 10.1177/10944281241245444
Linda Jakob Sadeh, Avital Baikovich, Tammar B. Zilber
This article proposes a framework for reflexive choice in qualitative research, centering on social interaction. Interaction, fundamental to social and organizational life, has been studied extensively. Yet, researchers can get lost in the plethora of methodological tools, hampering reflexive choice. Our proposed framework consists of four dimensions of interaction (content, communication patterns, emotions, and roles), intersecting with five levels of analysis (individual, dyadic, group, organizational, and sociocultural), as well as three overarching analytic principles (following the dynamic, consequential, and contextual nature of interaction). For each intersection between dimension and level, we specify analytical questions, empirical markers, and references to exemplary works. The framework functions both as a compass, indicating potential directions for research design and data collection methods, and as a roadmap, illuminating pathways at the analysis stage. Our contributions are twofold: First, our framework fleshes out the broad spectrum of available methods for analyzing interaction, providing pragmatic tools for the researcher to reflexively choose from. Second, we highlight the broader relevance of maps, such as our own, for enhancing reflexive methodological choices.
{"title":"Analyzing Social Interaction in Organizations: A Roadmap for Reflexive Choice","authors":"Linda Jakob Sadeh, Avital Baikovich, Tammar B. Zilber","doi":"10.1177/10944281241245444","DOIUrl":"https://doi.org/10.1177/10944281241245444","url":null,"abstract":"This article proposes a framework for reflexive choice in qualitative research, centering on social interaction. Interaction, fundamental to social and organizational life, has been studied extensively. Yet, researchers can get lost in the plethora of methodological tools, hampering reflexive choice. Our proposed framework consists of four dimensions of interaction (content, communication patterns, emotions, and roles), intersecting with five levels of analysis (individual, dyadic, group, organizational, and sociocultural), as well as three overarching analytic principles (following the dynamic, consequential, and contextual nature of interaction). For each intersection between dimension and level, we specify analytical questions, empirical markers, and references to exemplary works. The framework functions both as a compass, indicating potential directions for research design and data collection methods, and as a roadmap, illuminating pathways at the analysis stage. Our contributions are twofold: First, our framework fleshes out the broad spectrum of available methods for analyzing interaction, providing pragmatic tools for the researcher to reflexively choose from. Second, we highlight the broader relevance of maps, such as our own, for enhancing reflexive methodological choices.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"9 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140637754","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 : 2024-04-17DOI: 10.1177/10944281241240180
Stefanie Habersang, Markus Reihlen
Qualitative meta-studies (QMS) have emerged as a promising methodology for synthesizing qualitative research within organization and management studies. However, despite considerable progress, increasingly fragmented applications of QMS impede the advancement of the methodology. To address this issue, we review and analyze the expanding body of QMS in organization and management studies. We propose a framework that encompasses the core decisions and methodological choices in the formal QMS protocol as well as the reflective—yet often implicit—meta-practices essential for deriving meaningful results from QMS. Based on our analysis, we develop two guidelines to help researchers reflectively align formal methodological choices with the intended purpose of the QMS, which can be either confirmatory or exploratory.
{"title":"Advancing Qualitative Meta-Studies (QMS): Current Practices and Reflective Guidelines for Synthesizing Qualitative Research","authors":"Stefanie Habersang, Markus Reihlen","doi":"10.1177/10944281241240180","DOIUrl":"https://doi.org/10.1177/10944281241240180","url":null,"abstract":"Qualitative meta-studies (QMS) have emerged as a promising methodology for synthesizing qualitative research within organization and management studies. However, despite considerable progress, increasingly fragmented applications of QMS impede the advancement of the methodology. To address this issue, we review and analyze the expanding body of QMS in organization and management studies. We propose a framework that encompasses the core decisions and methodological choices in the formal QMS protocol as well as the reflective—yet often implicit—meta-practices essential for deriving meaningful results from QMS. Based on our analysis, we develop two guidelines to help researchers reflectively align formal methodological choices with the intended purpose of the QMS, which can be either confirmatory or exploratory.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"25 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140608152","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 : 2024-04-17DOI: 10.1177/10944281241246770
Anand P. A. van Zelderen, Theodore C. Masters-Waage, Nicky Dries, Jochen I. Menges, Diana R. Sanchez
Due to recent technological developments, vignette studies that have traditionally been done in text or video formats can now be done in immersive formats using virtual reality—but are such virtual reality video vignettes superior to traditional vignettes? To address this question, we examine participants’ experiences within a fictitious organization by comparing their responses to a relevant and particularly sensitive organizational phenomenon presented either through written text, a video recording, or a virtual reality experience. The results indicate that participants prefer more immersive methods, and that these increase their attention to critical study details. Moreover, this augments the effect sizes of several measured employee reactions—particularly those with high emotional content—suggesting that virtual reality technology offers a promising avenue for developing ecologically valid vignette studies to measure employee affect. To facilitate and expediate the use of virtual reality video vignettes in organizational research, we provide organizational scholars with a step-by-step instructional guide to develop immersive vignette studies.
{"title":"Simulating Virtual Organizations for Research: A Comparative Empirical Evaluation of Text-Based, Video, and Virtual Reality Video Vignettes","authors":"Anand P. A. van Zelderen, Theodore C. Masters-Waage, Nicky Dries, Jochen I. Menges, Diana R. Sanchez","doi":"10.1177/10944281241246770","DOIUrl":"https://doi.org/10.1177/10944281241246770","url":null,"abstract":"Due to recent technological developments, vignette studies that have traditionally been done in text or video formats can now be done in immersive formats using virtual reality—but are such virtual reality video vignettes superior to traditional vignettes? To address this question, we examine participants’ experiences within a fictitious organization by comparing their responses to a relevant and particularly sensitive organizational phenomenon presented either through written text, a video recording, or a virtual reality experience. The results indicate that participants prefer more immersive methods, and that these increase their attention to critical study details. Moreover, this augments the effect sizes of several measured employee reactions—particularly those with high emotional content—suggesting that virtual reality technology offers a promising avenue for developing ecologically valid vignette studies to measure employee affect. To facilitate and expediate the use of virtual reality video vignettes in organizational research, we provide organizational scholars with a step-by-step instructional guide to develop immersive vignette studies.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"78 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140608136","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 : 2024-04-13DOI: 10.1177/10944281241244760
Kai Liu, Yi Zheng, Daxun Wang, Yan Cai, Yuanyuan Shi, Chongqin Xi, Dongbo Tu
In recent decades, multidimensional forced-choice (MFC) tests have gained widespread popularity in organizational settings due to their effectiveness in reducing response biases. Detecting differential item functioning (DIF) is crucial in developing MFC tests, as it relates to test fairness and validity. However, existing methods appear insufficient for detecting DIF induced by the interaction between multiple covariates. Furthermore, for multi-category, ordered or continuous covariates, existing approaches often dichotomize them using a-priori cutoffs, commonly using the median of the covariates. This may lead to information loss and reduced power in detecting MFC DIF. To address these limitations, we propose a method to identify both main effect DIF and interactive DIF. This method can automatically search for the optimal cutoffs for ordered or continuous covariates without pre-defined cutoffs. We introduce the rationale behind the proposed method and evaluate its performance through three Monte Carlo simulation studies. Results demonstrate that the proposed method effectively identifies various DIF forms in MFC tests, thereby increasing detection power. Finally, we provide an empirical application to illustrate the practical applicability of the proposed method.
{"title":"A Framework for Detecting Both Main Effect and Interactive DIF in Multidimensional Forced-Choice Assessments","authors":"Kai Liu, Yi Zheng, Daxun Wang, Yan Cai, Yuanyuan Shi, Chongqin Xi, Dongbo Tu","doi":"10.1177/10944281241244760","DOIUrl":"https://doi.org/10.1177/10944281241244760","url":null,"abstract":"In recent decades, multidimensional forced-choice (MFC) tests have gained widespread popularity in organizational settings due to their effectiveness in reducing response biases. Detecting differential item functioning (DIF) is crucial in developing MFC tests, as it relates to test fairness and validity. However, existing methods appear insufficient for detecting DIF induced by the interaction between multiple covariates. Furthermore, for multi-category, ordered or continuous covariates, existing approaches often dichotomize them using a-priori cutoffs, commonly using the median of the covariates. This may lead to information loss and reduced power in detecting MFC DIF. To address these limitations, we propose a method to identify both main effect DIF and interactive DIF. This method can automatically search for the optimal cutoffs for ordered or continuous covariates without pre-defined cutoffs. We introduce the rationale behind the proposed method and evaluate its performance through three Monte Carlo simulation studies. Results demonstrate that the proposed method effectively identifies various DIF forms in MFC tests, thereby increasing detection power. Finally, we provide an empirical application to illustrate the practical applicability of the proposed method.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"9 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140551921","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 : 2024-01-31DOI: 10.1177/10944281241228186
Jaewoo Jung, Wenjun Zhou, Anne D. Smith
Text analysis, particularly custom dictionaries and topic modeling, has helped advance management and organization theory. Custom dictionaries involve creating word lists to quantify patterns and infer constructs, while topic modeling extracts themes from textual documents to help understand a theoretical domain. Building on these two approaches, we propose another text analysis approach called word-text-topic extraction (WTT), which enhances the efficiency and relevance of text analysis for the sake of theoretical advancement. Specifically, we first identify relevant words for a researcher's theoretical area of interest using word-embedding algorithms. That step is followed by extracting text segments from the textual corpus using a collocation process. Finally, topic modeling is applied to capture themes relevant to the specific theoretical area of interest. To illustrate the WTT approach, we explored one research area needing further theory development—innovation. Using 841 CEOs’ letters to shareholders, we found that our WTT approach provides nuanced features of innovation that differ across industry contexts. We guide researchers on decisions and considerations related to the WTT approach in order to facilitate its use in future studies.
{"title":"From Textual Data to Theoretical Insights: Introducing and Applying the Word-Text-Topic Extraction Approach","authors":"Jaewoo Jung, Wenjun Zhou, Anne D. Smith","doi":"10.1177/10944281241228186","DOIUrl":"https://doi.org/10.1177/10944281241228186","url":null,"abstract":"Text analysis, particularly custom dictionaries and topic modeling, has helped advance management and organization theory. Custom dictionaries involve creating word lists to quantify patterns and infer constructs, while topic modeling extracts themes from textual documents to help understand a theoretical domain. Building on these two approaches, we propose another text analysis approach called word-text-topic extraction (WTT), which enhances the efficiency and relevance of text analysis for the sake of theoretical advancement. Specifically, we first identify relevant words for a researcher's theoretical area of interest using word-embedding algorithms. That step is followed by extracting text segments from the textual corpus using a collocation process. Finally, topic modeling is applied to capture themes relevant to the specific theoretical area of interest. To illustrate the WTT approach, we explored one research area needing further theory development—innovation. Using 841 CEOs’ letters to shareholders, we found that our WTT approach provides nuanced features of innovation that differ across industry contexts. We guide researchers on decisions and considerations related to the WTT approach in order to facilitate its use in future studies.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"99 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139938974","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 : 2024-01-08DOI: 10.1177/10944281231223412
Balázs Kovács
Organizational research increasingly relies on online review data to gauge perceived valuation and reputation of organizations and products. Online review platforms typically collect ordinal ratings (e.g., 1 to 5 stars); however, researchers often treat them as a cardinal data, calculating aggregate statistics such as the average, the median, or the variance of ratings. In calculating these statistics, ratings are implicitly assumed to be equidistant. We test whether star ratings are equidistant using reviews from two large-scale online review platforms: Amazon.com and Yelp.com. We develop a deep learning framework to analyze the text of the reviews in order to assess their overall valuation. We find that 4 and 5-star ratings, as well as 1 and 2-star ratings, are closer to each other than 3-star ratings are to 2 and 4-star ratings. An additional online experiment corroborates this pattern. Using simulations, we show that the distortion by non-equidistant ratings is especially harmful in cases when organizations receive only a few reviews and when researchers are interested in estimating variance effects. We discuss potential solutions to solve the issue with rating non-equidistance.
{"title":"Five Is the Brightest Star. But by how Much? Testing the Equidistance of Star Ratings in Online Reviews","authors":"Balázs Kovács","doi":"10.1177/10944281231223412","DOIUrl":"https://doi.org/10.1177/10944281231223412","url":null,"abstract":"Organizational research increasingly relies on online review data to gauge perceived valuation and reputation of organizations and products. Online review platforms typically collect ordinal ratings (e.g., 1 to 5 stars); however, researchers often treat them as a cardinal data, calculating aggregate statistics such as the average, the median, or the variance of ratings. In calculating these statistics, ratings are implicitly assumed to be equidistant. We test whether star ratings are equidistant using reviews from two large-scale online review platforms: Amazon.com and Yelp.com. We develop a deep learning framework to analyze the text of the reviews in order to assess their overall valuation. We find that 4 and 5-star ratings, as well as 1 and 2-star ratings, are closer to each other than 3-star ratings are to 2 and 4-star ratings. An additional online experiment corroborates this pattern. Using simulations, we show that the distortion by non-equidistant ratings is especially harmful in cases when organizations receive only a few reviews and when researchers are interested in estimating variance effects. We discuss potential solutions to solve the issue with rating non-equidistance.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"22 12","pages":""},"PeriodicalIF":9.5,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139446588","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 : 2023-12-13DOI: 10.1177/10944281231216381
Arturs Kalnins, Kendall Praitis Hill
Variance inflation factors (VIF scores) are regression diagnostics commonly invoked throughout the social sciences. Researchers typically take the perspective that VIF scores below a numerical rule-of-thumb threshold act as a “silver bullet” to dismiss any and all multicollinearity concerns. Yet, no valid logical basis exists for using VIF thresholds to reject the possibility of multicollinearity-induced type 1 errors. Reporting VIF scores below a threshold does not in any way add to the credibility of statistically significant results among correlated variables. In contrast to this “threshold perspective,” our analysis expands the scope of a perspective that has considered multicollinearity and misspecification. We demonstrate analytically that a regression omitting a relevant variable correlated with included variables that exhibit multicollinearity is susceptible to endogeneity-induced bias inflation and beta polarization, leading to the possible co-existence of type 1 errors and low VIF scores. Further, omitting variables explicitly reduces VIF scores. We conclude that the threshold perspective not only lacks any logical basis but also is fundamentally misleading as a rule-of-thumb. Instrumental variables represent one clear remedy for endogeneity-induced bias inflation. If exogenous instruments are unavailable, we encourage researchers to test only straightforward, unambiguous theory when using variables that exhibit multicollinearity, and to ensure that correlated co-variates exhibit the expected signs.
{"title":"The VIF Score. What is it Good For? Absolutely Nothing","authors":"Arturs Kalnins, Kendall Praitis Hill","doi":"10.1177/10944281231216381","DOIUrl":"https://doi.org/10.1177/10944281231216381","url":null,"abstract":"Variance inflation factors (VIF scores) are regression diagnostics commonly invoked throughout the social sciences. Researchers typically take the perspective that VIF scores below a numerical rule-of-thumb threshold act as a “silver bullet” to dismiss any and all multicollinearity concerns. Yet, no valid logical basis exists for using VIF thresholds to reject the possibility of multicollinearity-induced type 1 errors. Reporting VIF scores below a threshold does not in any way add to the credibility of statistically significant results among correlated variables. In contrast to this “threshold perspective,” our analysis expands the scope of a perspective that has considered multicollinearity and misspecification. We demonstrate analytically that a regression omitting a relevant variable correlated with included variables that exhibit multicollinearity is susceptible to endogeneity-induced bias inflation and beta polarization, leading to the possible co-existence of type 1 errors and low VIF scores. Further, omitting variables explicitly reduces VIF scores. We conclude that the threshold perspective not only lacks any logical basis but also is fundamentally misleading as a rule-of-thumb. Instrumental variables represent one clear remedy for endogeneity-induced bias inflation. If exogenous instruments are unavailable, we encourage researchers to test only straightforward, unambiguous theory when using variables that exhibit multicollinearity, and to ensure that correlated co-variates exhibit the expected signs.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"24 4","pages":""},"PeriodicalIF":9.5,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139005307","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 : 2023-12-03DOI: 10.1177/10944281231216323
T. Köhler, Maria N. Rumyantseva, Catherine Welch
Qualitative research methods are deemed best suited to exploring novel phenomena and generating new concepts. Their potential to reevaluate existing theorizing, however, is underestimated. Qualitative restudies that return to the data and settings on which the original theories were built are a well-established tradition in other disciplines (e.g., history, sociology, and anthropology), but have received little recognition in management and organization studies. We introduce qualitative restudies as a powerful means to improve theorizing by revising or challenging theories that have become outdated or obsolete and establishing transferability and longevity of findings and interpretations. We provide a typology of qualitative restudy designs drawing on an integrative review of literature in management, strategy, and the social sciences and humanities. We highlight the main design and ethical considerations for researchers in undertaking a restudy. We argue for the strengths of restudies as lying in their possibilities for retheorizing, above and beyond verifying or updating prior studies. Restudies draw on the strengths of in-depth qualitative work to uncover how interpretations and theorizing are shaped by methodological traditions, historical contexts, existing societal structures, and researcher backgrounds.
{"title":"Qualitative Restudies: Research Designs for Retheorizing","authors":"T. Köhler, Maria N. Rumyantseva, Catherine Welch","doi":"10.1177/10944281231216323","DOIUrl":"https://doi.org/10.1177/10944281231216323","url":null,"abstract":"Qualitative research methods are deemed best suited to exploring novel phenomena and generating new concepts. Their potential to reevaluate existing theorizing, however, is underestimated. Qualitative restudies that return to the data and settings on which the original theories were built are a well-established tradition in other disciplines (e.g., history, sociology, and anthropology), but have received little recognition in management and organization studies. We introduce qualitative restudies as a powerful means to improve theorizing by revising or challenging theories that have become outdated or obsolete and establishing transferability and longevity of findings and interpretations. We provide a typology of qualitative restudy designs drawing on an integrative review of literature in management, strategy, and the social sciences and humanities. We highlight the main design and ethical considerations for researchers in undertaking a restudy. We argue for the strengths of restudies as lying in their possibilities for retheorizing, above and beyond verifying or updating prior studies. Restudies draw on the strengths of in-depth qualitative work to uncover how interpretations and theorizing are shaped by methodological traditions, historical contexts, existing societal structures, and researcher backgrounds.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"36 23","pages":""},"PeriodicalIF":9.5,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138605211","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 : 2023-11-13DOI: 10.1177/10944281231213068
Philipp Poschmann, Jan Goldenstein, Sven Büchel, Udo Hahn
In this article, we develop a methodological approach for organizational research regarding the construction of multidimensional and relational similarity measures by using the vector space model in natural language processing (NLP). Our vector space approach draws on the well-established premise in organizational research that texts provide a window into social reality and allow measuring theory-based constructs ( e.g., organizations’ self-representations). Using a vector space approach allows capturing the multidimensionality of these theory-based constructs and computing relational similarities between organizational entities ( e.g., organizations, their members, and subunits) in social spaces and with their environments, such as the organization itself, industries, or countries. Thus, our methodological approach contributes to the recent trend in organizational research to use the potential inherent in big (textual) data by using NLP. In an example, we provide guidance for organizational scholars by illustrating how they can ensure validity when applying our methodological contribution in concrete research practice.
{"title":"A Vector Space Approach for Measuring Relationality and Multidimensionality of Meaning in Large Text Collections","authors":"Philipp Poschmann, Jan Goldenstein, Sven Büchel, Udo Hahn","doi":"10.1177/10944281231213068","DOIUrl":"https://doi.org/10.1177/10944281231213068","url":null,"abstract":"In this article, we develop a methodological approach for organizational research regarding the construction of multidimensional and relational similarity measures by using the vector space model in natural language processing (NLP). Our vector space approach draws on the well-established premise in organizational research that texts provide a window into social reality and allow measuring theory-based constructs ( e.g., organizations’ self-representations). Using a vector space approach allows capturing the multidimensionality of these theory-based constructs and computing relational similarities between organizational entities ( e.g., organizations, their members, and subunits) in social spaces and with their environments, such as the organization itself, industries, or countries. Thus, our methodological approach contributes to the recent trend in organizational research to use the potential inherent in big (textual) data by using NLP. In an example, we provide guidance for organizational scholars by illustrating how they can ensure validity when applying our methodological contribution in concrete research practice.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"131 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136351297","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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