Pub Date : 2025-05-21DOI: 10.1177/10944281251334777
Farhan Iqbal, Michael D. Pfarrer
Content analysis has enabled organizational scholars to study constructs and relationships that were previously unattainable at scale. One particular area of focus has been on sentiment analysis, which scholars have implemented to examine myriad relationships pertinent to organizational research. This article addresses certain limitations in sentiment analysis. More specifically, we bring attention to the challenge of accurately attributing sentiment in text that mentions multiple firms. Whereas traditional methods often result in measurement error due to misattributing text to firms, we offer coreference resolution—a natural language processing technique that identifies and links expressions referring to the same entity—as a solution to this problem. Across two studies, we demonstrate the potential of this approach to reduce measurement error and enhance the veracity of text analyses. We conclude by offering avenues for theoretical and empirical advances in organizational research.
{"title":"Using Coreference Resolution to Mitigate Measurement Error in Text Analysis","authors":"Farhan Iqbal, Michael D. Pfarrer","doi":"10.1177/10944281251334777","DOIUrl":"https://doi.org/10.1177/10944281251334777","url":null,"abstract":"Content analysis has enabled organizational scholars to study constructs and relationships that were previously unattainable at scale. One particular area of focus has been on sentiment analysis, which scholars have implemented to examine myriad relationships pertinent to organizational research. This article addresses certain limitations in sentiment analysis. More specifically, we bring attention to the challenge of accurately attributing sentiment in text that mentions multiple firms. Whereas traditional methods often result in measurement error due to misattributing text to firms, we offer coreference resolution—a natural language processing technique that identifies and links expressions referring to the same entity—as a solution to this problem. Across two studies, we demonstrate the potential of this approach to reduce measurement error and enhance the veracity of text analyses. We conclude by offering avenues for theoretical and empirical advances in organizational research.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"45 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113624","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 : 2025-05-21DOI: 10.1177/10944281251339144
Diana Garcia Quevedo, Anna Glaser, Caroline Verzat
Online data are constantly growing, providing a wide range of opportunities to explore social phenomena. Large Language Models (LLMs) capture the inherent structure, contextual meaning, and nuance of human language and are the base for state-of-the-art Natural Language Processing (NLP) algorithms. In this article, we describe a method to assist qualitative researchers in the theorization process by efficiently exploring and selecting the most relevant information from a large online dataset. Using LLM-based NLP algorithms, qualitative researchers can efficiently analyze large amounts of online data while still maintaining deep contact with the data and preserving the richness of qualitative analysis. We illustrate the usefulness of our method by examining 5,516 social media posts from 18 entrepreneurs pursuing an environmental mission (ecopreneurs) to analyze their impression management tactics. By helping researchers to explore and select online data efficiently, our method enhances their analytical capabilities, leads to new insights, and ensures precision in counting and classification, thus strengthening the theorization process. We argue that LLMs push researchers to rethink research methods as the distinction between qualitative and quantitative approaches becomes blurred.
{"title":"Enhancing Theorization Using Artificial Intelligence: Leveraging Large Language Models for Qualitative Analysis of Online Data","authors":"Diana Garcia Quevedo, Anna Glaser, Caroline Verzat","doi":"10.1177/10944281251339144","DOIUrl":"https://doi.org/10.1177/10944281251339144","url":null,"abstract":"Online data are constantly growing, providing a wide range of opportunities to explore social phenomena. Large Language Models (LLMs) capture the inherent structure, contextual meaning, and nuance of human language and are the base for state-of-the-art Natural Language Processing (NLP) algorithms. In this article, we describe a method to assist qualitative researchers in the theorization process by efficiently exploring and selecting the most relevant information from a large online dataset. Using LLM-based NLP algorithms, qualitative researchers can efficiently analyze large amounts of online data while still maintaining deep contact with the data and preserving the richness of qualitative analysis. We illustrate the usefulness of our method by examining 5,516 social media posts from 18 entrepreneurs pursuing an environmental mission (ecopreneurs) to analyze their impression management tactics. By helping researchers to explore and select online data efficiently, our method enhances their analytical capabilities, leads to new insights, and ensures precision in counting and classification, thus strengthening the theorization process. We argue that LLMs push researchers to rethink research methods as the distinction between qualitative and quantitative approaches becomes blurred.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"16 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144113628","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 : 2025-03-18DOI: 10.1177/10944281251326062
Zihao Jia, Philseok Lee
The advent of machine learning and artificial intelligence has profoundly transformed organizational research, especially with the growing application of natural language processing (NLP). Despite these advances, managing long-sequence text input data remains a persistent and significant challenge in NLP analysis within organizational studies. This study introduces five different approaches for handling long sequence text data: term frequency-inverse document frequency with a random forest algorithm (TF-IDF-RF), Longformer, GPT-4o, truncation with averaged scores and our proposed construct-relevant text-selection approach. We also present analytical strategies for each approach and evaluate their effectiveness by comparing the psychometric properties of the predicted scores. Among them, GPT-4o, the truncation with averaged scores, and the proposed text-selection approach generally demonstrate slightly superior psychometric properties compared to TF-IDF-RF and Longformer. However, no single approach consistently outperforms the others across all psychometric criteria. The discussion explores the practical considerations, limitations, and potential directions for future research on these methods, enriching the dialogue on effective long-sequence text management in NLP-driven organizational research.
{"title":"Efficient Processing of Long Sequence Text Data in Transformer: An Examination of Five Different Approaches","authors":"Zihao Jia, Philseok Lee","doi":"10.1177/10944281251326062","DOIUrl":"https://doi.org/10.1177/10944281251326062","url":null,"abstract":"The advent of machine learning and artificial intelligence has profoundly transformed organizational research, especially with the growing application of natural language processing (NLP). Despite these advances, managing long-sequence text input data remains a persistent and significant challenge in NLP analysis within organizational studies. This study introduces five different approaches for handling long sequence text data: term frequency-inverse document frequency with a random forest algorithm (TF-IDF-RF), Longformer, GPT-4o, truncation with averaged scores and our proposed construct-relevant text-selection approach. We also present analytical strategies for each approach and evaluate their effectiveness by comparing the psychometric properties of the predicted scores. Among them, GPT-4o, the truncation with averaged scores, and the proposed text-selection approach generally demonstrate slightly superior psychometric properties compared to TF-IDF-RF and Longformer. However, no single approach consistently outperforms the others across all psychometric criteria. The discussion explores the practical considerations, limitations, and potential directions for future research on these methods, enriching the dialogue on effective long-sequence text management in NLP-driven organizational research.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"22 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143653938","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 : 2025-03-14DOI: 10.1177/10944281251318727
Joep P. Cornelissen, Mirjam Werner
Over the last two decades, much of management research has converged on the belief that one of its major aims is to identify the causal mechanisms that produce the phenomena that researchers seek to explain. In this paper, we review and synthesize the literature that has amassed around causal mechanisms. We do so by detailing the different methodological perspectives that are featured in management research, which we label as the contextual, constitutive, and interventionist perspectives. For each of these perspectives, we examine what it theoretically presupposes a mechanism to be, how this connects to methodological choices, and how this shapes the kind of mechanism-based explanations that each perspective offers. We also explore the main inferential challenges for each of these perspectives and offer specific methodological guidance in response. In this way, we aim to offer a common plank for theorizing and research on causal mechanisms in ways that recognize and harness the productive differences across different epistemologies and methodological traditions.
{"title":"What Are Mechanisms? Ways of Conceptualizing and Studying Causal Mechanisms","authors":"Joep P. Cornelissen, Mirjam Werner","doi":"10.1177/10944281251318727","DOIUrl":"https://doi.org/10.1177/10944281251318727","url":null,"abstract":"Over the last two decades, much of management research has converged on the belief that one of its major aims is to identify the causal mechanisms that produce the phenomena that researchers seek to explain. In this paper, we review and synthesize the literature that has amassed around causal mechanisms. We do so by detailing the different methodological perspectives that are featured in management research, which we label as the contextual, constitutive, and interventionist perspectives. For each of these perspectives, we examine what it theoretically presupposes a mechanism to be, how this connects to methodological choices, and how this shapes the kind of mechanism-based explanations that each perspective offers. We also explore the main inferential challenges for each of these perspectives and offer specific methodological guidance in response. In this way, we aim to offer a common plank for theorizing and research on causal mechanisms in ways that recognize and harness the productive differences across different epistemologies and methodological traditions.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"10 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143627436","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 : 2025-03-13DOI: 10.1177/10944281251323248
Yucheng Zhang, Yuyan Zheng, Dan Wang, Xiaowei Gu, Michael J. Zyphur, Lin Xiao, Shudi Liao, Yangyang Deng
In contemporary organizational research, when dealing with large heterogeneous datasets and complex relationships, statistical modeling focused on developing substantive explanations typically results in low predictive accuracy. In contrast, machine learning (ML) exhibits remarkable strength for prediction, but suffers from an unexplainable analytical process and output—thus ML is often known as a “black box” approach. The recent development of explainable machine learning (XML) integrates high predictive accuracy with explainability, which combines the advantages inherent in both statistical modeling and ML paradigms. This paper compares XML with statistical modeling and the traditional ML approaches, focusing on an advanced application of XML known as evolving fuzzy system (EFS), which enhances model transparency by clarifying the unique contribution of each modeled predictor. In an illustrative study, we demonstrate two EFS-based XML models and conduct comparative analyses among XML, ML, and statistical models with a commonly-used database in organizational research. Our study offers a thorough description of analysis procedures for implementing XML in organizational research, along with best-practice recommendations for each step as well as Python code to aid future research using XML. Finally, we discuss the benefits of XML for organizational research and its potential development.
{"title":"Shedding Light on the Black Box: Integrating Prediction Models and Explainability Using Explainable Machine Learning","authors":"Yucheng Zhang, Yuyan Zheng, Dan Wang, Xiaowei Gu, Michael J. Zyphur, Lin Xiao, Shudi Liao, Yangyang Deng","doi":"10.1177/10944281251323248","DOIUrl":"https://doi.org/10.1177/10944281251323248","url":null,"abstract":"In contemporary organizational research, when dealing with large heterogeneous datasets and complex relationships, statistical modeling focused on developing substantive explanations typically results in low predictive accuracy. In contrast, machine learning (ML) exhibits remarkable strength for prediction, but suffers from an unexplainable analytical process and output—thus ML is often known as a “black box” approach. The recent development of explainable machine learning (XML) integrates high predictive accuracy with explainability, which combines the advantages inherent in both statistical modeling and ML paradigms. This paper compares XML with statistical modeling and the traditional ML approaches, focusing on an advanced application of XML known as evolving fuzzy system (EFS), which enhances model transparency by clarifying the unique contribution of each modeled predictor. In an illustrative study, we demonstrate two EFS-based XML models and conduct comparative analyses among XML, ML, and statistical models with a commonly-used database in organizational research. Our study offers a thorough description of analysis procedures for implementing XML in organizational research, along with best-practice recommendations for each step as well as Python code to aid future research using XML. Finally, we discuss the benefits of XML for organizational research and its potential development.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"14 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143618540","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 : 2025-01-31DOI: 10.1177/10944281251314059
Tine Köhler, Anne Smith, Thomas Greckhamer, Jane Lê
{"title":"Feature Topic for ORM: Advanced Analytic Approaches to Theorize From Qualitative Research","authors":"Tine Köhler, Anne Smith, Thomas Greckhamer, Jane Lê","doi":"10.1177/10944281251314059","DOIUrl":"https://doi.org/10.1177/10944281251314059","url":null,"abstract":"","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"29 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143071560","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 : 2025-01-13DOI: 10.1177/10944281241310574
Cameron J. Borgholthaus, Alaric Bourgoin, Peter D. Harms, Joshua V. White, Tyler N. A. Fezzey
Nearly 2 decades ago, Cycyota and Harrison (2006) documented a concerning trend of declining executive survey response rates and projected a continued decrease in the future. Their seminal work has significantly influenced the methodologies of upper echelons survey research. Our study examines the manner in which Cycyota and Harrison’s paper has impacted the existing upper echelons literature and replicates their study by analyzing peer-reviewed studies published post-2006. We reveal that executive response rates have largely stabilized since Cycyota and Harrison’s initial findings. Furthermore, we expand upon their research by identifying specific geographical contexts and contact methodologies associated with higher (and lower) response rates. Finally, we lend insight into the evolving landscape of executive survey research and offer practical implications for future methodological endeavors in the upper echelons.
{"title":"Surveying the Upper Echelons: An Update to Cycyota and Harrison (2006) on Top Manager Response Rates and Recommendations for the Future","authors":"Cameron J. Borgholthaus, Alaric Bourgoin, Peter D. Harms, Joshua V. White, Tyler N. A. Fezzey","doi":"10.1177/10944281241310574","DOIUrl":"https://doi.org/10.1177/10944281241310574","url":null,"abstract":"Nearly 2 decades ago, Cycyota and Harrison (2006) documented a concerning trend of declining executive survey response rates and projected a continued decrease in the future. Their seminal work has significantly influenced the methodologies of upper echelons survey research. Our study examines the manner in which Cycyota and Harrison’s paper has impacted the existing upper echelons literature and replicates their study by analyzing peer-reviewed studies published post-2006. We reveal that executive response rates have largely stabilized since Cycyota and Harrison’s initial findings. Furthermore, we expand upon their research by identifying specific geographical contexts and contact methodologies associated with higher (and lower) response rates. Finally, we lend insight into the evolving landscape of executive survey research and offer practical implications for future methodological endeavors in the upper echelons.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"9 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968357","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 : 2024-12-17DOI: 10.1177/10944281241300952
Kira F. Schabram, Christopher G. Myers, Ashley E. Hardin
While other applied sciences systematically distinguish between manipulation designs, organizational research does not. Herein, we disentangle distinct applications that differ in how the manipulation is deployed, analyzed, and interpreted in support of hypotheses. First, we define two archetypes: treatments, experimental designs that expose participants to different levels/types of a manipulation of theoretical interest, and primes, manipulations that are not of theoretical interest but generate variance in a state that is. We position these and creative derivations (e.g., interventions and invariant prompts) as specialized tools in our methodological kit. Second, we review 450 manipulations published in leading organizational journals to identify each type's prevalence and application in our field. From this we derive our guiding thesis that while treatments offer unique advantages (foremost establishing causality), they are not always possible, nor the best fit for a research question; in these cases, a non-causal but accurate test of theory, such as a prime design, may prove superior to a causal but inaccurate test. We conclude by outlining best practices for selection, execution, and evaluation by researchers, reviewers, and readers.
{"title":"Manipulation in Organizational Research: On Executing and Interpreting Designs from Treatments to Primes","authors":"Kira F. Schabram, Christopher G. Myers, Ashley E. Hardin","doi":"10.1177/10944281241300952","DOIUrl":"https://doi.org/10.1177/10944281241300952","url":null,"abstract":"While other applied sciences systematically distinguish between manipulation designs, organizational research does not. Herein, we disentangle distinct applications that differ in how the manipulation is deployed, analyzed, and interpreted in support of hypotheses. First, we define two archetypes: treatments, experimental designs that expose participants to different levels/types of a manipulation of theoretical interest, and primes, manipulations that are not of theoretical interest but generate variance in a state that is. We position these and creative derivations (e.g., interventions and invariant prompts) as specialized tools in our methodological kit. Second, we review 450 manipulations published in leading organizational journals to identify each type's prevalence and application in our field. From this we derive our guiding thesis that while treatments offer unique advantages (foremost establishing causality), they are not always possible, nor the best fit for a research question; in these cases, a non-causal but accurate test of theory, such as a prime design, may prove superior to a causal but inaccurate test. We conclude by outlining best practices for selection, execution, and evaluation by researchers, reviewers, and readers.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"86 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841975","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 : 2024-12-13DOI: 10.1177/10944281241279790
Harriet Lingel, Paul-Christian Bürkner, Klaus G. Melchers, Niklas Schulte
In graded paired comparisons (GPCs), two items are compared using a multipoint rating scale. GPCs are expected to reduce faking compared with Likert-type scales and to produce more reliable, less ipsative trait scores than traditional binary forced-choice formats. To investigate the statistical properties of GPCs, we simulated 960 conditions in which we varied six independent factors and additionally implemented conditions with algorithmically optimized item combinations. Using Thurstonian IRT models, good reliabilities and low ipsativity of trait score estimates were achieved for questionnaires with 50% unequally keyed item pairs or equally keyed item pairs with an optimized combination of loadings. However, in conditions with 20% unequally keyed item pairs and equally keyed conditions without optimization, reliabilities were lower with evidence of ipsativity. Overall, more response categories led to higher reliabilities and nearly fully normative trait scores. In an empirical example, we demonstrate the identified mechanisms under both honest and faking conditions and study the effects of social desirability matching on reliability. In sum, our studies inform about the psychometric properties of GPCs under different conditions and make specific recommendations for improving these properties.
{"title":"Measuring Personality When Stakes Are High: Are Graded Paired Comparisons a More Reliable Alternative to Traditional Forced-Choice Methods?","authors":"Harriet Lingel, Paul-Christian Bürkner, Klaus G. Melchers, Niklas Schulte","doi":"10.1177/10944281241279790","DOIUrl":"https://doi.org/10.1177/10944281241279790","url":null,"abstract":"In graded paired comparisons (GPCs), two items are compared using a multipoint rating scale. GPCs are expected to reduce faking compared with Likert-type scales and to produce more reliable, less ipsative trait scores than traditional binary forced-choice formats. To investigate the statistical properties of GPCs, we simulated 960 conditions in which we varied six independent factors and additionally implemented conditions with algorithmically optimized item combinations. Using Thurstonian IRT models, good reliabilities and low ipsativity of trait score estimates were achieved for questionnaires with 50% unequally keyed item pairs or equally keyed item pairs with an optimized combination of loadings. However, in conditions with 20% unequally keyed item pairs and equally keyed conditions without optimization, reliabilities were lower with evidence of ipsativity. Overall, more response categories led to higher reliabilities and nearly fully normative trait scores. In an empirical example, we demonstrate the identified mechanisms under both honest and faking conditions and study the effects of social desirability matching on reliability. In sum, our studies inform about the psychometric properties of GPCs under different conditions and make specific recommendations for improving these properties.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"29 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142820668","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 : 2024-11-04DOI: 10.1177/10944281241284941
Richard F.J. Haans, Marc J. Mertens
Websites represent a crucial avenue for organizations to reach customers, attract talent, and disseminate information to stakeholders. Despite their importance, strikingly little work in the domain of organization and management research has tapped into this source of longitudinal big data. In this paper, we highlight the unique nature and profound potential of longitudinal website data and present novel open-source code- and databases that make these data accessible. Specifically, our codebase offers a general-purpose setup, building on four central steps to scrape historical websites using the Wayback Machine. Our open-access CompuCrawl database was built using this four-step approach. It contains websites of North American firms in the Compustat database between 1996 and 2020—covering 11,277 firms with 86,303 firm/year observations and 1,617,675 webpages. We describe the coverage of our database and illustrate its use by applying word-embedding models to reveal the evolving meaning of the concept of “sustainability” over time. Finally, we outline several avenues for future research enabled by our step-by-step longitudinal web scraping approach and our CompuCrawl database.
{"title":"The Internet Never Forgets: A Four-Step Scraping Tutorial, Codebase, and Database for Longitudinal Organizational Website Data","authors":"Richard F.J. Haans, Marc J. Mertens","doi":"10.1177/10944281241284941","DOIUrl":"https://doi.org/10.1177/10944281241284941","url":null,"abstract":"Websites represent a crucial avenue for organizations to reach customers, attract talent, and disseminate information to stakeholders. Despite their importance, strikingly little work in the domain of organization and management research has tapped into this source of longitudinal big data. In this paper, we highlight the unique nature and profound potential of longitudinal website data and present novel open-source code- and databases that make these data accessible. Specifically, our codebase offers a general-purpose setup, building on four central steps to scrape historical websites using the Wayback Machine. Our open-access CompuCrawl database was built using this four-step approach. It contains websites of North American firms in the Compustat database between 1996 and 2020—covering 11,277 firms with 86,303 firm/year observations and 1,617,675 webpages. We describe the coverage of our database and illustrate its use by applying word-embedding models to reveal the evolving meaning of the concept of “sustainability” over time. Finally, we outline several avenues for future research enabled by our step-by-step longitudinal web scraping approach and our CompuCrawl database.","PeriodicalId":19689,"journal":{"name":"Organizational Research Methods","volume":"140 1","pages":""},"PeriodicalIF":9.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142580273","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：