{"title":"Taking the Road Less Travelled: How Corpus-Assisted Discourse Studies Can Enrich Qualitative Explorations of Large Textual Datasets","authors":"Mathew Gillings, Mark Learmonth, Gerlinde Mautner","doi":"10.1111/1467-8551.12816","DOIUrl":null,"url":null,"abstract":"<p>How might interpretivist qualitative researchers tackle large data sets consisting of millions or even billions of words? Corpus-assisted discourse studies (CADS) is the approach we explore here. Specifically designed for the analysis of voluminous textual data, it offers a recognized empirical approach for making sense of such data. But it does so within an epistemology that understands language to be central in shaping our understanding of the world around us, so that CADS can assist researchers in revealing the social dynamics of the text – including the ideology and power that is latent in many such corpora. Bringing together the training of applied linguists and a management scholar, we discuss the background to CADS and its differences from text-mining approaches such as topic modelling, which have been more widely used in management studies to date. Focusing on the needs of people who are new to the approach, we then offer a worked example to show CADS’ potential in exploring a management-related corpus. Our paper concludes with a discussion of the strengths and weaknesses of the approach and its potential for future discursively orientated management research – especially in the context of the rise of ‘big data’.</p>","PeriodicalId":48342,"journal":{"name":"British Journal of Management","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1467-8551.12816","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"British Journal of Management","FirstCategoryId":"91","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/1467-8551.12816","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0
Abstract
How might interpretivist qualitative researchers tackle large data sets consisting of millions or even billions of words? Corpus-assisted discourse studies (CADS) is the approach we explore here. Specifically designed for the analysis of voluminous textual data, it offers a recognized empirical approach for making sense of such data. But it does so within an epistemology that understands language to be central in shaping our understanding of the world around us, so that CADS can assist researchers in revealing the social dynamics of the text – including the ideology and power that is latent in many such corpora. Bringing together the training of applied linguists and a management scholar, we discuss the background to CADS and its differences from text-mining approaches such as topic modelling, which have been more widely used in management studies to date. Focusing on the needs of people who are new to the approach, we then offer a worked example to show CADS’ potential in exploring a management-related corpus. Our paper concludes with a discussion of the strengths and weaknesses of the approach and its potential for future discursively orientated management research – especially in the context of the rise of ‘big data’.
期刊介绍:
The British Journal of Management provides a valuable outlet for research and scholarship on management-orientated themes and topics. It publishes articles of a multi-disciplinary and interdisciplinary nature as well as empirical research from within traditional disciplines and managerial functions. With contributions from around the globe, the journal includes articles across the full range of business and management disciplines. A subscription to British Journal of Management includes International Journal of Management Reviews, also published on behalf of the British Academy of Management.