{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:41:26Z","timestamp":1778272886479,"version":"3.51.4"},"reference-count":73,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T00:00:00Z","timestamp":1576540800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["OIR"],"published-print":{"date-parts":[[2019,12,17]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to offer a critical analysis of talent acquisition software and its potential for fostering equity in the hiring process for underrepresented IT professionals. The under-representation of women, African-American and Latinx professionals in the IT workforce is a longstanding issue that contributes to and is impacted by algorithmic bias.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>Sources of algorithmic bias in talent acquisition software are presented. Feminist design thinking is presented as a theoretical lens for mitigating algorithmic bias.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Data are just one tool for recruiters to use; human expertise is still necessary. Even well-intentioned algorithms are not neutral and should be audited for morally and legally unacceptable decisions. Feminist design thinking provides a theoretical framework for considering equity in the hiring decisions made by talent acquisition systems and their users.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Social implications<\/jats:title>\n<jats:p>This research implies that algorithms may serve to codify deep-seated biases, making IT work environments just as homogeneous as they are currently. If bias exists in talent acquisition software, the potential for propagating inequity and harm is far more significant and widespread due to the homogeneity of the specialists creating artificial intelligence (AI) systems.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>This work uses equity as a central concept for considering algorithmic bias in talent acquisition. Feminist design thinking provides a framework for fostering a richer understanding of what fairness means and evaluating how AI software might impact marginalized populations.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/oir-10-2018-0334","type":"journal-article","created":{"date-parts":[[2019,12,31]],"date-time":"2019-12-31T07:55:32Z","timestamp":1577778932000},"page":"383-395","source":"Crossref","is-referenced-by-count":95,"title":["Algorithmic equity in the hiring of underrepresented IT job candidates"],"prefix":"10.1108","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4990-6973","authenticated-orcid":false,"given":"Lynette","family":"Yarger","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fay","family":"Cobb Payton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bikalpa","family":"Neupane","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"2-3","key":"key2020062510440128000_ref401","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1111\/1468-2389.00235","article-title":"Applicant and recruiter reactions to new technology in selection: a critical review and agenda for future research","volume":"11","year":"2003","journal-title":"International Journal of Selection and Assessment"},{"key":"key2020062510440128000_ref001","unstructured":"Angwin, J., Larson, J., Mattu, S. and Kirchner, L. (2017), \u201cMachine bias\u201d, available at: www.propublica.org\/article\/machine-bias-risk-assessments-in-criminal-sentencing (accessed December 3, 2017)."},{"key":"key2020062510440128000_ref002","unstructured":"Atlassian (2018), \u201cState of diversity and inclusion in US tech: stats summary\u201d, available at: www.atlassian.com\/diversity\/survey\/2018 (accessed October 30, 2018)."},{"key":"key2020062510440128000_ref003","first-page":"1301","article-title":"Feminist HCI: taking stock and outlining an agenda for design","year":"2010"},{"issue":"3","key":"key2020062510440128000_ref402","first-page":"671","article-title":"Big data\u2019s disparate impact","volume":"104","year":"2016","journal-title":"California Law Review"},{"key":"key2020062510440128000_ref004","doi-asserted-by":"crossref","unstructured":"Bauer, T., Truxillo, D., Mack, K. and Costa, A. (2011), \u201cApplicant reactions to technology\u2010based selection: what we know so far\u201d, in Tippins, N., Adler, S. and Kraut, A. (Eds), Technology-Enhanced Assessment of Talent, Josey-Bass, San Francisco, CA, pp. 120-223.","DOI":"10.1002\/9781118256022.ch6"},{"issue":"1-2","key":"key2020062510440128000_ref005","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1111\/j.0965-075X.2004.00269.x","article-title":"Applicant reactions to different selection technology: Face-to-Face, interactive voice response, and computer-assisted telephone screening interviews","volume":"12","year":"2004","journal-title":"International Journal of Selection and Assessment"},{"issue":"1","key":"key2020062510440128000_ref006","first-page":"7","article-title":"Possible unconscious bias in recruitment and promotion and the need to promote equality","volume":"16","year":"2012","journal-title":"Perspectives: Policy and Practice in Higher Education"},{"key":"key2020062510440128000_ref070","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1257\/0002828042002561","article-title":"Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination","volume":"94","year":"2004","journal-title":"American Economic Review"},{"key":"key2020062510440128000_ref007","article-title":"All the ways hiring algorithms can introduce bias","year":"2019","journal-title":"Harvard Business Review"},{"key":"key2020062510440128000_ref008","article-title":"Can blind hiring improve workplace diversity?","year":"2018","journal-title":"Society for Human Resource Management"},{"key":"key2020062510440128000_ref009","unstructured":"Byrne, W. (2018), \u201cNow is the time to act to end bias in AI\u201d, Fast Company, February 2, available at: www.fastcompany.com\/40536485\/now-is-the-time-to-act-to-stop-bias-in-ai (accessed March 14, 2019)."},{"issue":"4","key":"key2020062510440128000_ref010","first-page":"317","article-title":"Applicant reactions and their consequences: review, advice, and recommendations for future research","volume":"4","year":"2003","journal-title":"International Journal of Management Review"},{"key":"key2020062510440128000_ref011","unstructured":"Chou, J., Murillo, O. and Ibars, R. (2017), \u201cIn pursuit of inclusive AI\u201d, Microsoft Research, available at: https:\/\/msdesignstorage.blob.core.windows.net\/microsoftdesign\/inclusive\/InclusiveDesign_InclusiveAI.pdf (accessed October 20, 2019)."},{"issue":"3","key":"key2020062510440128000_ref012","first-page":"185","article-title":"Applicants\u2019 perceptions on online recruitment","volume":"38","year":"2014","journal-title":"Management and Labour Studies"},{"key":"key2020062510440128000_ref013","unstructured":"Costanza-Chock, S. (2018, June 3), \u201cDesign justice: towards an intersectional feminist framework for design theory and practice\u201d, Proceedings of the Design Research Society, available at: https:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=3189696 (accessed March 13, 2019)."},{"issue":"2","key":"key2020062510440128000_ref014","first-page":"120","article-title":"Conscientious classification: a data scientist\u2019s guide to discrimination-aware classification","volume":"38","year":"2017","journal-title":"Big Data"},{"key":"key2020062510440128000_ref015","volume-title":"Data Feminism","year":"2019"},{"key":"key2020062510440128000_ref016","article-title":"How to hire with algorithms","year":"2016","journal-title":"Harvard Business Review"},{"key":"key2020062510440128000_ref017","unstructured":"Dastin, J. (2018), \u201cAmazon scraps secret AI recruiting tool that showed bias against women\u201d, Reuters, available at: www.reuters.com\/article\/us-amazon-com-jobs-automation-insight\/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G (accessed October 20, 2018)."},{"issue":"2-3","key":"key2020062510440128000_ref018","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1002\/hrm.20011","article-title":"Perceived fairness of web\u2010based applicant screening procedures: weighing the rules of justice and the role of individual differences","volume":"43","year":"2004","journal-title":"Human Resource Management"},{"issue":"4","key":"key2020062510440128000_ref019","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1111\/j.1540-4560.1996.tb01848.x","article-title":"Affirmative action, unintentional racial biases, and intergroup relations","volume":"52","year":"1996","journal-title":"Journal of Social Issues"},{"key":"key2020062510440128000_ref020","unstructured":"Edionwe, T. (2017), \u201cThe fight against racist algorithms: can we teach our machines to unlearn racism?\u201d, The Outline, May 22, available at: https:\/\/theoutline.com\/post\/1571\/the-fight-against-racist-algorithms?zd=1&zi=uhapmzdb (accessed May 14, 2019)."},{"key":"key2020062510440128000_ref021","unstructured":"Florentine, S. (2016), \u201cHow artificial intelligence can eliminate bias in hiring\u201d, CIO Magazine, December 22, available at: www.cio.com\/article\/3152798\/artificial-intelligence\/how-artificial-intelligence-can-eliminate-bias-in-hiring.html (accessed October 27, 2018)."},{"key":"key2020062510440128000_ref022","unstructured":"Giang, C. (2018), \u201cThe potential hidden bias in automated hiring systems\u201d, Fast Company, May 8, available at: www.fastcompany.com\/40566971\/the-potential-hidden-bias-in-automated-hiring-systems"},{"key":"key2020062510440128000_ref023","article-title":"This is how AI bias really happens \u2013 and why it\u2019s so hard to fix","year":"2019"},{"key":"key2020062510440128000_ref024","unstructured":"Higginbottom, K. (2018), \u201cThe pros and cons of algorithms in recruitment\u201d, Forbes, October 10, available at: www.forbes.com\/sites\/karenhigginbottom\/2018\/10\/19\/the-pros-and-cons-of-algorithms-in-recruitment\/#35464e677340 (accessed October 27, 2018)."},{"issue":"3","key":"key2020062510440128000_ref025","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1111\/j.1754-9434.2008.00058.x","article-title":"Stubborn reliance on intuition and subjectivity in employee selection","volume":"1","year":"2008","journal-title":"Industrial and Organizational Psychology"},{"key":"key2020062510440128000_ref026","unstructured":"Huet, E. (2017), \u201cFacebook\u2019s hiring process hinders its effort to create a diverse workforce\u201d, Bloomberg Technology, January 9, available at: www.bloomberg.com\/news\/articles\/2017-01-09\/facebook-s-hiring-process-hinders-its-effort-to-create-a-diverse-workforce (accessed January 25, 2018)."},{"key":"key2020062510440128000_ref027","volume-title":"Why Diversity Matters","year":"2015"},{"issue":"1","key":"key2020062510440128000_ref028","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1108\/10662240510577068","article-title":"Using the web to look for work: implications for online job seeking and recruiting","volume":"15","year":"2005","journal-title":"Internet Research"},{"key":"key2020062510440128000_ref029","unstructured":"Johansson, A. (2017), \u201cWhy millennials are demanding even more diversity in tech\u201d, Forbes, December 19, available at: www.forbes.com\/sites\/annajohansson\/2017\/12\/19\/why-millennials-are-demanding-even-more-diversity-in-tech\/#66bb36d4386b (accessed October 30, 2018)."},{"key":"key2020062510440128000_ref030","first-page":"118","article-title":"The law of implicit bias","volume":"969","year":"2016","journal-title":"California Law Review"},{"issue":"3","key":"key2020062510440128000_ref031","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1177\/0001839216639577","article-title":"Whitened R\u00e9sum\u00e9s: race and self-presentation in the labor market","volume":"61","year":"2016","journal-title":"Administrative Science Quarterly"},{"issue":"10","key":"key2020062510440128000_ref032","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1145\/2983270","article-title":"Battling algorithmic bias: how do we ensure algorithms treat us fairly?","volume":"59","year":"2016","journal-title":"Communications of the ACM"},{"issue":"3-4","key":"key2020062510440128000_ref033","first-page":"857","article-title":"Data-driven discrimination at work","volume":"857","year":"2017","journal-title":"William and Mary Law School Scholarship"},{"key":"key2020062510440128000_ref034","first-page":"807","article-title":"Simplicity creates inequity: implications for fairness, stereotypes, and interpretability","year":"2019"},{"issue":"2","key":"key2020062510440128000_ref035","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1111\/ijsa.12026","article-title":"Fairness perceptions in Web-based selection: impact on applicants\u2019 pursuit intentions, recommendation intentions, and intentions to reapply","volume":"21","year":"2013","journal-title":"International Journal of Selection and Assessment"},{"issue":"3","key":"key2020062510440128000_ref036","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1111\/ijsa.12144","article-title":"Patterns of change in fairness perceptions during the hiring process","volume":"24","year":"2016","journal-title":"International Journal of Selection and Assessment"},{"key":"key2020062510440128000_ref037","unstructured":"Lam, B. (2015), \u201cThe least diverse jobs in America\u201d, The Atlantic, June 29, available at: www.theatlantic.com\/business\/archive\/2015\/06\/diversity-jobs-professions-america\/396632 (accessed October 27, 2018)."},{"key":"key2020062510440128000_ref038","first-page":"19","article-title":"Information as a double-edged sword: the role of computer experience and information on applicant reactions towards novel technologies for personnel selection","volume":"18","year":"2018","journal-title":"Computers in Human Behavior"},{"issue":"1","key":"key2020062510440128000_ref039","first-page":"1","article-title":"Understanding perception of algorithmic decisions: fairness, trust, and emotion in response to algorithmic management","volume":"5","year":"2018","journal-title":"Big Data and Society"},{"key":"key2020062510440128000_ref040","unstructured":"Lohr, S. (2015), \u201cBanking start-ups adopt new tool for lending\u201d, The New York Times, January 18, available at: www.nytimes.com\/2015\/01\/19\/technology\/banking-start-ups-adopt-new-tools-for-lending.html (accessed December 3, 2017)."},{"issue":"3","key":"key2020062510440128000_ref041","first-page":"1693","article-title":"Applicant perspectives during selection: a review addressing \u2018So what?,\u2019 \u2018What\u2019s new?,\u2019 and \u2018Where to next?\u2019","volume":"46","year":"2017","journal-title":"Journal of Management"},{"key":"key2020062510440128000_ref042","article-title":"Hiring algorithms are not neutral","year":"2016","journal-title":"Harvard Business Review"},{"key":"key2020062510440128000_ref043","unstructured":"Miller, C. (2015), \u201cCan an algorithm hire better than a human?\u201d, The New York Times, June 25, available at: www.nytimes.com\/2015\/06\/26\/upshot\/can-an-algorithm-hire-better-than-a-human.html (accessed December 3, 2017)."},{"key":"key2020062510440128000_ref044","unstructured":"O\u2019Connor, C. (2016), \u201cBlack woman engineer launches \u2018blind\u2019 job match app to take bias out of tech hiring\u201d, Forbes, March 3, available at: www.forbes.com\/sites\/clareoconnor\/2016\/03\/03\/black-woman-engineer-launches-blind-job-match-app-to-take-bias-out-of-tech-hiring\/#228f757d2394 (accessed January 14, 2018)."},{"key":"key2020062510440128000_ref045","volume-title":"Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy","year":"2016"},{"issue":"41","key":"key2020062510440128000_ref046","doi-asserted-by":"crossref","first-page":"10870","DOI":"10.1073\/pnas.1706255114","article-title":"Meta-analysis of field experiments shows no change in racial discrimination in hiring over time","volume":"114","year":"2017","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"key2020062510440128000_ref047","unstructured":"Rayome, A. (2018a), \u201cFive eye-opening statistics about minorities in tech\u201d, TechRepublic, February 7, available at: www.techrepublic.com\/article\/5-eye-opening-statistics-about-minorities-in-tech (accessed March 15, 2018)."},{"key":"key2020062510440128000_ref048","unstructured":"Rayome, A. (2018b), \u201cWhy AI could be the tool your HR team needs to hire and retain the best talent\u201d, Tech Republic, January 2, available at: www.techrepublic.com\/article\/why-ai-could-be-the-tool-your-hr-team-needs-to-hire-and-retain-the-best-talent (accessed January 27, 2018)."},{"key":"key2020062510440128000_ref049","doi-asserted-by":"crossref","unstructured":"Rosenblat, A., Kneese, T. and Boyd, D. (2014), \u201cNetworked employment discrimination\u201d, Data and Society Working Paper, October 8, available at: www.datasociety.net\/pubs\/fow\/EmploymentDiscrimination.pdf (accessed October 13, 2018).","DOI":"10.2139\/ssrn.2543507"},{"issue":"3","key":"key2020062510440128000_ref050","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.hrmr.2008.07.004","article-title":"Not much more than platitudes? A critical look at the utility of applicant reactions research","volume":"18","year":"2008","journal-title":"Human Resource Management Review"},{"key":"key2020062510440128000_ref051","unstructured":"Sandvig, C., Hamilton, K., Karahalios, K. and Langbort, C. (2014), \u201cAuditing algorithms: research methods for detecting discrimination on Internet platforms\u201d, paper presented to Data and Discrimination: Converting Critical Concerns into Productive Inquiry, 64th Annual Meeting of the International Communication Association, May 22, Seattle, WA."},{"key":"key2020062510440128000_ref052","unstructured":"Scott, A., Klein, F. and Onovakpuri, U. (2017), \u201cTech Leavers, Kapor center for social impact\u201d, available at: www.kaporcenter.org\/wp-content\/uploads\/2017\/08\/TechLeavers2017.pdf (accessed March 13, 2019)."},{"key":"key2020062510440128000_ref053","first-page":"59","article-title":"Fairness and abstraction in sociotechnical systems","year":"2019"},{"key":"key2020062510440128000_ref054","article-title":"We\u2019re in a diversity crisis: cofounder of Black in AI on what\u2019s poisoning algorithms in our lives","year":"2018","journal-title":"MIT Technology Review"},{"issue":"2","key":"key2020062510440128000_ref055","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.hrmr.2015.01.002","article-title":"The influence of technology on the future of human resource management","volume":"25","year":"2015","journal-title":"Human Resource Management Review"},{"issue":"1","key":"key2020062510440128000_ref056","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.hrmr.2006.11.002","article-title":"Research in e-HRM: review and implications","volume":"17","year":"2007","journal-title":"Human Resource Management Review"},{"issue":"2","key":"key2020062510440128000_ref057","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/s10799-012-0117-x","article-title":"E-recruiting and fairness: the applicant\u2019s point of view","volume":"13","year":"2012","journal-title":"Information Technology and Management"},{"key":"key2020062510440128000_ref058","unstructured":"Thryft, A. (2019), \u201cNot a lot of debiasing, auditing tools yet\u201d, Electrical Engineering Times, May 5, available at: www.eetimes.com\/document.asp?doc_id=1334650 (accessed May 13, 2019)."},{"key":"key2020062510440128000_ref059","unstructured":"Tiku, N. (2018), \u201cThe dirty war over diversity inside Google\u201d, Wired Magazine, January 26, available at: www.wired.com\/story\/the-dirty-war-over-diversity-inside-google (accessed January 27, 2018)."},{"issue":"2","key":"key2020062510440128000_ref060","first-page":"39","article-title":"The importance of organizational justice in personnel selection: defining when selection fairness really matters","volume":"12","year":"2004","journal-title":"International Journal of Selection and Assessment"},{"key":"key2020062510440128000_ref061","unstructured":"Valentino-DeVries, J. (2013), \u201cBosses may use social media to discriminate against job seekers\u201d, Wall Street Journal, November 20, available at: www.wsj.com\/articles\/bosses-may-use-social-media-to-discriminate-against-job-seekers-1384979412 (accessed January 27, 2018)."},{"key":"key2020062510440128000_ref062","doi-asserted-by":"crossref","unstructured":"Vasconcelos, M., Cardonha, C. and Goncalves, B. (2017), \u201cModeling epistemological principles for bias mitigation in AI systems: an illustration in hiring decisions\u201d, available at: https:\/\/arxiv.org\/pdf\/1711.07111 (accessed January 27, 2018).","DOI":"10.1145\/3278721.3278751"},{"key":"key2020062510440128000_ref063","unstructured":"Wachter-Boettcher, S. (2017), \u201cAI recruiting tools do not eliminate bias\u201d, Time Magazine, October 25, available at: http:\/\/time.com\/4993431\/ai-recruiting-tools-do-not-eliminate-bias (accessed March 13, 2019)."},{"key":"key2020062510440128000_ref064","doi-asserted-by":"crossref","unstructured":"Walker, H.J., Helmuth, C.A., Feild, H.S. and Bauer, T.N. (2015), \u201cWatch what you say: job applicants\u2019 justice perceptions from initial organizational correspondence\u201d, Human Resource Management, Vol. 54 No. 6, pp. 999-1011.","DOI":"10.1002\/hrm.21655"},{"issue":"2-3","key":"key2020062510440128000_ref065","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1111\/1468-2389.00245","article-title":"Reactions to computerized testing in selection contexts","volume":"11","year":"2003","journal-title":"International Journal of Selection and Assessment"},{"key":"key2020062510440128000_ref066","doi-asserted-by":"crossref","first-page":"78","DOI":"10.5325\/jinfopoli.8.2018.0078","article-title":"How algorithms discriminate based on data they lack: challenges, solutions, and policy implications","volume":"8","year":"2018","journal-title":"Journal of Information Policy"},{"key":"key2020062510440128000_ref067","unstructured":"Winning, L. (2018), \u201cIt\u2019s time to prioritize diversity across tech\u201d, Forbes, March 13, available at: www.forbes.com\/sites\/lisawinning\/2018\/03\/13\/its-time-to-prioritize-diversity-across-tech\/#642deab616f8 (accessed October 30, 2018)."},{"key":"key2020062510440128000_ref068","first-page":"1","article-title":"A qualitative exploration of perceptions of algorithmic fairness","year":"2018"},{"key":"key2020062510440128000_ref069","first-page":"675","article-title":"Towards a feminist HCI methodology: social science, feminism, and HCI","year":"2011"},{"key":"key2020062510440128000_ref071","unstructured":"Eder, S. (2018), \u201cHow can we eliminate bias in our algorithms?\u201d, Forbes, June 27, available at: www.forbes.com\/sites\/theyec\/2018\/06\/27\/how-can-we-eliminate-bias-in-our-algorithms\/#781c4d99337e (accessed May 14, 2019)."}],"container-title":["Online Information Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/OIR-10-2018-0334\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/OIR-10-2018-0334\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:44:04Z","timestamp":1753397044000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/oir\/article\/44\/2\/383-395\/320780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,17]]},"references-count":73,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,12,17]]}},"alternative-id":["10.1108\/OIR-10-2018-0334"],"URL":"https:\/\/doi.org\/10.1108\/oir-10-2018-0334","relation":{},"ISSN":["1468-4527"],"issn-type":[{"value":"1468-4527","type":"print"}],"subject":[],"published":{"date-parts":[[2019,12,17]]}}}