[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127267-en":3,"doc-seo-127267-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127267,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Automatic identification of storytelling responses to past‐behavior interview questions via machine learning","Structured interviews often include past-behavior questions that prompt applicants to narrate work-experience stories. Many candidates struggle to produce effective stories, creating a need for scalable, automated support. This study applies machine learning to predict whether responses in transcribed interviews contain storytelling, and to estimate narrative content counts. Models achieved best accuracy of 78% for story presence and successfully estimated situation, task/action, and results utterance frequencies, enabling potential automated feedback.","Received: 28 April 2022  \nRevised: 17 March 2023  \nAccepted: 19 March 2023  \nDOI: 10.1111/ijsa.12428  \nRES EARCH ARTICLE  \nAutomatic identification of storytelling responses to past‐behavior interview questions via machine learning  \nAdrian Bangerter1 | Eric Mayor2  | Skanda Muralidhar3 |  \nEmmanuelle P. Kleinlogel4 | Daniel Gatica‐Perez3 | Marianne Schmid Mast5  \n1Institute of Work and Organizational Psychology, University of Neuchâtel, Neuchatel, Switzerland  \n2Department of Clinical Psychology and Epidemiology, University of Basel, Basel, Switzerland  \n3Idiap Research Institute, Martigny, Switzerland  \n4Centre d'Economie et de Management de  \nl'Océan Indien, University of Reunion Island, Saint‐Denis, France  \n5Department of Organizational Behavior, University of Lausanne, Lausanne, Switzerland  \nCorrespondence  \nAdrian Bangerter, Institute of Work and Organizational Psychology, University of Neuchâtel, Rue Emile‐Argand 11, 2000 Neuchâtel, Switzerland.  \nEmail: [adrian.bangerter@unine.ch](adrian.bangerter@unine.ch)  \nFunding information  \nSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung  \nAbstract  \nStructured interviews often feature past‐behavior questions, where applicants are asked to tell a story about past work experience. Applicants often experience difficulties producing such stories. Automatic analyses of applicant behavior in responding to past‐behavior questions may constitute a basis for delivering feedback and thus helping them improve their performance. We used machine learning algorithms to predict storytelling in transcribed speech of participants responding to past‐behavior questions in a simulated selection interview. Responses were coded as to whether they featured a story or not. For each story, utterances were also manually coded as to whether they described the situation, the task/action performed, or results obtained. The algorithms predicted whether a response features a story or not (best accuracy: 78%), as well as the count of situation, task/ action, and response utterances. These findings contribute to better automatic identification of verbal responses to past‐behavior questions and may support automatic provision of feedback to applicants about their interview performance.  \nKEYWO R DS  \nmachine learning, past‐behavior question, selection interview, storytelling  \nPractitioner points  \n• Past‐behavior questions constitute a best practice in selection interviews.  \n• Past‐behavior questions invite applicants to tell a story about what they did in a past work‐related situation.  \n• Applicants often fail to produce stories, and when they do, they tend to focus on describing the situation rather than what they did and what results they obtained.  \n• Coaching may help them improve their responses but is costly.  \n• Using machine learning, we accurately predict storytelling responses to past‐ behavior questions and their narrative content from transcripts of applicant responses.  \n• It is feasible to design systems for automatic delivery of feedback to applicants to improve their responses to past‐behavior questions.  \nThis is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.  \n© 2023 The Authors. International Journal of Selection and Assessment published by John Wiley & Sons Ltd.  \n\n| BANGERTER ET AL.  |  |  |  |\n| --- | --- | --- | --- |\n|  |  |  |  |\n\n|  \n377  \n1 | INTRODUCTION  \nStructured behavioral interviews constitute the state‐of‐the‐art in selection interview practice. They evidence high predictive validity and are gaining traction in many organizations worldwide (Kantrowitz et al., 2018; Levashina et al., 2014; Motowidlo et al., 1992; Roulin, 2017; Roulin et al., 2012; Turner, 2004) . Structured interviews often feature past‐behavior questions, where applicants are as","cbCaisqZWbjT11TZ","https://ap.wps.com/l/cbCaisqZWbjT11TZ","pdf",833515,1,12,"English","en",105,"# Introduction\n## Behavioral interviewing and storytelling responses to past‐behavior questions","[{\"question\":\"What problem does the study address in selection interviews?\",\"answer\":\"It addresses applicants’ difficulty producing well-structured stories when answering past-behavior questions, and the resulting challenge for recruiters and training efforts.\"},{\"question\":\"How does the research use machine learning?\",\"answer\":\"It uses machine learning algorithms on transcribed responses to predict whether a response features a story and to estimate how many utterances describe the situation, the task/action, and the results.\"},{\"question\":\"What performance did the models achieve?\",\"answer\":\"The best accuracy reported for predicting whether a response contains a story was 78%, alongside estimates of the counts of key narrative elements.\"}]","Automatic identification of storytelling responses to past‐behavior interview questions via machine learning | 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