Christine Dagmar Malin, Jürgen Fleiß, Isabella Seeber, Bettina Kubicek, Cordula Kupfer, Stefan Thalmann
{"title":"The application of AI in digital HRM – an experiment on human decision-making in personnel selection","authors":"Christine Dagmar Malin, Jürgen Fleiß, Isabella Seeber, Bettina Kubicek, Cordula Kupfer, Stefan Thalmann","doi":"10.1108/bpmj-11-2023-0884","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.</p><!--/ Abstract__block -->","PeriodicalId":47964,"journal":{"name":"Business Process Management Journal","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Business Process Management Journal","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1108/bpmj-11-2023-0884","RegionNum":3,"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
Purpose
How to embed artificial intelligence (AI) in human resource management (HRM) is one of the core challenges of digital HRM. Despite regulations demanding humans in the loop to ensure human oversight of AI-based decisions, it is still unknown how much decision-makers rely on information provided by AI and how this affects (personnel) selection quality.
Design/methodology/approach
This paper presents an experimental study using vignettes of dashboard prototypes to investigate the effect of AI on decision-makers’ overreliance in personnel selection, particularly the impact of decision-makers’ information search behavior on selection quality.
Findings
Our study revealed decision-makers’ tendency towards status quo bias when using an AI-based ranking system, meaning that they paid more attention to applicants that were ranked higher than those ranked lower. We identified three information search strategies that have different effects on selection quality: (1) homogeneous search coverage, (2) heterogeneous search coverage, and (3) no information search. The more applicants were searched equally often (i.e. homogeneous) as when certain applicants received more search views than others (i.e. heterogeneous) the higher the search intensity was, resulting in higher selection quality. No information search is characterized by low search intensity and low selection quality. Priming decision-makers towards carrying responsibility for their decisions or explaining potential AI shortcomings had no moderating effect on the relationship between search coverage and selection quality.
Originality/value
Our study highlights the presence of status quo bias in personnel selection given AI-based applicant rankings, emphasizing the danger that decision-makers over-rely on AI-based recommendations.
期刊介绍：
Business processes are a fundamental building block of organizational success. Even though effectively managing business process is a key activity for business prosperity, there remain considerable gaps in understanding how to drive efficiency through a process approach. Building a clear and deep understanding of the range process, how they function, and how to manage them is the major challenge facing modern business. Business Process Management Journal (BPMJ) examines how a variety of business processes intrinsic to organizational efficiency and effectiveness are integrated and managed for competitive success. BPMJ builds a deep appreciation of how to manage business processes effectively by disseminating best practice. Coverage includes: BPM in eBusiness, eCommerce and eGovernment Web-based enterprise application integration eBPM, ERP, CRM, ASP & SCM Knowledge management and learning organization Methodologies, techniques and tools of business process modeling, analysis and design Techniques of moving from one-shot business process re-engineering to continuous improvement Best practices in BPM Performance management Tools and techniques of change management BPM case studies.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
