[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-37278-en":3,"doc-seo-37278-105":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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},37278,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","How Forced Intervention Facilitates AI Adoption","The study investigates how forced intervention can promote AI adoption and reduce algorithm aversion among human workers. Evidence from a leading online education company shows that sales workers underutilize a matching algorithm and selectively apply it to low-quality leads. In a three-week field experiment, workers were forced to use or not use the algorithm, and the forced-use treatment causally increased subsequent algorithm usage by 15.8 percentage points. A theoretical model attributes the improvement to learning through unbiased experience, leading to more frequent adoption and broader use on high-quality leads, with managerial guidance on extrinsic interventions to form unbiased beliefs.","How Forced Intervention Facilitates AI Adoption  \nXinyu Cao 1 , Chenshan Hu2 , Jiankun Sun3 , Dennis J. Zhang4  \n1. CUHK Business School, The Chinese University of Hong Kong  \n2. Leeds School of Business, University of Colorado Boulder  \n3. Imperial Business School, Imperial College London  \n4. Olin Business School, Washington University in St. Louis  \nProblem Definition: While artificial intelligence (AI) technologies increasingly become powerful and useful in operations, human workers often resist adopting algorithms, known as algorithm aversion. This aversion can undermine the algorithm performance in practice. While numerous studies explored short-term mitigation strategies for such aversion, this paper investigates whether and why forced interventions can promote AI adoption and reduce algorithm aversion in practice.  \nMethodology/Results: Data from a leading online education company reveal that sales workers underutilize a new matching algorithm and often selectively use it on low-quality leads. The company conducted a field experiment where sales workers were forced to use or not use the algorithm for three weeks. Experimental results show that forcing workers to use the algorithm during the experiment causally increases their algorithm usage over the month after the experiment by 15 .8 percentage points. We develop a theoretical model to derive empirical strategies for exploring the mechanisms behind this improvement. Contrary to the traditional literature focusing on habit formation, our findings suggest learning is a key driver for algorithm adoption among workers over the month after the experiment. Specifically, forced algorithm use allows workers to experience the unbiased algorithm performance and positively adjust their beliefs about it. Consequently, after the experiment, workers use the algorithm not only more frequently but also more on high-quality leads.  \nManagerial Implications: The study empirically shows that forced intervention can effectively improve persistent algorithm use after the intervention, which is crucial for continuous development of the algorithms. More importantly, forced intervention breaks the vicious cycle of biased beliefs and selective usage by enabling workers to form unbiased evaluation of the algorithm efficacy and mitigate selective adoption on low-quality cases. This suggests that firms can implement extrinsic interventions or educational programs to help workers recognize the benefits of algorithms and develop unbiased beliefs about their capabilities, thus facilitating sustained algorithm usage.  \nKey words : AI adoption, algorithm aversion, learning, habit formation, field experiment  \n1 . Introduction  \nWith the explosion of data, the improvement of computing power, and the rapid development of machine learning algorithms, a large number of companies start to utilize artificial intelligence (AI) technologies or smart algorithms to improve their business practices. According to a survey by PwC in 2023, 73% of US companies have already adopted AI in their business, and 54% of the companies surveyed have implemented generative AI in some business areas.1 In particular, AI technologies are vastly transforming operations of numerous companies across various functions, such as product and service development,  \n1 [https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html](https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html)  \nsupply chain management, and customer service. A majority of these companies have witnessed revenue growth in the business functions using AI, as reported in a survey by McKinsey & Company.2  \nIn practice, the successful application of AI hinges on the broad adoption and sustained usage by users, particularly in its early development stage, since AI improvement relies on user-generated data. Unlike traditional technology innovations that are usually adopted after reaching maturity and demonstrating superior performance, AI is o","cbCaiuVVfB9SAB1G","https://ap.wps.com/l/cbCaiuVVfB9SAB1G","pdf",1768454,4,1,47,"English","en",105,"# Introduction\n## Problem definition: algorithm aversion\n## Research question and approach\n# Methodology and results\n## Field experiment design\n## Empirical findings and causal effects\n# Theoretical model and mechanism\n## Learning vs habit formation\n# Managerial implications\n## Breaking biased beliefs and selective usage\n## Practical interventions for sustained adoption","[{\"question\":\"What problem does the paper address regarding AI use at work?\",\"answer\":\"Workers may resist adopting algorithms, a phenomenon called algorithm aversion, which can undermine real-world algorithm performance.\"},{\"question\":\"How did the authors test whether forced intervention changes AI adoption?\",\"answer\":\"They conducted a field experiment in a sales context where workers were forced to use or not use a matching algorithm for three weeks.\"},{\"question\":\"Why does forced use lead to higher adoption after the intervention?\",\"answer\":\"The paper argues that learning is the key driver: forced use lets workers experience the algorithm’s unbiased performance and adjust beliefs, reducing selective use and increasing usage on better-quality 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