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Three types of expert knowledge are essential to effective industry assignment: definition-based knowledge (i.e., expert definitions of each industry), structure-based knowledge (i.e., structural relationships among industries as specified in an ICS), and assignment-based knowledge (i.e., prior firm-industry assignments performed by domain experts). Existing industry assignment methods utilize only assignment-based knowledge to learn a model that classifies unassigned firms to industries, overlooking definition-based and structure-based knowledge. Moreover, these methods only consider which industry a firm has been assigned to, ignoring the time-specificity of assignment-based knowledge, i.e., when the assignment occurs. To address the limitations of existing methods, we propose a novel deep learning-based method that not only seamlessly integrates the three types of knowledge for industry assignment but also takes the time-specificity of assignment-based knowledge into account. Methodologically, our method features two innovations: dynamic industry representation and hierarchical assignment. The former represents an industry as a sequence of time-specific vectors by integrating the three types of knowledge through our proposed temporal and spatial aggregation mechanisms. The latter takes industry and firm representations as inputs, computes the probability of assigning a firm to different industries, and assigns the firm to the industry with the highest probability. We conduct extensive evaluations with two widely used ICSs and demonstrate the superiority of our method over prevalent existing methods.<\/jats:p>","DOI":"10.25300\/misq\/2022\/17171","type":"journal-article","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T17:14:55Z","timestamp":1696007695000},"page":"1147-1176","source":"Crossref","is-referenced-by-count":7,"title":["Exploiting Expert Knowledge for Assigning Firms to Industries: A Novel Deep Learning Method"],"prefix":"10.25300","volume":"47","author":[{"given":"Xiaohang","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Information Management & Engineering, Shanghai University of Finance and Economics, Shanghai, China"}]},{"given":"Xiao","family":"Fang","sequence":"additional","affiliation":[{"name":"Lerner College of Business and Economics, University of Delaware, Newark, DE, USA"}]},{"given":"Jing","family":"He","sequence":"additional","affiliation":[{"name":"Lerner College of Business and Economics, University of Delaware, Newark, DE, USA"}]},{"given":"Lihua","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Information Management & Information Systems, School of Management, Fudan University, Shanghai, China"}]}],"member":"10933","published-online":{"date-parts":[[2023,9,1]]},"reference":[{"issue":"4","key":"2025082212320516800_b1-08_ra_10_25300_misq_2022_17171","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.2307\/41703508","article-title":"MetaFraud: A meta-learning framework for detecting financial fraud","volume":"36","author":"Abbasi","year":"2012","journal-title":"MIS Quarterly"},{"issue":"2","key":"2025082212320516800_b2-08_ra_10_25300_misq_2022_17171","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1111\/jofi.12122","article-title":"The importance of industry links in merger waves","volume":"69","author":"Ahern","year":"2014","journal-title":"The Journal of Finance"},{"key":"2025082212320516800_b3-08_ra_10_25300_misq_2022_17171","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1016\/j.neucom.2021.07.057","article-title":"Leveraging label hierarchy using transfer and multi-task learning: A case study on patent classification","volume":"464","author":"Aroyehun","year":"2021","journal-title":"Neurocomputing"},{"issue":"6","key":"2025082212320516800_b4-08_ra_10_25300_misq_2022_17171","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1287\/mnsc.2014.1930","article-title":"Simultaneously discovering and quantifying risk types from textual risk disclosures","volume":"60","author":"Bao","year":"2014","journal-title":"management Science"},{"issue":"5","key":"2025082212320516800_b5-08_ra_10_25300_misq_2022_17171","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1046\/j.1475-679X.2003.00122.x","article-title":"What\u2019s my line? 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