{"id":4351,"date":"2024-04-16T15:38:54","date_gmt":"2024-04-16T22:38:54","guid":{"rendered":"https:\/\/blogs.ubc.ca\/genemoolee\/?p=4351"},"modified":"2026-06-05T20:39:14","modified_gmt":"2026-06-06T03:39:14","slug":"unpacking-the-ai-blackbox-the-impact-of-ai-strategies-on-firm-performance-with-a-dual-lens-on-product-and-process-orientation","status":"publish","type":"post","link":"https:\/\/blogs.ubc.ca\/genemoolee\/2024\/04\/16\/unpacking-the-ai-blackbox-the-impact-of-ai-strategies-on-firm-performance-with-a-dual-lens-on-product-and-process-orientation\/","title":{"rendered":"Unpacking AI Transformation: The Impact of AI Strategies on Firm Performance from the Dynamic Capabilities Perspective"},"content":{"rendered":"

Park, Jaecheol, Myunghwan Lee, J. Frank Li, Gene Moo Lee<\/span> \u201cUnpacking AI Transformation: The Impact of AI Strategies on Firm Performance from the Dynamic Capabilities Perspective<\/span><\/span><\/a>,\u201d Work-in-Progress.<\/i><\/h4>\n
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  • Presentations: UBC (2024), CIST (2024), INFORMS (2024), SNU (2024), UMass (2024), BIGS (2024), KrAIS (2024), CityU Hong Kong (2025), NTU (2025), AIM (2025), ISR-PDW (2025)<\/li>\n
  • Best Paper Award at BIGS 2024<\/strong><\/li>\n
  • Best Student Paper Award at KrAIS 2024<\/strong><\/li>\n<\/ul>\n

    Artificial intelligence (AI) technologies hold great potential for large-scale economic impact. Aligned with this trend, <\/span>recent<\/span> studies explore the adoption <\/span>impact<\/span> of AI technologies on firm performance. However, they predominantly <\/span>measure<\/span> firms\u2019 <\/span>AI <\/span>capabilities with <\/span>input (e.g., labor\/job posting)<\/span> or output (e.g., patents)<\/span>, neglecting to consider the strategic <\/span>direction toward<\/span> AI<\/span> in business operations<\/span> and value creation<\/span>. In this paper, we <\/span>empirically <\/span>examine how firms\u2019 AI <\/span>strategic <\/span>orientation affects firm performance <\/span>from the dynamic <\/span>capabilities<\/span> perspective<\/span>. We <\/span>create a novel firm-year <\/span><\/span>AI strategic orientation<\/span><\/span> measure <\/span>by employing a large language model to <\/span>analyze<\/span> business descriptions in Form 10-K filings and identify an increasing trend <\/span>and changing status <\/span>of AI <\/span>strategies<\/span> among U.S. public firms<\/span>.<\/span> Our long-difference analysis shows that AI strategic orientation is associated with greater operating cost, capital expenditure, and market value but not sales<\/span>, showing the importance of strategic <\/span>direction<\/span> toward<\/span> AI to create business value.<\/span> By <\/span>further <\/span>dissecting firms\u2019 AI <\/span>strategic orientation<\/span> into <\/span><\/span>AI awareness<\/span><\/span>, <\/span><\/span>AI product orientation<\/span><\/span>, and <\/span><\/span>AI process orientation<\/span><\/span>, <\/span>we find <\/span>that <\/span>AI awareness is generally <\/span>not related to performance<\/span>, that<\/span> AI product orientation is associated with short-term increased operating expenses and long-term market value, and that AI process orientation is associated with long-term increased costs and sales. Moreover, we find the negative moderating effect of environmental dynamism on AI process orientation.<\/span> This study contributes to the recent AI <\/span>strategy and <\/span>management literature by providing the strategic role of AI orientation on firm performance.<\/span><\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

    Park, Jaecheol, Myunghwan Lee, J. Frank Li, Gene Moo Lee \u201cUnpacking AI Transformation: The Impact of AI Strategies on Firm Performance from the Dynamic Capabilities Perspective,\u201d Work-in-Progress. Presentations: UBC (2024), CIST (2024), INFORMS (2024), SNU (2024), UMass (2024), BIGS (2024), KrAIS (2024), CityU Hong Kong (2025), NTU (2025), AIM (2025), ISR-PDW (2025) Best Paper Award […]<\/p>\n","protected":false},"author":51140,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[45231],"tags":[1264,379631,849344,4381942,4381959,4372817,45318,4381950,424636,4523,4381949,4381948],"class_list":["post-4351","post","type-post","status-publish","format-standard","hentry","category-working-papers","tag-ai","tag-big-data","tag-deep-learning","tag-firm-level","tag-industry-dynamism","tag-industry-intelligence","tag-is-strategy","tag-llm","tag-machine-learning","tag-text-mining","tag-process","tag-product"],"_links":{"self":[{"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/posts\/4351","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/users\/51140"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/comments?post=4351"}],"version-history":[{"count":16,"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/posts\/4351\/revisions"}],"predecessor-version":[{"id":4702,"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/posts\/4351\/revisions\/4702"}],"wp:attachment":[{"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/media?parent=4351"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/categories?post=4351"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.ubc.ca\/genemoolee\/wp-json\/wp\/v2\/tags?post=4351"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}