Abstract

In the age of digital transformation, firms are increasingly seeking advanced technologies to drive exploratory innovation. Among these, deep learning (DL) has emerged as a powerful tool for analyzing vast and complex data to uncover novel insights. However, its role in supporting organizational knowledge absorption—particularly for integrating external codified and tacit knowledge—remains underexplored. While prior research acknowledges the importance of Information Technology (IT) in fostering innovation, few studies examine how DL specifically contributes to the absorption of diverse types of knowledge for exploratory innovation(Andrade-Rojas et al., 2024). The mechanisms by which DL facilitates innovation through knowledge integration from external sources, such as competitors, customers, or public data, are not well understood. This study draws on the Knowledge-Based View (KBV) of the firm (Grant, 1996) and absorptive capacity theory (Cohen & Levinthal, 1990) to conceptualize how DL technologies enhance firms’ ability to identify, assimilate, and exploit external knowledge for innovation purposes. We aim to develop a conceptual model explaining the role of DL in supporting exploratory innovation via knowledge absorption. The guiding research questions include: How does DL enable firms to capture codified and tacit knowledge from structured and unstructured data? In what ways does DL improve firms’ capacity to generate novel insights and foster exploratory innovation? This is a conceptual study at an early stage. We plan to build a theoretical model and subsequently validate it using firm-level innovation data and patent output. Future empirical work will employ a quantitative approach with firm surveys and archival innovation metrics. This research contributes to the IS literature by extending KBV and absorptive capacity theory to include deep learning as a strategic knowledge integration capability. Practically, it provides guidance for firms on deploying DL technologies to unlock innovation opportunities in a knowledge-intensive economy.

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