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Do digital technologies pay off? A meta-analytic review of the digital technologies/firm performance nexus

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  • Oduro, Stephen
  • De Nisco, Alessandro
  • Mainolfi, Giada

Abstract

The adoption of digital technologies (DTs) has become a predominant management practice in recent years. However, empirical findings on their performance outcomes are mixed and fragmented across cultures, contexts, and disciplines. Inspired by the resource-based view of the firm, resource orchestration theory, and contingency theory, this study aims to examine the extent to which four selected DTs—artificial intelligence (AI), big data analytics (BDAs), internet of things/cyber-physical system (IoT/CPS), and 3D printings (3DP)—impact the overall, financial, innovation, and operational performance of the firm through a meta-analysis. Employing a random-effects model and psychometric meta-analysis approach based on subgroup and meta-regression analyses, we examine 109 studies with 1,346,242 observations and 165 effect sizes from 2006 to 2021. The findings reveal that DTs have a positive and moderate effect on overall firm performance; however, the impact on innovation performance is the largest, followed by operational efficiency, and finally, financial performance. Moreover, the results show that the impact of AI on firm performance is the largest, while 3DP exerts the lowest impact. Furthermore, boundary-spanning conditions—contextual factors (i.e., firm size, firm age, sector, country development, sector technology intensity, time frame) and methodological factors (i.e., study type, sampling technique, type of performance measure)—moderate the focal relationship (i.e., account for between-study variance). The theoretical and managerial implications of these findings are highlighted.

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  • Oduro, Stephen & De Nisco, Alessandro & Mainolfi, Giada, 2023. "Do digital technologies pay off? A meta-analytic review of the digital technologies/firm performance nexus," Technovation, Elsevier, vol. 128(C).
    • Handle: RePEc:eee:techno:v:128:y:2023:i:c:s0166497223001475
    DOI: 10.1016/j.technovation.2023.102836
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