The Impact of Organizational Culture on AI adoption and Organizational Performance: A Mixed Methods Approach

Outlet Title

AMCIS 2024 TREOs

Document Type

Conference Proceeding

Publication Date

2024

Abstract

In recent years, the rapid advancement of artificial intelligence (AI) has created many opportunities for organizations to leverage existing enterprise databases and improve organizational performance. However, many organizations are experiencing numerous challenges to adopt AI and gain a competitive advantage. AI can potentially transform how organizations operate and compete in the business landscape, largely due to the availability of big data, innovative techniques, and infrastructure. In the organization's context, organizational culture is argued to significantly impact AI adoption and organizational performance. Organizational culture (OC) refers to the set of shared values, beliefs, and norms an organization holds and their impact on decision-making toward novel technologies. While prior research mainly focuses on AI's technological capabilities (Mikalef & Gupta, 2021), this study examines the interplay between organizational culture, AI adoption, and organizational performance. Specifically, the study seeks to investigate the following research questions: 1) To what extent does organizational culture impact AI adoption in the organization? And 2) To what extent does the adoption of AI in the organization impact organizational performance? Figure 1. depicts the proposed theoretical model. The study employs a mixed-method approach. Mixed methods can enable a deeper understanding of organizations' perceptions, attitudes, and beliefs about AI adoption. The study expects the findings to show that organizational culture has a positive, significant mediating or moderating impact on AI adoption and organizational performance. This study makes two main contributions, the first is that it contributes theory by proposing a model to assess the impact of organizational culture on AI adoption and organizational performance. The second contribution is that the study employs a mixed methods approach integrating both quantitative and qualitative data for triangulation to gain a richer understanding of the phenomena.

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