Impact of Scientific Integrity and Misconduct on Data-intensive Enterprises

Abstract

Data-intensive enterprises primarily focus on processing and managing large volumes of data for applications such as business analytics, decision support systems, social media platforms, e-commerce, financial services, and video-streaming services, using advanced technologies including artificial intelligence (AI) and generative artificial intelligence (Gen AI). These projects typically involve long development cycles, high capital investment, and reliance on highly skilled and costly personnel. Consequently, it is essential to understand factors that may affect their successful implementation and operation. This study examines whether issues related to scientific integrity and misconduct can adversely impact data-intensive enterprises and identifies measures to mitigate such effects. The study is particularly relevant in light of increasing reports of misconduct among technical personnel and recent observations that science and research integrity are facing a “growing crisis,” as noted by The Lancet in its July 6, 2024 editorial. The proposed methodology maps characteristics of scientific misconduct that have previously affected businesses onto data-intensive enterprises within the framework of Normal Accident Theory. Vulnerable areas associated with potential revenue loss, reputational damage, or both—across technological, operational, and social dimensions—are identified. Based on this analysis, a set of managerial recommendations is proposed to support business planners, senior executives, and managers in taking proactive measures to prevent catastrophic failures and to ensure the long-term integrity and sustainability of data-intensive enterprise operations.

Ähnliche Arbeiten