Human-centric digital transformation: Exploring human information behavior through three field studies in digital policy, artificial intelligence, and emerging technologies

In today's rapidly evolving digital landscape, organizations often struggle to implement human-centric digital transformation effectively. Despite significant investments in advanced technologies, many digital initiatives fail to resonate with users because they overlook how humans seek, process, and utilize information. This disconnect arises from a gap in aligning technological advancements with the complex informational behaviors and preferences of individuals. Addressing this gap, this thesis investigates human-centric digital transformation through three distinct studies: the implementation of digital policies like General Data Protection Regulation (GDPR) and their reliance on understanding customers’ informational proactiveness; the application of machine learning algorithms such as life-event targeting to address customer uncertainties during decision-making; and the integration of emerging technologies like Artificial Intelligence (AI)-infused Augmented Reality (AR) in industrial settings, focusing on aligning their use with workers’ information processing capabilities. These studies explain how organizations can tailor digital transformation efforts to individuals' informational characteristics, offering insights into achieving human-centric digital strategies.
Through three comprehensive studies corresponding to the stages of Digitization, Digitalization, and Digital Innovation, the research examines how aligning technological initiatives with human informational attributes enhances the success of digital transformation efforts. The first study explores how tailoring consent acquisition strategies to customers' information proactiveness under regulations like GDPR enhances compliance and trust, emphasizing the importance of human-centric approaches in the initial digitization stage. The second study investigates how addressing customers' information-seeking needs during uncertain decision-making periods improves the effectiveness of machine learning applications like life-event targeting, highlighting the value of personalized engagement in the digitalization stage. The third study examines how adapting advanced technologies like AI-infused augmented reality to match workers' information processing capabilities enhances productivity in industrial settings, underscoring the necessity of considering human factors in the digital innovation stage.
Collectively, these studies affirm that prioritizing human-centric strategies aligned with end-users' informational behaviors will improve specific outcomes within each stage of digital transformation. While effectiveness was measured through stage-specific metrics—such as compliance rates in GDPR implementation, engagement rates in life-event targeting, and productivity enhancements with AR—these findings collectively demonstrate how aligning technology with informational behaviors fosters more impactful digital transformation efforts. By understanding and integrating how individuals seek, process, and utilize information, organizations can design and implement digital solutions that resonate deeply with users, leading to higher engagement, better adoption rates, and meaningful impacts. This thesis concludes that technological advancements alone are insufficient; success in digital transformation depends on the harmonious integration of technology with human informational characteristics. Future work could explore additional informational characteristics and contexts to further refine human-centric strategies, ensuring that digital transformation efforts remain aligned with the evolving ways humans interact with information. By adopting these approaches, organizations can navigate the complexities of the digital landscape more effectively, fostering a more inclusive, efficient, and responsive digital ecosystem.