Knowledge Path Versus Goal-driven Strategy for Digital Transformation

Traditional organizational strategy is meant to create change from how your customers think you rationalize sales and support to what your customers and you need. By fostering a sense of stability, providing consensus view, directing resources, and controlling activities, strategic planning is an expected cornerstone in modern organizations. Over the past eighty years, goal-driven strategies have led digital transformations to unlock new markets, enhanced value-driven revenues, and created a closer kinship between customers and providers. While one presumes goal-driven strategies are best practice for digital transformation, this article explores knowledge paths as an alternative approach. Habitual, emotional, associative, rule-based, and knowledge paradigms do not require embedded goals. Using a systems approach that clarifies the ontological differences between influences and objects, we propose a new explanation for limiting goal-directed strategies. Applying a mapping function that relates functional performance to loss, we construct a novel dataset allowing us to compare like kind results of goals and knowledge-path strategies. These findings offer organizations, with strong identities and experienced management teams, rapid integration of digitally transformed processes into a comprehensive business model. We illustrate how data (rather than process) organizes product/service competition and objectifies purchasing behaviors.