The Nadler–Tushman congruence model stands out as a well-known and effective framework for navigating organizational change (Nadler & Tushman, 1989, 1997). Despite the prevalent discussions on organizational ambidexterity in recent years, few people are aware that both of these concepts originated from the same researcher around the same period. Although these ideas followed separate developmental paths, their shared origins underscore a common research interest. This paper aims to explore Tushman's research and respond to the convergence of these two concepts after approximately 30 years. Based on Tushman's suggestion that the integration of multiple logics through an overarching identity is necessary, this paper considers and suggests a new integrated model. This model can help formulate a paradigm for dynamic organizational change that aligns with today's organizational environment.

In studies on buyer–supplier relationships, interdependence is a commonly used keyword; however, its definition and measurement items have not received much attention. By reviewing the literature, this study found two streams in studies associated with interdependence, with each stream having certain commonalities in its measurement items. In the power-related context based on Resource Dependence Theory by (1) Pfeffer and Salancik (1978), it tends to use the percentage of sales accounted for by the trading partner, the substitutability of trading partners, and the switching cost, whereas in the task-related context based on (2) Thompson’s (1967) technology, dependence on the traded input and output is often used as measurement items for interdependence. In fact, both logics are implemented in Toyota Motor Corporation’s supplier management, using multiple suppliers to avoid resource dependence while some parts are determined by technology.

Data usage has a major impact on the corporate innovation process by providing more multifaceted and clarified customer insights than those that have been available before. However, data-driven development relies on objective data, and as a result of the constant pursuit of rationality, we are confronted with the opposite constraint of user trends. Project L, as discussed in this paper, adopted data-driven development after its official release; however, as product updates were led by user data, the product tended to be updated conservatively and for the existing users, which resulted in a slowdown in the active user growth rate. Fortunately, the development team size was not reduced even after the product was released; hence, Project L was able to change into a development policy in which the ratio of idea-driven development and data-driven development could be adjusted based on user trends. The result revealed that they were equally successful in capturing new users and retaining existing ones.