The Tushman–Rosenkopf Model (Cyclical Model of Technological Change)

 Introduction

The Tushman–Rosenkopf Model, also known as the Cyclical Model of Technological Change, describes innovation as a patterned and repetitive process influenced by technological and organizational dynamics. The theory argues that industries do not evolve through continuous, smooth improvement; instead, they move through cycles of stability and disruption. The model integrates two core dimensions: the nature of technological change (radical vs. incremental) and organizational adaptation (flexibility vs. structural stability). These dimensions together explain why firms succeed during stable periods but often struggle when disruptive innovations emerge.

The model identifies four key stages: technological discontinuity, era of ferment, dominant design, and incremental innovation. These stages repeat over time, forming a cycle of technological evolution.

1. Technological Discontinuity

A technological discontinuity refers to a breakthrough that fundamentally alters existing technologies, processes, or business models. Such changes often make prior competencies obsolete. For example, in the energy sector, the development of large-scale lithium-ion battery storage disrupted conventional grid systems by enabling decentralized renewable energy solutions. Established utilities faced challenges as energy generation shifted from centralized plants to distributed solar-plus-storage models.

2. Era of Ferment

Following a discontinuity, industries experience an era of ferment, marked by experimentation, uncertainty, and competing technological approaches. Firms test alternative designs, and no clear standard exists. In agriculture, the emergence of precision farming led to competing technologies such as drone-based crop monitoring, AI-driven soil sensors, and satellite analytics. During this phase, investments are risky, and survival depends on adaptability rather than efficiency.

3. Emergence of a Dominant Design

Eventually, one configuration becomes the dominant design, setting industry standards and reducing uncertainty. In online education, the Learning Management System (LMS) integrated with AI-based analytics has become a dominant design, combining content delivery, assessment, and learner tracking into a single platform. Once this standard emerged, firms aligned their products and strategies around it.

4. Incremental Innovation and Stability

After a dominant design is established, innovation becomes incremental. Firms focus on optimization, cost reduction, and performance enhancement. For instance, in logistics, once automated warehouse systems became standardized, companies concentrated on improving picking speed, energy efficiency, and predictive maintenance rather than redesigning the entire system.

Renewal of the Cycle

Over time, new technologies trigger another discontinuity, restarting the cycle. This cyclical perspective helps managers understand when to exploit existing capabilities and when to explore new ones.

 

Caselet: The Evolution of Smart Warehousing

The logistics industry provides a clear illustration of the Tushman–Rosenkopf Model. Traditional warehouses relied heavily on manual labor and basic inventory software. The introduction of robotic automation and AI-driven demand forecasting represented a technological discontinuity. This led to an era of ferment, where firms experimented with autonomous mobile robots, robotic arms, and vision-based sorting systems. Different layouts and software ecosystems competed for dominance.

Over time, a dominant design emerged: AI-integrated warehouse management systems combined with collaborative robots (cobots). Once this standard stabilized, companies such as large e-commerce and retail firms shifted toward incremental innovation—fine-tuning robot speed, reducing error rates, and optimizing energy use. Today, the industry is again approaching a new discontinuity with the integration of digital twins and generative AI, signaling the start of another innovation cycle. This case highlights how technological change repeatedly reshapes industries through predictable yet disruptive cycles.

 References:

• Utterback, J. M. (1994). Mastering the dynamics of innovation: How companies can seize opportunities in the face of technological change. Harvard Business School Press.
• Christensen, C. M. (1997). The innovator’s dilemma: When new technologies cause great firms to fail. Harvard Business School Press.
• Perez, C. (2002). Technological revolutions and financial capital: The dynamics of bubbles and golden ages. Edward Elgar Publishing.