8.4 The Stabilisation of Qualitative and Quantitative Traits

Marcos Antonio de Lima Filho, PhD.

The departure from established research traditions on disruption, fundamentally anchored in the concepts of dominant designs and the punctuated equilibrium model, has culminated in an embarrassing gap between theory and practice. As examined in prior sections, Christensen innovated by introducing several new premises into his definition of disruption (Section 6.2). While seemingly positive, this paradigm shift narrowed disruption into an overly specific phenomenon, making disruption theory fall short in explaining the realities of industrial innovation. Moreover, this new paradigm might have amplified the pro-innovation bias that Rogers (1983) warned about, resulting in a pro-disruption bias that blinds practitioners to ubiquitous stability patterns observed in technological evolution.

To address these theoretical issues, I believe that scholars should revisit and reintegrate the founding principles of disruption research — dominant designs and the punctuated equilibrium model — into the core of disruptive innovation theory. However, the mere reintroduction of these concepts is not enough. Scholars and practitioners should also recognise disruptive innovation as part of an evolutionary process, acknowledging its position as one of many patterns observed within the evolution of industries. By drawing parallels with natural selection, we can then conceptualise a more diverse range of patterns beyond disruption, such as directional innovation (Chapter 7), stabilising innovation (Chapter 8), and purifying innovation (Chapter 9).

These concepts steer us away from an undue focus on disruptive changes, enabling us to recognise patterns of stability, gradual incremental shifts, and technological obsolescence within a wider evolutionary process. Without concepts characterising such patterns, scholars and practitioners might continue to overlook these prevalent patterns of design evolution, thereby perpetuating the pro-disruption bias. The predominance of disruptive innovation in current discourse is, therefore, not a reflection of its prevalence, but rather a consequence of the concepts we use to make sense of the world. Acknowledging other patterns of innovation is, thus, a necessary corrective to balance our understanding of technological evolution.

Several concepts were drawn to explain what could disrupt the stability of industries, such as the emergence of a new dominant design (Anderson & Tushman, 1990), or product architecture (Henderson & Clark, 1990). This latter explanation, on which Christensen relied “extensively upon”, is a direct continuation of the research pioneered by Abernathy and associated researchers (Abernathy & Utterback, 1978; Abernathy et al., 1983; Abernathy & Utterback, 1985). As such, the concept of dominant designs can reconcile disruption research with an array of research traditions and, by extension, with models of punctuated equilibrium and evolutionary theory. Integrative efforts like these could enable scholars to reestablish their shared identity and so combat the growing fragmentation of disruption research (Hopp et al., 2018).

Despite the usefulness of dominant designs in providing insights about the stabilisation of product architectures, this concept still does not capture the full complexity of stabilising selection in the evolution of designs. Although there is a significant correspondence between stabilising innovation and dominant designs, there are differences in their respective levels of analysis. The stabilisation of design traits can be observed across multiple levels, whereas dominant designs are more appropriate to higher order traits, such as product architectures.

For instance, consider the case of stabilising selection observed in the evolution of commercial airliners (Figure 8.4.1). The stability of this trait, however, reveals little about the architecture of these aircraft: narrow-body airliners, regional jets, and large wide-bodied aircraft all perform at roughly the same altitude and speed. A similar pattern is observed in the stabilisation of smartphone thickness (Figure 5.3.2), with diverse form factors conforming to a 9mm thick profile. Therefore, while these instances of stabilising selection do not explicitly characterise a dominant design, they do represent signs of technological stasis and therefore deserve a dedicated concept for their accurate representation.

In summary, dominant designs and stabilising selection represent two distinct concepts that operate at different levels of a design hierarchy. By definition, dominant designs belong to higher levels of the design hierarchy: “A dominant design usually takes the form of a new product or set of features” (Utterback & Suárez, 1993). They synthesise various independent technological innovations introduced in earlier products, establishing an overall hierarchy of technical concern and influence (Abernathy et al., 1983).

To illustrate the difference between dominant designs and stabilising selection, consider the evolution of smartphones. Since 2014, the touchscreen slate has become the dominant form factor, embodied in over 96% of all smartphone models launched. This variable (form factor) informs the major architecture of a product. In contrast, stabilising selection can operate across multiple levels of the design hierarchy, affecting both numeric and categorical variables.

Numeric variables tend to stabilise around certain values that represent a balance between functionality, aesthetic appeal, cost, and technological feasibility. In the evolution of smartphones, some numeric variables such as thickness and display resolution started to gravitate towards an “optimal” range. Consumers wanted slim, sleek devices, but not at the expense of battery life. Consequently, manufacturers aimed for a balance, leading to a stabilisation in device thickness (Figure 5.3.2). Similarly, display resolution improved rapidly at first, but it gradually began to stabilise as further increases became less discernible to the human eye and more taxing on the battery (Figure 5.3.3).

Categorical variables also experience stabilising selection. Figure 8.4.2 shows that most smartphone models now share a common set of sensors, components, and network capabilities. As time passed, these features gradually became the “norm”, being adopted by almost all devices in the market, contributing to a sort of uniformity in design and functionality. Moreover, Android and iOS emerged as the dominant operating systems (Figure 6.5.1). The diffusion of these features has considerable implications for competition and product design. When product designs begin to standardise, the opportunities for product differentiation shrink. This could make it harder for companies to distinguish their products, which can lead to price-based competition.

Design stability (or maturity) also shapes how users will perceive innovations. In the evolution of smartphones, features that were once considered innovative are now taken for granted and perceived as basic requirements. In the early 2000s, the ability to connect to Wi-Fi was a differentiating feature in a smartphone; it was a sign of advanced technology and connectivity. However, as Wi-Fi became more widespread, it transitioned from being a unique selling point to a default expectation. Nowadays, consumers naturally expect the features presented in Figure 8.4.2 in every smartphone. At this stage, such features lose their ability to build product differentiation.

In conclusion, while dominant designs capture some aspects of stability, this concept does not encompass broader patterns of stability in product evolution. Dominant designs, by their very definition, tend to be associated more closely with the higher levels of the design hierarchy. They represent a design paradigm that consumers and manufacturers alike accept as the “standard” or “norm”. In comparison, stabilising selection can occur at all levels of product design, from the architecture down to the individual components and features. This distinction is evident in the development of smartphones and commercial aircraft. Even with the emergence of dominant designs, stabilising selection has persisted through the ongoing integration of novel components and subsystems (Figure 8.4.3).

Therefore, innovation research is still in need of a concept that could characterise the stabilising selection of technologies. By reclaiming the concept of dominant designs, the theory of disruptive innovation can be reconnected with earlier research traditions and the model of punctuated equilibrium. Still, this reintegration with evolutionary theory could be further strengthened by the concepts of stabilising innovation and purifying innovation. Without concepts to define such patterns, practitioners will remain desensitised to these ubiquitous patterns of design evolution, and the pro-disruption bias will persist.

Stabilising innovation is hereby defined as a selection process that promotes evolutionary conservation in the design of products, services, or systems, reducing the overall variability and divergence in design features over time. To put it in operational terms, stabilising innovation can be characterised by the following:

Stabilising innovation consists of the conservation of a qualitative trait (materials, components, features, or form factor) or quantitative trait (such as size, speed, fuel consumption, computational power, etc).

For illustration, the conservation of qualitative traits can be observed in the stabilisation of design concepts (categorical variables) such as form factors, software platforms, product architectures, and etc. As for the conservation of quantitative traits, this simply refers to the stabilisation of quantifiable variables, such as smartphone thickness and display resolution (Figures 5.3.2 and 5.3.3), or aircraft operational speed and flying altitude (Figures 4.3.1 and 8.4.1).