Rethinking disruptive innovation: unravelling theoretical controversies and charting new research frontiers

ABSTRACT

For more than two decades, disruptive innovation theory (DIT) has stood as one of the most influential business theories of the 21st century. Despite its widespread impact and the extensive scholarly work it has inspired, DIT has encountered divergent views among academics, sparking debates over its evolution and applicability. This essay dives into these debates, methodically unraveling and highlighting key critiques and controversies shaping the discourse around DIT. These controversies include definitional ambiguity, the case-study conundrum, challenges in generalizability, issues in defining a unit of measure, biases in assessing DIT outcomes, and the exploration of its predictive and prescriptive potential. By synthesizing and recalibrating existing theoretical disputes and distinguishing between critique and controversies, we shed new light on often overlooked or unresolved issues, particularly outcome bias and the theory’s performative capabilities beyond its predictive or prescriptive scope. Introducing a novel “challenger-incumbent perspective,” our analysis extends the discussion beyond the traditional dichotomy between established organizations and disruptors. In conclusion, we pave the way for future research directions, aiming to deepen our understanding and enhance the practical application of this influential yet contested concept.

KEYWORDS:

• Disruptive innovation
• theoretical controversies
• incumbent bias
• challenger
• predictive, performative, prescriptive
• business bodel, technology

Introduction

Disruptive innovation theory (DIT) is prominent in management science, corporate strategy, and contemporary cultural discourse. Its roots trace back to the groundbreaking work of Bower and Christensen (1995), “Disruptive Technologies: Catching the Wave”, and it has since undergone substantial refinement, notably shifting from “disruptive technologies” to “disruptive innovation” in Christensen and Raynor’s influential 2003 work. Today, DIT is acknowledged as a pivotal business theory that has matured through rigorous academic dialogue and refinement by various scholars over the past quarter of a century.

Nevertheless, DIT’s widespread acceptance and evolution have not insulated it from contentious scholarly discourse. Indeed, DIT has been the fulcrum of substantial theoretical disputes, particularly in the last decade. These controversies are the nucleus of our thematic exploration into disruptive innovation. As encapsulated by Yu and Chieh (2008), DIT finds itself at the epicentre of an “incandescent debate”, a debate that we endeavour to unravel in this essay.

Our objective is multifaceted: to illuminate the primary theoretical controversies embroiling DIT; present contrasting perspectives; explicate areas of confusion; identify potential gaps in the existing research; and propose directions for future scholarship. We aim not merely to synthesise the current body of knowledge on DIT but also to challenge, revise, and extend our comprehension of disruptive innovation. In doing so, we aim to provide fresh insights and stimulate further scholarly discourse around this critical facet of innovation management.

We make three main contributions. First, we synthesise and recalibrate a vast array of the existing DIT theoretical disputes to pinpoint six key controversies that merit further scholarly attention. Second, we expand upon conventional understandings of DIT by shining a spotlight on the bias in DIT. This bias has two aspects: a focus on incumbents; and an implicit assumption that innovation is positive. While some scholars have gone beyond a focus on incumbents to include the perspectives of the challenger or disruptor, we introduce a fresh ‘challenger – incumbent perspective’ and redirect attention beyond established organisations and disruptors. Regarding the pro-innovation bias, we highlight disruptive innovations that negatively impact society and advocate examining the interplay between various contextual factors that contribute to disruptive innovation and determine its broader impact on society. Third, while recent studies have emphasised the role of performativity in disruptive innovation (Kumaraswamy et al., 2018), and demonstrated how framing and language can enact reality (Snihur et al., 2022), they have focused mostly on disruptors. We address this shortcoming by offering a challenger – incumbent perspective that requires an examination of the interactions and contestations between disruptors and incumbents. Finally, we propose potential avenues for future research to deepen our understanding and application of this influential, yet contested, concept.

DIT: critiques and controversies

Disruptive innovation theory has garnered considerable attention and debate within the academic community. Scholars have frequently presented critiques centring on the methodology and scope of DIT, suggesting refinements and broadening perspectives. These critiques have sought to polish and perfect, not disprove, or invalidate, the accepted theory. While these contributions have fostered the evolution of DIT, several controversies continue to pose fundamental challenges to its core tenets and could redefine, or even invalidate, the theory. By clearly separating the methodological and scope-related critiques from the deeper, more substantial controversies that question DIT’s theoretical foundations, we not only bring clarity to the ongoing discourse but also underscore the importance of this demarcation to address the challenges within DIT. This distinction aids in understanding the breadth and depth of theoretical deliberations and paves the way for greater enrichment and fortification of the theoretical underpinnings of DIT.

DIT: critiques

While guiding practice, management theories are perpetually under the microscope of academic scrutiny, and DIT exemplifies this ongoing dynamic between theory and critique. Although its foundational principles have revolutionised business strategy paradigms, DIT has attracted a range of critiques focused on furthering its development. These critiques, distinct from the foundational controversies that we discuss later, aim to enhance the theory’s granularity, breadth, or depth. They primarily address the theory’s methodological choices and analytical scope rather than questioning its core assumptions. By thoroughly examining these critiques, we gain a deeper appreciation of the multifaceted nature of DIT and its evolutionary trajectory. This section delves into seven main critiques of DIT, exploring their origins, implications, and how they pave the way for the theory’s ongoing enrichment.

An over-reliance on historical case studies

Christensen’s use of case studies to develop DIT has generated significant disagreement, which we elaborate upon in the controversy section. The general critique pertains to DIT’s dependence on retrospective case studies (Cohan, 2000, Danneels, 2004; Kumaraswamy et al., 2018; Yu & Chieh, 2008). These authors argued that this method, employed by Christensen, is susceptible to hindsight bias, reducing DIT’s predictive reliability. This critique originated in the broader academic discourse on the validity and reliability of case-study methodologies (Cepeda & Martin, 2005; Da Mota Pedrosa et al., 2012); left unaddressed, it could escalate into controversy territory. This may fundamentally challenge the empirical robustness of DIT and diminish its credibility and value in academic and practitioner circles. However, it might also invite researchers to refine and bolster DIT by incorporating diverse methodologies, enriching the empirical basis, and enhancing the theory’s overall rigour.

A simplistic classification of innovation

The critique concerning DIT’s binary classification of innovations as sustaining or disruptive, as highlighted in works by Cohan (2000), Danneels (2004), Kumaraswamy et al. (2018), and Yu and Chieh (2008), underscores the need for a more nuanced framework. These authors argued that categorising innovations strictly as sustaining or disruptive fails to capture the breadth and diversity of innovation. Examples such as 3D printing, blockchain, and augmented/virtual reality have created market disruptions beyond the ‘incremental product/service improvements’ (sustaining) and ‘lower service attributes catering to overlooked/under-served markets’ (disruptive) innovations. Given the multifaceted nature of innovations in practice, a binary classification system does not fully encompass the spectrum of technological advancements. Moreover, the widespread use of terms like ‘disruptive innovation’ in popular culture and corporate jargon may have led inadvertently to their diluted application in academic research. Addressing this critique invites the analysis of diverse disruptive phenomena, with a view to enhancing the theory’s capacity to reflect the complexities of innovation.

A narrow focus on technology and market factors

This critique concerns a predominant focus on technological attributes and market factors. This focus has the potential to obscure other critical elements, such as socio-political, institutional, and firm-level factors, which play an existential role in the emergence and adoption of disruptive innovations. Several examples illustrate this critique. For instance, the adoption and impact of 3D printing were shaped by its potential to transform business models and manufacturing practices and to create new market opportunities (Hahn et al., 2014). Blockchain technology’s development was driven by factors like regulatory policies and digital security perceptions beyond just technological innovation (Saadatmand & Daim, 2019). The trajectory of AR/VR has been affected by its multidimensional disruptive potential in various sectors (Guo et al., 2019). A narrow focus on technology and market factors in DIT can lead to the intricate web of other influential elements being overlooked, calling for a more integrative approach to understanding disruptive innovations.

Insufficient consideration of strategic responses

Disruptive innovation theory has been critiqued for understating or oversimplifying the strategic choices and actions that both incumbents and challengers can take in response to potential disruptions (Cohan, 2000; Danneels, 2004; Kumaraswamy et al., 2018; Yu & Chieh, 2008). These studies propose that the theory should account for the dynamic strategic interactions and contingencies involved in disruption scenarios. Consider the auto industry’s adoption of autonomous vehicles. Traditional automakers and new entrants are navigating evolving business models, regulatory landscapes, and industry dynamics, illustrating the multifaceted strategic responses that DIT often overlooks (Skeete, 2018). By failing to delve deep into these strategic nuances, DIT fails to recognise the complexity of firm-level responses. Addressing these strategic intricacies would present a more accurate depiction of the range of responses deployed by both incumbents and challengers (Ansari & Krop, 2012).

Sampling on the dependent variable

Somewhat related to the earlier case-study-method critique is the suggestion that the theory was developed through cherry-picking cases. Danneels (2004) criticised Christensen’s method for selecting only successful cases of disruption and failing to examine unsuccessful or potentially disruptive technologies that did not materialise. Markides (2006) echoed this, arguing that this selection bias undermines the theory’s credibility and generalisability, failing to consider the spectrum of innovation outcomes. Returning to autonomous vehicles, for instance, demonstrates how regulatory and industry dynamics can impact the success or slow adoption of disruptive technologies. While technological advancements in autonomous vehicles are significant, the industry has faced challenges such as regulatory barriers and market readiness, offering lessons beyond successful disruption cases (Skeete, 2018). By focusing predominantly on successful disruptions, DIT might have inadvertently overlooked the nuances and lessons learned from unsuccessful or potential disruptions that never took off. Addressing this methodological concern could pave the way for a more balanced understanding of disruptive innovation, incorporating insights from both sides of the disruption spectrum, ‘the disruptor and the disrupted’.

Neglect of high-end disruption

This critique pertains to DIT’s failure to account for the phenomenon of high-end disruption, where a new product or service is introduced at a high price point and initially targets the upper market before cascading downward (Yu & Chieh, 2008). This omission limits the theory’s applicability to a range of innovation scenarios. Govindarajan and Kopalle (2006) affirmed this oversight by explicitly discussing the concept of high-end disruption; they argued that the original DIT application did not account for this phenomenon. For instance, the example of the iPhone referenced by Weeks (2015) challenges DIT’s boundaries. Contradicting the traditional portrayal of disruptive innovation as a cheaper, initially inferior technology targeting a neglected user base, the iPhone was an expensive, high-quality innovation catering to a coveted user base. Nonetheless, some researchers (Alberti-Alhtaybat et al., 2019) categorised the iPhone as a form of disruptive innovation, while others identified it as a theoretical white space in DIT (Yu & Chieh, 2008). Similarly, the introduction and widespread adoption of mobile phones, pre-dating the iPhone, also counter traditional DIT metrics: mobile phones debuted as lower-performing but pricier devices that simplified customers’ lives and appealed to a mainstream audience, eventually overtaking landlines (Zagorsky, 2019). The high-end disruption critique underscores the evolving nature of market disruptions, emphasising that not all disruptions fit the traditional mould of DIT. The iPhone and mobile phone examples are instances where innovations targeted premium markets before trickling down to other market segments. Recognising high-end disruption can broaden DIT’s scope to address the multifaceted dynamics of market disruptions.

An overemphasis on individual firms

This critique pertains to DIT’s tendency to focus on the individual firm, as opposed to broader innovation ecosystems, as the primary unit of analysis. Kumaraswamy et al. (2018) argued that the theory should pay more attention to the role of innovation ecosystems, comprising multiple interdependent actors, in enabling or constraining disruption. Likewise, Adner and Kapoor (2010) emphasised the importance of considering the broader innovation ecosystem, agreeing with the critique of DIT’s firm-centric focus. The development of blockchain technology and the advent of autonomous vehicles underscore these critiques. Both cases highlight the interconnectedness and collaborative dynamics among various stakeholders. For the blockchain ecosystem, stakeholders include developers, users, regulatory bodies, and actors in the financial and technology sectors (Saadatmand & Daim, 2019). Stakeholders in the autonomous vehicle ecosystem include automakers, technology firms, regulatory authorities, urban planners, and end users (Skeete, 2018). Both examples challenge the firm-centric perspective of DIT. Adopting an ecosystem perspective in DIT expands the theoretical framework by acknowledging the roles of various stakeholders in shaping the innovation process, and it offers practitioners insights into collaborative strategies and the importance of aligning with ecosystem dynamics. Including these dynamics can enhance DIT’s remit for navigating disruptive challenges and opportunities.

DIT critiques: summary

We have identified seven major critiques, ranging from methodological approaches to the theory’s application. While these critiques have suggested specific areas for improvement within the framework of DIT, they differ from the broader theoretical controversies that we address next.

Critiques of DIT broadly aim to strengthen and clarify the theory rather than challenge its foundational principles. However, when these theoretical critiques are overlooked or inadequately addressed, they can evolve into contentious theoretical controversies. Unresolved critiques begin to question the core assumptions and applicability of DIT, triggering a more fundamental debate. The ensuing discussion delves into the more polarised debates, where different controversies challenge not just specific elements but also the very foundations of DIT, including its overarching assertions, principles, and applications. Controversies reflect more profound disagreements about the theory’s scope and relevance in today’s rapidly evolving innovation landscape.

DIT: controversies

Every so often, a theoretical controversy rattles the foundations of well-established theories within a scholarly domain. Like the enduring ‘nature versus nurture’ discourse in behavioural psychology, such controversies catalyse the evolution, clarification, and refinement of theories, especially within less empirically grounded fields. This ongoing debate charts the trajectory of social science evolution more broadly, germinating from the philosophies of Ancient Greece and China, gathering momentum through the musings of Enlightenment-era philosophers such as Kant and Hume, and culminating in a vibrant empirical dialogue, with the advent of psychology as a social-science discipline in the 20th century. Such pivotal debates catalyse theoretical development by challenging entrenched norms and shaping the academic landscape.

This section delves into the pivotal theoretical controversies that challenge the underpinnings of DIT, distinguishing them from critiques that seek to refine the theory. Notwithstanding the discord surrounding DIT, we build on the work of scholars who rank among the most frequently cited authors within this contentious stream. We have distilled six key controversies among DIT scholars. Starting with definitions – the bedrock of theoretical development – we navigate the labyrinth of these theoretical controversies and draw on illustrative examples. These are 1) definitional ambiguity, 2) the case-study conundrum, 3) the question of generalisability, 4) the challenge of defining a unit of measure, 5) DIT outcome bias, and 6) the predictive, prescriptive, and performance potential of DIT. More profound than critiques, these controversies address the fundamental disagreements at the core of DIT.

Definitional ambiguity

While theory building should be based on clear definitions, DIT has been plagued with definitional ambiguity (Dubin, 1970; Eisenhardt, 1989; Wacker, 2004). For a theory to achieve legitimacy and widespread acceptance, it must crystallise clear, mutually agreed definitions, particularly around its key concepts (Wacker, 2004). This translates into reaching a consensus on the semantics of the terms “disruption” and “innovation”. Nevertheless, as illustrated by several scholars (Danneels, 2004; Gilbert, 2003; Govindarajan & Kopalle, 2006; Kassicieh et al., 2002; Leifer et al., 2000; Walsh et al., 2002; Yin et al., 2017), a unifying definition of disruptive innovation that has secured universal acceptance remains elusive. Christensen’s decision to introduce the prefix ‘disruption’ to ‘innovation’ has further muddied the waters – a choice that he retrospectively questioned (Christensen, 2014; King & Baatartogtokh, 2015). Weeks (2015) underscored this issue by identifying a primary trigger for criticisms targeted at DIT: the absence of a sufficiently rigorous definition for disruptive innovation. This vagueness has resulted in interpretational inconsistencies in understanding disruptive innovation. The literature substantiates this claim, with a kaleidoscope of definitions for disruptive innovation surfacing over time. This has birthed what could be construed as ‘definitional myopia’, marked by a somewhat circular and self-referential approach to defining the concept. Danneels (2004) noted the lack of explicit criteria to discern what qualifies as a disruptive technology, provoking inquiries about whether disruption is a typology of innovation or a yardstick to measure success in achieving managerial objectives. These diverse opinions obfuscate the creation of a consistent taxonomy. Critics have even posited that the theory is riddled with circular definitions (Sood & Tellis, 2011).

Initiatives have been undertaken to engender a more coherent understanding of disruptive innovation. Govindarajan and Kopalle (2006) amalgamated various contributions to craft a framework that encapsulates Christensen’s typology of DIT while incorporating insights from other thought leaders. Corsi and diMinin (2014) imbued DIT with a geographical dimension via the ‘reverse’ disruptive innovation concept. Markides (2006) redirected the spotlight from the incumbent to encompass both consumer and producer.

The broader theme of innovation has also undergone a protracted history of definitional evolution, dating back to the 1930s. Joseph Schumpeter, for instance, refrained from articulating a specific theory about innovation; yet he was unequivocal about its ramifications. Drawing parallels with Charles Darwin’s The Origin of Species, Schumpeter conceptualised innovation as a radical force that instigates irreversible, monumental organisational shifts with significant economic implications (Schumpeter, 1939). This conceptualisation of innovation as a market-level phenomenon represents a marked departure from the technology-centric interpretation of radicalness (Govindarajan & Kopalle, 2005), or what Christensen categorised as sustaining innovation (Christensen, 1997).

A recurring theme in innovation theory development is the dichotomous categorisation, such as ‘sustaining’ versus ‘disruptive’ innovation. Examples of such dual-category definitions span from programmed versus non-programmed (March & Simon, 1958) to exploratory versus exploitative (Jansen et al., 2006) and transitional versus transformational innovation (Kashaboina, 2019). This bifurcation reaches sociological realms with identity-sustaining versus identity-challenging innovations (Tripsas, 2009). While reflecting the multidimensional nature of the innovation process, the abundance of definitions contributes to definitional ambiguity that impedes the maturation of DIT. This lack of conceptual clarity can also obstruct the theory’s operationalisation, potentially constraining its utility for practitioners and policymakers.

Broadening the scope to other fields reveals similar challenges. In environmental science, for instance, the term ‘sustainability’ has been subject to definitional crises, leading to considerable controversy in academic literature (Bond & Morrison-Saunders, 2011; Skjølsvold, 2013; Struik et al., 2014) and a patchwork of environmental regulations and corporate practices that often lack consistency and coherence. Governments and corporations have struggled to effectively implement sustainability initiatives, often because of differing interpretations of sustainability (Moslehpour et al., 2022). This real-world consequence of theoretical ambiguity in environmental science is a cautionary tale for DIT. Just as the environmental sector grapples with the implications of this conceptual uncertainty, DIT faces similar risks of misinterpretation and misapplication, where definitional ambiguity leads to divergent understandings and applications of the theory, with potential practical consequences.

Clarifying foundational terms in DIT is not just an academic exercise but also a prerequisite for the theory’s coherent development and practical application. As demonstrated in environmental science, unresolved ambiguities can lead to fragmented and ineffective practices, underscoring the urgency to establish a clear and robust framework for DIT.

The case-study conundrum

The conundrum arises from questions about whether a case-study approach in DIT stands up to rigorous academic scrutiny and applicability to go beyond mere criticism. While using case studies as a strategy for theory building is a well-regarded practice to create theoretical constructs (Eisenhardt, 1989), it is important to ensure the integrity and applicability of case-study-based theory development. This includes applying established guidelines such as careful case selection based on clear criteria, transparent and consistent analysis methods, and continuous refinement of theoretical constructs in light of new data (Eisenhardt & Graebner, 2007).

Beyond the critique of Christensen’s case-study method, there has been significant disagreement over the veracity of his case-study approach in DIT (Cohan, 2000; King & Baatartogtokh, 2015; Lepore, 2014; Pedrosa et al., 2012). Lepore (2014, p. 12) even noted that “Christensen’s sources are often dubious and his logic questionable”. However, these concerns should be assessed in light of established case-study research practices, including deliberate case selection and definition of boundaries (Denzin & Lincoln, 2011). Indeed, using an extreme exemplar can serve as a powerful catalyst for theoretical discourse (Siggelkow, 2007; Yin, 1994).

Christensen’s case selections, while not flawless, resonate with the tradition of case-study-based theory development, wherein researchers typically choose intriguing and pertinent cases. The process of distinguishing cases from “interesting” to “non-interesting” is a strategic aspect of this methodology (Summary, 1971). Critiques suggesting that Christensen handpicked his case studies tended to overlook the fact that this is an inherent component of the case-study methodology (Weeks, 2015). It is also noteworthy that Tushman and Anderson’s (1986) seminal work on “technological discontinuities”, which significantly influenced DIT, also relied on carefully selected case studies. This work remains among the most frequently cited in the theoretical field of innovation, underlining the acceptance of their methodological approach.

We acknowledge that developing theory from case studies can be challenging. The selection of cases must be representative to enable the development of generalisable theories (Eisenhardt & Graebner, 2007). This provokes a crucial question for DIT: Do most cases used to develop disruptive innovation theory meet its core theoretical tenets?

King and Baatartogtokh (2015) undertook to answer this question by surveying a cohort of 79 DIT academic experts. They evaluated the application of DIT using the cases cited by Christensen. The survey utilised four elements of DIT as criteria: incumbents’ improvement along a trajectory of sustaining innovation; the pace of sustaining innovation outstripping customer needs; incumbents possessing, but failing to exploit, the capability to respond; and incumbents faltering because of disruption. In this manner they identified 77 disruptive innovations, with several cases not being a good fit with all of its conditions and predictions.

Controversies also stem from dismissing companies such as Bucyrus and Seagate Technologies as examples of failing to innovate. Lepore (2014) argued that, despite facing disruptive challengers, these firms not only survived but also prospered. Lepore contradicted Christensen’s (1997) suggestion that newcomers invariably terminate incumbents, pointing out that firms such as Western Digital regained market dominance after initially giving way to disruptive newcomers. Studies by King and Tucci (1999) and Chesbrough (2003) supported this assertion, noting that incumbent firms were less likely to exit the market despite disruptive competition.

This leads us to two critical issues. First, there needs to be a more precise definition of what disruption means for an incumbent (referring to definitional ambiguity). If disruption is measured in existential terms vis-à-vis an incumbent, Lepore’s dismissal of the common application of DIT is justified. As per Danneels (2004), disruptive technologies often result in newcomers replacing incumbents, although it is not clear whether this implies the incumbent’s collapse or simply a loss of market dominance. If disruption is defined as a temporary loss of sector lead, Lepore’s dismissal is less impactful. This ambiguity warrants further clarification. Even Weeks, who disagreed with Lepore’s rebuke of Christensen’s case-study methodology, found the selection of Seagate Technologies questionable. According to Weeks (2015), Christensen’s initial 1989 analysis of Seagate’s failure in the 3.5-inch disk-drive market might have seemed valid at the time, but subsequent data contradicted some of Christensen’s initial conclusions. As Christensen did not update his analysis of Seagate, it was arguably a missed opportunity to improve his central thesis.

While Christensen’s case-study approach aligned with established research practices, his selection and interpretation of these cases have been contentious. Furthermore, the definitional ambiguity surrounding the term ‘disruption’ complicates interpreting and evaluating these case studies. DIT’s robustness and reliability can be enhanced through rigorous selection and interpretation of cases, coupled with clear definitions. Addressing these challenges directly will not only strengthen DIT but also contribute to the evolution of case-study methodology in academic research in general.

The question of generalizability

A growing controversy around DIT pertains to its generalisability. Scholars (e.g., Eisenhardt & Graebner, 2007) have argued that if the case studies that form the basis of a theory are not broadly representative, the theory itself might not be generalisable. This concern is particularly poignant when considering instances of disruption that do not seem to fit the DIT model, such as the iPhone’s disruption of the mobile-phone market or Uber’s taxi-industry disruption. This selection bias has been argued to undermine the theory’s credibility and generalisability. Taking the controversy further, Choi et al. (2020) pointed out that the indiscriminate application of DIT in explaining the success of new entrants can lead to misinterpretations and inappropriate strategic applications. Their rebuttal of DIT’s generalisability emphasised the need for a more elastic application of DIT to specific market situations.

However, this controversy around generalisability raises an important question: Must a theory be universally applicable to be valid? Does the existence of a few exceptions necessarily invalidate a theory? In response, some scholars have proposed viewing DIT not as a universally applicable theory but as one of several typologies of innovation. For example, Schmidt and Druehl (2008) developed a framework that included four types of innovation, with disruptive innovation being just one of them. They, however, did not delve into the intricacies of disruptive innovation consistent with Christensen’s approach. Others have proposed extending the scope of DIT to create a broader taxonomy of innovation types. In this context, any case study featuring an incumbent disrupted by a newcomer offering a lower-quality product or service to a previously overlooked customer base could be categorised as a Christensen disruption type. This outcome-based application focuses on the incumbent as the unit of measure, addressing some theoretical issues with DIT. Govindarajan and Kopalle (2006) further supported this typological approach, which differentiated between radicalness, a technology-based dimension of innovations, and disruptiveness, a market-based dimension. This distinction anchored DIT to a single unit of measure. While the applicability and generalisability of DIT remain subject to ongoing research, these disputes do not detract from the theory’s phenomenological utility. While the scope and generalisability of DIT continue to be debated, these disagreements do not diminish the practical value of the theory in understanding and navigating market disruptions.

Of course, even widely acclaimed theories in other fields have attracted controversy regarding generalisability. One example is prospect theory (Tversky & Kahneman 1992). Research in behavioural economics (e.g., Erner et al., 2013; Ruggeri et al., 2020; Sharp & Salter, 1997) has raised questions about the theory’s general applicability. Thus, even well-established theories are subject to controversy regarding their applicability to diverse scenarios.

The challenge of defining a unit of measure

Although defining the unit of measure is instrumental to building theory (Saljo, 2009), DIT has struggled with this challenge. Critics have pointed out that Christensen’s work appears to shift between different units of measure in various contexts: the market (Bower & Christensen, 1995); the organisation (Charitou & Markides, 2003; Christensen & Raynor, 2003; Christensen et al., 2000); managerial agency (Christensen, 2009); and the business model (Cozzolino et al., 2018, Johnson et al., 2008). Some have even accused Christensen of sampling on the dependent variable (Danneels, 2004). However, it is useful to consider that these shifts may represent Christensen’s attempts to refine and evolve his theory in response to scholarly, peer-reviewed feedback and criticism. DIT was undoubtedly incomplete in the early stages, and these adaptations could signify the theory’s refinement. In his seminal work, The Structure of Scientific Revolutions, Thomas Kuhn noted that even the most successful models have limitations, and recognising them often leads to a paradigm shift (Kuhn, 1996).

Learning theories tend to be beleaguered by multiple units of measure, generating many differences in theoretical perspectives that are “seemingly irreconcilable” (Alexander et al., 2009, p. 1). Saljo (2009) suggested this as a normative practice in the learning field because a phenomenon’s unit of measure (conceptualisation) corresponds to a theoretical framework. Continuing with this logic, learning is rich with phenomenological dynamism, requiring differing theoretical frameworks and lending itself to many units of measure. Even the term “learning” suffers from definitional ambiguity, according to Saljo (2009).

The variability in DIT’s unit of measure reflects its evolutionary nature and intersects with other controversies, such as definitional ambiguity. How a theory’s key concepts are defined and empirical examples selected inherently influences the chosen unit of measure. It should not be surprising that when a theory is grappling with clear definitions and representative case studies, it is challenging to establish a consistent unit of measure. Thus, addressing these interconnected controversies is indispensable to developing an integrated approach to refining the theory’s conceptual clarity, methodological rigour, and measurement consistency.

Outcome bias

An ongoing controversy within DIT has revolved around the concept of outcome bias, a critical factor influencing the interpretation and development of theories related to disruptive phenomena. Outcome bias can skew theory building by leading researchers to judge a theory’s validity based on the success or failure of its outcomes rather than the soundness of the theory’s underlying rationale and methodology (Brownback & Kuhn, 2019; Madan et al., 2014; Suri, 2011). This overarching bias encompasses two aspects: incumbent survivor bias; and pro-innovation bias (Weeks, 2015). As discussed in Hannan and Freeman (1977), survivor bias tends to over-represent the success of disruptive innovations, leading to an overemphasis on cases where disruptors prevail and incumbents falter. Similarly, pro-innovation bias, defined by Rogers (1995), implies an inherent assumption that disruptive innovations are universally beneficial, overlooking their potential negative impacts. Addressing these biases is necessary for a more nuanced and realistic assessment of disruptive innovations.

Incumbent survivor bias

This aspect of bias concerns DIT’s focus on the incumbent’s viewpoint and how incumbents can sidestep disruption from emerging competitors (Nagy et al., 2016; see also Ansari and Krop 2012). This incumbent-focused perspective of DIT was also evident in The Innovator’s Dilemma (Christensen, 1997), which outlined an existential rubric for managers in established firms. This model advised managers to assess potential disruptive innovation threats from challengers using a binary heuristic: Was the disruptive innovation an existential threat, or was it an opportunity to seize? While Fraser and Ansari (2020) suggested broadening this constrained framing of DIT to include a more comprehensive “multiplexed framework” for managers responding to disruptive threats, and considerations beyond existential threats, they still emphasised the incumbent side of the disruptive innovation ledger. Exceptions include studies on the “disruptor’s dilemma” (Ansari et al., 2016; Petzold et al., 2023), where firms introducing disruptive innovations into multisided ecosystems face the challenge of gaining the support of the disrupted incumbents and addressing this dilemma by continually adjusting their strategy and relational positioning. Similarly, Snihur et al. (2018) notion of the “disruptor’s gambit” explains how a disruptor reveals its intentions early on through effective framing, followed by a rapid adaptation of its business model to meet ecosystem needs and dislodge powerful incumbents through business model innovation. DIT needs to address the challenges faced by both incumbent firms and disruptors or challengers, as well as the strategies they develop in evolving ecosystems.

Pro disruptive bias, or “disruption is always good” bias

Regarding the bias towards pro-innovation, the prevailing sentiment suggests that broadly diffused innovations generate positive societal changes. This idea is encapsulated in E. M. Rogers’ assertion that such innovations “should be neither re-invented nor rejected” (Weeks, 2015, p. 1). However, prefixing “innovation” with “disruption” has not always been favourably received. For instance, Lepore (2014) strongly opposed what she perceived as glorifying disruptive innovation. However, few academic studies share this perspective. Satell (2015), in his aptly titled piece “Let us stop arguing about whether disruption is good or bad”, suggested that successful disruption does not necessarily need to be destructive but can catalyse valuable shifts in mental or theoretical models.

A valid concern has been the potential over-application of DIT by academic and professional communities, possibly due to an excessive focus on its upending potential (Teixeira, 2019). While technology has immense potential to enhance the human condition, it can also wreak societal havoc (Rushkoff, 2019). The proliferation of fake news, facilitated by technological advancements, is a case in point. The real-time detection of fake news on major social media platforms has become almost impossible (Aral, 2021, Sahoo & Gupta, 2021). While these technological advances have revolutionised information dissemination, they have also contributed to the formation of filter bubbles, the proliferation of fake news, and an increasingly polarised society (Aral, 2021, Spohr, 2017; Wang et al., 2023). The content often results from multiple operations, such as resizing, cropping, and recompression, which conceal signs of digital manipulation (Leonardi & Treem, 2020). Such harmful, disruptive innovations pose grave threats to fundamental institutions such as education, religion, and journalism (Lepore, 2014).

Undoubtedly, disruptive innovation can also negatively impact society and culture on a broader scale. However, as Millar et al. (2010) pointed out, there is a significant lack of scholarly input regarding disruptive innovation’s broader societal and systemic implications and the interplay between social, technical, environmental, and economic factors. The case of financial derivatives exemplifies the potential collateral damage that ill-conceived innovations can inflict upon society (Williams, 2010). Here, innovation should be perceived not as an improvement but as a technological discontinuity (Tushman & Anderson, 1986) that disrupts the existing order, regardless of whether it has positive or negative societal consequences.

ChatGPT-3.5 is an artificial intelligence chatbot that was developed by startup Open AI. It became the fastest-growing consumer software application in history, gaining over 100 million users within a couple of months (Fyfe, 2022). The November 2022 release of ChatGPT-3.5 offers a compelling example of disruptive innovation being a double-edged sword. For instance, in the education sector, with its ability to process vast amounts of data, GPT can revolutionise teaching and research by providing personalised learning experiences and facilitating access to knowledge and analyses that drive discoveries. However, its potential for misuse as a sophisticated cheating tool is a growing and immediate concern (Fyfe, 2022). Students may leverage GPT’s advanced language-generation capabilities to craft essays and research papers, bypassing the critical learning process involved in such tasks. Educators fear that this disruptive innovation might erode educational standards and foster a culture of dishonesty (Greitemeyer & Kastenmüller, 2023).

Recognising and addressing outcome biases is the first step to developing a balanced understanding of disruptive innovations. This ensures that theory building in this domain transcends simple success or failure metrics and incorporates multiplexed vantage points (incumbents and challengers). Together, the two kinds of outcome bias – incumbent bias and pro-innovation bias – impact how the theory is constructed and validated.

The predictive, prescriptive, and performative potential of disruptive innovation theory

This controversy has centred on DIT’s predictive (ex ante) utility, which has sparked heated debate. DIT has been described as a theory about why businesses fail that ‘does not explain change’ and ‘makes a very poor prophet’ (Lepore, 2014, p. 21). While some have argued that ‘disruptive innovation can only be reliably identified in hindsight’ (Wolfe, 2016, p. 1), suggesting that DIT is limited to retrospective (ex post) applications, others have advocated its prospective, ex ante predictive applications. Consequently, the discourse around DIT’s predictive ability highlights the necessity for precise and quantifiable criteria that managers and innovators can leverage to foresee and manoeuvre through disruptive trends. Moreover, delving into DIT’s predictive and performative potential enriches our theoretical grasp of disruptive innovation and helps to guide strategic decision-making in real-world scenarios.

Christensen (2009) focused on managerial agency rather than DIT’s predictive or prescriptive merits. This shift suggested that specific managerial actions can yield reasonably predictable results – an ex ante (predictive/prescriptive) application of the theory. Weeks offered a more hopeful outlook: ‘While the current predictive power of the framework is limited, it could be greatly improved with a rigorous research agenda subjected to thorough peer review’ (Weeks, 2015, p. 428). Many scholars have affirmed the ex ante predictive application of DIT (Adner, 2002; Christensen & Raynor, 2003; Danneels, 2004; Govindarajan & Kopalle, 2006; Hang et al., 2011; Nagy et al., 2016; Schmidt & Druehl, 2008; Sood & Tellis, 2011). Govindarajan and Kopalle (2006) proposed that a retrospective, ex post comprehension can inform ex ante predictions of specific precursors to disruptive innovation events. However, they cautioned against consistently establishing management rewards linked to hard-to-measure key performance indicators (KPIs), such as market size, growth rate, and profitability.

Ignoring the signs of disruptive innovation from emerging competitors can have severe consequences, such as loss of market share, decreased status, or even the bankruptcy or dissolution of an organisation (Bower & Christensen, 1995). Nagy et al. (2016, p. 119) posed a fundamental question: “But how can an organizational manager know if a particular technology will result in a market disruption or even affect their organization?” This question underscored the necessity for predictive managerial action guided by a DIT framework. Christensen emphasised a deeper understanding of consumers within a specific market to identify and foster opportunities for disruptive innovation (Christensen, 2009; Christensen et al., 2016). We argue that a necessary component of understanding consumers should encompass identifying negative sentiments as adverse antecedents to disruption, characterised as disillusionment, distrust, or dissatisfaction with conventional offerings from incumbents or the overall marketplace in a given sector. For example, Shi et al. (2024) document how the Chinese firm Xiaomi premised its innovation on addressing the frustrations of an underserved niche of tech-savvy users and enthusiasts in China who wanted a “local Apple” – or an affordable, home-grown alternative to the upscale and expensive iPhone. By allowing personalisation, and soliciting and rapidly incorporating user feedback, they stimulated collaborative interactions and cultivated a sizeable base of dedicated users. Despite having no smartphone production competencies, within a decade the firm became the leading player in China and neighbouring India – the two largest smartphone markets globally.

To illustrate the real-world implications of DIT’s predictive, prescriptive, and performative potential, we consider the journalism sector. This industry, marked by rapid technological changes and evolving consumer preferences, is an exemplary case for examining how DIT can be applied to understand and anticipate market disruptions. The industry is marked by deep public distrust in the fake news era, particularly towards social media as a news source (Newman et al., 2020). The systemic, high degree of negative sentiment raises essential questions about the causality of disruptive innovation. For instance, does negative market sentiment indicate a potential vulnerability to disruption for incumbent print and broadcast news organisations (‘adverse antecedents’)? At what point does the negative sentiment foreshadow (or otherwise predict) the disruption of traditional journalism? Could these conditions serve as a disruptive prescription or a wake-up call for emerging players? The case of the journalism sector, especially in the era of fake news, exemplifies how DIT can be applied to predict and navigate industry disruptions. This example underscores the theory’s practical utility in identifying market vulnerabilities and opportunities for innovation, reflecting the broader dynamics in disruptive innovation.

Regarding the controversy surrounding the predictive utility of DIT, Kumaraswamy et al. (2018) pivoted the discussion by proposing a performative perspective. They suggested that ex ante prescriptive applications of DIT do not necessarily have to be predictive. Instead, they conceptualised the prescriptive application of DIT as “performativity”, where managerial actions and decisions are not strictly about making accurate predictions but rather serve as actionable blueprints for future innovations. These blueprints, articulated by visionary leaders, aim to jumpstart the innovation journey, reflecting a dynamic and proactive approach to leveraging DIT in shaping future market landscapes. We delineate performativity as a prescription converted into managerial discourse and action with anticipated outcomes, with the caveat that “results may vary”. The practice of creating actionable steps based on ex post insights can benefit both incumbents and challengers by encouraging experimentation (e.g., Vittori et al., 2024) with innovative ideas “shaped by memories of the past, aspirations for the future, and contextualized by the settings within which they operate” (Kumaraswamy et al., 2018, p. 1033). Snihur et al. (2022) demonstrated how skilful framing of innovation by new entrants is performative in addressing the challenges posed by incumbents. Language and framing used by entrepreneurial ventures, such as a business-model pitch or projections about the future, don’t just describe but also constitute and actualise “the realities” that they envision through the actions they entail.

DIT controversies: summary

The controversies surrounding DIT underscore the intricate and multifaceted nature of the theory and illuminate the practical challenges inherent in its application. These profound disagreements, extending beyond academic discourse, highlight fundamental challenges in understanding and operationalising disruptive innovation, as well as stretching its boundaries. These debates serve as a reminder of the tension between established paradigms and emerging perspectives, underlining the necessity for ongoing dialogue and refinement in theoretical and practical realms.

Discussion

While disruptive innovation theory continues to evolve, scholars have increasingly argued that its application remains theoretically undercapitalised, lacking robust empirical support. This highlights the need for a more rigorous application of DIT grounded in research-based evidence, moving beyond theoretical postulations to practical verifications. Contradictory examples such as the iPhone and mobile phone adoption preceding it, present a Schrödinger’s cat dilemma that underscores the theory’s challenges. These examples illustrate the complexity of applying DIT in real-world scenarios, where the line between disruption and innovation can be blurred.

The subjective nature of defining disruptive innovation suggests that DIT’s application requires a nuanced contextual understanding. This calls for a more abductive approach resonating with US Supreme Court Justice Potter Stewart’s famous remark about pornography: “I know it when I see it” (Lattman, 2007, p. 1). Acknowledging these challenges, DIT holds potential for predictive, prescriptive, and performative) applications.

Theoretical contributions

This analysis of DIT scrutinises its current state and contributes to its evolution by introducing a novel “challenger – incumbent template” for understanding disruption. We expand the focus of DIT beyond the traditional lens centred on established organisations by highlighting the dynamic interplay between disruptors and incumbents. Our approach responds to the call for a broader, more inclusive, understanding of disruptive processes. By identifying and recalibrating key controversies within DIT, we aim to refine management theories where there is a lack of consensus, as exemplified in the case of DIT.

Our endeavour to synthesise existing theoretical disputes in DIT has led us to identify six key controversies warranting further scholarly attention. These controversies, drawn from diverse perspectives in DIT literature, provide valuable insights into the multifaceted nature of disruption. One central controversy involves the definition of “disruption” itself, with the current discourse suggesting a shift from Christensen’s original framework towards a more inclusive view that considers both disruptors and incumbents. We go beyond the incumbent and pro-innovation biases in traditional interpretations of DIT to conceptualise disruption as a complex, interactional phenomenon. For example, the automotive industry’s response to Tesla’s entry, and the hospitality industry’s reaction to the emergence of Airbnb, illustrate the need to examine not just disruptors or incumbents but also the interactions between these entities. Our challenger – incumbent perspective thus reveals disruption as a multifaceted and interactive process, demanding a more nuanced approach to understanding its mechanisms and implications.

Implications for practice

The refined understanding of DIT, particularly through the lens of the challenger – incumbent template, offers significant practical implications that extend far beyond the theoretical discourse. Our perspective provides businesses and policymakers with a more nuanced and flexible guide for navigating the complexities of market disruptions, fostering a deeper appreciation of the dynamics at play in the modern business landscape.

In strategic decision-making, organisations across sectors can harness the power of the challenger – incumbent template to inform their approaches. With this new understanding, incumbent companies are better positioned to recognise potential disruptors early on. This awareness can cultivate agility in their response to emerging innovations. In contrast, challengers can strategically identify and exploit vulnerabilities within established markets, such as in sectors where a critical mass of negative consumer sentiment (adverse antecedents) has developed, signalling potential opportunities for disruption. Examining the dynamic interplay between disruptors and incumbents can be valuable for businesses seeking to anticipate and prepare for market shifts. Companies can adjust their business models by closely monitoring the signals of impending disruption and converting potential disruptive threats into opportunities for innovation. Acknowledging the dual nature of disruptive innovations – their capacity to drive progress alongside potential societal and economic consequences – also broadens the application of DIT. We call upon businesses and policymakers to consider these two facets of innovation in tandem. A more balanced approach will help to foster sustainable and ethical innovation practices, ensuring that advancements in one area do not have unintended negative impacts on others.

The interconnection between theoretical controversies in DIT, and their practical and performative implications, cannot be overstated. Businesses stand to benefit from these theoretical insights, which can help to critically assess and refine their innovation strategies. By aligning these strategies with broader market trends and consumer needs, firms can ensure that their innovation efforts are effective and resonate with their target audiences and market conditions.

Future research in disruptive innovation theory

It becomes clear from our exploration that, while DIT has evolved, the theoretical landscape remains ripe for further scholarly exploration. One particularly intriguing area is the ex ante prescriptive examination from the challenger’s perspective and how they navigate the intricate balance of cooperation and competition with incumbents in an evolving ecosystem.

Understanding consumer sentiment in predicting disruptive innovation presents more fertile ground for research. Bower and Christensen (1995), among others, have suggested that incumbents may become susceptible to disruption by focusing too much on their customers. However, the opposite of this – ignoring widespread negative sentiment – could pose an equally significant risk. Delving into the nuances of consumer dissatisfaction, especially among early adopters, could provide valuable insights into the adverse antecedents that foreshadow disruption.

Expanding the scope of DIT to encompass both challengers’ and incumbents’ perspectives can reveal a more multifaceted view of disruption. This approach, which we advocate, moves beyond the traditional incumbent-centric bias of DIT. It involves also examining disruptors and understanding how their market entry triggers responses from established players. This interactional (Gray et al., 2015) perspective could significantly enrich our understanding of disruptive innovation as a complex, multifaceted phenomenon.

The performative nature of DIT also warrants deeper investigation. Recent studies by Kumaraswamy et al. (2018) and Snihur et al. (2022) have shed light on how framing language and ‘hype’ can shape realities in the context of disruptors. Our expanded viewpoint encourages considering these dynamics more broadly within the context of challenger – incumbent interactions. For instance, it is worth investigating how Apple’s introduction of the iPhone and its performative framing influenced consumer expectations and the mobile-phone ecosystem, leading to disruptions across multiple industries. Similarly, the ongoing discourse and activities surrounding generative AI (e.g., ChatGPT) are performative in shaping the technology’s disruptive potential.

The role of DIT in shaping responses to the societal and ethical challenges posed by disruptive innovations is an area that requires further attention. The adverse effects of disruptive innovations, such as the psychographic – algorithmic curation of news on social media, call for a balanced approach that considers potential societal impacts. The path ahead for DIT research is both vast and varied. Exploring these avenues could provide a richer, more nuanced understanding of disruptive innovation and its varied impacts. Such research will advance academic discourse and offer practical insights for navigating the ever-evolving landscape of business innovation.

Moving forward, we need to synthesise insights from both the critiques and the controversies, enabling a comprehensive approach to DIT theory building. Critics have called for more research on how disruptive innovations affect societies and how to leverage, mitigate, or manage systemic disruption.

Conclusion

This essay has journeyed through the intricate landscape of disruptive innovation theory, underscoring the evolving nature of this theory in management and innovation. Christensen’s observation that some of his harshest critics may have overlooked developments arising subsequent to The Innovator’s Dilemma (Christensen, 2014) reminds us of the importance of viewing theories as dynamic entities in motion, continually adapting and expanding to meet the complexities of market dynamics. In addressing the prominent critiques and controversies within DIT, our work broadens the understanding of this theory, moving beyond a limited perspective to illuminate underexplored areas. The introduction of the challenger – incumbent template offers a more nuanced perspective for understanding disruption innovation.

We encourage future scholars to approach disruptive innovation theory with an appreciation of its dynamic nature, recognising that its development reflects an ongoing process rather than a fixed set of principles. It is beneficial to view the evolution of DIT within the broader context of academic discourse, acknowledging that assessments made at various stages of its development contribute to its continuous refinement and expansion. Therefore, rather than dismissing DIT based on specific temporal assessments, it is more constructive to consider these as part of the theory’s natural progression and growth. For instance, to dismiss Newton’s Principia based only on the advancements of modern physics would overlook the fundamental role it played in the evolution of scientific understanding. Recognising this evolutionary process lends itself to appreciating DIT’s enduring relevance and adaptability, ensuring its continued application and development in the face of changing business and technological landscapes.

Disclosure statement

No potential conflict of interest was reported by the author(s).