Technological Speciation : Navigating New Needs through Trial and Error – A Rifle Case Study

doi:10.21203/rs.3.rs-3996089/v1

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Technological Speciation : Navigating New Needs through Trial and Error – A Rifle Case Study

https://doi.org/10.21203/rs.3.rs-3996089/v1

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Technological speciation is a crucial concept for understanding the emergence of new technologies and describes the adaptation of existing technologies adapt to novel application domains in response to emerging needs. Prior research has primarily utilized case-centered qualitative approaches. While some quantitative studies have been conducted, they have not fully captured the complexity of technological speciation, with the methodologies used for observation also falling short. This study aims to enhance previous discussions by rigorously validating the process of technological speciation, focusing on Levinthal (1998) through empirical evidence, and uncovering the managerial strategic implications observable in the speciation process of emerging technologies. This study emphasizes products as intermediaries between markets and technologies, developing indicators based on the similarity between antecedent and descendant technologies, considering the path dependency of technology, and applying these to real product data. It hypothesizes that significant changes at the inception of these indicators mark the beginning of technological speciation. Analysis reveals that new technologies emerge through an adaptive process, systematically addressing needs through trial and error, with shifts in needs serving as the catalyst. This aligns with detailed discussions in existing qualitative studies on the technological speciation process. This study proposes an analytical method for examining technological speciation by exploring the interaction between markets and technologies from an evolutionary perspective, using product data as a mediator. Additionally, the study highlights the importance for companies aiming to enter new markets of accurately identifying new needs, exploring adjacent technologies, and adopting iterative, small-scale productization strategies to navigate the adaptation process effectively.

JEL : O32, O33

Market needs-technology interaction

path dependency

product phylogenetic tree

technological speciation

trial and error

In an era marked by increasing technological uncertainty, accurately identifying and utilizing the emergence of new markets in response to evolving demands is crucial (Andriani & Cattani 2022; Baldwin & Von Hippel 2011; Cattani & Malerba 2021; Malhotra et al. 2021). The emergence of such markets fundamentally requires technological discontinuities (Anderson & Tushman 1990; Tushman & Anderson 1986), which arise from the accumulation of continuous and incremental technological innovations (Bergek et al. 2011). Previous research efforts to understand technological discontinuity have focused on identifying emerging technologies and analyzing patterns of technological emergence. Among these endeavors, technological speciation has been highlighted as a critical concept (Adner & Levinthal 2002; Carignani et al. 2019; Levinthal 1998; Moehrle & Caferoglu 2019), addressing the adaptation of existing technologies to new application domains spurred by emerging needs (Adner & Levinthal 2002; Levinthal 1998). This concept suggests that new technologies do not emerge in an abrupt manner; instead, they evolve through a continuous adaptive process to fulfill new needs, underlining the importance of various attempts and failures within this process. These studies aim to elucidate the process of new technology emergence, offering strategic insights for decision-makers targeting new market opportunities.

To explore technological discontinuity through the lens of technological speciation, numerous studies have undertaken case analyses focusing on specific technologies (Adner & Levinthal 2002; Carignani et al. 2019; Cattani 2006; Garnsey et al. 2008; Levinthal 1998; Mokyr 1990). These analyses provide in-depth insights into the adaptive process by which technologies evolve, considering the multifaceted factors influencing technology emergence. However, the difficulty in generalizing from case studies and the potential for subjective interpretation have led researchers to supplement these analyses with quantitative methods. Frenken and Nuvolari (2004) posited that demand diversity drives technological speciation, employing simulations to measure entropy as evidence. Similarly, Moehrle and Caferoglu (2019) introduced a model based on bi-gram learning from technology patents to predict the application fields where speciation is likely to occur, offering a more objective perspective on the phenomena of technological speciation.

Although previous studies have utilized quantitative models to validate the occurrence of technological speciation, they have not adequately addressed how technological emergence transitions from existing to new technologies in novel environments, spurred by the rise of new needs. This gap leaves unanswered the critical question often explored in qualitative research: how does technological speciation manifest? Given that speciation is seen as a branching phenomenon within the evolutionary relationship between antecedents and descendants, these studies struggled to comprehensively explain the speciation process by considering each technology in isolation.

To bridge this gap, the present study aims to clarify the mechanism of technological speciation through an evolutionary framework that supports the quantitative evaluation of this phenomenon. It adopts a methodology based on the similarity between antecedent and descendant technologies, noting a significant decrease in similarity as new technologies emerge through the evolutionary process. By tracking numerical changes at critical junctures where technological mutations occur, this approach seeks to chart the course of technological speciation from these mutations.

The study analyzed 585 rifles manufactured between 1840 and 2000. The defense industry, particularly through the lens of weapons development, provides a robust framework for identifying needs via doctrine, making rifles an ideal candidate for studying the interplay between needs and technology due to their long history and diversity (Shafer 2023). Notably, doctrinal shifts, especially following major conflicts, offer a unique opportunity to investigate technological speciation by analyzing rifles developed before and after such paradigmatic changes. This examination within the warfare context aims to uncover strategic insights into technological emergence.

The structure of this study is organized as follows: Section 2 revisits the concept of technological speciation and related literature, highlighting their importance and shortcomings. Section 3 elaborates on the definition and theoretical background of the indicators used, detailing their components and calculation methods. Section 4 presents the empirical analysis that elucidates the process of technological speciation using these indicators. Section 5 examines the distinctive features of the technological speciation process and how it diverges from prior studies. Section 6 summarizes the study's main findings and their implications, concluding with a reflection on the significance of researching technological speciation and suggesting future avenues for indicator refinement.

2.1 Technological speciation as an evolutionary process

Technological speciation elucidates the phenomenon of discontinuous technological change, wherein novel technologies supplant existing ones (Adner & Levinthal 2002; Levinthal 1998), delineating the adaptation of current technologies to new application domains in response to evolving needs. This process engenders the evolution of existing technologies into new forms, fostering distinct evolutionary paths or technological trajectories guided by the novel selection criteria of the application domain (Adner & Levinthal 2002; Carignani et al. 2019; Cattani 2006; Garnsey et al. 2008; Moehrle & Caferoglu 2019). The abundance of resources in these new domains catalyzes diverse and swift transformations, culminating in the emergence of technologies that manifest as rapid and radical shifts, emblematic of discontinuous change (Levinthal 1998).

Investigations into the causes and patterns of technological speciation, as outlined in Table 1, have predominantly utilized case-centered qualitative analyses. These investigations contend that the abrupt advent of new technologies does not merely forge new markets by displacing extant technologies; instead, it entails a continuous adaptive process whereby existing technologies evolve to meet the demands of the new market as novel needs surface. However, the reliance on qualitative methods, with their focus on the evolution of specific technologies, introduces a degree of subjective interpretation by researchers, presenting challenges in generalizing outcomes. To surmount these limitations, quantitative research methodologies have been integrated alongside qualitative approaches.

Table 1

Overview of recent technology speciation research.
Research	Research objective	Technology	Analytic Approach
Mokyr (1990)	A pattern of technological change	Telegraphy	Qualitative analysis (Theoretical study)
Levinthal (1998)	A process of technology speciation	Wireless communication	Qualitative analysis (Case study)
Adner and Levinthal (2002)	Patterns of technology speciation and its implication	Wireless communications & CAT scanner	Qualitative analysis (Case study)
Frenken and Nuvolari (2004)	The impact of diversity on technological speciation	Steam Engine	Quantitative analysis (Entropy analysis)
Cattani (2006)	Extending the concept of technological speciation to firms	Fiber-optics	Qualitative analysis (Case study)
Garnsey et al. (2008)	Applying spin-off innovation to technology speciation	RISC (Reduced Instruction Set Computer)	Qualitative analysis (Case study)
Carignani et al. (2019)	An emergence process of technology based on the "Woesian perspective"	Jet-engine	Qualitative analysis (Case study)
Moehrle and Caferoglu (2019)	A proposal of Technological speciation prediction method	Camera	Quantitative analysis (Semantic patent analysis)

Frenken and Nuvolari (2004) advanced a theoretical framework for understanding technological speciation through simulation, adopting an evolutionary perspective akin to that of genetic evolution. Their analysis incorporated shifts in entropy and measures of information interdependence based on technological characteristic data, illuminating the foundational causes of technological speciation.

Building on earlier research by Frenken et al. (1999), which investigated industry niches and characteristics using established entropy methods, this study enhances the discourse through simulations based on the NK-model, showcasing speciation propelled by new demands. However, it stops short of delving into the continuous adaptive process inherent in speciation. Another pivotal quantitative analysis by Moehrle and Caferoglu (2019) introduced a predictive framework for technological speciation, utilizing patent data and similarity analysis to anticipate changes in application fields. While this research offers forecasts based on an understanding of technological speciation as influenced by shifts in application domains, it falls short in explicating how new technologies evolve from pre-existing ones. In essence, while both studies contribute data-driven quantitative methodologies informed by the concept of technological speciation, they lack comprehensive insights into the detailed patterns of technological evolution previously addressed through qualitative inquiry.

To thoroughly explore technological speciation, it is crucial to elucidate the adaptation of existing technologies to new application domains. This necessitates an examination of the emergence of new technologies from an evolutionary standpoint, acknowledging the profound impact of antecedent technologies on their successors. Moreover, the selection of research subjects that facilitate the observation of the interplay between markets and technologies is imperative. Consequently, this study proposes to develop an analytical methodology centered on products as the intermediaries between markets and technologies. Embracing a product-focused approach yields profound insights into the complex dynamics between technology and market forces.

Therefore, this investigation endeavors to introduce a product-centric evolutionary analysis methodology that can quantitatively assess the speciation patterns of technology previously delineated through qualitative research, with a particular emphasis on Levinthal (1998) as a foundational reference. By applying this methodology, we aim to corroborate the patterns of technological speciation highlighted in prior studies through the lens of evolutionary trajectories observable in actual product markets. Additionally, this approach seeks to extract strategic insights into the emergence of new technologies, framed within the context of technological speciation.

The process of technological speciation unfolds through distinct stages, starting with the identification of new needs that herald the creation of a new application domain, thereby stimulating the demand for innovative technologies (Levinthal, 1998). In response, various technologies surface to address these emerging needs, characterized by an adaptive process that fosters interaction between markets and producers, with products serving as the medium of this interaction (Adner & Levinthal 2001; Christensen & Rosenbloom 1995). At this stage, suppliers prioritize responding to evolving market needs over the exploration of technological potential, favoring the application of existing technologies (exaptation) to the invention of new ones (Adner & Levinthal 2001; Dosi 1982; Mowery & Rosenberg 1979).

The inherent uncertainty and lack of information pose challenges for producers in predicting the market's selection criteria accurately (Adner & Levinthal 2002; Mokyr 1990; Ziman 2003). Consequently, producers venture into a broad exploration of alternatives within the realm of all conceivable variations, navigating their perceived space of possibilities (Ziman 2003). This exploration may involve utilizing existing technologies as-is or innovating through the recombination of existing knowledge. Amid significant influences from existing technologies, producers weigh various alternative approaches (Arthur 1989; Basalla 1988; Coombs & Hull 1998; Meyer & Schubert 2007; Mowery & Rosenberg 1979; Rosenberg 2009; Rycroft & Kash 2002; Ziman 2003).

Upon the market release of a product, producers garner direct feedback from consumers (Coombs & Hull 1998; Fleck 2000), which serves to refine and clarify previously ambiguous market needs, thereby significantly influencing suppliers' future strategic directions (Ziman 2003). Through this iterative adaptation process, technologies develop a path dependence, guiding the evolution of new technologies. Ultimately, this leads to the emergence of a new technological species, characterized by designs that are aligned with market needs, while maintaining a connection to their evolutionary lineage (David 1997; Garnsey et al. 2008; Meyer & Schubert 2007; Rycroft & Kash 2002).

This methodology elucidates a systematic approach to distinguishing between technological species by examining product similarities. Products evolving in a technologically congruent manner, strongly influenced by their antecedents and demonstrating path dependence, are categorized within a single species. In contrast, products that significantly deviate from their antecedent influences and establish new categories upon branching are recognized as a distinct species. Through such analysis of technological similarities among products, the phenomenon of technological speciation can be understood more clearly. The proposed approach not only corroborates the mechanisms of technological speciation outlined in prior studies but also highlights the underlying principles of technological emergence characteristic of this process.

2.2 History of speciation of rifles

A historical examination of rifles shows three principal technological species indicatives of distinct eras. Up to the mid-19th century, muskets were prevalent. The early 20th century saw the ascendancy of bolt-action rifles in the firearms industry, which were subsequently superseded by automatic rifles from the mid-20th century onward (McNab 2004; Westwood 2005). These transitions between technological species often aligned with the evolving doctrinal needs during warfare periods.

In the 19th century, European military doctrine favored linear infantry tactics, promoting concentrated fire from organized formations, primarily employing muskets. This strategy enhanced firing efficacy but compromised mobility. The Battle of Jena-Auerstedt in 1806, where Prussian forces encountered Napoleon's artillery-supported infantry, underscored the superiority of tactics that prioritized mobility, as exemplified by the French. This doctrinal shift underscored the emerging requirement for infantry tactics that combined firing efficacy with mobility.

In response, 1841 witnessed the introduction of the Dreyse rifle, equipped with a bolt-action mechanism facilitating both “fire and maneuver.” This innovation proliferated across Europe, and by World War I (1914–1918), when such tactics had become widespread, bolt-action rifles had entirely supplanted muskets (Ford 2017; Zabecki 2015), illustrating a clear evolution in military technology reflective of changing doctrinal needs.

During the Industrial Revolution in the mid-19th century, alongside the mass production of firearms, reserve forces were organized, emphasizing shooting and mobility. This era underscored the doctrinal need for smaller forces to effectively confront larger ones. Denmark, following its defeat in the Second Schleswig War against Germany in 1864, sought doctrinal innovation, particularly a new rifle design that could offer advantages in close combat. This demand catalyzed the development of early models of automatic rifles, marking a significant shift in military technology.

World War I significantly emphasized the criticality of close combat, with battles frequently transpiring within the confines of trenches, revealing the limitations of slow-firing bolt-action rifles. The necessity for automatic rifles grew (Holmes 2006), a demand further underscored by World War II (1939–1945), as urban warfare and close-quarter battles became more common. The Vietnam War (1964–1972) saw extensive use of automatic rifles, demonstrating their superiority and leading to the obsolescence of bolt-action rifles (Pauly 2008).

This evolutionary trajectory, illustrated in Fig. 1, was predominantly driven by doctrinal shifts stemming from warfare, propelling the development of new technologies supported by national interests. This progression exemplifies technological speciation, where new technologies emerge within application domains characterized by new needs and abundant resources, serving as new selection criteria.

This study introduces the technological speciation degree (TSD), utilizing actual product data to validate an index for analyzing patterns of technological speciation and to elucidate the technological evolutionary characteristics observed throughout this process.

3.1 Overall process

The analytical framework of this study, shown in Fig. 2, begins with the collection of product data comprising technological characteristics, from which a phylogenetic tree is constructed. Subsequently, all lineages are delineated from this tree, identifying the Primary lineage that most accurately represents the evolutionary phenomenon. The TSD of products within this Primary lineage is then calculated to discern patterns of technological speciation, offering insights into the continuous adaptation and evolution of technologies in response to shifting doctrinal needs and warfare strategies.

3.2. Data

In the assessment of the TSD for studying technological speciation, selecting pertinent data is fundamental. The data must accurately reflect shifts in needs and application domains, ensuring that the technological attributes of chosen products are thoroughly documented. Additionally, it is vital to choose product categories that have witnessed technological advancements over time due to sustained development.

Rifles, as a product category, fulfill these criteria and are deemed appropriate for the quantitative analysis of technological speciation. Weapons such as rifles provide insights into evolving needs through military doctrines, facilitating the analysis of need changes via extensive doctrinal documentation. This framework enables the examination of how new technologies arise with the formation of new application domains (Blasko 2011; Holley Jr. 2004; Kim et al. 2021; Pauly 2008; Smith & Smith 1973). Moreover, rifles, relative to other weaponry, boast a vast and varied array of specimens, with comprehensive data over an extended timeframe being meticulously preserved. The historical exploration of rifles across various studies spanning over a century renders them an exemplary subject for research, where the interplay between demand and technological development is distinctly evident (Shafer 2023).

Consequently, this study's quantitative analysis utilizes rifle data primarily from Westwood (2005), complemented by information from Jane's Defense Data Service (JDDS) and Walter (2006) to fill in any data gaps. This compilation resulted in a dataset encompassing 585 rifle data points. The technological traits of the rifles within this dataset are succinctly encapsulated, as illustrated in Table 2.

Table 2

Technological attributes of rifles detailed.
	Variables	Description	Variables	Description
Continuous Variables (9)	Cal_mm	Caliber(mm)	Cart_mm	Cartridge length(mm)
	L_mm	Length(mm)	Folded_L_mm	Folded Length(mm)
	BarL_mm	Barrel Length(mm)	W_kg	Weight(kg)
	Year	Released year	MV_ms	Muzzle Velocity(m/s)
	Gr_n	No. of grooves
Categorical Variables (6)	Name	Name of rifle	Bullpup	Bullpup design
	OM	Operating Mechanism	Detailed_OM	Detailed Operating Mechanism
	Type	Feeding System	Country	Manufacturing Country

This research specifically focuses on rifles introduced post-1841, a pivotal year marking the advent of key modern rifle features such as rifling and specialized ammunition. Data collection commenced with the introduction of the first bolt-action rifle, the Dreyse rifle, in 1841 (Westwood 2005), providing a comprehensive foundation for analyzing the evolution of rifle technology and its diversification over time.

3.3 Extracting phylogenetic trees for lineage extraction.

To quantify the degree of path dependence for a product, it is essential to elucidate ancestor-offspring relationships. This is achieved by constructing a phylogenetic tree based on product similarities, which delineates the evolutionary trajectory of a given product.

The conceptualization of a product phylogenetic tree draws parallels between products and biological organisms, facilitating the identification of ancestral and descendant relationships. This approach allows for a detailed analysis of the technological evolution process and its patterns (Basalla 1988; Baum & Smith 2013; Jeong & Lee 2024; Khanafiah & Situngkir 2006; Lee et al. 2022; Valverde & Solé 2015). Utilizing an algorithm-based phylogenetic tree visualization method, as proposed by Lee et al. (2022), this study meticulously extracted rifle lineages. In the phylogenetic tree diagram, nodes symbolize individual products (rifles), while edges denote the ancestor-offspring relationships.

The relationship between antecedent and descendant products is discerned through their technical attributes. A product ${Z}_{i,t}$ introduced at time t is represented as a vector of its various technical characteristics ${x}_{j}$. To standardize the diverse units of measurement, continuous variables underwent normalization to a scale ranging from 0 to 1 via minimum-maximum scaling.

$$\begin{array}{c}{Z}_{i,t}=\left\{{x}_{j} is a distinct technical characteristic in {Z}_{i,t}\right\}.\#\left(1\right)\end{array}$$

This vector representation of product attributes enables the quantitative assessment of similarity. Considering that a product typically encompasses a mix of continuous and categorical variables, the Gower similarity metric, founded on the Gower distance, was utilized for these calculations (Gower 1971). The Gower similarity (S) is computed as follows: For a categorical variable, the similarity between product ${Z}_{1,t}$ from period t and ${Z}_{2,t+n}$ from period t + n is assigned a value of 1 if the technical characteristic ${x}_{i}$ matches, and 0 if it does not. For continuous variables ${x}_{j}$, the absolute difference between the normalized variables is deducted from 1, transforming the distance metric into a similarity score. These scores across all variables are aggregated and averaged by the total number of variables defining the product, yielding an overall similarity score:

$$\begin{array}{c}{S}_{{x}_{i}, categorical feature}\left({Z}_{1,t}, {Z}_{2,t+n}\right)=\left\{\begin{array}{c}1, if {x}_{i,{Z}_{1}}= {x}_{i,{Z}_{2}}\\ 0, if {x}_{i{,Z}_{1}}\ne {x}_{i{,Z}_{2}}\end{array}\right..\#\left(2\right) \end{array}$$

$$\begin{array}{c}{S}_{ {x}_{j}, continuous feature}\left({Z}_{1,t}, {Z}_{2,t+n}\right)=1-\left|{x}_{j,{Z}_{1,t}}- {x}_{j,{Z}_{2,t+n}}\right|.\#\left(3\right) \end{array}$$

$$\begin{array}{c}{S}_{Gower similarity}\left({Z}_{1,t}, {Z}_{2,t+n}\right)=\frac{\sum {S}_{{x}_{i}, categorical features}+\sum {S}_{ {x}_{j}, continuous features}}{N\left(Number of total features\right)}.\#\left(4\right) \end{array}$$

This systematic procedure is applied to all products, culminating in the construction of an adjacency matrix that captures the similarities between them. Following the creation of this similarity matrix, each product ${ Z}_{i,{t}_{i}}$is linked to the product introduced prior that exhibits the highest similarity, thereby designating it as its antecedent.

$$\begin{array}{c}{Antecedent Z}_{j,{t}_{j}} of descendant{ Z}_{i,{t}_{i}}=argmax\left(S\left({Z}_{j,{t}_{j}}, {Z}_{i, {t}_{i}}\right)\right) , i \ne j and {{ t}_{i}>t}_{j}\#\left(5\right) \end{array}$$

This methodology embraces the features of technological evolution, suggesting that each technology traces its origins to an antecedent within its search domain (Ziman 2003). By repetitively applying this process, each product identifies its direct predecessor, thereby establishing a lineage (L) that maps its evolutionary journey back to a shared ancestor.

$$\begin{array}{c}L\left({Z}_{i}\right)=\left\{for \forall {Z}_{j}, {Z}_{j} are antecedents of {Z}_{i}, from origin {to Z}_{i}\right\}.\#\left(6\right) \end{array}$$

Upon applying these operational definitions, distinct evolutionary patterns emerge for each lineage. However, to address the aim of this study—which is to dissect the process of technological speciation—focusing on a singular primary lineage that accurately represents the broad evolutionary phenomena became essential. To this end, the study adopts betweenness centrality as a metric to assess the centrality of each product within the network. Betweenness centrality quantifies the proportion of all shortest paths between pairs of nodes that pass through a specific node, as encapsulated in the formula provided:

$\begin{array}{c}Betweenness Centrality\left(v\right)={C}_{B}\left(v\right)= \sum _{s\ne v\ne t}\frac{{\sigma }_{st}\left(v\right)}{{\sigma }_{st}}.\#\left(7\right) \end{array}$ * s, v, t are all different nodes in a network

In this formula, the betweenness centrality of a node v, denoted ${C}_{B}\left(v\right),$ is the sum of the fraction of all shortest paths from node s to node t that pass through node v, where ${\sigma }_{st}$ is the total number of shortest paths from node s to node t and ${\sigma }_{st}$(v) is the number of those paths passing through node v. Products with high ${C}_{B}\left(v\right)$ values are deemed crucial within their networks, acting as conduits for information flow between nodes (Newman 2005; Veremyev et al. 2017). These pivotal products, marking significant points of evolutionary branching, delineate the technological evolutionary path within a product group, thus defining the primary lineage as expressed below:

$$\begin{array}{c}Primary lineage= argmax\left(\sum _{v\in lineage}{C}_{B}\left(v\right)\right).\#\left(8\right) \end{array}$$

3.4 Measurement of Technological Speciation Degree(TSD)

Isolating the lineage that encompasses ancestral products enables the quantification of continuity, reflecting the extent of influence exerted by antecedents on a specific product. $Path-dependency\left(L\right)$ is conceptualized as the average similarity from a common ancestor to the product under consideration. This average similarity metric serves to evaluate the central tendency within a product's lineage, offering insights into the product's evolutionary trajectory (Lee et al. 2023).

$$\begin{array}{c}Path-dependency\left(L\right)=P\left(L\right)=\frac{{\sum }_{i= target product}^{N-1}S\left({Z}_{i},{Z}_{ancestor of {z}_{i}}\right)}{{N}_{Lineage}-1} .\#\left(9\right) \end{array}$$

For the dataset's initial product, $Path-dependency\left(L\right)$ warrants special attention due to the lack of historical data regarding its precursors. If empirical evidence designate this product as the genesis of its species, it is attributed the lowest path-dependency value derived from the primary lineage. Conversely, if not explicitly the origin, the product is assigned the mean path-dependency value from the primary lineage, ensuring a consistent baseline for analysis.

$$\begin{array}{c}P\left(L\right) of root= \left\{\begin{array}{c}argmin P\left(Primary Lineage\right), if it is the origin of its species\\ \frac{\sum P\left(Primary lineage\right)}{N-1}, otherwise\end{array}\right.\#\left(10\right) \end{array}$$

In adherence to the defined operational concept of path-dependency, the TSD for a product ${Z}_{t}$ introduced at time t is determined by the variance between the TSD of the lineage just before its introduction (t-1) and the TSD at the moment of its introduction. This calculation facilitates a detailed examination of the evolutionary divergence or continuity each product manifests within its lineage.

$$\begin{array}{c}Technological Speciation Degree\left({Z}_{t}\right)=TSD\left({Z}_{t}\right)= P\left(L\left({Z}_{t-1}\right)\right)- P\left(L\left({Z}_{t}\right)\right).\#\left(11\right) \end{array}$$

If the $TSD\left({Z}_{t}\right)$ yield a positive outcome, it indicates a diminution in lineage path-dependency relative to the period before the introduction of ${Z}_{t}$. This scenario suggests a pronounced probability that the newly introduced product will be classified as belonging to a distinct species from its predecessors. Conversely, a negative TSD value signals an augmentation in continuity leading up to the advent of the product in question, denoting a diminished likelihood of speciation. This approach underscores the significance of path-dependency and divergence in understanding the dynamics of technological speciation.

Figure 1 exemplifies the practical application of path-dependency and TSD assessments. Path-dependency for product ${Z}_{1,t}$ is determined by calculating the mean similarity across all its antecedents within lineage $L\left({Z}_{1,t}\right)$. This average similarity serves as an indicator of the trend in path-dependency or coherence within that lineage. Relying solely on the immediate antecedent to measure changes in similarity might not accurately capture the essence of path-dependency. For instance, consider two products, and ${Z}_{2,t}$, introduced at the same time t and depicted as an empty circle and an empty triangle, respectively. Despite showing the same level of similarity to their immediate antecedents, a thorough analysis incorporating the mean similarity from all preceding antecedents within the lineage reveals divergent narratives. Specifically, ${L(Z}_{1,t})$ exhibits a consistently higher degree of similarity throughout its evolutionary history compared to ${L(Z}_{2,t}$), indicating a greater path-dependency for ${L(Z}_{1,t}$).

The TSD metric quantifies fluctuations in path-dependency. A shift towards a new technological species typically indicates a novelty that reduces the product's path-dependency in relation to its predecessors. Within a lineage, a significant decrease in path-dependency with the introduction of a new product suggests a higher likelihood of triggering speciation. The magnitude of this decrease acts as an indicator of the degree of speciation. For example, in Fig. 1, comparing product ${Z}_{3.t+1}$(represented by a square) with ${Z}_{4.t+1}$ (represented by a diamond) demonstrates that, although both maintain identical similarity levels with their immediate antecedents, ${TSD(Z}_{3.t+1})$ is considerably higher than ${TSD(Z}_{4.t+1})$. This indicates that the greater the existing path-dependency in a product, the more significant the impact of a new product's introduction on path-dependency. Consequently, if a product emerges as a new species within a lineage characterized by a well-established design, the change in path-dependency will be more pronounced compared to a lineage without such a design foundation. This pronounced change underscores the extent of speciation, enabling a clearer divergence from an established species.

The comprehensive methodology outlined can be visually summarized as follows, which is also illustrated in Fig. 2: Initially, data encompassing the technological attributes of various products is gathered. This data forms the basis for constructing a phylogenetic tree, mapping the products based on their technological lineage. From this tree, multiple lineages are identified, among which the primary lineage that most succinctly captures the evolutionary trends is distinguished. The TSD values for products within this primary lineage are then calculated, enabling the identification of patterns indicative of technological speciation.

4.1. Derivation of Primary Lineage from Rifle Phylogenetic Tree

In the exploration of technological speciation, the TSD framework was applied to an extensive dataset of rifles. The initial step involved creating a similarity-based phylogenetic tree diagram for rifles, as illustrated in Fig. 4. This diagram revealed a total of 292 lineages, with each lineage encompassing between 2 to 25 products. A notable observation from the tree, particularly when rifle features were color-coded by their operating mechanism (OM), was the distinct clustering of lineages sharing the same OM color. This pattern highlights the OM's critical role in defining technological species. The transition from bolt-action rifles (blue cluster) to automatic rifles (red cluster) is marked by a specific segment connecting the Fiscal Guard Carbine and the Madsen-Rasmussen 1986 rifle, signifying a crucial point in the speciation process where automatic rifles begin to diverge from their bolt-action predecessors. Post this juncture, most of the descendants trace back to the automatic rifle lineage, indicating a significant evolutionary shift.

To ascertain the primary lineage from the identified lineages, the lineage boasting the highest aggregate of betweenness centrality values across its nodes was selected. Table 3 lists six lineages ordered by their descending betweenness centrality values, each labeled by the name of its terminal descendant product. The lineage L(CGAS5-C2), with the paramount betweenness centrality sum, was identified as the primary lineage following the operational criteria. Highlighted in green in Fig. 4, this lineage acts as the structural core from which all other lineages emanate, succinctly depicting the phylogenetic tree's overall structure. Importantly, it captures the divergence of the automatic rifle segment from bolt-action rifles, rendering it an exemplary lineage for studying the speciation phenomenon.

Table 3

Lineages ranked by highest betweenness centrality.
No.	Lineage	Betweenness centrality
1	L(CGAS5-C2)	0.0802277250907388
2	L(SR-88-A (C)), L(Zastava M85)	0.08015747570542092
3	L(SCS 70/90)	0.08009308043554621
4	L(Colt C8 (C))	0.07995550872263203
5	L(Stgw 90 (C)), L(B2S(C)), L(AC-556F)	0.07988818639503573
6	L(Stgw90)	0.0795691371033837

4.2 Analysis of Technological Speciation Degree in Rifles

The analysis of the TSD for products within the primary lineage, as presented in Table 4, reveals critical insights into the speciation process.

Table 4

TSD values for products within the primary lineage.
No.	Year	Name of products	TSD
1	1841	M1841 Dreyse needle-gun
2	1866	M1866 Chassepot rifle	-0.04698
3	1869	M1869 Vetterli rifle of Swiss	0.031184
4	1871	M1871 of Swiss	-0.02282
5	1878	Gras M1878 Marine Rifle	-0.01147
6	1882	1882 Naval Rifle	-0.00707
7	1886	Fiscal Guard Carbine	-0.00187
8	1896	Madsen-Rasmussen M1896	0.038627
9	1908	Mondragon M1908	0.017765
10	1916	Browning Automatic Rifle	0.002645
11	1927	M1918A1	-0.00926
12	1930	FN M30	-0.00693
13	1932	FN TypeD	-0.00595
14	1942	Mkb 42(H)	-0.00334
15	1943	StG44(Sturmgewehr, called MP43)	-0.00451
16	1949	AK-47	-0.0037
17	1950	AK-S	0.002558
18	1960	AKM-S	-0.00351
19	1965	RK-62(F)	-0.00304
20	1970	SC 70 Carbine	-0.00199
21	1972	7.62mm AR / MG	-0.00129
22	1973	5.56mm SAR	-0.00153
23	1980	SG 543(Carbine)	-0.00206
24	1989	Vektor R6	-0.00203
25	1993	CGAS5-C2	-0.0018

Notably, three distinct segments were identified where TSD values were greater than 0, with a particularly significant observation in the second segment, where a consecutive positive trend was noted over three iterations. These products, highlighted by a lime color in Fig. 4, signify pivotal moments in the lineage's evolution.

The dynamic trends of TSD changes are further visualized in Fig. 5.

Each data point represents a product from the primary lineage, plotted against the year of its release on the horizontal axis and its corresponding speciation degree on the vertical axis. The color coding of lines, indicative of the operational mechanism, serves as a crucial interpretative tool for analyzing speciation events. The analysis delineates three peaks: the first emerges in the nascent phase of bolt-action rifles, the second aligns with the introduction of automatic rifles, and the third occurs during the maturation of automatic rifle development. According to our initial framework, bolt-action rifles experience an additional speciation event following their initial divergence from the Dreyse rifle. In contrast, automatic rifles undergo three sequential speciation processes post their inception from bolt-action rifles, culminating in another distinct speciation occurrence. These speciation events are underscored by the emergence of new needs, further investigated through an examination of the technical attributes and manufacturing contexts of each pivotal product to gain a detailed understanding of the speciation dynamics.

4.3 Discussion

The Dreyse rifle represents the inaugural speciation from musket rifles (Westwood 2005), marking the first significant peak. The subsequent peak, associated with the Vetterli rifle, signifies another critical speciation event within the bolt-action rifle mechanism. The development backdrop of the Vetterli rifle during a period of rapid adoption of bolt-action rifles across Europe, with a growing emphasis on fire and maneuver tactics, highlights Switzerland's strategic intent to bolster its military capabilities. As a neutral nation amidst major powers, Switzerland sought to enhance its arsenal with advanced weaponry conducive to fire and maneuver strategies. This doctrinal imperative facilitated the Vetterli rifle's creation in 1869, combining the repeater system from lever-action mechanisms into the bolt-action framework, initially designed for single shots. This innovation, incorporating a magazine for increased ammunition capacity, significantly reduced reloading times, thus augmenting the rate of fire during maneuver and marking a notable advancement in rifle technology.

The trend analysis of the TSD for bolt-action rifles post-Vetterli introduction reveals a pattern of consistent evolution with TSD values remaining below zero, indicating a steady evolutionary path that maintains similarities with prior designs. Furthermore, Fig. 4 highlights the Vetterli rifle's pivotal role in lineage longevity. Lineages incorporating the Vetterli rifle exhibit sustained survival and diversification, whereas those diverging earlier demonstrate a trend towards extinction with fewer descendants. This pattern underscores the critical impact of integrating the repeating system into the bolt-action mechanism, solidifying its status as the predominant technology within its lineage through a process of trial and error.

The second significant peak in Fig. 5 marks the speciation of Automatic rifles from bolt-action rifles, characterized by three consecutive technological shifts. The Madsen-Rasmussen M1896 rifle marks the beginning of this speciation phase, introducing a novel recoil mechanism borrowed from machine guns for loading. Despite its innovative approach, the rifle saw limited use due to stability challenges associated with recoil-based loading and the technological inability to implement a fully automatic system, resulting in only semi-automatic loading capabilities.

Subsequently, the Mondragon rifle from Mexico represents the second phase in this evolutionary process. Driven by similar strategic imperatives to Denmark, Mexico aimed to equip its infantry with automatic rifles. Despite technological limitations restricting it to automatic loading (Westwood 2005), the Mondragon rifle enhanced stability through a gas-operated mechanism, diverging from the machine gun's operational system. Although the Mondragon rifle did not achieve widespread acclaim, its production and distribution through SIG's subcontracting and sales efforts played a crucial role in disseminating automatic rifles internationally. The transition from bolt-action to automatic rifles, marked by incremental advancements and the resolution of technical challenges, reflects a broader narrative of military technology adapting to evolving doctrinal requirements and battlefield conditions.

The introduction of the browning automatic rifle (B.A.R.) marked the culmination of the technological speciation process, representing the first implementation of a fully automatic firing mechanism in rifles. Developed by the United States in response to the demands of frequent close combat encountered during World War I, the B.A.R. was swiftly incorporated into the battlefield. This innovation extended beyond the existing self-loading technology to facilitate fully automatic firing, setting a new standard for automatic rifles. Despite its weight and cumbersome nature, the B.A.R. served as the archetype for future automatic rifles, integrating automatic reloading and firing in a singular operation.

For approximately 50 years following the advent of the B.A.R., the speciation degree consistently registered values below zero, indicating an evolutionary phase characterized by sustained path dependency. Post-B.A.R., the primary lineage exclusively comprised gas-operated fully automatic rifles. As illustrated in Fig. 4, lineages that diverged prior to the technological advancements leading to the B.A.R. exhibit a reduced number of descendants and diminished longevity. Conversely, lineages stemming from the B.A.R. demonstrate robust survival, reflecting a process of adaptation through trial and error that culminated in the formation of a distinct technological species tailored to specific operational needs.

The third peak, despite its lower speciation degree relative to earlier speciation, showcases distinct technological advancements. This phase introduced the functionality of folding the buttstock, rendering the firearm more compact and better suited for urban warfare and close-quarters combat. This modification, aimed at enhancing portability and effectiveness in close combat scenarios, signifies another phase of technological speciation that emerged post-World War II, in line with the growing doctrinal focus on close-quarters engagement. Although the scale of this speciation was less pronounced than its predecessors, it nonetheless represents a significant evolutionary shift, driven by the tactical necessity of adapting to close combat situations.

The characteristics of the speciation process, as elucidated by the study's findings, include:

Continuous evolution based on doctrinal shifts: The speciation process unfolds continuously, reflecting adaptations to evolving military doctrines rather than occurring as abrupt, disjointed changes. This gradual evolution underscores the adaptive nature of technological development in response to changing tactical and operational needs.

Survivability and divergence of lineages: Certain lineages that diverge without experiencing adequate continuous evolution tend to have a limited lifespan or leave behind few descendants. This observation not only corroborates the detailed portrayal of the continuous speciation process highlighted in previous qualitative research but also emphasizes the role of trial and error within this evolutionary framework.

These insights reveal the dynamic interplay between technological innovation and military doctrine, where the demands of warfare drive the evolution of technology through a process of ongoing adaptation and refinement. The speciation process, characterized by both incremental advancements and periods of significant change, reflects the complex relationship between technological capabilities and operational requirements. The study's experimental results thus contribute to a deeper understanding of the mechanisms underpinning technological speciation, highlighting the importance of flexibility, experimentation, and adaptation in the evolution of military technologies.

This study introduced a novel quantitative methodology to explore technological speciation, shedding light on the intricate dynamics between market forces and technological innovation. It successfully delineated the critical segments of technological speciation by examining shifts in path dependence throughout the evolution of specific product groups, offering a granular view at the level of individual technologies. The investigation conclusively demonstrated that technological speciation is intricately linked to doctrinal shifts. It revealed how the evolution of selection criteria and a rich environment conducive to experimentation are pivotal drivers of speciation. Furthermore, it was observed that the speciation process often involves the amalgamation of existing technologies, navigating through a landscape marked by trial and error to establish distinct evolutionary paths.

This study not only corroborated the concept of technological speciation through quantitative analysis but also highlighted the crucial role of emerging needs as catalysts for novel technological developments within the market. It underscored the necessity of embracing trial and error as an inherent part of fulfilling these emerging needs. Moreover, the study posited that persistent productization and the exploration of technologies, beyond initial endeavors, are essential for inducing meaningful technological transformations, thereby emphasizing a critical strategy for technological advancement.

A recurring theme across the identified speciation segments was the advent of new doctrinal needs, precipitating the development of novel technologies and the integration of existing external technologies into new products. Notably, this process did not transpire abruptly but evolved gradually, seeking increasingly apt solutions to meet these emerging needs (Carignani et al. 2019). This pattern asserts that technological speciation unfolds not as an isolated event but as a continuous evolutionary journey. This process is characterized by a process of adaptation, systematically addressing specific selection criteria through the relentless exploration of existing technologies, facilitated by the collective recognition of common needs among multiple stakeholders. Concurrently, this collective recognition engenders a multitude of trials and errors throughout the speciation process, further enriching the tapestry of technological evolution.

As illustrated in Fig. 4, following the initial divergence of bolt-action rifles from musket rifles, lineages incorporating the Vetterli rifle, which introduced a repeating system to more adeptly meet doctrinal needs, demonstrated consistent survival and produced numerous descendants. Conversely, lineages that predated the adoption of the repeating system in the Vetterli rifle either faced extinction or were limited in their proliferation. Similarly, in the context of automatic rifles, lineages emerging after the introduction of the Browning Automatic Rifle within the technological speciation segments showed sustained survival, whereas those before it either became extinct or were markedly fewer. This pattern suggests that within the evolutionary process, driven by trial and error to address changing needs, only a subset of the generated diversity is retained, selected based on practical usage criteria. Additionally, this reveals the inherent costs associated with trial and error. This finding underscores the importance of focusing on the gradual evolutionary process and its interplay with antecedent technologies, rather than solely on the advent of new technologies, to comprehensively understand the emergence of novel technological solutions. Particularly, acknowledging the role of trial and error in this evolutionary trajectory underscores the critical need for a deeper understanding of needs, coupled with a feedback mechanism to ascertain the alignment of technology with those needs through practical application.

This study contributes two significant academic insights. First, it elucidates technological speciation through the lens of the evolutionary process's trial and error. Second, it highlights the importance of precisely identifying needs and employing a feedback mechanism to evaluate the suitability of technologies for those needs within the trial and error dynamics of technological evolution (Nelson 2008; Sosna et al. 2010; Wagner & Rosen 2014; Ziman 2003).

Furthermore, this research adopted a novel approach to quantitatively analyze technological speciation. Methodologically, the examination of products facilitated an exploration of the interplay between market demands and technological capabilities, presenting a quantitative framework that is adaptable across various product domains. Specifically, the methodology proposed in this study elucidates technology evolution through the lens of supply and demand interactions, emphasizing the significance of market-released products.

The strategic implications derived from this study encompass the following: Analyzing the process of technological speciation offers valuable insights for decision-making in generating new demand and formulating new products grounded in the dynamics of technological emergence. To introduce a technology distinct from current offerings, a thorough understanding of the selection criteria shaped by the needs within the application domain is imperative. This necessitates a process of garnering feedback through market interactions via products and devising solutions that meld with existing technologies to address these needs effectively.

Furthermore, the adaptation process, which involves refining needs through market engagement, incurs trial and error costs, thus necessitating significant resources—the latter serving as a critical driver of technological speciation. Given that initial product offerings may not fully capture the market's needs, there is a heightened risk of failure in the early stages. Additionally, the challenge of pinpointing specific needs may lead to product extinction or stunted growth. However, by navigating through trial and error within the market and fostering a lineage of descendants, products can gradually carve out stable technological lineages. This iterative process, while resource-intensive, fosters diversity and sharpens the selection criteria in alignment with market needs. Sustained efforts to cater to these evolving needs pave the way for the continuous emergence of technology.

Achieving this goal necessitates a shift from the pursuit of perfect products at their inception towards a strategy of releasing small-scale products more frequently to gain a deeper understanding of market needs. Proactive exploration of technologies across different lineages and their integration with existing offerings are vital. Importantly, assimilating feedback and technological insights from utilizing diverse technologies can significantly augment the efficacy of technological recombination. Actively facilitating the quest for solutions to more precisely defined needs—through the frequent recombination of small-scale products informed by past failures—becomes a cornerstone for fostering technological innovation and speciation.

However, this study is subject to certain limitations. First, the construction and analysis of the phylogenetic tree are significantly contingent upon the availability, quantity, and quality of product data, demanding considerable effort to secure reliable data. To mitigate the impact of data gaps, the study undertook comprehensive data collection from diverse literature sources, ensuring that the lineage diagram resonated with widely accepted interpretations and consensus. Moreover, while the analysis was confined to rifle data, future endeavors should aim at broadening the research scope to encompass technological speciation phenomena across a variety of products, enhancing the generalizability of the findings. Second, the current methodology presupposes continuity for initial products owing to the absence of historical data on preceding innovations, necessitating inferred continuity for these products based on specific assumptions. To address this, an extensive review of relevant literature was undertaken to ascertain whether the product under consideration indeed marked the inception of speciation. Future research endeavors are imperative to establish objective and compelling continuity metrics, facilitating a more refined and precise analysis.

Acknowledgement

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT).(No.NRF-2022R1A2C1091917)

Funding

This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT).(No.NRF-2022R1A2C1091917)

Ethical Conduct

This research does not involve human participants and animals.

Data Availability Statement

The data and code of this study are available from the corresponding author upon request

Competing Interests

The authors declare that they have no conflict of interest.

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Technological Speciation : Navigating New Needs through Trial and Error – A Rifle Case Study

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Abstract

Figures

1. Introduction

2. Literature review

2.1 Technological speciation as an evolutionary process

2.2 History of speciation of rifles

3. Methodology

3.1 Overall process

3.2. Data

3.3 Extracting phylogenetic trees for lineage extraction.

3.4 Measurement of Technological Speciation Degree(TSD)

4. Result

4.1. Derivation of Primary Lineage from Rifle Phylogenetic Tree

4.2 Analysis of Technological Speciation Degree in Rifles

4.3 Discussion

5. Conclusion

Declarations

References

Additional Declarations

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Version 1