A hierarchical taxonomy of business model patterns
Although business model innovation (BMI) is essential to remaining competitive, many firms fail at it. A promising approach is building on reoccurring successful solutions – business model patterns (BMP) – as a blueprint for BMI. However, existing patterns face constraints subject to a high diversity and overlaps among patterns. In addition, literature do not consider relations among BMPs, which limits their potential for BMI. This paper develops a hierarchical taxonomy of BMPs including generalizations and specializations based on inheritance. We conduct a literature review to identify patterns and a cluster analysis to create an inductive structure, followed by a qualitative analysis. The resulting hierarchical taxonomy includes 194 elements. It is the first hierarchical taxonomy of BMPs. The hierarchy addresses the diversity of patterns and overlaps with inheritance. It aids research to structure and understand BMPs. For practice, the taxonomy allows for the application of patterns and supports BMI.
KeywordsBusiness model Business model pattern Taxonomy Hierarchical structure Cluster analysis
JEL classificationsO310 Innovation and Invention Processes and Incentives
Market dynamics are changing at an ever-increasing pace and thus becoming more demanding for firms (D'Aveni et al. 2010; Teece 2018; El Sawy and Pereira 2013). Better information and a broader selection of firms has led to a shift in bargaining power toward customers (Teece 2010). To win this battle for customer attention, firms need to shorten development cycles, which increases competition and turbulence in the market (Schneider and Spieth 2014; Teece 2018). Consequently, firms have to adapt to market dynamics and changing demand continuously.
Business models (BMs) are a crucial aspect to remaining competitive in these turbulent markets (Martins et al. 2015; de Reuver et al. 2013; De Reuver et al. 2009). A BM defines how firms create, deliver, and capture value in a market (Teece 2010).1 Firms adapt BMs to cope with changing market dynamics by harmonizing the business strategy, internal processes, and information systems (Al-Debei and Avison 2010; Schneider and Spieth 2014; Teece 2018).
However, many firms fail when trying to align BM change with dynamic market requirements (Christensen et al. 2016). Changing an entire BM can involve enormous transformations for an organization (Foss and Saebi 2017). Thus, it is not surprising that this concept of BM change or adaption, termed as Business Model Innovation (BMI), enjoys increasing popularity (Foss and Saebi 2017). However, practitioners often build on trial-and-error experimentation to innovate their BM and fail likewise (Martins et al. 2015; Chesbrough 2010; Sosna et al. 2010; Morris et al. 2005). One reason is a lack of supporting frameworks and tools (Osterwalder and Pigneur 2013; Veit et al. 2014; Heikkilä et al. 2016; Weking et al. 2018a).
A promising approach that supports BMI is learning from recurring phenomena that have proven to be successful in the past in different industries or contexts: business model patterns (BMPs) (Amshoff et al. 2015). BMPs describe successful BM instances or components of it that are applicable on other firms (Osterwalder and Pigneur 2010; Gassmann et al. 2014; Amshoff et al. 2015). BMPs can either be used in isolation or in a combination to form a new complete BM or describe a BM instance (Osterwalder and Pigneur 2010; Böhm et al. 2017). We see BM instances as concrete real world BMs (Osterwalder et al. 2005). BMPs sometimes appear under different names, for instance BM archetypes (Bocken et al. 2014; Weill et al. 2005; Eickhoff et al. 2017; Weking et al. 2018b) or BM configurations (Taran et al. 2016). Gassmann et al. (2014) found that 90% of BMIs in practice are a combination of existing BMPs.
However, current BMP literature faces limitations that restrict their applicability in research and practice. There is a variety of different BMP (i.e., Gassmann et al. (2014), Taran et al. (2016) or Remané et al. (2017), which differ in two dimensions. First, BMPs differ in the covered BM elements. On the one hand, a BMP can relate to one distinct element of a BM, such as the pattern channel maximization (Remané et al. 2017), which refers to the BM element value delivery. On the other hand, a BMP can relate to several BM elements such as the pattern merchant model (Remané et al. 2017), which addresses the BM elements value creation, delivery and capture. Second, BMPs differ in the level of abstraction. BMPs can address a low level of abstraction, such as the pattern flexible pricing (Remané et al. 2017) or they can approach a high level of abstraction, such as the pattern multi-sided platform (Osterwalder and Pigneur 2010). Further, an HR broker is a specific form of a multi-sided platform, where a platform provider matches buyers and sellers. Thus, these differences in covered elements and level of abstraction lead to BMPs overlapping in terms of content and substance. Hence, this variety of BMPs leads to overlaps in both dimensions: the degree of coverage and the content resulting from differences in the level of abstraction. Ultimately, this results in a complex and chaotic collection of BMPs, which is hard to use when innovating a BM.
Two contributions aim to resolve this complex collection by structuring BMPs (Taran et al. 2016; Remané et al. 2017). However, no framework provides a compelling categorization that addresses the variety of BMPs in the covered BM elements, level of abstraction and resulting overlaps. To address these issues, it is important to characterize (Remané et al. 2017) and cluster individual BMPs (Taran et al. 2016), but also to identify a structure with relations among BMPs that describes many levels of abstraction with generalizations, specializations and inheritance.
The purpose of this work is to structure BMPs consistently and to leverage their potential for BMI. This paper develops a hierarchical taxonomy for BMPs. The taxonomy separates patterns present in the extant literature according to different degrees of coverage and levels of abstraction mitigating the issue of overlapping patterns. We build on an iterative taxonomy development approach (Nickerson et al. 2013) to tackle the complex field of BMPs by developing a hierarchical structure among BMPs. First, we perform an empirical-to-conceptual iteration with an agglomerative clustering of BMPs to generate an inductive structure (Kaufman and Rousseeuw 2009; Struyf et al. 1997). Second, we draw on a conceptual-to-empirical iteration with qualitative analysis to derive hierarchical levels within the structure. Scholars and practitioners can build on the hierarchical taxonomy to understand and use BMPs. The hierarchical structure helps to reduce the complexity of BMPs and to increase their applicability in the context of increased market dynamics.
the Business Model Canvas with nine dimensions (Osterwalder and Pigneur 2010),
the Magic Triangle with four dimensions (Gassmann et al. 2014),
the BM framework according to Abdelkafi et al. (2013) with five main elements,
the unified BM framework (Al-Debei and Avison 2010) as a conceptual BM framework and
All of them cover the following elements to characterize a BM instance: value proposition, value delivery, value creation and value capture. In addition, there are BM frameworks that do not directly address value-based elements, but specific aspects. The casual loop diagram (Casadesus-Masanell and Ricart 2010; Casadesus-Masanell and Ricart 2011) as a logic oriented BM framework uses choices and consequences to describe BM instances and highlights their reinforcing cycles. The matrix-shaped BM framework according to Weill et al. (2005) focuses on four BM archetypes (i.e., creator, distributor, landlord and broker) and the type of asset involved (i.e., financial, physical, intangible and human) (Weill et al. 2011). IBM’s component business model (Chesbrough 2010; Pohle et al. 2005) illustrates the category of specialization-focused BM frameworks. It includes an accountability level (i.e., direct, control and execute) and does not cover a direct value capture dimension. Besides specialized BM frameworks, there are also BM frameworks tailored toward a specific context: digital BMs (Bock and Wiener 2017), big data (Hartmann et al. 2016), FinTechs (Eickhoff et al. 2017), car sharing (Remané et al. 2016), platform BMs (Täuscher and Laudien 2018), or sustainable BMs (Upward and Jones 2016). The large amount of frameworks as well as their differences emphasize the ambiguity of the concept of BMs.
BMPs are a promising solution to reduce the complexity in characterizing BMs with BM frameworks. BM literature provides many different collections of BMPs with diverse amounts of BMPs. Osterwalder and Pigneur (2010) deduce five BMPs. Gassmann et al. (2014) define 55 BMPs. Both use their BM framework to derive and describe typical BMPs including related example cases. Two contributions build on BMPs from literature. Taran et al. (2016) initially found 97 BMPs and conclude with 71 different BMPs. Remané et al. (2017) started with 356 BMPs and result with 182 different BMPs.
However, the current literature about BMPs has two main limitations. First, the multitude of general BM frameworks leads to a wide range of BMPs that address different BM elements, i.e., one or many. Consequently, some patterns include only a few BM elements, whereas others describe holistic BMs. Osterwalder and Pigneur (2010) deduce five BMPs that change the general setup of a BM and influence all BM elements and many areas of a firm (e.g., long tail, multi-sided platform or open business model). Gassmann et al. (2014) define BMPs that vary in their addressed BM elements. Some BMPs focus on a few elements of a BM. Examples are the patterns pay what you want and subscription addressing mainly value capture mechanisms, and white label addresses mainly the value proposition. Others affect all elements of a BM, such as no frills, peer-to-peer or two-sided market. Likewise, the work of Taran et al. (2016) covers BMPs influencing all BM elements, such as broker (i.e., “bring together buyers and sellers and facilitate transactions”) and BMPs influencing only a few BM elements. Channel maximization (i.e., “product is distributed through as many channels as possible to create the broadest distribution possible”), for example, refers to the value delivery. Remané et al. (2017) similarly covers very different BMPs. Examples are e-mail (i.e., “communicate with stakeholders via e-mails rather than print and mail”) that influences the value delivery only, whereas connection (i.e., “provide physical and/or virtual network infrastructure to gain (internet) access”) or software firms (i.e., “create software and license/sell it”) describe holistic BMs.
Second, there is a variety in the level of abstraction of BMPs. Some are specializations, while others are generalizations of BMPs. Multi-sided platforms, for example, bring together two or more customer segments (Hein et al. 2018c). The presence of each segment creates value for the other segments (Remané et al. 2017; Osterwalder and Pigneur 2010). Thus, multi-sided platforms are generalizations of brokerage that define two segments as buyers and sellers and add a commission fee (Remané et al. 2017; Weill et al. 2005). Further specializations are financial broker, HR broker, physical broker and information broker (Remané et al. 2017; Weill et al. 2005). Another example is subscription where customers regularly pay upfront for products or services (Remané et al. 2017; Rappa 2001). Specializations are flat-rate, where the customer receives unlimited access and membership where the access to products or services and the time-dependent payment is the focus (Remané et al. 2017; Gassmann et al. 2014; Tuff and Wunker 2010). These differences in the level of abstraction of BMPs and in the covered BM elements leads to overlapping BMPs and increased complexity. Collections of BMPs are hard to apply for BMIs.
Two contributions aim to reduce this complexity by creating a comprehensive structure for characterizing BMPs. Taran et al. (2016) introduce the five-V framework. It clusters the 71 BMPs into five dimensions: value proposition, value segment, value configuration, value network, and value capture. Remané et al. (2017) introduce a matrix-shaped BM taxonomy. They used BMPs to create a morphological box for characterizing BMs. The BM framework has four initial dimensions based on Günzel and Holm (2013): value proposition, value delivery, value creation and value capture (Remané et al. 2017). Remané et al. (2017) include two hierarchical levels in the form of prototypical as holistic patterns and solution patterns as specific building blocks. Both studies focus on clustering and classifying existing BMPs by deriving typologies or BM frameworks to reduce complexity (Taran et al. 2016; Remané et al. 2017). They both cover the basic four elements ranging from value proposition, to value delivery, to value creation, and value capture. They can characterize BMPs as well as BM instances from practice.
However, both frameworks focus only on characterizing BMPs. BM literature address neither the variety in covered BM elements of BMPs nor the diversity in the level of abstraction of BMPs nor the resulting overlaps among BMPs. Likewise, general BM frameworks are not able to address these issues. The four BM elements are not enough to address the main drawbacks of BMPs. Current literature only characterizes individual BMPs. Despite the importance of reducing complexity among BMPs by structuring, no paper has taken into account the relations and hierarchical structures among BMPs yet. Thus, this paper focuses on relations among BMPs in the form of a hierarchical taxonomy of BMPs covering specializations and generalizations based on the inheritance of characteristics of BMPs to address the differences in covered BM elements, the diverse abstraction levels, and the resulting overlaps among BMPs.
We followed a two-step research approach. First, we used a structured literature review (Webster and Watson 2002) to identify a comprehensive set of BMPs. Second, we used an iterative taxonomy development approach (Nickerson et al. 2013) to structure BMPs according to their relationships.
To identify articles with BMPs and similar constructs, we built on a literature review conducted by Remané et al. (2017). With a literature review according to Webster and Watson (2002), they identified 182 different BMPs out of 22 collections of BMPs and six reviews of BMP collections. To ensure the validity of their findings, we conducted a follow-up literature review based on Webster and Watson (2002) to cross validate and supplement their results. We used the four databases: ProQuest – Business, EBSCOhost, Science Direct and Scopus with the following search string: “‘Business model*’ AND (characteristics OR framework* OR taxonomy OR pattern* OR design OR development OR evolution)”. We reviewed 776 papers, from which we have chosen 33 relevant articles. The search included articles in academic journals and conference proceedings written in English. We included only articles that focus on BMPs or similar constructs that meet the definition of BMPs. We found two more papers through a backward and forward search resulting in 35 papers.
In the coding process, two researchers iteratively checked and consolidated the BMPs presented in each publication to ensure intercoder reliability. We confirm the comprehensiveness of the list of BMPs according to Remané et al. (2017) and found only two additional patterns (i.e., data as a service and R&D contractor). Overall, we derived a set of 184 BMPs.
Next, two researchers coded each of the BMPs to verify their relevance according to three criteria. First, we include only patterns that cover at least one of nine building blocks of the Business Model Canvas (Osterwalder and Pigneur 2010). We have chosen the Business Model Canvas for this relevance criterion and the coding in the first iteration for three main reasons. First, it is a widely applied and practical BM framework (Massa et al. 2017). Second, it is a general BM framework and not specific for certain contexts. Third, with nine dimensions and two to ten characteristics each, it is very comprehensive (Osterwalder and Pigneur 2010). Thus, we exclude patterns that do not cover any BM element and do not meet the definition of BMPs. An excluded example is e-mail (i.e., “communicate with stakeholders via e-mails rather than print and mail”) (Strauss and Frost 2016; Remané et al. 2017). Second, BMPs must not be specific for one industry. BMPs that are specific for one industry do not meet the definition of BMPs. An excluded example is misdirection for search engines (i.e., “send customers to locations different from what they initially searched for if the searched company did not pay sufficient listing fees to the search engine”) (Clemons 2009; Remané et al. 2017). Other examples are BMPs for the electric vehicle industry (Bohnsack et al. 2014). Third, BMPs must not solely build on a business practice that has established itself as common practice. Excluded examples are customer relationship management (i.e., “collecting and integrating all information on each customer touch point”) and enterprise resource planning (i.e., “use an integrated back office system to optimize business processes and thereby reduce cost”) (Strauss and Frost 2016; Remané et al. 2017). To ensure intercoder reliability and internal validity, two researchers alternatively created (researcher A) and revised (researcher B) the coding until both agreed. We excluded 19 patterns and concluded with 164 BMPs for the taxonomy.
Coding scheme based on Osterwalder and Pigneur (2010) with added characteristics (*) and an example coding of razors/ blade (italic) (own illustration)
Getting the job done
Asset/ service sale
Lending/ Renting/ Leasing
Strategic alliances between non-competitors
Strategic partnerships between competitors
The second iteration followed a conceptual-to-empirical approach (Nickerson et al. 2013) to analyze and validate the clusters qualitatively. A qualitative analysis is necessary since a cluster analysis cannot recognize the different levels of abstraction of BMPs. Further, we validate the clusters qualitatively. Figure 2 summarizes the development process. It shows the quantities of BMPs in each cluster (1. Iteration) or subtree (2. Iteration) and includes initial names for clusters (1. Iteration). Two researchers studied all BMPs in one cluster to detect generalizations (step 4c) and specializations (step 5c) and to revise the taxonomy continuously (step 6c). BMPs with a higher level of abstraction became superordinate BMPs. If there was no high-level BMP that covers the intersection of low-level characteristics, we created a new BMP. We also split high-level clusters by building on lower-level clusters that resulted in 27 and 51 clusters from the analysis. For example, the value proposition cluster from the first iteration has 70 elements (see Fig. 2). Thus, we used the low-level clusters within the value proposition cluster to further differentiate BMPs. Subordinate clusters supported the separation between payment/ pricing models, revenue streams, target customers, value propositions and development processes. Other clusters could be used with almost no changes for the hierarchical structure (i.e., merchant model, multi-sided platforms and value network). For splitting and merging clusters and forming the hierarchical levels, we highly built on subordinate clusters from the first iteration that resulted from the analysis with 27 and 51 clusters. Eventually, the classification structure included hierarchical relations and all ending conditions were met (step 7).
Business model pattern taxonomy
A current limitation of BMPs is that they have varying degrees of coverage in terms of BM elements and have different levels of abstraction. Some BMPs are straightforward and illustrate how firms can adapt their value stream (e.g., membership), while others touch all aspects of a BM (e.g., multi-sided platform). The consequence is that BMPs are overlapping, hard to compare, and thus not easy to use when innovating a business model. Existing BMP frameworks (Remané et al. 2017; Taran et al. 2016) are designed to illustrate and define patterns. Thus, they are not intended to analyze relations among BMPs or to address the variety in the degree of coverage, the different levels of abstraction and the overlaps. This paper builds on hierarchical relations among BMPs and creates a hierarchical taxonomy including generalizations and specializations based on inheritance to address all three issues. This work’s literature review reveals 164 BMPs. Using an iterative taxonomy development method (Nickerson et al. 2013), we derive a hierarchical taxonomy with eight BMPs on the top level of abstraction and three further levels including more detailed BMPs. Since an instance of a BM in practice can comprise many BMPs, more than one BMP within one branch or subtree can apply to one complete instance of a BM.
The hierarchical taxonomy shows eight overarching BMPs that comprise dominant and holistic BMPs or cover common BM elements (i.e., value proposition, value delivery, value creation and value capture). On the one hand, two of eight high-level BMPs of the taxonomy cover holistic and well-known BMs. First, the merchant model describes wholesalers and retailers of goods and service (Remané et al. 2017). This BMP has existed for a long time and has been digitalized during e-commerce (Rappa 2001). Second, the multi-sided platform describes serving two or more customer segments, where the presence of each segment creates value for the other segments (Remané et al. 2017). This BMP similarly have been around for a long time (Osterwalder and Pigneur 2010). However, multi-sided platforms spread heavily with the rise and support of information technology (Parker et al. 2017; Hein et al. 2018a). Examples are Google, Facebook and Visa (Osterwalder and Pigneur 2010; Parker et al. 2017; Hein et al. 2018b; Schreieck et al. 2018). Both high-level BMPs, merchant model and multi-sided market, draw on a long history and show enormous business success in practice (Hein et al. 2016). The inductively derived taxonomy shows that both stand out as two very dominant BMPs in the BMP literature. On the other hand, the remaining six of eight high-level BMPs of the taxonomy address common elements of BM frameworks: value proposition, value delivery, value creation and value capture. The subtree value proposition addresses to the identically named BM element. The subtree customer groups refers to the value delivery, whereas the subtrees value proposition development and value network refer to the value creation. Payment/ pricing models and revenue streams address the value capture element. Consequently, the taxonomy confirms dominant and common elements of BM frameworks. Moreover, the taxonomy highlights two dominant BMPs. For both aspects, dominant BM elements and dominant BMPs, it provides further specifications with its hierarchical structure of BMPs.
The resulting hierarchical taxonomy of BMPs addresses three shortcomings of literature. First, it creates a structure for the various BMPs in literature including the relations among BMPs. It considers individual BMPs as well as relations among them and thus mitigates the complexity of the large amount of BMPs in extant literature. Patterns are easier to find in the hierarchical structure than in an alphabetically sorted list. For example, if a user is looking for a pricing model, she can look at this subtree and see possible options. Second, the hierarchical structure takes into account the diversity of BMPs concerning their various degrees of coverage in terms of BM elements. The taxonomy with its different levels and relations among BMPs covers all kinds of different degrees of coverage and hence explains overlaps. The six of eight high-level BMPs that address common elements of BM frameworks and BMs clearly differentiate BMPs concerning their essence. The remaining two high-level BMPs (i.e., merchant model and multi-sided market) express two common holistic BMs. The taxonomy further specifies these BMs with lower-level BMPs, namely specializations. In this way, the taxonomy mitigates the various degrees of coverage in terms of BM elements by structuring BMPs according to BM elements and common holistic BMPs. Hence, it also clarifies overlaps in the dimension of coverage. Third, the taxonomy addresses the various hierarchical levels of BMPs with specializations and generalizations based on inheritance. BMPs inherit characteristics of superior BMPs and, thus, are specializations of BMPs on a higher level. While BMPs on a higher level in the taxonomy address a higher level of abstraction, BMPs on a lower level in the taxonomy also show a lower level of abstraction and cover BM elements in detail. In this way, it also clarifies overlaps in the dimension of abstraction levels. Summarizing, the taxonomy considers the variety in the covered BM elements of BMPs and the diversity in the level of abstraction of BMPs and incorporates overlapping BMPs with its hierarchical structure.
This work has three main implications for theory. First, to the best of our knowledge, this is the first inductively derived BM classification as well as the first classification considering relations among BMPs. It is the first BM taxonomy that address the diversity of BMPs concerning their various degrees of coverage, different hierarchical levels of BMPs, and overlaps of BMPs and relations among BMPs. The taxonomy helps to structure and understand the vast amount of BMPs available in literature. In contrast to existing BM frameworks (Osterwalder and Pigneur 2010; Gassmann et al. 2014), the hierarchical taxonomy of BMPs is able to characterize individual BMPs and BM instances from practice. Additionally, it allows for putting a BMP or BM instance in relation to other BMPs. In this way, BMs can be analyzed against the backdrop of other BMPs and in a higher order structure of BMPs with higher and lower levels of abstraction. Second, the taxonomy further serves as an extendable structure for future BMPs as well as current BMPs that literature does not cover yet. In contrast to existing BM frameworks, the taxonomy defines hierarchical dimensions for classifying BMPs and for describing them. The taxonomy functions as an overall structure. Currently, there are two holistic and overarching BMPs, namely merchant model and multi-sided market as well as six overarching BMPs that address different BM elements. Sparse parts of the taxonomy show possible areas for areas for new BMPs and future research. Third, the hierarchical structure as a supporting tool for BMI addresses several calls for research. The hierarchical taxonomy represents a holistic, exhaustive and systematic classification structure for BMs (Fielt 2013) including the derivation of specific sub-classes of BMs (Veit et al. 2014). It supports the conceptual modeling and formalization of BMs (Osterwalder and Pigneur 2013).
For practice, the hierarchical BM taxonomy allows for the application of BMPs. The taxonomy consists of BMPs with examples cases from practice in a hierarchical structure. The structure makes it easier to use than an alphabetically sorted list of BMPs, and the example cases provide the basis for analogical thinking (Gavetti and Rivkin 2005). Thus it helps practitioners to identify related BMPs (sharing the same parent node) to find a creative solution for a specific problem of their BM (e.g. payment/ pricing models). Furthermore, practitioners can characterize their current BM with the taxonomy of BMPs. They can decide for each branch and BMP if it is relevant for their current business or not. Then, they can identify analogies to BMPs and related example cases from literature. Practitioners can assess possible opportunities for BMI based on the taxonomy, the BMPs and example cases. For instance, they can assess related patterns within one branch as possible incremental BMI or analyze different branches as possible radical BMI. Here, the hierarchical taxonomy as a graphic tree helps to visualize the initial and planned combination of BMPs within an intended BMI. The taxonomy further shows the path that has to be traveled in the hierarchical structure for a certain BMI. This visualizes the changes of the current BM that are necessary to reach the target BM. In this way, the hierarchical taxonomy of BMPs can serve as a practical tool to support BMI. It addresses numerous calls for research. It helps to find options for BMI and new and viable BM alternatives as well as its visualization (Osterwalder and Pigneur 2013; Veit et al. 2014). The taxonomy supports incremental, i.e. similar BMPs within one branch, as well as radical changes of BMs, i.e. leaping from one branch to another, with example cases for each BMP (El Sawy and Pereira 2013).
This work has some limitations. First, the taxonomy solely relies on BMPs from literature. Thus, we cannot ensure that the taxonomy includes all available BMPs. There are probably new BMPs in practice that literature does not yet cover. However, we argue that the taxonomy is extendable and provides a good basic structure that is able to integrate future BMPs. Second, the taxonomy development process and especially the coding of BMPs as well as the second iteration with the conceptual to empirical approach can be subject to the researchers’ interpretations of BMPs definitions. However, two researchers discussed the coding and matchings iteratively to prevent a possible bias. Third, there are limitations regarding the taxonomy’s applicability in practice. Avoiding superficial analogies is important for strategy development (Gavetti and Rivkin 2005). An analogical case (source) has to be understood thoroughly before its similarities and differences can be assessed and it can be translated into a target case (Gavetti and Rivkin 2005). The taxonomy cannot consider the contextual factors and strategic path dependencies of an applying firm. Practitioners may find possible opportunities with example cases in the taxonomy. However, the taxonomy can only partly support practitioners in evaluating a specific BMP for their context and strategy by providing analogies in the forms of definitions and example cases (Gavetti and Rivkin 2005). Nevertheless, the taxonomy supports BMI in practice by structuring the many BMPs and make them utilizable. Fourth, the taxonomy has some sparse areas. Some dimensions of the structure are more detailed than others and include more BMPs. For example, brokerage as a specialization of multi-sided platform has many specializing BMPs, whereas trust intermediaries or buy/ sell fulfillment have no specializing BMPs. We can see that e-commerce BMPs (e.g., online advertisement) and digital BMPs dominate the taxonomy. The reason for this is that we included BMPs from literature only. This leads to promising areas for future research.
The hierarchical taxonomy for BMPs provides four main opportunities for future research. First, in order to address sparse areas of the taxonomy, future research can investigate new BMPs and extend the taxonomy. The taxonomy reveals two overarching and holistic BMPs, namely merchant model and multi-sided platform. Future research can investigate whether both types are dominant and successful types in practice and extend the hierarchical structure with new patters that further characterize both types. Likewise, the taxonomy shows six overarching BMPs that address BM elements. Future research can investigate in and extend these subtrees. For this purpose, the taxonomy serves as an overall structure and supports the identification of areas for new BMPs. Second, future research can use the taxonomy to describe certain BM instances and developments of a BMI. Like in practice, future research can apply the hierarchical taxonomy to characterize BM instances (e.g., an initial BM and a target BM) with existing BMPs to describe case studies, for example. Third, this work is a first step towards an ontology of BMPs and towards a BM distance measure. For now, the taxonomy includes hierarchical relations only. However, it would be interesting and further facilitate the usage of the taxonomy to include all kinds of relations. This ontology of BMPs would illustrate cross relations within the hierarchy, for instance BMPs that complement or exclude each other. Excluding examples are disintermediation, integrator and orchestrator. Whereas disintermediation and integrators aim to cover more parts of the value chain, an orchestrator tries to focus on core competencies, outsource remaining activities and only coordinate the value chain. An ontology of BMPs would further support a BM distance measure. With an indented BMI including an initial BM (initial combination of BMPs) and a target BM (target combination of BMPs), the hierarchical taxonomy and ontology can support the calculation of a distance between these BMs (combinations of BMPs). It would indicate how many changes of the current BM are necessary to reach the target BM and suggest how revolutionary the BMI would be. Fourth, the hierarchical taxonomy including the definitions of BMPs (see appendix Table 2) can be developed further as a practical tool. For example, a software tool implementing the hierarchical BM taxonomy would strengthen its practical relevance. In this way, the hierarchy can support practitioners with characterizing their current BM with BMPs and suggest possible opportunities for BMI. A hierarchical questionnaire based on the taxonomy can provide guidance for characterizing a firm’s BM. Building on the current BMP combination, the tool can suggest possible opportunities for incremental BMI based on the hierarchy. Possible opportunities for revolutionary BMI can be suggested based on a case study database of successful BMIs where the initial and the target BM is characterized with the hierarchical taxonomy of BMPs. Hence, the hierarchy of BMPs can serve as an underlying logic of a practitioner-oriented tool. Overall, the hierarchical taxonomy of BMPs opens up fruitful areas for future research with theoretical as well as practice relevance.
In increasingly turbulent markets and environments, BMs, their fit to a firm’s strategy and the capability to innovate BMs are essential to remain competitive (Martins et al. 2015; Zott and Amit 2008). In research, the concepts of BMs and BMI are gaining more and more attention (Massa et al. 2017; Foss and Saebi 2017). However, innovating a BM is a complex task and many firms fail (Christensen et al. 2016). One approach to supporting BMI is building on successful solutions of the past, i.e. BMPs (Gassmann et al. 2014; Amshoff et al. 2015). However, BMPs and available collections of BMPs have three major limitations that restrict their applicability in research and practice. First, a large amount of BMPs exists with diverse degrees of coverage, i.e., covered BM elements. Second, BMPs show diverse levels of abstraction and resulting overlaps. Third, extant literature only characterize individual BMPs without considering the relations among BMPs to address diversity, hierarchy levels and overlaps. In order to mitigate these issues, this paper develops a hierarchical taxonomy of BMPs that includes generalizations and specializations among patterns based on inheritance to address this diverse degree of coverage, diverse hierarchy levels and overlapping BMPs.
In order to develop this hierarchical structure, we first build on a literature review (Webster and Watson 2002) to identify BMPs and second on an iterative taxonomy development approach (Nickerson et al. 2013). We coded all 164 BMPs according to Osterwalder and Pigneur (2010) and conducted an agglomerative cluster analysis, followed by a qualitative analysis to come up with an inductive structure. The resulting hierarchical taxonomy of BMPs includes 194 elements in its four levels of abstraction. On its highest level, it reveals two overarching, holistic BMPs (i.e., merchant model and multi-sided market) and six overarching elements of BMs. It is the first hierarchical taxonomy of BMPs, which takes into account relations among BMPs. The hierarchical structure reduces complexity by structuring the large amount of BMPs, respecting the diversity in the degree of coverage and abstraction levels and addresses overlaps with inheritance. It structures the complex field of BMPs and helps researchers to understand BMPs. For practice, the taxonomy allows for the application of BMPs, supports BMI and, thus, addresses several calls for research (Fielt 2013; Osterwalder and Pigneur 2013; Veit et al. 2014; El Sawy and Pereira 2013). The hierarchical taxonomy is extendable and, hence, serves as a robust foundation for further research with yet unidentified BMPs.
For helpful comments and suggestions, the authors would like to thank two anonymous reviewers and the editors. This research is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft – DFG) as part of the ‘Collaborative Research Center 768: Managing cycles in innovation processes – Integrated development of product service systems based on technical products’ (TP C1), and the Center for Very Large Business Applications (CVLBA)@TUM. In addition, this work is part of the TUM Living Lab Connected Mobility (TUM LLCM) project and has been funded by the Bavarian Ministry of Economic Affairs, Energy and Technology (StMWi) through the Center Digitisation.Bavaria, an initiative of the Bavarian State Government.
- Andrew, J. P., & Sirkin, H. L. (2006). Payback: reaping the rewards of innovation. Boston: Harvard Business Press.Google Scholar
- Applegate, L. M. (2001). E-business models: Making sense of the internet business landscape. In G. Dickson & G. DeSanctis (Eds.), Information technology and the Future Enterprise: New Models for Managers (pp. 49–94). Upper Saddle River: Prentice Hall.Google Scholar
- Bienstock, C., Gillenson, M., & Sanders, T. (2002). The complete taxonomy of web business models. Quarterly Journal of Electronic Commerce, 3(2), 173–186.Google Scholar
- Bock, M., & Wiener, M. (2017).Towards a Taxonomy of Digital Business Models – Conceptual Dimensions and Empirical Illustrations. In Thirty Eighth International Conference on Information Systems, Seoul, South Korea. Google Scholar
- Böhm, M., Weking, J., Fortunat, F., Müller, S., Welpe, I., & Krcmar, H. (2017). The business model DNA: Towards an approach for predicting business model success. In 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017) (pp. 1006–1020). St. Gallen, SwitzerlandGoogle Scholar
- Casadesus-Masanell, R., & Ricart, J. E. (2011). How to design a winning business model. Harvard Business Review, 89(1/2), 1–9.Google Scholar
- Christensen, C. M., Bartman, T., & Van Bever, D. (2016). The hard truth about business model innovation. MIT Sloan Management Review, 58(1), 31–40.Google Scholar
- De Reuver, M., Bouwman, H., & Maclnnes, I. (2009). Business model dynamics: A case survey. Journal of Theoretical and Applied Electronic Commerce Research, 4(1), 1–11.Google Scholar
- Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95–104.Google Scholar
- Eickhoff, M., Muntermann, J., & Weinrich, T. (2017). What do FinTechs actually do? A Taxonomy of FinTech Business Models. In Thirty Eighth International Conference on Information Systems (ICIS 2017), Seoul, South Korea.Google Scholar
- Eisenmann, T. R. (2001). Internet business models: Text and cases. Boston: Irwin/McGraw-Hill.Google Scholar
- El Sawy, O. A., & Pereira, F. (2013). Business modelling in the dynamic digital space: An ecosystem approach (Vol. 1, SpringerBriefs in digital spaces). Heidelberg: Springer-Verlag.Google Scholar
- Fielt, E. (2013). Conceptualising business models: Definitions, frameworks and classifications. Journal of Business Models, 1(1), 85–105.Google Scholar
- Fleisch, E., Weinberger, M., & Wortmann, F. (2014). Business models and the internet of things. White Paper (pp. 1–19): Universität St. Gallen, Bosch Internet of Things & Services Lab.Google Scholar
- Gassmann, O., Frankenberger, K., & Csik, M. (2014). The business model navigator: 55 models that will revolutionise your business. Harlow: Pearson.Google Scholar
- Gavetti, G., & Rivkin, J. W. (2005). How strategists really think. Harvard Business Review, 83(4), 54–63.Google Scholar
- Hanson, W., & Kalyanam, K. (2007). Principles of Internet marketing: South-Western College Publishing.Google Scholar
- Heikkilä, M., Bouwman, H., Heikkilä, J., Haaker, T., Lopez-Nicolas, C., & Riedl, A. (2016). Business model innovation paths and tools. In Twenty Ninth Bled eConference (pp. 571–587).Google Scholar
- Hein, A., Schreieck, M., Wiesche, M., & Krcmar, H. (2016). Multiple-case analysis on governance mechanisms of multi-sided platforms. In Multikonferenz Wirtschaftsinformatik (MKWI 2016) (pp. 9–11). Ilmenau: Germany.Google Scholar
- Hein, A., Böhm, M., & Krcmar, H. (2018a). Platform Configurations within Information Systems Research: A Literature Review on the Example of IoT Platforms. In Multikonferenz Wirtschaftsinformatik (MKWI 2018), Lüneburg, Germany. Google Scholar
- Hein, A., Böhm, M., & Krcmar, H. (2018b). Tight and loose coupling in evolving platform ecosystems: The cases of Airbnb and Uber. In W. Abramowicz & A. Paschke (Eds.), Business Information Systems (BIS). Lecture Notes in Business Information Processing (Vol. 320, pp. 295–306). Cham: Springer.Google Scholar
- Hein, A., Scheiber, M., Böhm, M., Weking, J., & Krcmar, H. (2018c). Toward a Design Framework for Service-Platform Ecosystems. In Twenty Sixth E uropean Conference on Information Systems (ECIS 2018), Portsmouth, UK. Google Scholar
- Johnson, M. W. (2010). Seizing the white space: Business model innovation for growth and renewal. Boston: Harvard Business Press.Google Scholar
- Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: an introduction to cluster analysis (Wiley series in probability and statistics). Hoboken: Wiley.Google Scholar
- Linder, J., & Cantrell, S. (2000). Changing Business Models: Surveying the Landscape. A Working Paper from the Accenture Institute for Strategic Change: Accenture.Google Scholar
- McClain, J. O., & Rao, V. R. (1975). Clustisz: A program to test for the quality of clustering of a set of objects. Journal of Marketing Research, 12(4), 456–460.Google Scholar
- Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook for visionaries, game changers, and challengers. Hoboken: Wiley.Google Scholar
- Osterwalder, A., & Pigneur, Y. (2013). Designing business models and similar strategic objects: The contribution of IS. Journal of the Association of Information Systems, 14(5), 237–244.Google Scholar
- Osterwalder, A., Pigneur, Y., & Tucci, C. L. (2005). Clarifying business models: Origins, present, and Future of the Concept. Communications of the Association for Information Systems, 16(1), 1–25.Google Scholar
- Pohle, G., Korsten, P., & Ramamurthy, S. (2005). Component business models - Making specialization real. IBM Business Consulting Services (pp. 16): IBM Institute for Business Value.Google Scholar
- Rappa, M. (2001). Managing the digital enterprise: Business models on the Web. http://digitalenterprise.org/models/models.html. Accessed 5/13/2018.
- Remané, G., Nickerson, R. C., Hanelt, A., Tesch, J. F., & Kolbe, L. M. (2016). A taxonomy of Carsharing business models. In Thirty Seventh International Conference on Information Systems (ICIS 2016), Dublin, Ireland.Google Scholar
- Schreieck, M., Hein, A., Wiesche, M., & Krcmar, H. (2018). Governance der Akteure einer digitalen Plattform. In M. Wiesche, P. Sauer, J. Krimmling, & H. Krcmar (Eds.), Management digitaler Plattformen. Informationsmanagement und digitale Transformation (pp. 53–66). Wiesbaden: Springer Gabler.CrossRefGoogle Scholar
- Struyf, A., Hubert, M., & Rousseeuw, P. (1997). Clustering in an object-oriented environment. Journal of Statistical Software, 1(4), 1–30.Google Scholar
- Tapscott, D., Lowy, A., & Ticoll, D. (2000). Digital capital: Harnessing the power of business webs. Boston: Harvard Business School Press.Google Scholar
- Tuff, G., & Wunker, S. (2010). Beacons for business model innovation. In Doblin, Deloitte Consulting LLP. Chicago, USA.Google Scholar
- Veit, D., Clemons, E., Benlian, A., Buxmann, P., Hess, T., Kundisch, D., Leimeister, J. M., Loos, P., & Spann, M. (2014). Business models - an information systems research agenda. Business & Information Systems Engineering, 6(1), 45–53.Google Scholar
- Webster, J., & Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly, 26, xiii-xxiii.Google Scholar
- Weill, P., & Vitale, M. (2001). Place to space: Migrating to e-business models. Boston: Harvard Business School Press.Google Scholar
- Weill, P., Malone, T. W., D’Urso, V. T., Herman, G., & Woerner, S. (2005). Do some business models perform better than others? A study of the 1000 largest US firms. MIT Center for coordination science working paper, 226.Google Scholar
- Weill, P., Malone, T. W., & Apel, T. G. (2011). The business models investors prefer. MIT Sloan Management Review, 52(4), 16–20.Google Scholar
- Weking, J., Brosig, C., Böhm, M., Hein, A., & Krcmar, H. (2018a). Business Model Innovation Strategies for Product Service Systems – An Explorative Study in the Manufacturing Industry. In Twenty Sixth European Conference on Information Systems (ECIS 2018), Portsmouth, UK. Google Scholar
- Weking, J., Stöcker, M., Kowalkiewicz, M., Böhm, M., & Krcmar, H. (2018b). Archetypes for Industry 4.0 Business Model Innovations. In Twenty Fourth Americas Conference on Information Systems (AMCIS 2018), New Orleans, LA, USA. Google Scholar
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.