Business model patterns for 3D printer manufacturers

Patrick Holzmann (Innovation Management and Entrepreneurship, Alpen-Adria-University Klagenfurt, Klagenfurt, Austria)
Robert J. Breitenecker (Innovation Management, Johannes Kepler Universitat Linz, Linz, Austria)
Erich J. Schwarz (Innovation Management and Entrepreneurship, Alpen-Adria-University Klagenfurt, Klagenfurt, Austria)

Journal of Manufacturing Technology Management

ISSN: 1741-038X

Article publication date: 13 June 2019

Issue publication date: 23 November 2020




The purpose of this paper is to analyze the business models that 3D printer manufacturers apply to commercialize their technologies. The authors investigate these business models and analyze whether there are business model patterns. The paper describes the gestalt of the business model patterns and discusses differences and similarities.


The authors review the literatures on business models and 3D printing technology. The authors apply a componential business model approach and carry out an in-depth analysis of the business models of 48 3D printer manufacturers in Europe and North America. The authors develop a framework focusing on value proposition, value creation and value capture components. Cluster analysis is used to identify business model patterns.


The results indicate that there are two distinct business model patterns in the industry. The authors termed these patterns the “low-cost online business model” and the “technology expert business model.” The results demonstrate that there is a relationship between business model and technology. The identified patterns are independent of age, company size and country of origin.

Research limitations/implications

The empirical results complement and extend existing literature on business models. The authors contribute to the discussion on business models in the context of novel technology. The technology seems to influence the gestalt of the business model. The sample is limited to European and North American companies and the analysis is based on secondary data.


This is the first empirical study on the business models of 3D printer manufacturers. The authors apply an original mixed-methods approach and develop a framework that can function as a starting point for future research. 3D printer manufacturers can use the identified business model patterns as blueprints to reduce the risk of failure or as a starting point for business model innovation.



Holzmann, P., Breitenecker, R.J. and Schwarz, E.J. (2020), "Business model patterns for 3D printer manufacturers", Journal of Manufacturing Technology Management, Vol. 31 No. 6, pp. 1281-1300.



Emerald Publishing Limited

Copyright © 2019, Patrick Holzmann, Robert J. Breitenecker and Erich J. Schwarz


Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at

1. Introduction

Manufacturing companies are facing severe challenges. Advances in technology, global competition and readily available information led to an increased comparability of offers. As a result, power shifted away from the manufacturers toward the customers. In addition, also customer preferences changed significantly. For instance, individualization has become a major trend (Rachinger et al., 2018). Furthermore, customers frequently demand the simultaneous improvement of quality and a reduction of costs (Brettel et al., 2014). As a response, companies promote the implementation of digitalization, automation and ICT (Oesterreich and Teuteberg, 2016). In order to fulfill this shift companies also have to alter their production technologies (Rylands et al., 2016).

3D printing is considered a major technological driver fostering this paradigm shift (Petrick and Simpson, 2013; Ghobakhloo, 2018) as it might enable more efficient and effective production of prototypes and goods (Hannibal and Knight, 2018). Regarding the extent of its potential future impact, scholars have already drawn comparisons between 3D printing and ICT (Rayna and Striukova, 2016). However, 3D printing has yet to live up to expectations (Maresch and Gartner, 2018). The 3D printing industry is still small but rapidly growing. In 2016, 3D printer manufacturers’ industry revenues grew by 17.4 percent and were an estimated $6.063 billion (Wohlers Associates, 2017). Despite increasing market shares (Manyika et al., 2013), overall technology diffusion is still slow (Wohlers Associates, 2017; Holzmann et al., 2018; Yeh and Chen, 2018). Lately, also several governments, such as the USA, UK (Schniederjans, 2017), Germany, the Netherlands, China and South Korea started to actively promote the adoption of 3D printing technology (Forbes, 2018).

Besides its primary application in rapid prototyping and for small production runs, 3D printing started to enter also consumer households (Hannibal and Knight, 2018). 3D printing allows manufacturers to shift productive activities at least to some extent to consumers (Bogers et al., 2016). Major drivers that enable this development are the expiration of critical patents for fused deposition modeling (FDM) and stereolithography (SLA) printers and the advent of the Replication Rapid (RepRap) Prototyper project (Wohlers and Caffrey, 2014). The literature refers to individual 3D printing as home fabrication (Rayna and Striukova, 2016). Home fabrication deals, for instance, with the production of appliances, tools or replacement parts (McKinsey, 2017).

Research on economic and business effects of this novel technology is still insufficient (Weller et al., 2015; Rayna and Striukova, 2016; Öberg et al., 2018). Previous research primarily aims to investigate 3D printings potential for manufacturing optimization (Öberg et al., 2018) and technology entrepreneurship (Gartner et al., 2015). However, there is limited research on business models in the 3D printing industry (Öberg et al., 2018). Bogers et al. (2016), for instance, showed that 3D printing can enable consumer goods manufacturers to shift toward more consumer-centric business models. Rayna and Striukova (2016) investigated how 3D printing is affecting business model innovation. Holzmann et al. (2017) identified business models for user entrepreneurs. Öberg et al. (2018) analyzed current literature on business models and 3D printing. They conclude that there is a need for more holistic views, especially on business model components.

We seek to address this research gap by shedding further light on the relationship between business models and novel technology. Previous research has concluded that business models are crucial in the adoption of new technology (Chesbrough, 2010; Morris et al., 2013). Scholars (e.g. Gambardella and McGahan, 2010; Teece, 2010; Zott et al., 2011) argue that business models are important to unlock technology’s full commercial potential. However, developing a viable business model to commercialize especially novel technology is a challenging task and the risk of failure is high (Cavalcante, 2013).

We aim to investigate the business models that companies are using to commercialize 3D printers for rapid prototyping and home fabrication purposes and formulate the following research questions:


How do 3D printer manufacturers commercialize their products?


Which business model patterns do 3D printer manufacturers apply?


What is the gestalt of these business model patterns?

To answer these research questions, we introduce 3D printing and briefly discuss the technology’s advantages. In addition, we present prerequisites for its usage. We then address business models and their constituting components as well as the nexus between business models and technology. We apply a componential business model approach and carry out an in-depth analysis of the business models of 48 3D printer manufacturers located in North America and Europe. The results of our analysis provide insights into constitutional variables for novel technology commercialization. Our findings indicate that there are two distinct business model patterns. We extract the gestalt of these business model patterns and identify differences and similarities. Finally, we discuss if these patterns can function as blueprints for other 3D printer manufacturers by highlighting potential advantages and disadvantages. Our study contributes to a better knowledge base of the nexus between business model and novel technology. Thus, this paper extends existing literature in business model research and technology entrepreneurship. Finally, we derive managerial implications, discuss the limitations of our work and suggest potential future research directions.

2. Theoretical background

2.1 A brief introduction to 3D printing

3D printing is an additive manufacturing process that builds objects by adding layer upon layer of a material (e.g. ceramics, metals and polymers) (Steenhuis and Pretorius, 2017; Wohlers Associates, 2017). Thus, it differs significantly from injection molding and subtractive manufacturing processes. Areas of application are primarily rapid prototyping and small production runs (Appleyard, 2015; Weller et al., 2015). Due to its specific characteristics, 3D printing technology is considered a major driver for digitalization (Rayna and Striukova, 2016). For instance, 3D printing has major advantages regarding cost-effective production and production speed (D’Aveni, 2015). First, applying 3D printing processes can reduce the costs of setting up a venture, since they do not require expensive tools, forms, or punches (Holmström et al., 2010; Berman, 2012). Second, 3D printers enable short setup time. Thus, they are able to reduce time-to-market and allow swift order processing. Third, economies of scale are not applicable to 3D printing, thus increasing the attractiveness to manufacture individualized products (Berman, 2012; Holmström et al., 2017). In addition, in-time production circumvents the need to stockpile huge numbers of products (Holmström et al., 2010; Berman, 2012; Lipson and Kurman, 2013). Fourth, 3D printing allows for printing almost any form, thus overcoming design or construction-related constraints (Petrovic et al., 2011; D’Aveni, 2015). Fifth, 3D printing can also positively affect environmentally friendly production due to a reduction of input material needed and the amount of waste (Berman, 2012; Holmström et al., 2017). Furthermore, biodegradable filaments are becoming more widely available. Taken together 3D printing technologies might enable more efficient and effective production of prototypes and individualized goods (Hannibal and Knight, 2018).

However, companies can only attain these advantages if their organizations fulfill certain requirements. First, manufacturing complex parts in one part is a challenging task (Holmström et al., 2010; Berman, 2012) that assumes the successful combination of hardware and software (Petrick and Simpson, 2013). Compared to the traditional way of casting and assembling various parts, this approach requires CAD-designs that are more sophisticated. Thus, there is a pressing need for designers that master the requirements and challenges of designing fully printable complex objects. Second, the design and production of complex 3D printed parts can be time consuming. Thus, companies that adopt 3D printing technology also have to be aware to adjust and intensify their value creation processes toward digitalization. Consequently, these companies need to implement a fundamentally different logic of thinking based on the principles of digitalization (Holzmann et al., 2018).

Besides its primary application in rapid prototyping and for small production runs, 3D printing started to enter also consumer households (Hannibal and Knight, 2018). Currently, several companies target the consumer markets with affordable printers (Holzmann et al., 2017). Target customers for these inexpensive 3D printers are primarily makers and tinkerers. The literature refers to individual 3D printing as home fabrication (Rayna and Striukova, 2016). Home fabrication deals, for instance, with the production of appliances, tools or replacement parts (McKinsey, 2017). Teenage Engineering a Swedish synthesizer manufacturer altered its value creation by eliminating warehousing of replacement parts. The company offers customers to purchase 3D printable files. Customers can now print as many parts as they want at home or use a 3D printing service. In addition, they can even modify the parts and can thus create a unique appearance for their synthesizers (3D printing, 2012).

3D printing has the potential to transform manufacturing and alter services (Gartner et al. 2015). Due to its specific characteristics, 3D printing technology provides novel ways for companies to create value for customers. However, capturing value from 3D printing is crucial. Prior research (e.g. Rayna and Striukova, 2016) highlighted that this can be a challenging task. There is a pressing need for viable business models that underscore the technology’s advantages and enable profitable commercialization (Bogers et al., 2016). Hence, the question that remains is what value based on 3D printing can companies provide to customers? How can they create this value and how can they successfully capture value for themselves through their business models?

2.2 On the nexus of business models and technology

Business models have evolved as a distinct unit of analysis (Zott and Amit, 2013; Tongur and Engwall, 2014). Special issues on business models and business model innovation in top-tier academic journals, such as Long Range Planning (2010, 2013, 2018), R&D Management (2014) and Strategic Entrepreneurship Journal (2015), acknowledge the topic’s importance for the scientific community. Further, various studies (e.g. Aspara et al., 2010; Brettel, et al., 2012; Hu, 2014; Velu, 2015) have concluded that a business model is of value to companies, since it can positively influence their performance. Despite the fields growing importance, empirical research on business models is still scarce. Several scholars (e.g. Spieth et al., 2014; Wirtz et al., 2016; Teece, 2018) have therefore underlined the importance of a reasonable knowledge base of business models.

Business models aim to provide holistic and structured templates of how companies do business (Zott et al., 2011; Zott and Amit, 2013). Following the componential approach, business models comprise particular business model components that provide explanation of a company’s business model (Baden-Fuller and Mangematin, 2013). Scholars consider the concept of value as the common element in business model research (George and Bock, 2011). There is a growing consensus that business models consist of value proposition, value creation and value capture components (e.g. Shafer et al., 2005; Desyllas and Sako, 2013; Bocken et al., 2014; Holzmann et al., 2017). The value proposition comprises bundles of products and services (Osterwalder et al., 2005; Morris et al., 2005; Morris et al., 2006). It defines the benefits that potential customers attain from these bundles (Mason and Spring, 2011). The value proposition can contain a portfolio of valuable benefits for customers (Morris et al., 2005, Johnson et al., 2008). The value creation component specifies the key processes and external partners, as well as the communication and distribution channels (Johnson et al., 2008; Bocken et al., 2014) required to create value and ultimately to harness the value proposition. The value capture component encompasses revenue generation options, cost structures and profits (Bolton and Hannon, 2016; Rayna and Striukova, 2016). These three components are inextricably linked with each other and need to be aligned (Foss and Saebi, 2017; Kiel et al., 2017). Taken together the combination of these three business model components defines the gestalt of a business model (Shafer et al., 2005).

Research on the nexus between technology and business models is still in progress (Cavalcante, 2013). Baden-Fuller and Haefliger (2013) note that literature has frequently disregarded the importance of business models as enablers for value creation through novel technology. A growing number of studies assumed the existence of strong interdependencies between technology and business model (e.g. Kodama, 2004; Tongur and Engwall, 2014). The discussion is driven by the assumption that mere technology development is no longer sufficient for firm success (Doganova and Eyquem-Renault, 2009). For Chesbrough (2007) a technology per se has no inherent value. Scholars point out that the business model provides an important link from technology to firm performance (Baden-Fuller and Haefliger, 2013). Christensen (2006) concludes that the business model is the fundamental challenge when marketing technologies. The business model can open novel opportunities for technology application and exploitation that lead to previously inaccessible profit generation (Suoto, 2015). However, the number of studies that empirically investigate the nexus between business model and technology is still insufficient. The present study aims to contribute to a better understanding of the role of business models in commercializing novel technology.

3. Empirical study

3.1 Data collection

Due to the gestalt of our research questions, we employed an explorative research design. We decided to apply a mixed-methods approach (Tashakkori and Teddlie, 2003). A mixed-methods approach combines qualitative and quantitative research. Further, it incorporates a unique set of ideas and practices (Creswell, 2014), and allows creating a more comprehensive and complete picture of the focal phenomenon through a combination of strengths of different research methods (Tashakkori and Teddlie, 2003). In line with previous research on business models (Morris et al., 2013; Holzmann et al., 2017; Täuscher and Laudien, 2018), we chose to collect secondary data. First, we consulted the Wohler’s Report to identify relevant 3D printer manufacturers (Wohlers and Caffrey, 2014). We searched for North American and European 3D printer manufacturers that target rapid prototyping or home fabrication applications. In a next step, we checked whether these companies provide sufficient information about their business models. We gathered information from websites, company profiles (e.g. from the chamber of commerce) and official media releases. We identified 16 companies listed in the Wohler’s Report that fulfilled all criteria. In addition, we applied a web search to identify further companies. We used online search engines to identify additional 3D printer manufacturers that fulfill our criteria. This search resulted in 32 companies increasing our final sample to 48 companies. Prior research has shown that this methodology is suitable for analyzing business models (Holzmann et al., 2017; Täuscher and Laudien, 2018). The whole data collection period lasted from 2014 until end 2015.

We apply a componential business model approach (Baden-Fuller and Mangematin, 2013). Our focus on a single industry allowed us to emphasize the more relevant variables that constitute the business models. Further, this approach allows identifying potential business model patterns and drawing comparisons between these patterns in order to detect similarities and differences (Morris et al., 2013). We developed a framework for our analysis focusing on value proposition, value creation and value capture components. We analyzed these components and their potential specifications. Two raters systematically categorized the collected information by means of qualitative content analysis (Mayring, 2000). We followed a mixed coding approach and a clear predefined coding strategy. First, raters systematically searched for information on the three business model components. Second, we assigned collected information to business model subcomponents (e.g. partner integration, distribution channels and communication channels) and variables derived from the literature (deductive category assignment). Third, raters screened the collected content for further subcomponents and variables (inductive category formation). In case a rater identified a new subcomponents or variable, the raters discussed the meaning, interpretation and assignment to business model components and checked for ambiguity. After raters reached common agreement about the new variable, they repeated the entire process and again analyzed the content. In line with previous research (Morris et al., 2013; Holzmann et al., 2017; Täuscher and Laudien, 2018), we used binary variables to assess whether a specification is present in a respective company’s business model or not. This approach allowed the setup of a category system that describes the three business model components for the quantitative part of the analysis. We controlled the data coding for consistency by calculating the interrater agreement. Interrater agreement was 91.25 percent and Cohen’s κ was 0.760. Thus, there was substantial agreement between the two raters (Landis and Koch, 1977).

3.2 Methodology

We identified cluster analysis as an appropriate statistical method to analyze business models. The method is an appropriate explorative approach to identify distribution patterns in a data set and to summarize cases with similar specifications into homogenous clusters (Ketchen and Shook, 1996). In our case, cluster analysis enables discovering distinct business model patterns from the overall data set of business model specifications. Business models within a cluster are homogeneous and distinct from business models in other clusters. The applied method allows discovering and describing the identified similarities and differences in the business models.

We applied a TwoStep Cluster approach in IBM SPSS Statistics 23. TwoStep Clustering is applicable even in case of large data sets, with categorical data and results with an optimal number of clusters (SPSS Inc., 2001). Due to the binary-scaled variables in our data set, we calculated distances between business model specifications based on Log-Likelihood distance measure. Further, TwoStep Clustering allowed us to include all identified business model variables in our analysis. Thus, it enabled a more precise cluster description. We selected the most appropriate cluster model based on the Bayesian information criterion (BIC). The BIC is a global goodness of fit statistic allowing the comparison of different cluster solutions and identifying the cluster model that best describes the data (Magidson and Vermunt, 2002). We also ran robustness checks with alternative clustering approaches. We applied latent class analysis (LCA) and hierarchical cluster analysis (complete linkage method with Russel and Rao similarity measure). These alternative clustering approaches led to comparable results.

In order to describe the cluster solution, we calculated mean values of clustering variables. This calculation further allowed us to draw profile plots. These plots highlight the similarities and differences concerning business model specifications. To test for statistical differences concerning business model specifications as well as descriptive variables we applied independent-sample t-test, χ2 test, Fisher’s Exact test and non-parametric Mann–Whitney U test.

3.3 Measurement

To measure the three business model components we generated dichotomous variables for all identified business model specifications. Regarding the value proposition component, we build on the forms of value proposed by Kim and Mauborgne (2000) and Morris et al. (2006). Further, we found that companies in our sample propose additional forms of value. Taken together, our framework comprises sixteen variables (see Table I). We assigned a value of 1 (yes) for fulfilled conditions, otherwise 0 (no).

The value creation component comprises three subcomponents (integration of partners into the value chain, distribution channels and communication channels) (e.g. Osterwalder et al., 2005). These three subcomponents comprise 12 variables (see Table II). The integration of partners into companies’ value creation processes comprises three variables: the company cooperates with companies, customers (Osterwalder and Pigneur, 2010) or academic institutions (inductive category formation). There are three distribution channels: retailers, stores and web shops (Osterwalder and Pigneur, 2010). According to literature (Osterwalder and Pigneur, 2010) emails and web pages can be used for communication. In addition, we found the following communication channels: advertisements, blogs, press releases and social media (inductive category formation). Every company in our sample has a web page and provides an e-mail address; therefore, we had to exclude these two variables from the analysis due to missing variance. Again, we assigned the value of 1 to fulfilled conditions and 0 to those not applicable to the company’s business model.

The value capture component comprises two subcomponents (revenue sources, payment methods) (e.g. Timmers, 1998; Osterwalder et al., 2005) and ten variables in total (see Table III). There are various revenue sources, for instance, leasing, rental and sales (Osterwalder and Pigneur, 2010). In addition, we found reselling of consumables (Inductive category formation). The payment methods address which options companies offer and how customers prefer to pay (Osterwalder and Pigneur, 2010). We found bank transfer, Bitcoin, cash, cash on delivery (COD)/invoicing, credit card and PayPal (inductive category formation). Again, we assigned the value of 1 to fulfilled conditions, and 0 to those that did not apply.

3.4 Descriptives

Our sample consists of 23 (47.9 percent) European companies and 25 (52.1 percent) from North America (see Table IV). They are on average 5.8 years old (SD=7.24), with the youngest companies one year and the oldest company 33 years old. There are no significant differences (t=1.574; df=31.51; p=0.125) in the firm age between the European companies (mean=4.13; median=3.0; SD=3.58) and those from North America (mean=7.28; median=3.0; SD=9.28). The majority (51.4 percent) has less than ten employees. Three companies (8.1 percent) have more than 500 employees. We also found no significant differences in the employee size classes between the companies in Europe and those in North America (Mann–Whitney U=206.0; p=0.254).

3.5 Results

The TwoStep Cluster algorithm selects based on the BIC a two-cluster solution (BIC 1 Cluster: 1796.5; BIC 2 Cluster: 1792.7; BIC 3 Cluster: 1841.9). Thus, a two-cluster solution best describes the data. There are significant differences regarding certain business model specifications between the two identified business model clusters (see Table V).

We drew profile plots of the variables in the cluster model, to illustrate cluster differences and similarities (see Figures 1–3).

3.6 Cluster descriptions

The companies in the larger cluster (75 percent) apply the “low-cost online model.” These companies aim to provide value for customer through quality printers at low cost. In addition, they emphasize convenience by offering easy-to-use printers. Further, they propose fast and durable printers. On the other hand, they put little effort on innovation leadership. In line with this, they seldom offer specific software to operate the printers. Security, flexibility, environmental friendliness, training and expertise are less characteristic (see Figure 1). Regarding the value creation component, these companies prefer to do business online via web shops. In addition, they also sell their printers via retailers. They often use social media and their own blogs for communication. Advertisements and press releases are less frequent. They primarily cooperate with other companies. Some companies integrate customers into their value creation processes. They seldom cooperate with academic institutions (see Figure 2). Regarding the value capture component, the low-cost online business model relies on selling printers. Further, some companies are reselling consumables. Two companies offer leasing options. Another company rents out its printers. Due to their online business model, these companies also focus on digital and online payments, for instance, PayPal, credit cards and bank transfers. Cash payments and COD/invoicing are less frequent. One company also accepts bitcoin payments (see Figure 3).

The smaller cluster (25 percent) applies a business model termed the “technology expert model.” Technology experts emphasize to provide value via expertise, innovation leadership and quality. Further, they focus on flexible printers for which they offer specific trainings. In addition, they often provide specific software. They mind security and environmental friendliness. Conversely, there is no focus on low-cost offerings and customer productivity. These companies put less emphasize on customer service, speed, convenience, reliability and variety (see Figure 1). Regarding, the value creation component, companies show higher probabilities to integrate academic institutions into their value creation. Further, they cooperate with other companies but seldom with customers. Their primary distribution channels are retailers. Some companies operate physical stores. They are less likely to do business online and seldom run a web shop. Social media is the most common communication channel followed by press releases. Advertisements and blogs were not found in this cluster (see Figure 2). Regarding the value capture component, selling printers are used most frequently. In addition, some companies are reselling consumables. They do not offer leasing and rental options. Bank transfer is the most widespread payment option followed by COD/invoicing. PayPal and credit card payments are less frequent. None of the companies offers cash or bitcoin payments (see Figure 3).

We found no significant differences between the low-cost online and the technology expert business model concerning country of origin (χ2=0.028, df=1, p=0.868). There are also no significant differences between the two clusters regarding employee size classes (Mann–Whitney U=102.0, p=0.181) and company age (t=1.768, df=45, p=0.084). In total, 91.6 percent of companies in the low-cost online cluster manufacture extrusion-based 3D printers, while in the technology expert cluster 41.6 percent offer products based on this technology (χ2=13.642, df=1, p<0.001). The technology expert cluster predominantly (75 percent) offers 3D printers that utilize other processes like stereolithography or sintering compared to 8.3 percent of companies in the other cluster (χ2=21.333, df=1, p<0.001).

4. Discussion

Based on the business model specifications we are able to describe the business models of 3D printer manufacturers. The most commonly used specifications regarding value proposition are quality (n=42), convenience (n=30) and reliability (n=24) (see Table V). The least frequent specifications are customer service (n=10), innovation leadership (n=8) and customer productivity (n=3). Regarding the value creation component, the integration of company partners in the value chain is most frequent (n=34). The preferred distribution channel is the web shop (n=37) and social media is the most frequent communication channel (n=42). Selling printers is the most frequent source of revenue (n=47). Interestingly, PayPal (n=29) is the most frequent payment method followed by credit card (n=27). It is interesting to see that, 3D printer manufacturers commercialize their novel products through business models that are rather traditional. Previous research has shown that companies are able to develop novel products based on new technology. However, they frequently fail to develop new business models to market their products (Cavalcante, 2013).

Further, our results indicate that there are two distinct business model patterns to commercialize printers that serve rapid prototyping and/or home fabrication applications. The majority of companies in our sample applies the low-cost online pattern. Their primarily extrusion-based printers do not differ significantly in terms of specifications (e.g. build volume). Thus, they create value for customers through focusing on convenience and reliability. They aim to provide simple plug-and-play solutions that need little setup time and maintenance. Previous research concluded that user friendliness is still low which can lead to bad experiences. Especially for new technology, this hinders or even prevents adoption (Steenhuis and Pretorius, 2016). However, convenient and user-friendly printers provide pleasant experiences and potentially stimulate further purchases. In the manufacturing industry, these printers are primarily used for rapid prototyping purposes. Consumers apply them for home fabrication applications. Lately, this market is growing even more rapidly than industrial systems (Wohlers and Caffrey, 2014). Due to an already relatively large and further growing number of companies in this market, price competition between companies is fierce. Thus, the low-cost online pattern requires cutting costs to remain competitive. Companies applying this pattern aim to cut costs through a strong emphasize on digital aspects in their business models. Thus, this business model may function also as role model for digitalization within and across industry borders. The majority of companies are “born digital.” They were founded after important patents expired. The advent of the RepRap project catalyzed the emergence of affordable printers. These printers targeted primarily tinkerers and hobbyists online. Later, companies such as Makerbot started to address home fabrication on a larger scale. Today, these companies are also supporting their customers’ digitalization efforts with their inexpensive 3D printers. These printers are often the first customers acquire. They offer the possibility to get familiar with the novel technology at reasonable prices. For instance, trial-and-error learning enables customers to understand and master the digital value chain from designing to manufacturing. Thus, they foster not only the diffusion and adoption of 3D printing but also the shift toward digitalization.

The technology expert pattern aims to provide value through innovation leadership. These companies frequently market novel features that are new to the industry (e.g. automatic bed levelling). Often companies delineate their expertise through the number of filed patents and awards they have won. Due to their expertise, there printers are able to handle, for instance, various input materials. In order to secure optimal printing results, they provide specific trainings for customers. In these trainings, customers learn about the various possibilities and settings. Some manufacturers provide specific software for their printers. The correct usage of this software is also part of the training programs. In order to provide state-of-the-art technology, these companies often cooperate with academic institutions. However, this approach is also a major cost driver and affects the price structure. Focusing on lower prices is thus not the preferred option. This business model follows rather conventional value creation logics. Selling the printers is the most frequent revenue source. Interestingly, none of the companies in this cluster seeks to circumvent charging high prices by leasing printers to their customers. This approach would enable them to address customers’ expectations regarding high quality and lower prices simultaneously. Learnings from other industries, for instance, the automotive industry where companies achieved cutting costs without necessarily lowering quality (Williamson, 2010), for instance through value innovation (Kim and Mauborgne, 2005), could be a viable option also for 3D printer manufacturers.

Besides the differences between the two business model patterns, there are also some similarities (see Table V). Regarding the value proposition, both patterns focus strongly on quality. The correct assessment of potential and performance of a novel technology can be a difficult task. The companies might aim to diminish customers’ risk to spend money on a printer that cannot meet their performance expectations through a strong emphasize on quality. A rather surprising finding is that customer service is not a major aspect in either of the two business model patterns. This finding is even more surprising for the technology expert pattern. Previous research (e.g. Mellor et al., 2014) has shown that customer service is important for technology adoption. Companies applying low-cost online pattern focus strongly on printers that are reliable and easy to use. That might affect their customer service. Maybe companies assume that convenience and reliability at least partly substitute customer service. For low-cost online companies it might also be a strategic decision to cut costly customer service activities. It is possible, that technology experts assume that they can at least partly substitute customer service through their specific training. Another possible explanation could be that manufacturers assume that customers interested in buying a 3D printer have the required technical expertise that enables them to solve at least minor issues themselves. Further, there are strong online 3D printing communities (e.g. RepRap Wiki) and a growing number of local Fab labs and maker spaces where likeminded people provide hints and advice. Lately, a growing number of companies (e.g. Makerbot, Ultimaker) have taken advantage of expert users by hosting their own 3D printing communities. The companies might assume that these communities can at least partially replace the tasks of internal customer service departments. However, this lack of customer service might be a major factor that technology adoption is still below expectations.

Concerning the value capture component, companies in both clusters rely primarily on rather conservative revenue sources. Almost all companies are only selling their printers. Currently, only three companies utilize other revenue sources such as leasing (two companies) or renting (one company). This finding opens up possibilities for business model innovation. Leasing and renting are common revenue sources in other industries (e.g. car and truck industry) and highly appreciated by customers, because they enhance their flexibility. Offering leasing and renting options might attract more customers and again foster technology adoption. Further, companies could transfer and adapt successful business model patterns from other industries. For instance, the famous razor-and-the-blade model originally created by Gillette has been transferred to various other industries (e.g. Nespresso coffee machines; inkjet printers and cartridges). This model might also be applicable to 3D printing. This could be a viable option especially for manufacturers producing also consumables. In addition, companies could contemplate about adopting successful business models from the service industries. Software-as-a-service, for instance, might function as inspiration for “printer-as-a-service” or other novel options.

5. Implications, limitations and future research

There is a growing literature stream on business models in the context of new technology (e.g. Kodama, 2004; Tongur and Engwall, 2014). However, the overall number of empirical studies within this research domain is still limited. Our empirical findings contribute to the discussion on business models in the context of novel technology, complementing existing research within this stream (e.g. Doganova and Eyquem-Renault, 2009; Simmons et al., 2013; Bohnsack et al., 2014; Tongur and Engwall, 2014). Prior research aimed to address the nexus between business model and technology and assumed that the two closely related. However, there is a lack of empirical investigations. The present study aims to contribute to a better understanding of this relationship by providing empirical results. In line with previous research (e.g. Christensen, 2006; Cavalcante, 2013), our empirical results show that there is indeed a link between business models and technology. Companies commercialize extrusion-based printers primarily using the low-cost online pattern. On the other hand, manufacturers of, for instance, sinter or stereolithography printers more frequently apply the technology export pattern. Thus, the technology seems to affect also the gestalt of the business models used for technology commercialization. We extract the gestalt of the business models within 3D printing and thus contribute to further clarification of the nexus between technology and business model.

3D printing is a prominent topic in many research domains. However, research on business and economic aspects of the technology is still limited (Weller et al., 2015; Rayna and Striukova, 2016; Öberg et al., 2018). We complement and extend previous studies on business models in 3D printing (Bogers et al., 2016; Holzmann et al., 2017, Öberg et al., 2018). Prior research has primarily addressed the potential business model innovations that 3D printing technology can trigger in different contexts (e.g. consumer goods manufacturing or user entrepreneurship). This research takes a step back and presents the business models that 3D printer manufacturers apply to market the technology. Thus, this research extends the perspective on business models within the 3D printing context. The successful commercialization of the technology is an essential precondition in the development of further 3D printing applications and technology-related services. Thus, the business model is of paramount importance. To the best of our knowledge, this study is the first to investigate the business models of 3D printer manufacturers empirically.

Further, our work contributes to the extensive literature on business models in general. Business models have evolved as a topical unit of analysis, however capturing and evaluating business models requires further clarification (Morris et al., 2013). Despite the growing number of business model research, empirical studies are still scarce. There is a lack of quantitative and mixed-methods studies on business models (Wirtz et al., 2016). Empirical research relies primarily on qualitative methodologies examining either single or few case studies. Thus, results only provide limited ability for generalization (Zott and Amit, 2007). The present study enlarges the number of empirical studies by applying a rigorous mixed-methods approach to investigate the business models. We provide an original approach to measure and analyze business models. The applied approach permitted the discovery of business model patterns using a componential approach. Thus, contributing to the call for more holistic views on business model components (e.g. Öberg et al., 2018). The applied design including the developed framework and the identified business model specifications could be a starting point for further business model research and further mixed-methods studies.

Our findings are important for managerial practice. The identified business model patterns can function as blueprints for new entrants and existing companies. However, sticking to these blue prints provides advantages as well as disadvantages. On the positive side, companies can potentially reduce their risk of failure by using these patterns as blueprints. Replication of already proven business model patterns can significantly shorten the development process and reduce the number of trial-and-error iterations. Thus, relying on proven patterns can be an efficient and effective strategy. On the downside, an exact imitation of business model patterns prevents the creation of novel and unique business models. Thus, when applying business model patterns companies face an inherent trade-off between risk reduction and innovativeness. To circumvent this trade-off, companies might use these patterns as starting points for business model innovation. For instance, companies could modify or extend existing patterns by focusing on previously uncovered customer needs or revenue sources. This might be a viable strategy to differentiate from competition. Another potential option could be the acquisition of companies that apply a different business model. Stratasys, the inventor of FDM, for instance, acquired the rapidly growing consumer-grade printer manufacturer Makerbot.

We are fully aware that our study has limitations. The results of this study are based on a European and North American sample. Thus, they might not be applicable to other regions. Future research could target additional regions and draw comparisons to our study. Further, studies on the business model design of 3D printing service providers would complement to our study and would foster the development of a holistic picture of the 3D printing industry. Another constraint is tied to our data. Our analysis is based on secondary data, thus, limiting the number of variables in the analysis. For instance, we could not assess financial aspects and cost structures. Further, due to the usage of secondary data we had to rely on dichotomous dummy variables. Thus, limiting the richness of the data.

Future research endeavors could further investigate the decision making on why and when companies decide to apply proven business model patterns instead of developing novel business models. In addition, future research should also examine whether the identified business model patterns remain stable over time. In the case of changes in the patterns, scholars should investigate the reasons for the change as well as the gestalt of the change.


Plotted means of value proposition variables

Figure 1

Plotted means of value proposition variables

Plotted means of value creation variables

Figure 2

Plotted means of value creation variables

Plotted means of value capture variables

Figure 3

Plotted means of value capture variables

Variables measuring value proposition

ConvenienceKim and Mauborgne (2000)The 3D printer is very convenient for its users (e.g. a simple, easy-to-use printer)
Customer productivityKim and Mauborgne (2000)The company claims that the 3D printer is able to increase customers’ productivity
Customer serviceMorris et al. (2006)The company focuses on customer service (e.g. a 24/7 service hotline)
Environmental friendlinessKim and Mauborgne (2000)The company has a special focus on environmental issues (e.g. usage of biocompatible filament, low emission printers)
ExpertiseMorris et al. (2006)The company claims that it has much expertise (e.g. industry experience, expertise in printer development, filed patents)
FlexibilityInductive formationThe 3D printer is flexible (e.g. it can handle different printing materials/filaments)
Innovation leadershipMorris et al. (2006)The company claims that its products are innovative and novel (e.g. its printers being the first that provide a specific feature)
Low costMorris et al. (2006)The company claims that it offers its 3D printers at low prices
QualityMorris et al. (2006)The company offers high-quality 3D printers (e.g. printers with high accuracy, award-winning product)
ReliabilityKim and Mauborgne (2000)The 3D printer is very reliable (e.g. printers need little or no maintenance)
SecurityKim and Mauborgne (2000)The 3D printer is secure (e.g. usage of its printer is safe for everyone)
SoftwareInductive formationThe company provides its own specific software for the best printing results
SpeedInductive formationThe 3D printer is fast (e.g. it produces objects faster than the industry standard)
TrainingKim and Mauborgne (2000)The company offers special trainings to operate its 3D printers
VarietyInductive formationThe company offers a variety of different products (e.g. a broad range of printers, upgrades for printers, or additional accessories such as scanners)

Variables measuring value creation

Subcomponent (source)VariableSourceDescription
Partner integration (Osterwalder et al., 2005)Academic partnersInductive formationThe company cooperates with academic institutions
Company partners(Osterwalder and Pigneur, 2010)The company cooperates with other companies (e.g. manufacturers of consumables)
Customer partners(Osterwalder and Pigneur, 2010)The company integrates customers into its value creation (e.g. through a forum)
Distribution channels (Osterwalder et al., 2005)Retailer(Osterwalder and Pigneur, 2010)The company uses retailers to market its printers
Store(Osterwalder and Pigneur, 2010)The company sells printers in its own physical store
Web shop(Osterwalder and Pigneur, 2010)The company sells printers via its own web shop
Communication channels (Osterwalder et al., 2005)AdvertisementsInductive formationThe company uses online advertisements
BlogInductive formationThe company uses a blog
E-mail(Osterwalder and Pigneur, 2010)The company provides an e-mail address.
Press releasesInductive formationThe company offers provides press releases
Social mediaInductive formationThe company uses social media channels (e.g. Facebook)
Web page(Osterwalder and Pigneur, 2010)The company has a web page

Variables measuring value capture

Subcomponent (source)VariableSourceDescription
Revenue sources (Osterwalder et al., 2005)Leasing(Osterwalder and Pigneur, 2010)The company offers to lease printers
Rental(Osterwalder and Pigneur, 2010)The company offers to rent printers
Reselling consumablesInductive formationThe company offers third-party consumables
Sale(Osterwalder and Pigneur, 2010)The company sells its printers
Payment methods (Osterwalder et al., 2005)Bank transferInductive formationThe company accepts bank transfer
BitcoinInductive formationThe company accepts bitcoin payments
CashInductive formationThe company accepts cash
COD/InvoicingInductive formationThe company accepts COD/invoicing
Credit cardInductive formationThe company accepts credit cards
PayPalInductive formationThe company accepts PayPal payments

Sample descriptives

No.CountryFoundation yearApplicationTechnologyBusiness model pattern
 1.USA2013RPEmbedded Electronics from Layered AssemblyTechnology expert
 2.NLD2014RP/HFExtrusionLow-cost online
 3.BEL2014HFExtrusionLow-cost online
 4.CAN2014HFExtrusionLow-cost online
 5.BEL2014RPExtrusionTechnology expert
 6.CAN2014HFExtrusionLow-cost online
 7.CAN2014HFExtrusionLow-cost online
 8.GBR2013HFExtrusionLow-cost online
 9.BEL2013HFExtrusionLow-cost online
10.CAN2013HFExtrusionLow-cost online
11.USA2013RPExtrusionTechnology expert
12.USA2013HFExtrusionLow-cost online
13.AUT2013RPExtrusionTechnology expert
14.SWE2013RPExtrusionLow-cost online
15.USA2012HFExtrusionLow-cost online
16.USA2012HFExtrusionLow-cost online
17.USA2012RP / HFExtrusionLow-cost online
18.FIN2012HFExtrusionLow-cost online
19.GER2012RPExtrusionLow-cost online
20.BEL2012HFExtrusionLow-cost online
21.GER2012RPExtrusionLow-cost online
22.USA2012HFExtrusionLow-cost online
23.CAN2012HFExtrusionLow-cost online
24.CAN2012HFExtrusionLow-cost online
25.BEL2012HFExtrusionLow-cost online
26.GBR2012HFExtrusionLow-cost online
27.USA2011HFExtrusionLow-cost online
28.BEL2011HFExtrusionLow-cost online
29.BEL2011HFExtrusionLow-cost online
30.GER2011HFExtrusionTechnology expert
31.CAN2011HFExtrusionLow-cost online
32.CAN2011HFExtrusionLow-cost online
33.USA2011RPStereolithographyTechnology expert
34.AUT2011RPLithography-based ceramic manufacturingTechnology expert
35.GER2010HFExtrusionLow-cost online
36.USA2009HF/RPExtrusionLow-cost online
37.USA2009HFExtrusionLow-cost online
38.CAN2009HFExtrusionLow-cost online
39.BEL2009HF/RPExtrusionLow-cost online
40.BEL2009RPExtrusionLow-cost online
41.DK2009RPSinteringLow-cost online
42.GER2006RPSinteringTechnology expert
43.USA2005RPSinteringTechnology expert
44.SWE1997RPElectronic beam meltingTechnology expert
45.USA1994RPSmooth curvature printingTechnology expert
46.USA1989RP/HFExtrusionTechnology expert
47.USA1986RP/HFStereolithographyLow-cost online
48.USA1982RPStereolithographyLow-cost online

Notes: Application – RP, Rapid prototyping; HF, Home fabrication

Cluster solution

VariableTotal (n=48)Total (mean) (n=48)Technology expert (mean) (n=12)Low-cost online (mean) (n=36)DiffFisher’s Exact test
Value proposition
Low cost190.400.530.5310.483**
Environmental friendliness150.310.420.280.140.808
Customer service100.
Innovation leadership80.170.420.080.347.200*
Customer productivity30.0600.080.089.063
Value creation
Partner integration
 Company partners340.710.580.750.171.210
 Customer partners90.
 Academic partners60.120.330.060.276.349*
Distribution channels
 Web shop370.770.250.940.6924.570**
Communication channels
 Social media420.870.750.920.172.286
 Press releases100.
Value capture
Revenue sources
 Reselling consumables80.
Payment methods
 Credit card270.560.080.720.6414.928**
 Bank transfer180.380.250.420.171.067

Notes: Level of significance: *p<0.05; **p<0.01


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Corresponding author

Patrick Holzmann is the corresponding author and can be contacted at:

About the authors

Patrick Holzmann is Vice Head at the Department of Innovation Management and Entrepreneurship at Alpen-Adria Universitaet Klagenfurt, Austria. He has received his PhD in entrepreneurship and innovation management. His research interest includes business models, technology, entrepreneurship and innovation management.

Robert J. Breitenecker is Professor of Global Business Studies and Head of the Institute for Innovation Management at the Johannes Kepler University Linz. He has studied mathematics, obtained his PhD in Statistics, and received his State Doctorate for Business Management at Alpen-Adria Universitaet Klagenfurt. His research interests include entrepreneurship, innovation management and spatial and multivariate statistical methods.

Erich J. Schwarz is Professor and the Dean of The School of Management and Economics at the Alpen-Adria Universitaet Klagenfurt, Austria and the Head of the Department of Innovation Management and Entrepreneurship. He received his PhD in the field of Economic Engineering at Technical University of Graz and his State Doctorate for Business Management at University of Graz. His research interests are in the field of entrepreneurship, innovation management and technology management.

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