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Lean startup for materials ventures and other science-based ventures: under what conditions
is it useful?
View the table of contents for this issue, or go to the journal homepage for more 2015 Transl. Mater. Res. 2 035001
(http://iopscience.iop.org/2053-1613/2/3/035001)
Transl. Mater. Res. 2 (2015) 035001
1. Introduction
The lean startup methodology (LSM) is a methodology for new venture development that suggests that entrepreneurs translate their vision in explicit, falsifiable hypotheses regarding the customer problem, the solution offered by the venture, and the business model that is going to be used to deliver the solution (Eisenmann et al 2011, Tanev et al 2015). The hypotheses are then subjected to iterations of consciously designed qualitative and quantitative research. Test results feed back into an improved understanding of the customer problem, the proposed solution and the business model in an iterative fashion (Tanev et al 2015). Easy-to-modify prototypes, demos (Maurya 2012), and minimum viable products (Ries 2011) are used early on for getting customer feedback on the proposed solution.
The LSM is often applied in software-driven ventures that address a business-to-consumer market. Popular examples are Dropbox (Eisenmann et al 2011), Ries’s IMVU online metaverse (Ries 2011), and Maurya’s Cloud-Fire media-sharing venture (Maurya 2012). Recently, LSM is applied in corporations (Furr and Dyer 2014, Owens and Obie 2014) with examples from HP (Kuhn 2014), General Electric (Nelson 2014), and Telefonica (Jurado Apruzzese and Olano Mata 2014). Also, there are examples of applying LSM to other application contexts such as hardware (Jurado Apruzzese and Olano Mata 2014, Nelson 2014), government services (Park 2014), and even churches (Howard 2014).
In these examples, entrepreneurs who applied LSM were able to reduce time to market and development costs through its early focus on customer value. Entrepreneurs can identify and test critical assumptions of their venture before they engage the development of a final product, that may not meet market expectations (Blank 2013). Ries states: ‘When one is choosing among the many assumptions in a business plan, it makes sense to test the riskiest assumptions first. If you can’t find a way to mitigate these risks toward the ideal that is required for a sustainable business, there is no point in testing the others.’ (Ries 2011, p 119). If uncertainty and risk is revolved, we believe the entrepreneurs are more likely to engage in entrepreneurial action (Harms and Meierkord 2008).
Lean startup for materials ventures and other science-based
ventures: under what conditions is it useful?
Rainer Harms1, Yorgos Marinakis2 and Steven T Walsh2
1 University Twente, NIKOS/IGS, Postbus 217, 7500 AE Enschede, The Netherlands
2 University of New Mexico, Anderson School of Management, Albuquerque, NM 87131, USA
E-mail: r.harms@utwente.nl, ymarinak@unm.edu and walsh@unm.edu
Keywords: entrepreneurship, lean startup, market uncertainty, materials, MEMS, technology entrepreneurship, technology uncertainty
Abstract
Materials-based ventures face a high degree of technology uncertainty and market uncertainty when engaging in the technology entrepreneurial process. Recently, the lean startup methodology (LSM) has been introduced to practice and education as an integrated approach on how entrepreneurs can resolve these uncertainties when starting up a business. While the literature provides examples of LSM’s successful application in a range of application areas, its focus application tends to be on consumer software. The purpose of this article is to discuss the degree to which LSM can be applied to the context of technology entrepreneurship. We find that LSM has strengths in addressing market uncertainty, but is largely silent on addressing technology uncertainty. In situations of a low degree of technology readiness, strongly intertwined process and product innovation processes such as those common in materials translation, and of addressing business markets, LSM may not suffice. We discuss the case of competence leveraging as one where technology uncertainty is lower for materials companies and illustrate the benefits on LSM in this context.
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The software B2C context offers conditions under which the LSM can be applied effectively. As consumer software is based on established programming languages and architectures, its technology risks seems to be low. Its market risks can be considerable. However, software B2C markets offer a large number of prospective clients that can be drawn on for problem- and solution investigations. Also, most software-based products and services can be easily modified, which enables rapid product iterations. For most materials and science-based ventures, however, the technology risk is quite considerable. Their customers are often businesses, which limit possible experiments, and their products cannot be easily modified.
In addition to different degrees of market risk and technology risk, materials technologies do not follow the traditional sequence of ‘first product innovation, then process innovation’ (Abernathy and Utterback 1978). Materials-based product and process innovations occur simultaneously (Linton and Walsh 2003). This creates a different set of hurdles and implications for those developing entrepreneurial action by utilizing the LSM-concept of minimal viable products. LSM suggests getting some artefact out in front of the customer for them to touch, feel and interact with. This causes problems utilizing the Lean start up process with material based products which have an exceptionally tight link between process and product development. Furthermore, the consequences of this exceptionally tight link between process and product has implications for continuous improvement since any change in the process changes the product (Yanez et al 2010). For example, this is why many large chemical, materials and semiconductor firms, all examples of materials companies, have adopted copy-exact policies, most famously in Intel manufacturing settings, when setting up new manufacturing processes (Walsh et al 2015). The intellectual property centered on bounding problems and generating innovations in the materials sector has greater constraints.
2. Intellectual foundations of LSM
The LSM is a practitioner-driven methodology that has its roots in Blank’s Customer Development Model, which was developed further by Ries (2011) and others (Maurya 2012). It now has become popular among practitioners worldwide and is developed further in LSM communities. Its intellectual roots are in the planning school and the learning school in entrepreneurship. It builds on concepts such as ‘disciplined entrepreneurship’ (Sull 2004), ‘hypothesis-driven entrepreneurship’ (Eisenmann et al 2011), ‘probe and learn’ (Lynn et al 1996), and discovery-driven planning (McGrath and MacMillan 1995).
When starting their venture, conventional wisdom suggests that entrepreneurs either ‘plan or storm the castle’ (Brinckmann et al 2010). The planning school suggests that entrepreneurs can identify and solve uncertainties that their venture is faced with by careful business planning before they start their venture (Blank 2013). The learning school contests the value of business planning. Instead, entrepreneurial learning scholars (Wang and Chugh 2014) argue that that entrepreneurs continually ‘improve their knowledge [which allows them to] evaluate rival schemes of action in the light of logic and experience’ (Harper 1999, p 6 and p 9). Hence, uncertainties are identified and revolved as they emerge. In what is also known as the ‘affordable loss-principle’, this allows entrepreneurs to explore multiple strategic options before their funds run out (Maine et al 2015).
Descriptive entrepreneurial learning literature shows that entrepreneurs improve their knowledge and refine their initial, often vague idea via social interactions (Gemmell et al 2012). In these social interactions, entre-preneurs test and refine basic assumptions of their venture idea, such as assumptions about latent demand, assumptions about the viability of a possible solution, and assumptions about the business model (Harper 1999). However, these social and active experiments are not always consciously designed (Gemmell et al 2012). This may result in social biases such as social desirability bias, and cognitive biases such as optimism bias, confirmation bias, the planning fallacy, and the sunk cost fallacy (Eisenmann et al 2011). Hence, entrepreneur’s decisions may be based on biased information.
LSM as an approach to new venture creation is closer to the learning school, but emphasizes a methodological rigor that allows entrepreneurs to address the biases that may creep into ad-hoc experimentation (Eisenmann et al 2011), while at the same time keeping the flexibility that is the strength of the learning approach. Here the LSM proposes that entrepreneurs ought to test the most critical assumptions first (Ries 2011). In what follows we address the issue of technological uncertainty and market uncertainty as key areas of critical assumptions and how they relate to materials-based ventures (Maine and Garnsey 2006). We acknowledge that the areas of experimentation need to go beyond the assessment of the customer problem (market uncertainty) and the identification of a solution to this problem (technological uncertainty). Practitioners should scrutinize all areas of a business model such as those depicted in the Business Model Canvas (Osterwalder and Pigneur 2010) or the Lean Canvas (Maurya 2012). Yet the differences between software B2C and material applications is most striking in those areas.
3. LSM outside consumer software
3.1. Addressing technology uncertainty
Technology uncertainty emerges when development takes longer than expected, the technology may not work at all or does not scale well, or a firm’s technology is superseded by other, competing technologies (Kim and Vonortas
2014). Technology uncertainty is a function of technology readiness, R&D degree of difficulty, and technology need value (Mankins 2009). When researchers only know basic principles (low degree of technology readiness), fundamental scientific breakthroughs are needed to increase technology readiness (high degree of R&D difficulty), and if the technology is critically important to the product or service (high degree of technology need value), then technology risk is highest.
Technology risks in consumer software seem to be low, as can be inferred from its wide-spread market di f-fusion. For materials and nanotechnology, technology risks may be higher, as only a small percentage of R&D projects in those fields deal with scaling to mass production or serial production (Brousseau et al 2010). Many potentially economically viable technologies for micro and nanotechnology as well as for pharmaceutics are likely to become available in the distant future only (Sahal 1981, Walsh 2004, Walsh et al 2015). So before technology entrepreneurs can engage in addressing a customer problem, they need to be confident that they can develop the technology towards maturity and scale it to economically viable production before their funds run out (see the cases on scaling from Maine and Garnsey 2006). Arguably, learning about market needs is a futile exercise if it is unlikely that the technology can be developed towards maturity in the mid-term.
It seems that LSM is for the most part agnostic in regards to how technological uncertainty affect the commer-cialization process when many have shown that these are extremely important (Walsh and Linton 2011). These and other authors (Prahalad and Hamel 1990) show that the commercialization process is exceptionally different. Further there is another important element which differentiates the hurdles involved in technology commerciali-zation, which is the intensity of the degree of strategic importance of a technology (Walsh and Groen 2013). The process of commercializing disruptive and emerging technology is quite different from commercializing sustain-ing technology (Walsh et al 2002, Mangematin and Walsh 2012). Finally, 21st century challenges are causing the creation of multiple root technology based solutions and forcing the search for management of technology com-mercialization specifics rather than universals (Tierney et al 2013).
To conclude, Ries states ‘We have the capacity to build almost anything we can imagine. The big question of our time is not Can it be built? but Should it be built?’ (Ries 2011, p 263). In the same vein, Blank explains ‘why lean start-up changes everything’ (Blank 2013). The idea of technology uncertainty shows that we may not be able to build anything yet, and the LSM may not change every startup process. It is under conditions of market uncertainty that the LSM plays out its strengths.
3.2. Addressing market uncertainty
Market uncertainty exists when it is not clear whether there is a market for a certain product, or whether such a market can be created. It refers to uncertainty about the types of customer needs, and the degree to which a particular technology or product can satisfy those needs (Kim and Vonortas 2014). According to the LSM, market uncertainty can be resolved by getting customer information very early in the startup process. At the very beginning of a new venture process, the LSM asks entrepreneurs to investigate the existence of a user problem, the users’ ‘job-to-be-done’ (Furr and Dyer 2014), or ‘pain point’ (Maurya 2012). Entrepreneurs collect this information by using qualitative market research (Belk et al 2012) and by ‘getting out of the building’ (Blank
2013). According to the LSM, an entrepreneur does not need to have sophisticated research skills to perform this type of market research. Also, entrepreneurs only need to observe or interview around 15 potential users to identify a large share of user problems (Griffin and Hauser 1993).
The information thus gathered helps to reduce market uncertainty, for example misunderstandings between technology provider and technology user on the utility of the technology (Gourville 2006), and other potential barriers to technology adoption such as the not-invented-here syndrome (de Araújo Burcharth et al 2014), or the adopters’ fear of failure (Harms and Linton 2015).
While early market research helps to resolve market uncertainty, there are challenges when applied to B2B markets. First, entrepreneurs need to have a very close access to the user to follow him in his daily activities (Belk et al 2012, Furr and Dyer 2014). Some corporate customers will not allow this for security, legal, and secrecy reasons. Here, entrepreneurs may use workarounds such as contacting customers at industry fairs, communities of inter-ests, or using private referrals. It remains questionable whether the data thus gathered are rich enough to resolve market uncertainty. Second, users and buyers are distinct in corporations. Entrepreneurs need not only identify user needs but also the decision parameters of buying centers (Schneider and Wallenburg 2013). Third, travel distance becomes an issue, as corporate customers may be dispersed globally. On the upside, many technology
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markets may have a smaller number of customers, so that ethnographic market research can be done with less customers (Goffin et al 2012), or even directly in the context of product co-development with one key customer. Summing up these problems, LSM can be applied in B2B markets as well, but it is more difficult to do so.
Following the resolution of uncertainty about customer needs is the investigation into whether those customer needs can be satisfied by the proposed solution. The LSM proposes to use tentative solutions that are market-tested to create a baseline of customer feedback. These tentative solutions can be easy-to-modify prototypes, demos (Maurya 2012), and minimum viable products (Ries 2011). Information to resolve this market uncertainty can be collected via qualitative and quantitative market research. Customer feedback suggests changes to the product, and a new version of the product is tested again. For materials-based companies, the use of prototypes may be a key challenge.
First, prototyping creates challenges for entrepreneurs that operate in B2B markets. First, a key asset of an entrepreneur is his credibility, defined here as the belief that he can realize the product that he promises (Rieh et al 2007). By offering rough prototypes, entrepreneurs may damage their credibility. The reputation damage may have more serious consequences when the total number of potential buyers is low. Also, it is most important to be known as a reliable technology leader when targeting early majority customers as they are more conservative than technology enthusiasts (Easingwood and Harrington 2002). Second, reputation loss may also extend to other business units an entrepreneur may have. Many science based ventures have multiple business units, for example when they operate a consultancy next to the new business (Shane 2004). This spillover effect may be lessened when the new business operates under a different name that clearly signifies its experimental nature, such as ‘Telefonica labs’ or ‘Google labs’ (Furr and Dyer 2014). Third, the number of potential buyers may be too small to carry out multiple rounds of statistically significant product tests, even though one needs a lower number of respondents for statistically significant answers when the population is less numerous.
Second, the ease of development refers to the ease with which a product can be modified. In software, features can be added, modified or deleted at a rapid pace. Software engineering practices such as agile development (Dyba and Dingsøyr 2008) allow programmers to change a software product in weeks, days, or sometimes in even less time (Gothelf and Seiden 2012). In hardware and materials, change cycles are slower (Tanev et al 2015) and ridden with technological readiness issues, despite advances in additive manufacturing (Gartner et al 2015) and com-puting power that is used in the R&D process (Thomke 1998). Materials designers understand that designing a new materials-based product requires a simultaneous design of a process and a product (Linton and Walsh 2008). Further, there is a major focus on finding common processes that can generate products with increased complex-ity through the addition of process steps or batch development (Tierney et al 2012). A good example of this is the CMOS semiconductor manufacturing process which initiated with less than 40 steps and 50 years later can reach nearly 1000 steps when making today’s most advances semiconductors (Walsh et al 2005).
4. LSM in the context of technology and market uncertainty
The purpose of this article is to discuss when the LSM can be applied to materials and science-based ventures. A first key characteristic of most of these ventures is that they operate under a high degree of technological uncertainty that needs to be resolved to a degree to which the actual products can be developed in a specified timeframe. A second key characteristic is that they often serve business markets. Together with the tight link of materials’ product and process innovation, LSM may be less suitable for resolving market uncertainty. The following figure (see figure 1) addresses different approaches to entrepreneurial action under different conditions
of market and technology uncertainty. We acknowledge that entrepreneurs may use the tools mentioned in the matrix in combination, and in different degrees of maturity. Hence, this tables serves well as a simplified overview.
When market uncertainty is high, and technology uncertainty is low, LSM can be used to improve the knowl-edge about customer problems and solutions. The low technical uncertainty allows entrepreneurs to develop rudimentary prototypes and deliver the final product. For example, Ries (2011) describes how he should have used the LSM to discover the market uncertainties around the way users would use the chat environment that he and his team were developing. Early and systematic customer inquiry would have revealed that users would rather not invite people from their friends lists to the chat, but rather make new contacts. This knowledge would have saved the team the effort to program interfaces.
A low degree of both market and technology uncertainty characterizes much of the small business context, where low-tech, imitative ventures such as retailers, or traditional services are found. Here, information from the market and the technology is known or easily obtainable. Hence, formal business planning may increase perfor-mance by introducing a certain level of long-term orientation and foresight, formalization, and control (Kraus et al 2006), while the ‘iteration required by lean startup or probe-and-learn approaches will add costs and time to the process’ (Koen 2015, p 10).
Should the market uncertainty be low and the technology uncertainty be relatively high, structured approaches to innovation management such as stage gate processes could help to reduce technology uncertainty and speed up
time-to-market. Traditional stage gate processes assume a relatively low degree of market uncertainty, as they require that some initial business case is made already early on. It seems that misunderstandings about the market are the Achilles’ heel of NPD processes, as Cooper (1988) identified a lack of market analysis as the most common problem that leads to new product failure. Recent examples from industry show how science based ventures apply the stage-gate type processes. For example, Cohen et al (1998) describe gate systems at ExxonMobile Chemicals, Shaw et al (2001) describe a ‘business gate framework’ for the chemical industry, and von Ehr describes how Zywex uses a state-gate type of process for new product development (von Ehr 2008).
In situations where both types of uncertainty are high, large R&D-heavy companies are best suited to develop the technology while keeping an eye on emerging market needs. For example, Cooper (2006, p 24) describes how researchers in EXXON’s Metallocene project initially had ‘ … some gummy stuff with interesting properties’, but with unclear market and technical properties. At this stage, it is too early to specify user needs or the ability of that technology (which has not been fully developed yet), so business planning, LSM, or traditional stage-gate processes can be ruled out. Cooper proposes a stage-gate variant for technology development projects with initial, technology-driven gates and increasing commitment and specification as the technological properties become more clear. Such a technology stage gate is often more flexible than the traditional stage gate in that the criteria for the next steps are developed in a step-by-step approach, with an emphasis on the technological aspects of a project (Ajamian and Koen 2002). For university-based ventures, a lab-to-market roadmap suggests a methodology for successful technology translation (Windheim and Myers 2014). Patient money and the acceptability of long-term goals is the key here (Maine and Garnsey 2006). For all situations of high technological uncertainty, real options thinking with a particular emphasis on high risk, high-reward science-based ventures, can also inform entrepre-neurs to make improved investment decisions (Casault et al 2014).
A particular situation of low technology uncertainty and high market uncertainty exists when an entrepreneur leverages his technological competencies to new products and markets (Danneels 2007). These technological competencies can be fungible technologies such as materials that can be applied to many different uses and mar-kets. For example, carbon fibers are used in rackets, bikes, helmets, in the aviation industry, and in the medical industry (Liu and Liu 2011). However, the scope of opportunities that an entrepreneur becomes aware of or can create may be limited.
First, opportunity recognition is path dependent and based on successful historical experiences. For example, Shane (2000) illustrates that the 3D printing technology was applied to architecture by an entrepreneur versed in that field, and in pharmaceuticals by a domain specialist from this area. This illustrates that entrepreneurs tend not to operate outside of their core knowledge domain (Gemmell et al 2012). Second, even when entrepreneurs engage users to identify novel applications of a technology, the user’s creativity may be influenced by how the entrepreneur presents his technology. For example, Keinz and Prügl (2010) illustrate a case where a company engaged external user communities to develop novel applications for sensors embedded in a glove. The novel application that the users came up with did not expand the uses of the sensor beyond the glove. Artefacts such as existing products may be a double-edged sword: on the one had they can be valuable starting points for creativity, on the other hand they can limit the solution space (Keinz and Prügl 2010). Third, market experimentation can be
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Entrepreneurs who apply LSM can expand the potential solution space that may be available to them. First, LSM allows for cheap and fast experiments to (de-)validate assumptions about customer needs. Fast and cheap tests allow entrepreneurs to address multiple different uses of their technology. Second, the LSM calls for early and direct customer interaction. This can reduce the impact of path-dependent opportunity recognition, as entrepre-neurs go beyond their previous learned knowledge by engaging with customers to identify the customers’ ‘job-to-be-done’ (Furr and Dyer 2014) or ‘pain points’ (Maurya 2012). Third, the LSM asks entrepreneurs to consciously select potential target groups that may go well beyond the social circle he or she is familiar with.
When an entrepreneur leverages technology competencies, some technological uncertainty remains. First, there is the case of application technologies, those ‘whose application is further developed than the underlying scientific fundamentals’ (Fischbach 2013, p 338, also for the example of carbon composites). An entrepreneur in the materials company of the Danneels (2007, p 522) study explains: ‘We know some of that [the physical
proper-ties of the material and process in question; the author] on a very elementary level, but not on the level that we need to become a coatings business and play in the semiconductor industry or coat a reaction vessel for a processing plant’. Second, there is the case of additional product characteristics of complementary technology in different markets. For example, Liu and Liu (2011) report that to enter the aviation market, their case company had to add fire-proofing to the initial material, and to enter the bike market, the case company had to develop a complementary technology to join the carbon parts. Hence, technological uncertainty may emerge when a technology is leveraged to new markets (Maine and Garnsey 2006).
5. Conclusion
The discussion showed that LSM can have potential when applied outside of the B2B software market. However, we agree with Hackett (2012, p 1) who argues that ‘Lean Startup methodology requires significant customization to work in an operational setting. This becomes truer when applied outside of software and product development’. We are beginning to see these customizations. First, LSM- projects in the life science industries suggest that regulatory issues, buying center issues with hospitals as buyers, and insurer issues complicate the market by being sources of ‘risky assumptions’ (Ries 2011, p 119). Second, advanced materials ventures are often upstream in the value chain. On the upside, many market partners can be engaged in the further development and commercialization of an invention (Maine 2013). On the downside, there are numerous market partners that try to capture the value crated by materials-based innovation (Maine and Garnsey 2006). If this is so, then LSM practitioners in materials ventures should emphasize not only the ‘need’ and the ‘solution’, but also the ‘network’ component as a source of ‘risky assumptions’ that can make or break their venture. Lubik and Garnsey (2015) illustrate the utility of experimentation for the full business model for materials-based ventures, and argue that young materials based ventures can leverage their upstream university ecosystem for entrepreneurial learning.
Future research needs to go beyond anecdotal evidence and provide deeper insights into the conditions under which hypothesis-driven entrepreneurship can be applied most effectively. For example, Harms (2015) showed that team learning and psychological safety mutually reinforce good performance of LSM-based projects. New research could analyze whether the emphasis that LSM puts on speedy learning results in faulty decisions (Perlow et al 2002). Also, an emphasis on speed could make entrepreneurs neglect sufficient preparation and incubation that is needed for creative solutions. Another question could be the extent to which learning sequences between systematic and flexible experimentation (Bingham and Davis 2012) can yield superior performance. Finally, materials-based startups often require partners to commercialize their invention (Teece 1988, Lubik and Garnsey 2015). These partners often shape the direction in which a ven-ture evolves (Sarasvathy 2001). Market selection may be driven more through available capital than through careful experimentation (Lubik and Garnsey 2015). Hence, the influence of early stakeholders on entrepre-neurial experimentation warrants attention. We encourage practitioners and researchers to contribute to these relevant discussions.
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Biographies
Rainer Harms gained a Diploma in Economics and a PhD in Entrepreneurship at WWU
Münster. He was appointed as Assistant Professor for Innovation and Entrepreneurship at AAU Klagenfurt. In 2009 he joined NIKOS at University Twente where he is now Associate Professor for Entrepreneurship. He has held visiting positions at Università Autonoma Barcelona, WU Vienna, and JKU Linz. He is associate Editor of Creativity and Innovation Management. Currently he works in the fields of technology entrepreneurship and entrepreneurial learning.
Yorgos Marinakis is a registered US patent attorney and is also admitted to the New
Mexico and Massachusetts State Bars. He has a BA in mathematics from San Jose State University; a PhD in the biological sciences, a JD and an MBA from the University of New Mexico; and is a management PhD candidate at the University of Twente. He has worked for Lockheed Missiles and Space Company, Lockheed Martin Technical Operations, and Fidelity Management and Research Company. He currently has a patent law practice and is a partner in a design firm.
Steven T Walsh is a world renowned consultant, serial entrepreneur. As a serial
entrepreneur he helped to attract over $40 million dollars to his firms. He has helped to initiate emerging technology venture funds and has both management and engineering degrees from RPI.
Dr Walsh is both a ‘Distinguished Professor’ at the University of New Mexico and the Institute professor for Entrepreneurial renewal of Industry at the University of Twente. He is recognized as a top worldwide researcher in TIM and technology entrepreneurship. He has served or is serving on the editorial board of JSBM, JM3, Technovation, TFSC, EMJ and CMM.
