The study conducted by Harms and Schwery (2020) had a two-fold aim. First, researchers aimed at developing an operationalization of Lean Startup Capability (LSC). Their second purpose was to investigate whether the use of LSC by startups was positively associated with their performance. During their research, Harms and Schwery (2020) developed a five-factor operationalization of LSC, which included “customer orientation, hypothesis testing, iterative experimentation, validation, and learning” (p. 208). In order to answer the second research question, the researchers utilized the quantitative approach and received 100 valid responses from software startups located in Berlin. The findings indicated that LSC was positively related to the performance of startups. The practical implication of this research is that it gives educators and entrepreneurs, especially those dealing with software ventures, some confidence to use and advocate for Lean Startup (LS).
Addressed Gaps in the Literature
The authors revealed that most research on LS is qualitative, which hinders the transition of this research field from the nascent to the intermediate stage. Furthermore, sparse quantitative research on LS is focused only on experimentation, while LS is not limited to experimentation. Therefore, Harms and Schwery (2020) addressed these gaps in the literature by conducting quantitative research, developing a multifaceted operationalization of LSC, and investigating the impact of LSC on performance.
Ideas and Arguments Found Stimulating
The first stimulating idea is that entrepreneurs gain new entrepreneurial knowledge from experimentation. According to Harms and Schwery (2020), experimentation is an integral part of entrepreneurs’ learning process, which is preceded by forming assumptions and hypotheses and followed by the emergence of new experiences. This idea is notable because it sheds light on the process of generating entrepreneurial knowledge and emphasizes the importance of entrepreneurs’ creativity and risk-taking required for experimentation. Another interesting argument is that a faulty application of LSC can take a toll on startups’ performance.
Examples of such faulty applications include creating a biased sample of customers for surveys, applying inappropriate empirical methods, and not learning from the results (Harms & Schwery, 2020). This idea supports the need for future research in the field of LSC to help practitioners adopt valuable experimentation practices and avoid making mistakes that would negatively influence their startup performance.
Questions and Concerns with the Main Claims
The authors’ main claim is that LS is an iterative process consisting of more than just experimentation. In the article, Harms and Schwery (2020) answer two questions related to this claim. The first question is how LSC can be measured and what elements should be used to operationalize this concept. The second question is whether the use of LSC by ventures increases the performance of startups and what conditions are necessary for this positive influence. One more concern with this claim that one can think of is the impact of the context on the relationship between the use of LSC and performance. For example, the costs of experimentation in a particular industry or location can outweigh the benefits of the LSC application, thus having a negative effect on startup performance.
Similarities and Disagreements Reported in The Literature
Harms and Schwery (2020) identified similarities in various researchers’ understanding of LS. During their literature review, they discovered that most scholars saw LS as a means of experimentation for entrepreneurs and emphasized the importance of experiments for the development of new ventures. While Harms and Schwery (2020) agree with the significance of experimentation for startup development, they argue that LS is more than experimentation. It also contains such elements as learning, iteration, and customer insights, which are included in the LSC operationalization proposed by the authors.
The researchers also reported the differences between their findings regarding the relationship between LSC and performance and those of other scholars. First of all, Harms and Schwery (2020) used a quantitative approach to their study, which allowed them to show that LSC consists of several capabilities, as opposed to prior qualitative research that focused only on experimentation. Further, while Harms and Schwery (2020) found a positive relationship between LSC and startup performance, some other scholars found no such relationship. This difference can be attributed to dissimilar contexts of studies. In some contexts, the cost of experimentation can be higher than the obtained benefits, which causes LSC to have no positive influence on performance.
Future Research Directions
Harms and Schwery (2020) suggest that future research should refine their operationalization of LSC because their operationalization is the first of its kind, so it can have some flaws. Furthermore, future research can investigate the impact of the use of LSC on performance in ventures operating in industries other than software, as well as located in different regions, in order to ensure the generalizability of the results. Scholars may also explore what specific factors have the most influence on performance. Additionally, researchers can undertake longitudinal studies to establish the causal relationships between the application of LSC and performance. Future research may also investigate how LS can be combined with other methods of new venture development.
A Recent Work Missing
A recent study conducted by Bocken and Snihur (2020) aimed at addressing the academic critique of LS and providing a more positive perspective on this concept. Like Harms and Schwery (2020), Bocken and Snihur (2020) argue that LS is an iterative process directed toward decreasing uncertainty and promoting learning. According to the researchers, LS does not include the process of forming a vision and creating innovative business ideas (Bocken & Snihur, 2020). Instead, LS is responsible for generating hypotheses based on the previously created ideas, testing them during the experimentation, and learning from the outcomes. Thus, the operationalization of LS proposed by Harms and Schwery (2020) appears to be broader than that of Bocken and Snihur (2020) because it includes customer insight in LS.
References
Bocken, N., & Snihur, Y. (2020). Lean startup and the business model: Experimenting for novelty and impact. Long Range Planning, 53(4), 101953. Web.
Harms, R., & Schwery, M. (2020). Lean startup: Operationalizing lean startup capability and testing its performance implications. Journal of Small Business Management, 58(1), 200-223. Web.