What influences the speed of prototyping? An empirical investigation of twenty software startups

What influences the speed of prototyping? An empirical investigation of twenty software startups Anh Nguyen Duc1, Xiaofeng Wang2, Pekka Abrahamsson1

1Department of Computer and Information Science (IDI), NTNU NO-7491 Trondheim, Norway 2Free University of Bozen-Bolzano Piazza Domenicani 3, 39100 Bolzano, Italy
{anhn, pekkaa}@ntnu.no, xiaofeng.wang@unibz.it Abstract. It is essential for startups to quickly experiment business ideas by building tangible prototypes and collecting user feedback on them. As prototyping is an inevitable part of learning for early stage software startups, how fast startups can learn depends on how fast they can prototype. Despite of the importance, there is a lack of research about prototyping in software startups. In this study, we aimed at understanding what are factors influencing different types of prototyping activities. We conducted a multiple case study on twenty European software startups. The results are two folds; firstly we propose a prototype-centric learning model in early stage software startups. Secondly, we identify factors occur as barriers but also facilitators for prototyping in early stage software startups. The factors are grouped into (1) artifacts, (2) team competence, (3) collaboration, (4) customer and (5) process dimensions. To speed up a startup’s progress at the early stage, it is important to incorporate the learning objective into a well-defined collaborative approach of prototyping. Keywords: prototype, MVP, prototyping-learning loop, validated learning, speed, software startups 1 Introduction With the startup movement, software industry is witnessing a paradigm shift from serving customer requirements to creating customer value. The challenge for software companies is no longer primarily on implementing customer requirements, but rather on finding customer demands and providing a solution that delivers customer value [2]. Addressing uncertainty in both solution and problem domains has often been ad- hoc and based on guesswork, which becomes one of the main reasons for failing startup companies [3]. A demand on systematic approaches to manage the uncertainty has led to an increased research interest on Lean Startup [4], New Product Development (NPD) [5], software startups [6] and continuous experimentation [7].
This is the author’s version of the work. It is self-arhived at Arxiv. The definite version was published in: Nguyen-Duc A., Wang X., Abrahamsson P. (2017) What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups. In: Baumeister H., Lichter H., Riebisch M. (eds) Agile Processes in Software Engineering and Extreme Programming. XP 2017. Lecture Notes in Business Information Processing, vol 283. Springer, Cham, https://doi.org/10.1007/978-3-319-57633-6_2

In a competitive environment such as software industry, time-to-market is becoming more and more critical as a success factor for startup companies. Business ideas under development once revealed can be easily threatened by high speed copycats [9]. Moreover, competitors can also follow an on-going journey of validating product-market fit and arrive faster in the destination. Regardless of company sizes and application domains, the knowledge of influencing factors for a quick learning loop is important for software startups to form best-fit strategy in developing business experimentation [10].
A ‘Build-Measure-Learn’ loop, as a central concept of the Lean Startup methodology, aims at speeding up the new product development cycle [4]. The central part of the loop is to build a representation of the business, a so-called Minimum viable product (MVP), to collect feedback from customers and to learn from that. Steve Blank emphasizes the goal of MVPs is “to maximize learning through incremental and iterative engineering” [2]. In the startup context, developers quickly and iteratively develop a software application to validate business ideas [12]. As such, the study of validated learning can be beneficial from Software Engineering (SE) concepts and practices, such as rapid prototypes and evolutionary prototypes [13, 14, 15]. Consequently, the time-to-release of prototypes is essential to determine the total time in the validated learning loop. Software startup research is increasingly recognized by researcher’s community, with many practical aspects, such as User Experience, Software practices, compete

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