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Software startups
(2016)
Software startup companies develop innovative, software-intensive products within limited time frames and with few resources, searching for sustainable and scalable business models. Software startups are quite distinct from traditional mature software companies, but also from micro-, small-, and medium-sized enterprises, introducing new challenges relevant for software engineering research. This paper’s research agenda focuses on software engineering in startups, identifying, in particular, 70+ research questions in the areas of supporting startup engineering activities, startup evolution models and patterns, ecosystems and innovation hubs, human aspects in software startups, applying startup concepts in non-startup environments, and methodologies and theories for startup research. We connect and motivate this research agenda with past studies in software startup research, while pointing out possible future directions. While all authors of this research agenda have their main background in Software Engineering or Computer Science, their interest in software startups broadens the perspective to the challenges, but also to the opportunities that emerge from multi-disciplinary research. Our audience is therefore primarily software engineering researchers, even though we aim at stimulating collaborations and research that crosses disciplinary boundaries. We believe that with this research agenda we cover a wide spectrum of the software startup industry current needs.
The business plan is one of the most frequently available artifacts to innovation intermediaries of technology-based ventures' presentations in their early stages [1]–[4]. Agreement on the evaluations of venturing projects based on the business plans highly depends on the individual perspective of the readers [5], [6]. One reason is that little empirical proof exists for descriptions in business plans that suggest survival of early-stage technology ventures [7]–[9]. We identified descriptions of transaction relations [10]–[13] as an anchor of the snapshot model business plan to business reality [13]. In the early-stage, surviving ventures are building transaction relations to human resources, financial resources, and suppliers on the input side, and customers on the output side of the business towards a stronger ego-centric value network [10]–[13]. We conceptualized a multidimensional measurement instrument that evaluates the maturity of this ego-centric value networks based on the transaction relations of different strength levels that are described in business plans of early-stage technology ventures [13]. In this paper, the research design and the instrument are purified to achieve high agreement in the evaluation of business plans [14]–[16]. As a result, we present an overall research design that can reach acceptable quality for quantitative research. The paper so contributes to the literature on business analysis in the early-stage of technology-based ventures and the research technique of content analysis.
This paper broadens the resource-based approach to explaining survival of new technology-based firms (NTBFs) by focusing on the entrepreneur's ability to transform resources in response to triggers resulting from market interactions. Network theory is used to define a construct that allows determining the status of venture emergence (VE).The operationalization of the VE construct is built on the firm's value network maturity in the four market dimensions customer, investor, partner, and human resource. Business plans of NTBFs represent the artifact that contains this data in the form of transaction relation descriptions. Using content analysis, a multi-step combined human and computer coding process has been developed to empirically determine NTBFs' status of VE.Results of the business plan analysis suggests that the level of transaction relations allows to draw conclusions on the status of VE. Moreover, applying the developed process, a business plan coding test shows that the transaction relation based VE status significantly relates to NTBFs' survival capabilities.
This paper builds upon the widely-used resource-based approach to explaining survival of new technology-based firms (NTBFs). However, instead of looking at the NTBF's initial resource configuration, a process-oriented perspective is taken by focusing on the entrepreneur's ability to transform resources in response to triggers resulting from market interactions. Transaction relations reflect these interactions and are thus operationalized with a suggested method for measuring the status of venture emergence (VE) applicable to early-stage NTBFs. NTBFs' value network maturity is reflected in the number and strength of their transaction relations in the four market dimensions customer, investor, partner, and human resource. Business plans of NTBFs represent the artifact that contains this data in the form of transaction relation descriptions. Using content analysis, a multi-step combined human and computer coding process has been developed to annotate and classify transaction relations from business plans in order to empirically determine NTBFs' status of VE. Results of the business plan analysis suggest that the level of transaction relations allows to draw conclusions on the VE status. Moreover, applying the developed process, first analysis of a business plan coding test shows that the transaction relation based VE status significantly relates to NTBF survival capability.
We examine to what extent a transaction relation-based value network maturity status of New Technology-Based Firms (NTBFs) is related to their survival. A specific challenge of NTBFs is their lack of market-orientation, which is why the maturity of the ties they form towards the market in terms of customers, financiers, personnel and partners is supposed to be a strong indicator for survival. We analyze a sample of 170 NTBFs by capturing their value network status from business plans and defining their survival status using secondary research. Simple statistical tests and regressions suggest that the official registration of the business is a pre-step for survival that requires industry-specific value network dimension strengths. A sub-sample survival analysis shows that for all NTBFs that have reached registration, regardless of their industry, a stronger customer value network maturity dimension prevents from failure and is thus a significant predictor for survival. Moreover, the analyses partly support the idea that NTBFs from the IT sector are less dependent on a strong value network in the financier dimension to survive. The results are of relevance for both practitioners and researchers in the innovation system: a better understanding of the factors impacting on NTBF survival can help to provide more tailored support services for young firms, increase the effectiveness of resource allocations, and provide a basis for further research.
Validity of the business model is a key indicator for buying into ventures in the early-stage. Business models of early-stage ventures decrease in validity when developing the business over the progressing stages of the business life-cycle. By doing so, the ventures are validating their business model when building transaction relationships to the surrounding value network. In prior research, we developed a research design based on existing business innovation proposals (onepager, pitch decks, business plans) that is assumed to evaluate the status of business model validation. The core hypothesis of the research design is that transaction relations represent a strong anchor between the business model and the business reality, thus providing information on the business model validity. In this research, we test this hypothesis by designing and analyzing a survey that was directed to founders taking part in a business plan competition. We compared the relationships described in the submitted business plans to the relations explicitely stated in the follow-up questionnaire. We identified that the described relations to customers, investors, and people (human resources) match the relationships expressed in questionnaires quite well. A significant disagreement, however, exists in the relationships to suppliers. We conclude that there is still a theoretical and empirical gap that leads to disagreement between business plans and reality in the group of suppliers.
New Technology-Based Firms (NTBFs) learn their business in the early-stages of their life-cycle. As a central element of the entrepreneurial learning process, the business model describes the value-creation functions that are conceptualized in different stages of the NTBF’s life-cycle. Transaction relations connect the model with the business reality and ideally mature in strength over time to a functioning value-network. This chapter describes the development of a research design that determines, extracts, and evaluates semantics constructs of this entrepreneurial learning out of a convenient sample and three cohorts of business plans submitted to a business plan award between 2008 and 2010. The analysis shows empirical evidence for the survival and growth of those NTBFs that exhibit a balanced status of entrepreneurial learning in the maturity of the value-network that can be characterized as early startup-stage. The empirical findings of the network theory based business plan analysis will allow for a better explanation of the performance in the entrepreneurial process that is discussed for NTBFs based on theory of organizational learning.
Business coaching is believed to effectively improve survival and success chances of new technology-based firms (NTBFs). However, not much empirical evidence on the support measure's effectiveness is available. Therefore, a pragmatic two-armed Randomized Controlled Trial (RCT) to test the effect of tactical business coaching on NTBF survival capabilities was designed and, for the most part, carried out. However, due to a lower than expected sample size and great attrition between groups, the RCT reveals deviations from the trial design that impede a thorough data assessment. Based on the data given, a first data analysis does not reveal significant differences in survival capability between the two groups. Thus, to provide guidance for future RCTs in business contexts, lessons learned about how to deal with trickle samples and experiment constellations with third parties carrying out the intervention are drawn.