Complexity in Entrepreneurship, Innovation and Technology Research

Although entrepreneurships is recognized as a complex field, existing research does not pay enough attention to capturing the essence of its complexity. I argue that mixed methods designs offer a solid foundation for bridging this gap. To build my argument, I review the key assumptions and dimensions that make entrepreneurship a complex scientific field, discuss the structure of complexity and compare and contrast different research paradigms in terms of their ability to capture complexity. I will then show that mixed methods designs based on the pragmatic paradigm are philosophically better suited than mono-method designs to capture complex phenomena in entrepreneurship. The paper concludes with an integrative framework to guide research and practice along this direction and discusses the implications of this view for studying complexity in entrepreneurship.

Networks define a key entity in entrepreneurship and have spurred an enormous amount of research. Nonetheless, research lacks studies on entrepreneurial contexts and opportunities. This is due to the common separation of research on networks between the macro-level of structure, conducted by quantitative methods, and the micro-level of agency, conducted by qualitative methods. Mixed methods provide ways to bridge this separation of structure and agency and grasp the complexity of entrepreneurial action from a multidimensional perspective. Hence, mixed methods are crucial for conducting studies to answer urgent questions of the research field and inform theory building. This chapter guides researchers in applying mixed methods of network research in entrepreneurship. It gives an overview of different research designs with several examples and recommendations. The chapter focuses on the integration of qualitative approaches into mixed methods because first of all, they have been neglected and training is required, and secondly, qualitative approaches show promise to address current gaps in entrepreneurship research.

The central contribution of this chapter consists in exploring the implications of a memetic perspective for dealing with complexity in (social) entrepreneurship. The line of argument can basically be divided into four aspects. First, it is argued that memes, especially their mental representations, can be conceptualized in the context of (cultural) schemata that have an impact on the perception and discovery of entrepreneurial opportunities. Second, a memetic view of creativity also suggests that opportunity creation strongly depends on memes. Third, viewing social entrepreneurship as a meme(plex) allows us to compare it with related concepts. Moreover, we argue that by focusing on the properties of social entrepreneurial opportunities we can get to the core of the social entrepreneurial process. In this chapter, we are focusing on social entrepreneurial opportunities that can be understood as the intersection of the set of ‘opportunities to solve a societal problem’ and the set of ‘profitable business opportunities’. This conception represents the vantage point for the fourth part of this contribution, where we argue that, in order to facilitate the propagation of the social entrepreneurship meme within (for-profit) organizations, a systematic analysis of the firm’s social network as well as its memeplex is advisable.

Both entrepreneurial and educational processes are often characterized by a high degree of dynamics and uncertainty. The assumption that similar initial conditions cause similar results does not apply to these properties. Based on the current state of the art of entrepreneurship, the underlying assumption of this elaboration is that entrepreneurship can be learned. One issue concerning this field of research is that, to date, it has not succeeded in obtaining a deep level of insight into the actual process of entrepreneurship education without reducing its complexity. The concern of this theoretical elaboration is to draw closer to this ‘black box’ by applying the Theory of Synergetics, a complexity theory of self-organization. First, the central concepts of entrepreneurship and their meaning for the development of an entrepreneurial mindset are given. Then, we present an overview of educational topics and introduce the conception of complexity and its consequences for entrepreneurship education. Finally, entrepreneurship education is displayed as a self-organized process by using the Theory of Synergetics. The results offer new insights, explanatory concepts and opportunities for further research.

This chapter reviews a line of research that studies several different theoretical questions in entrepreneurship through novel applications of computer simulation. All of the simulation studies reviewed are based on a shared game-theoretical modeling framework that allows a high level of integration with existing theories. What made these simulations unique was their firm grounding in the theory of the entrepreneurial market process from Austrian economics, and the lack of previous simulation studies in the entrepreneurship field. The focus is on how and why the cooperative game theory framework was chosen, the justification and process of applying the simulation method and the lessons learned from doing so. The aim is to provide entrepreneurship scholars with a better understanding of where and why computer simulation may add something of value to their research as a tool for the analysis of complex systems. The reviewed studies involve artificial economies with a small number of agents, demonstrating that the emergence of complex macro patterns from micro behaviors does not require large numbers of agents.

This chapter demonstrates the application of a qualitative formal method, logical formalization, to organization and management theory. Organizational arguments are usually phrased out in some natural language in the first place. After separating the premises (facts, definitions) of a natural language argument from its conclusions (predictions), this preprocessed text is translated into a logical language. Then, experimentation can begin if the logical formulae standing for the verbal premises imply the putative conclusions as formal theorems. If not, what kind of modifications can make these outcomes follow? What other theorems are implied from the same argument core? A substantial advantage of using symbolic logic over many branches of applied mathematics is that logical models can quite closely map the intended meaning of assertive sentences, while the deduction of conclusions can proceed with the rigor of mathematical proofs. The examples highlight how different logical languages, different dialects, can be used to the idiosyncrasies of the subject. The proof and the translation process from natural language statements to logical models are supported by user-friendly theorem-prover softwares. The appliers of the method need not be logic experts; what they need are analytical skills, sharp eyes at formula evaluation, and some stamina. The promise of using symbolic logic is combining the flexibility of qualitative reasoning with exactness in drawing conclusions from complex arguments. The chapter is to show how and in which extent logical formalization can fulfill this promise.

Whereas qualitative research methods have become increasingly popular in the broader social sciences, they remain less prominent in entrepreneurship, innovation, and technology research. In the face of increased complexity of empirical phenomena in these fields of research, we make the case for qualitative research methods as one valuable methodological option for researchers to gain a better understanding of complex phenomena. By unbundling the challenges that the empirical examination of complex phenomena imposes on researchers, we outline that qualitative research methods are particularly well-suited to cope with these challenges. To shed more light on how researchers may use qualitative data to draw theoretical inferences, we propose an analytical approach for the examination of complex phenomena that systematically discloses the inner logic of these phenomena in a step-wise process. In a case study of strategic innovation trajectories in haute cuisine restaurants, we illustrate how researchers may apply this analytical procedure ‘in practice.’ Our illustrative analysis indicates that, despite the severe challenges that researchers face when exploring complex phenomena, the proposed analytical procedure provides a practical and helpful guide for researchers to gain a better theoretical understanding of complex phenomena based on qualitative data.

Research on effectuation has the opportunity to advance from an intermediate development stage along two main directions. First, effectuation exists as an alternative to classical models for testing hypotheses; and second, effectuation has introduced a grounded approach that envisions new theoretical elements and further theories. In this chapter, I discuss the potential effectuation has for both theory testing and theory building. I present the methods from which effectuation theory emerged, namely the think-aloud method, and their protocols and content analysis. I offer hints for the implementation of such methods and provide an illustration from a related research project. I also discuss sample size and elicitation tool construction. I conclude with limitations of the effectuation approach to research.

This chapter aims at showing the benefits of using vignette based factorial surveys for examining complex and sensitive topics in entrepreneurship, innovation and technology research. The example of research on entrepreneurial ethics is used to illustrate potential methodological problems. A literature review on entrepreneurial ethics shows that factorial surveys have rarely been applied despite its benefits with respect to disentangling the effects of interrelated variables and reduction of social desirability. As the rare reception might be due to lack of knowledge in how to design and carry out such studies, a guideline is provided in how to set up and conduct a factorial survey.

Family firms form the majority of enterprises in almost all national economies. While public corporations consist of the two components ownership and business, family firms have a third component: the family. In the past, brand management and family firms have been seen as two separate fields of research. This empirical study focuses on brand management of family firms, which can be seen as a complex interdisciplinary field of research. Taking the complexity into account, a model based on the brand identity model of Burmann, Halaszovich, and Hemmann (Identitätsbasierte Markenführung. Springer-Gabler, 2012) and qualitative interviews that were conducted with German family firm managers, is developed. This model, the so-called “markencloud”, categorizes the different aspects of brand management in family firms and gives family firm owners additional hands-on information on their company’s brand. The “markencloud” does not only illustrate complexity from a scientific point of view, it also works as a practitioners’ tool to support a family firm’s brand management.

Complex value creation networks have evolved as a substantial challenge for entrepreneurship in many industries. Value Delivery Architecture Modeling is a new approach to respond to this challenge by enabling people to understand the value creation network and by supporting the successful positioning of a company within this network. Consequently, Value Delivery Architecture Modeling allows for analyzing, evaluating and designing business models and their embeddedness in the value creation network. Value Delivery Architecture Modeling is based on the combination of the new business modeling language Value Delivery Modeling Language and semi-formal ontologies. The initial application of this new approach in the area of fast charging infrastructure in Germany shows promising results. The developed artifacts create an explicit frame of reference for the value creation network which can be useful in various situations. Value Delivery Architecture Modeling hereby addresses the understanding about the value network and enables the creation of novel value propositions.

Although the roots of action research trace back to the works of Kurt Lewin throughout the 1940s, there exists a great nescience concerning this methodology. This is largely due to a kind of Babylonian confusion where, depending on the literature stream, small differences result in new labeling and inconsistent usage of definitions across the methodological derivatives. However, the core action research approach has the potential to derive pioneering research results. In entrepreneurship research, the understanding of complex phenomenon through retrospective sense-making with self-reporting instruments has its inherent limitations. Action research through its real-time assessment by a researcher who is involved in the ongoing process and plans frequent learning circles, can result in superior research results. To reach this goal, the action researcher needs to follow specific rules and procedures in his or her research endeavor. The acquisition of individual social research skills is essential as they directly influence the results’ quality.

Qualitative Comparative Analysis (QCA) is a powerful method originating in the fields of political science and sociology, where it is becoming a mainstream method. This article analyzes the state of QCA applications in business and management (B&M) research by conducting a structured literature review, which results in the identification of 96 studies between 1995 and 2015. Additionally, the knowledge basis of those articles is analyzed by means of a citations analysis. The 5,141 unique citations serve to also structure the research front using a bibliometric coupling analysis. The results point towards a somewhat deferred development of QCA in the discipline, which has recently undergone a quantum leap with regard to the number of publications as well as the advance of the method application. The current development is strongly determined by the originator of the method, Charles Ragin, and by the first studies applying QCA in business and management. Yet, the research front is only loosely connected, underlining that QCA remains at an early stage of adoption in business and management. The chapter gives three recommendations for future QCA studies and predicts a progressing profile formation of QCA in business and management research that can contribute to the adoption of configurational thinking within the discipline.

This study, by analyzing a sample composed of some of the best Italian small and medium-sized enterprises (SMEs) that are engaged in a lean effort, adopts a multi-method approach to investigate the complex relationship between the level of implementation of lean systems, the adoption of a set of “high involvement” management behaviors and a sustained financial performance, controlling for such variables as the duration of the “lean journey” and the size of the firm. Through fuzzy-set analysis, this study focuses on the process of financial value creation within a firm due to the lean transformation and on the multifaceted relation among its determinants. Our results corroborate the perspective of lean environments as complex and integrated sociotechnical systems, confirming that the configurational approach is the most appropriate to analyze them. From this study emerges that the best performing lean companies, according to the adopted financial criteria, are those that wholly embrace the lean philosophy, follow its principles and apply its tools for years. The role of the “high involvement” management behaviors appears to be essential, since they create an environment where the lean techniques and tools can be more effective.

Young innovative companies (YICs) are becoming increasingly prominent in the debate on industrial policy because of their role as drivers of industry and the economy. The aim of this research was to determine which variables associated with the entrepreneur and the creation of YICs enable access to public entrepreneurship policies. This analysis compared Mas-Tur and Simón-Moya’s (2015) results (obtained using regression analysis) with results yielded by Qualitative Comparative Analysis (QCA). Using the QCA methodology, we identified causal configurations that lead companies to receive subsidies, or conversely, that lead companies not to receive subsidies. We thus observed differences in findings obtained using these two techniques.

QCA reduces complexity and richness of each individual case through the process of Boolean minimization. This poses a challenge for future development of QCA as a case study method. We address this challenge and propose complementing QCA with cross-impact analysis. This latter method provides an in-depth, holistic analysis of a single case by focusing on the set of factors that are an essential part of each case, and focuses on capturing and analyzing interactions between these factors. That is, after deriving causal explanations, researchers can return to the cases and capture their complexity and interactions. Application of both methods is demonstrated in this paper in the context of ICT adoption and use in Croatian SMEs. While QCA provides a macro overview of a number of cases and identifies seven key factors that influence SMEs’ adoption of ICT, cross-impact analysis has a case-based focus that provides additional insights into SMEs actual experiences and challenges with ICT use. The alignment between the two methods produces important implications for the future development of QCA towards in-depth case analysis and exploring the complexity of each case.

The aim of the chapter is to present the lessons learnt from the application of the configurational method to the evaluation of the “SPIN” public regional program. The objective of this complex program was to increase the intensity of knowledge transfer (KT) between universities and enterprises. After pilot implementation of the Model, there was a huge need for an evaluation study which would provide recommendations relating to the enhancement of the success of the further applications. Comparative case study methodology was applied in order to cope with the complexity of the program. As a result of the analysis, three models of implementation were distinguished.

Entrepreneurial small- and medium-sized enterprises (SMEs) are regularly operating in very complex settings. The methods and tools used by SME research to account for that complexity are commonly derived from only one of both worlds: Either the world of entrepreneurship with its emphasis on personal traits and characteristics of the entrepreneur, or from the world of large corporations and its focus on singular issues, such as processes or organizations. SME research is stuck in the middle, being the step-child of two unlikely parents who live in worlds apart. Specific research, targeted at entrepreneurial small and medium enterprises as a whole, could help to close this gap and to integrate the different approaches in a comprehensive context. A holistic view of the formation and growth process as well as on later stages, using a company-related perspective, is needed in SME research. One approach that could prove helpful is configurational analysis using the concept of Entrepreneurial Orientation. Configurational approaches are helpful particularly in ongoing transformation phases, as common in young companies. Embedding of Entrepreneurial Orientation in the context of the company therefore could establish an instrument that would make it possible to analyze especially small and medium enterprises in all phases of their lifecycle appropriately. The complexity of the enterprise as such as well as of its environment can hereby be described and analyzed in a holistic way, independent of the stage and age of the company, thus providing a bridging of the gap described above.

Small business management and entrepreneurship are clearly related phenomena but certainly not synonymous. We seek to capture and disentangle this complex relationship between both fields by employing a large-scale survey of small business and entrepreneurship scholars and a content analysis of published research from both areas, from which we derive an implicit consensual definition of each field and the interface between them. Our findings suggest the presence of a relatively strong common bond within the fields that enables researchers to reflect multiple perspectives, while still maintaining each field’s distinctiveness.

One of the major complexities of innovation as well as entrepreneurship and technological change processes is that larger or lesser financial and non-financial risks and uncertainties are parts of these processes, since the courses as well as outcomes of the processes are not known in beforehand. Assuming that managers and employees within entrepreneurship, innovation and technology firms are themselves concerned about this aspect of their businesses, it becomes pertinent for researchers to gain an understanding of what their perceptions regarding risks and uncertainties are. Relevant research designs could include e.g. surveys or interviews, whereas this chapter focuses on a possible supplementary approach consisting in using the securities reports submitted to the authorities as a new kind of source for relevant information. The chapter makes use of illustrative quotes from the sections in the securities reports of four biotechnology companies located in the USA and Japan, countries where self-assessments regarding risks the company is facing was made mandatory from 2005 and 2004 respectively. The chapter also includes a section with some suggestions as for how this new source may be utilized within future entrepreneurship, innovation and technology research, and concludes with stating that there are significant promises in connection with using the source, although there are also some challenges.

Innovation and Entrepreneurship are complex activities. They are also primarily language and relationship based. That is, it is largely through verbal communications (speech and text) that ideas are developed and business transacted. New methods are arising which are changing the way that we understand and can investigate innovation and entrepreneurship. Big Data Analytics allow researchers to uncover relationships and meaning in text documents, using a mix of quantitative and qualitative methods. This chapter shows that the complexity issues in innovation and entrepreneurship research with text comes from three sources. The first form of complexity is technical complexity. The second source of complexity is from language itself. The third source of complexity is in the concept itself. Each of these is discussed in detail. Complexity can either be addressed by simplifying the data or finding a mechanism for dealing with the complexity. A method of text data analytics using Corpus and Computational Linguistics deals with the complexity without eliminating data, allowing for a more nuanced investigation of innovation and entrepreneurship. The methodology is demonstrated by investigating how technological innovation and entrepreneurship are discussed in the United States Congress, using a corpus from 1981 to 2014.