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In today's volatile world, established companies must be capable of optimizing their core business with incremental innovations while simultaneously developing discontinuous innovations to maintain their long-term competitiveness. Balancing both is a major challenge for companies, since different types of innovation require different organizational structures, operational modes and management styles. Established companies tend to excel in improving their current business through incremental innovations which are closely related to their current knowledge base and competencies. However, this often goes hand in hand with challenges in the exploration of knowledge that is new to the company and that is essential for the development of discontinuous innovations. In this respect, the concept of corporate entrepreneurship is recognized as a way to strengthen the exploration of new knowledge and to support the development of discontinuous innovation. For managing corporate entrepreneurship more effectively, it is crucial to understand which types of knowledge can be created through corporate entrepreneurship and which organizational designs are more suited to gain certain types of knowledge. To answer these questions, this study analyzed 23 semi-structured interviews conducted with established companies that are running such entrepreneurial activities. The results show (1) that three general types of knowledge can be explored through corporate entrepreneurship and (2) that some organizational designs are more suited to explore certain knowledge types than others are.
Forschungsfrage: Welche Rollen lassen sich in Corporate Entrepreneurship identifizieren? Wie unterscheiden sich diese anhand verschiedener Merkmale und welche Fähigkeiten scheinen besonders relevant für ihre erfolgreiche Ausführung?
Methodik: Explorative Studie mit 56 semi-strukturierten Interviews mit Corporate-Entrepreneurship-Aktivitäten im DACH-Raum
Praktische Implikationen: Ein genaues Verständnis über die jeweiligen Rollen, ihre Unterschiedlichkeiten und Anforderungen ist notwendig, um die verschiedenen Corporate-Entrepreneurship-Aktivitäten mit passendem Personal zu besetzen.
Die Automobilindustrie steht wirtschaftlich aktuell besser da, als von manchem erwartet. Sie steht aber gleichzeitig großen Herausforderungen gegenüber, denn wir erleben die Überlagerung dreier Transformationen, deren Auswirkungen sich wohl in keinem Markt so gravierend niederschlagen wie in diesem. Um hierbei die Rolle als Leitmarkt zu erhalten, braucht es mehr Veränderungsintelligenz und eine noch höhere Innovationsdynamik. Diese sind mit beidhändigen Organisationen zu erreichen, die die Ambidextrie beherrschen, gleichzeitig das Kerngeschäft zu optimieren und mit strategischer Innovation Zukunft zu erfinden.
Text produced by entrepreneurs represents a data source in entrepreneurship research on venture performance and fund-raising success. Manual text coding of single variables is increasingly assisted or replaced by computer-aided text analysis. Yet, for the development of prediction models with several variables, such dictionary-based text analysis methods are less suitable. Natural language processing techniques are an alternative; however, the implementation is more complex and requires substantial programming skills. More work is required to understand how text analytics can advance entrepreneurship research. This study hence experiments with different artificial intelligence methods rooted in Natural Language Processing and deep learning. It uses 766 business plans to train a model for the automated measurement of transaction relations, a construct which is an indicator for new technology-based firm survival. Empirical findings show that the accuracy of construct measurement can be significantly increased with automated methods and improves with larger amounts of training data. Language complexity sets limits to the precision of automated construct measurement though. We therefore recommend a hybrid approach: making use of the inherent advantages of combining automated with human coding until the amount of training data is sufficiently large to substitute the human coding completely. The study provides insights into the applicability of different text analytics methods in entrepreneurship research and points at future research potential.
Despite the increased attention dedicated to research on the antecedents and determinants of new venture survival in entrepreneurship, defining and capturing survival as an outcome represents a challenge in quantitative studies. This paper creates awareness for ventures being inactive while still classified as surviving based on the data available. We describe this as the ‘living dead’ phenomenon, arguing that it yields potential effects on the empirical results of survival studies. Based on a systematic literature review, we find that this issue of inactivity has not been sufficiently considered in previous new venture survival studies. Based on a sample of 501 New Technology-Based Firms, we empirically illustrate that the classification of living dead ventures into either survived or failed can impact the factors determining survival. On this basis, we contribute to an understanding of the issue by defining the ‘living dead’ phenomenon and by proposing recommendations for research practice to solve this issue in survival studies, taking the data source, the period under investigation and the sample size into account.
Female Entrepreneurship has gained interest over the last 20 years. Therefore, this paper analyses 7,320 articles of the research field ‘women in entrepreneurial context’ published in 885 journals. The sample is analyzed by using a machine learning and text mining based methodological approach. Aiming to provide a broad overview over the research literature, 41 clusters and 11 superordinate topics were identified. Major developments of research attention are outlined by analyzing bibliometric data of the period from 2000 to 2020. Overall growth in terms of research attention measured by the development of yearly citations per article is best noticeable in clusters ‘corporate social responsibility’, ‘brand’, and ‘corporate (-governance)’, and in superordinate topics ‘performance’, ‘education’, and ‘corporate (board/ management)’. There are also indicators for an overall increase of research attention and cluster variety. The synthesis provides an insight into most trending superordinate topics. Therefore, this literature review gives a comprehensive and descriptive overview as well as an insight into thematic trend developments of the research field.
Prior quantitative research identified in the text of technology-based ventures' business plans distinctive performance patterns of evolving business models. Accordingly, interactions with customers, financiers, and people and the patenting strategy's status evolved and served as indicators of early-stage tech ventures' performance. With longitudinal data from five venture cases, this research sheds light on the evolving business model by validating the performance patterns, and elucidating how and why the ventures' business models evolved. Based on a generic systems theory framework for the indicators, the explanatory case studies re-contextualize the performance patterns taken from the snapshot perspective of business plans to the longitudinal perspective of technology-based ventures' life-cycle. This research confirms the relation of business model patterns of digital and non-digital ventures to the performance groups of failure, survival, or success and suggests a broader systems perspective for further research.
Corporate Entrepreneurship (CE) has now evolved into an imperative innovation practice of established companies. Despite organizational design models for CE activities and companies' frequent initiation of new activities, effectively managing them remains a challenging endeavor which results in disappointment about the outcomes of CE and its early termination. We assume specific types of goals for CE as one element of this unresolved management issue. While both practice and literature address goals in different contexts, no uniform picture has emerged so far. Although goals are commonly used to categorize CE activities, they seldomly seem to be the core subject of investigation. Based on this preliminary analysis and consolidation, we put the goals of CE in focus. In a systematic literature review, we reveal aspects of goals to unmask the different types of goals and their underlying dimensions and characteristics. Our review contributes to a better understanding of goals by (1) organizing relevant literature on goals of CE in a specific classification process, (2) describing dimensions and attributes for a systematic classification of CE goals; and (3) providing a framework showing differences of goals for the CE context. We conclude with a discussion and hints for future research paths.