Dirk Schneckenberg

Dirk Schneckenberg
Rennes School of Business · Strategy & Innovation

PhD

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62
Publications
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Introduction
Dirk Schneckenberg is Associate Professor of Strategy & Innovation at Rennes School of Business, France. His research interests are in Strategy and Digital Innovation, Business Model Innovation, Knowledge Management, Open Innovation, and Higher Education and Innovation. C.V: https://sites.google.com/view/dirkschneckenberg/home || Google Scholar : http://bit.ly/t2Ejml

Publications

Publications (62)
Article
The cognitive perspective in entrepreneurship research has predominantly evolved around static conceptions of cognition at the level of individual reasoning. Recently, the emerging stream of situated entrepreneurial cognition asserts that the environment substantially influences the inherent knowledge structures of entrepreneurial reasoning. It cla...
Conference Paper
Digitalization has been heavily transforming Real Estate (RE) around the world in the last decade. Many old processes and practices are the center of aim for change, innovation, new way of operation, more efficient use of resources and alternative ways of solving the same and also new problems. RE has just started to interact and cooperate with sta...
Article
Full-text available
Recent developments in the innovation literature suggest that even when an organisation truthfully implements the adopted R&D policy, it may still fail to achieve its intended goals, a phenomenon called means-ends decoupling. We employ a systematic literature review to answer the question of "what is the current state of knowledge in the phenomenon...
Article
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The field of paradox studies keeps struggling to put the notion of paradox into the very centre of organizational life and managerial decision-making, with mixed success. We argue that this research ambition can be realized much more effectively by anchoring the field in three interrelated conceptual approaches which build on paradox as the paradig...
Article
In this perspective piece, we aim to broaden understanding of business model innovation (BMI) by expanding and deepening extant theoretical dimensions and positions in the literature. While a significant amount of research has been carried out on the phenomenon of BMI, the current theory does not fully capture the broad diversity and inherent impli...
Article
Do software vendors propose, create, and capture value in the era of digital transformation? Drawn on the literature of business models, digital innovation, and firms' capabilities, we examine this cutting-edge research question. We conducted a multiple case research of 10 software vendors operating in Germany and Austria. The thematic analysis yie...
Article
While extant research on entrepreneurial ecosystems has focused on macro-level factors influencing the ecosystem's development, the role and impact of entrepreneurial practices have been neglected. The objective of this study is to address this research gap and to shed light on the micro-level practices of entrepreneurs who support sustainability t...
Article
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This paper seeks to expand our understanding of sustainable entrepreneurial ecosystems by investigating the interrelation between contextual factors and sustainable entrepreneurial activities of sharing ventures. While the sharing economy is considered as a potential pathway to a more sustainable society; ambiguous activities of some sharing ventur...
Article
How to build a coherent narrative of organizational identity in a socially contested field? Through an inductive study of the sharing economy, we analyzed how managers deal with conflicting collective identities and develop coherent organizational identity narratives through label work. Our findings reveal that managers responded to the social cont...
Cover Page
Full-text available
Artificial Intelligence is perhaps the most promising architectural innovation in the 21st Century and will impact all aspects of society and businesses in far greater magnitude than any previous digital revolution (Makridakis, 2017). Given its powerful technological capabilities, AI is a promising enabler for organizations: It facilitates new vent...
Article
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Entrepreneurs designing novel business model configurations face cognitive biases that derive from limited mental capacity to deal with complex and uncertain decision contexts. Building on the notion of the business model as an idiosyncratic mental representation that organizes managerial understanding of value creating and value capture, we invest...
Article
Managing goals is a key network management function and is critical in the implementation of industrial R&D projects. In this paper, we explore the implementation of an industrial R&D project, focusing in particular upon the role of means-ends decoupling work to understand how the goals are managed. We combine several data sources in our case resea...
Article
Despite the importance of managerial reasoning in designing business models to handle exogenous change, little is known about its cognitive foundations. We address this gap with a comparative analysis of how managers rethink business model configurations to provide value in the emerging collaborative consumption economy. As customer behaviors shift...
Article
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The proliferation of innovation contests has fostered community-based idea evaluation as an alternative to expert juries to filter and select new product concepts at the fuzzy front end of corporate R&D innovation. We refer to this phenomenon as open evaluation, as all registered participants can engage in jury activities like voting, rating, and c...
Article
Full-text available
As a research subject, business model innovation spans the strategy, innovation, and entrepreneurship fields. Yet, despite the importance of the concept, prior work has paid little attention to how decision-makers cope with uncertainty and gain understanding about interdependencies in new business model configurations. To address this gap, we combi...
Article
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Purpose – Firms increasingly integrate a wide range of actors in the early ideation and concept creation phases of innovation processes leading to the collection of a large number of ideas. This creates the challenge of filtering the most promising ideas from a large number of submissions. The use of external stakeholders into the evaluation and se...
Conference Paper
Full-text available
Freemium represents an essential business model in the accelerating mobile applications industry. While Freemium models contribute more than 90% of the revenue generated in Google Play and Apple’s app store, extant research lacks to date a systematic typology. Therefore, this paper aims at developing a classification scheme for Freemium business mo...
Article
Research on the phenomenon of business model (&) innovation is taking place in related perspectives. Next to innovation and entrepreneurship literature, theories of firm strategy closely relate to the ongoing conceptualization of the business model concept. We first review in this article the linkage between the business model, innovating the busin...
Article
Microfoundations of dynamic capabilities have become a central concept for strategy and innovation research. Yet, despite recent developments in information technologies which facilitate data flow and information management in firms, little is known about how organizational learning and knowledge management nurture microfoundations of innovative ca...
Conference Paper
Full-text available
Consumer behaviour patterns shift in the emerging sharing economy from owning to sharing possessions and create new market opportunities for economic actors. Current literature lacks understanding about the cognitive processes relating to the design of new business models. We identify in this case research six cognitive processes which undergird ma...
Article
Gamification has recently been receiving increased attention in corporate innovation and business research alike. In this article, we first outline the main streams of research on gamification in the creativity and innovation literature. We then introduce the selection of contributions to this special section by theoretically embedding them in thei...
Article
Purpose – The purpose of this paper is to inquire how large multinational firms can develop and implement knowledge-sharing measures that move their corporate strategy towards the open innovation paradigm, since open innovation becomes increasingly important as source for competitive advantage. Design/methodology/approach – We review the literatur...
Article
Background: Cloud computing has recently received interest in information systems research and practice as a new way to organise information with the help of an increasingly ubiquitous computer infrastructure. However, the use of cloud computing in higher education institutions and business schools, as well as its potential to create novel learning...
Article
Full-text available
Business model innovation is receiving increased attention in corporate practice and research alike. We propose in this article a role-based approach to categorize the literature and argue that the respective roles of explaining the business, running the business, and developing the business can serve as three interrelated perspectives to present a...
Article
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Research on business model development has focused on the relationships between elements of value conceptualization and organization having a linear sequence in which business models are first designed and then implemented. Another stream of research points to business model development with these elements interacting in a cyclical manner. There is...
Article
Our paper presents a cross-sectional study of incentive systems for open innovation practices. Organisations face the challenge to design and implement strategic incentive systems which reward active contributions of individuals to open innovation practices. We refer to contributions from psychology and economics to develop a framework for organisa...
Article
Introduction The concept of action competence Towards a concept of eCompetence Holistic measures for faculty development Methodology for the survey Main findings Conclusions and limitations References
Article
This paper explores the corporate adoption of Web 2.0 technologies to enhance knowledge flows within and across firms. It first investigates the nature of the Web 2.0 phenomenon. As social mechanisms determine the use of technologies in corporate environments, a literature review is carried out to understand how governance models influence the use...
Article
Based on a theoretical framework for the concept of eCompetence of academic staff, this chapter develops explores principles for the design of faculty development measures. It carries out a literature review that identifies key components and combines them into a model of action competence, which serves as point of departure to develop a concept fo...
Article
This paper explores the role that eCompetence of faculty members play in the integration of eLearning in higher education. Learning technologies have the potential to enhance educational innovation, but the eLearning adoption rate of faculty in universities is so far disappointing. The motivation and capability of faculty to use information and com...
Article
eCompetence combines the motivation and capability of faculty members to use Information and Communication Technologies (ICT). This paper develops a theoretical framework for the concept of eCompetence of academic staff, and it explores principles for the design of respective faculty development measures. A literature review identifies key componen...
Chapter
Twelve years have passed since Peter Drucker made his threatening prediction for the future of universities. But, while we have left the twentieth century behind us, the university as an institution has been quite stable in its capacity to adapt and serve society – and continues to do so today. One proof for the evolution of higher education instit...
Article
This paper discusses the potential of learning technologies to foster competence development of students. It aims to improve understanding of pedagogical conditions that have to be met to establish a competence orientation in e-learning. We review the literature to summarise recent changes in e-learning, identify attributes of web 2.0 technologies,...
Chapter
This chapter develops a theoretical framework for the concept of e-Competence, and it investigates the principles of the methodical design of competence development measures for faculty. e-Competence is grounded in the motivation and capability of faculty members to use information and communication technologies (ICT). A literature review extracts...
Article
Background: Academic staff have a key role to play in the innovation efforts of universities aiming to exploit the potential of web-based learning technologies. Although learning technologies are an important building block of educational innovation, the eLearning adoption rate of European academic staff appears disappointing. The majority of curri...
Article
Full-text available
Purpose – The purpose of this paper is to discuss the potential of Web 2.0 technologies for knowledge management and to explore how corporate governance models influence the adoption of Web 2.0 for organisational learning and knowledge exchange. Design/methodology/approach – The paper begins with a literature review to understand the phenomenon of...
Conference Paper
This paper assumes that the sustainable use of eLearning technologies in higher education depends on the ability of university leadership to actively involve faculty in the innovation process. The motivation and capability of academic teachers to effectively use Information and Communication Technologies (ICT) in teaching and learning relates to a...
Article
Full-text available
In this paper we argue that e-learning is changing from a primarily distributive mode to a more collaborative mode. This enables e-learning to step up the ladder, and to turn to competence oriented pedagogical models. Three steps are taken: First the changes e-learning is currently undergoing are explored and summarised, secondly the concept of com...
Article
Auf dem Weg zu einem strategischen Personalmanagement von akademischen Lehrenden in der Nutzung von ICT - Ansatze zur Messung von eCompetence Die eCompetence von akademischen Lehrenden stellt einen Aspekt der technologiebasierten Innovation von Universitaten dar. Wahrend konzeptionelle Ansatze fur eCompetence und eine Reihe von Masnahmen fur Person...
Article
Driven by technological innovation, Europe is moving towards a post-industrial society in which knowledge has been identified as the key factor for growth. As the creation of new knowledge is the core business of universities, the European Union needs an innovative and effective higher education system. The model of eCompetence development proposes...
Article
Full-text available
eCompetence research represents one aspect of the discussion on the integration of new technologies in universities. Its main interest is on the role of the human factor in this process of technological innovation and institutional change. eCompetence is, at its core, dealing with the development of personal competences in the creative use of ICT....
Article
Full-text available
E-learning plays a key role in the innovation process of universities. The development and integration of learning technologies into higher education has been pushed forward by an alliance of policy makers, technology investors and university management. The concept of e-learning includes a variety of aspects that in its combination cause a fundame...
Article
This paper presents an experimental approach to use web 2.0 tools for the re-design business courses on the idea of competence development. Web 2.0 technologies have the potential to move education from knowledge transfer to competence development. But while web technologies are currently changing the competitive landscape of com- panies in modern...
Article
Full-text available
Based on findings from a large-scale international survey on eCompetence measures, the key assumption of this paper is that a successful development of eStrategies and management of technology-driven innovation in universities depends on their ability to actively involve faculty in the organisational change process [Schneckenberg 2007]. The motivat...

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Projects (5)
Project
Dirk Schneckenberg is Associate Professor of Strategy & Innovation at Rennes School of Business, France. His research interests cover strategy and digital innovation, business model innovation, knowledge management, open innovation, and innovating higher education. He has published four books and over 100 peer-reviewed contributions in journals and conference proceedings. Dirk is a member of the European Group for Organizational Studies (EGOS) and the European Academy of Management (EURAM). He is co-chairing the tracks 'Business model - strategy, innovation, and entrepreneurial venturing', and 'Artificial intelligence as an enabler for venture creation, innovation, and organizational change' at the annual EURAM conference. Dirk has taught strategic management, organisational innovation, design thinking, knowledge management in organisations, and qualitative research methods at the undergraduate, graduate, doctorate, and executive levels. His research and teaching have been awarded on various occasions. Address for correspondence: Dr. Dirk Schneckenberg, ESC Rennes School of Business, Strategy & Innovation Department, 2, rue robert d'Arbrissel CS 76522, 35065 Rennes Cedex, France. Tel: +33 2 99 33 48 23; e-mail: dirk.schneckenberg@gmail.com.
Project
Special issue call for papers in Technological Forecasting and Social Change. Link to the call for papers: https://www.journals.elsevier.com/technological-forecasting-and-social-change/call-for-papers/artificial-intelligence-as-an-enabler-for-innovation Artificial Intelligence (AI) and its associated Machine Learning (ML) capabilities are considered to be the next General-Purpose Technologies (GPTs) that will impact all areas of economy and society at large (Montes & Goertzel, 2019), perhaps at the same or greater magnitude as previous GPTs such as steam engine, electricity, internal combustion engine, and computers (Brynjolfsson, Rock, & Syverson, 2017). The pervasive nature of AI holds considerable potential for disrupting both management practices (von Krogh, 2018) and strategies of business in all industries (Agrawal, Gans, & Goldfarb, 2018). In this call for papers, we focus on the impact of AI on the innovation processes of organizations. While AI can influence both the production and the characteristics of a wide range of products and services, Cockburn, Henderson, and Stern (2018) suggest that AI may impact the innovation process itself by serving as a new general-purpose method of invention. Brynjolfsson and McAfee (2017, p. 19) state that the impact of AI on business and the economy “will be reflected not only in their direct contributions but also in their ability to enable and inspire complementary innovations.” For instance, AI-based learning may “automate discovery across many domains where classification and prediction tasks play an important role” and “radically alter scientific and technical communities’ conceptual approaches and framing of problems” (Cockburn et al., 2018, p.7). Makridakis (2017) proposes that the automation of routine research tasks may allow R&D teams to focus on more creative and innovative tasks. As such, AI can potentially affect the way organizations manage and conduct R&D to develop new products and services. In the pharmaceutical industry, AI has been used to predict candidate selection for trials, identify target proteins, and automate molecule design, all of which have halved the development cost of certain drugs and considerably reduced the time to market through higher approval rate. In marketing, AI can leverage the myriad of consumer data to predict the successful features of a future product or service, which will reduce the notoriously high rate of failure for any new product or service. At an organizational level, AI can lower entry barriers to resource-constrained organizations by drastically reducing R&D costs. In this scenario, an increasing number of small firms will be able to use AI and ML capabilities to produce incremental innovations which otherwise would have been forgone due to inherent high search costs. This call for papers encourages the submission of work that examines the question of how AI may impact the innovation processes of organizations in all domains. We are particularly interested in work that looks at AI as an enabler for innovation, for instance, when AI improves the ways firms organize and conduct R&D to increase both the quantity and the quality of new products and services, independently of the domain (marketing, finance, production and manufacturing, logistics, etc.), product and service, or industry. However, please note we are not interested in papers that investigate AI-based applications in new products and services. For example, we realize that AI as a GPT may lead to the commercialization of many new products and services that incorporate some of these technologies (see Hengstler, Enkel, & Duelli, 2016). Autonomous cars, smart speakers, and personalized recommendations on e-commerce platforms may be examples of AI-based products and services for consumers, while fraud detection models, targeted advertising, and drones may be examples of AI-based products and services for businesses. These new products and services may be AI-based, but they are not the outcomes of a novel process of innovation. Therefore, we outline below some of the questions that are relevant to this call (this list is non-exhaustive): Organizing R&D How does AI impact the existing structure and organization of R&D?What potential benefits may AI bring in the organizing or R&D (lower cost, automation of tasks, shorter timelines, etc.)? How do these benefits come around?In contrast, what are the challenges of introducing new structures and organizing in existing R&D units?How might AI impact the role of humans in R&D teams? Innovation Processes How can AI become a general-purpose method of invention?How are existing innovation processes in a particular domain affected by the introduction of AI capabilities?What are the challenges and key success factors for an organization to implement AI capabilities in its innovation processes?What benefits does AI bring in terms of innovation outputs (number of innovations, nature of innovation, quality of innovation, type of innovation)? The Role of Data in AI-based Innovation Innovation using AI is highly dependent upon access to data, how can organizations or industries work to facilitate the sharing data?Similarly, how can a “market for data” be created that benefits both users (those who “supply” data) and organizations (those who use data)?What is the role of data in AI-based innovation methods?When do organizations need more or fewer data to innovate? What are both the external and internal conditions that command the volume of data they need?What industries, products, or services are more prone to data management challenges? Who owns the data, and when does data privacy hamper the innovative capability of an organization or industry? AI as an enabler for small and new organizations Since AI lowers search cost, it facilitates access to innovation activities for firms with constrained resources, but how does this process take place?What are the R&D tasks that will be made more accessible to small firms and new ventures?How might small firms and new ventures access critical data for building their AI algorithms? The average timeline for Special Issue publication based on historic data for article submission/review and issue production for the Elsevier titles are provided below: first submission date: 1 November 2020 final submission deadline: 30 January 2021 The final acceptance deadline (for guest editors): 30 October 2021 (Final decision on each of the manuscript must have been made by this date)
Project
Special issue call for papers from International Journal of Entrepreneurial Behavior & Research The submission portal for this special issue will open in October 2020. Link to call for papers: https://www.emeraldgrouppublishing.com/products/journals/call_for_papers.htm?id=8852 Guest Editors: Yann TRUONG, Burgundy School of Business, France Yann.truong@bsb-education.com Dirk SCHNECKENBERG, Rennes School of Business, France dirk.schneckenberg@rennes-sb.com Rachid JABBOURI, Burgundy School of Business, France Rachid.jabbouri@bsb-education.com Aims and Scope Artificial Intelligence (AI), broadly defined as an overarching science that is concerned with intelligent algorithms (Agrawal, Gans, & Goldfarb, 2018), is about to disrupt businesses and societies at large in greater magnitude than any previous technological revolutions (Makridakis, 2017). As a general-purpose technology (Cockburn, Henderson, & Stern, 2018), AI and its associated sub-category technologies will affect virtually all businesses across industries by disrupting their current practices (von Krogh, 2018). Unsurprisingly, AI has been gaining momentum among management scholars from all disciplines (Obschonka & Audretsch, 2019), as evidenced by the number of calls for papers in 2019 in top-tier management journals including MISQ, Journal of Business Research, Small Business Economics, European Journal of Marketing, and the Journal of the Association for Information Systems. Similarly, research investigating the impact of AI in the field of entrepreneurship is in a nascent stage (Obschonka & Audretsch, 2019) but ought to rapidly become a major area of focus as the immense potential of AI technologies provides a considerable leverage for the pursuit of entrepreneurial activities (Nambisan, 2017; von Briel, Davidsson, & Recker, 2018). Specifically, the automation and predictive capabilities of AI can be leveraged throughout all stages of the entrepreneurial process, that is, the identification, development, and exploitation of entrepreneurial opportunities (Shane, 2000). For instance, AI can facilitate the process of venture creation by expediting market studies and product testing in the exploration phase of venturing with automated data collection on social media, and improving market targeting and positioning in the exploitation phase of venturing with the help of predictive models. A concrete example is that of biotechnology ventures which have improved their R&D performance by using predictive models to find candidate molecules with optimal properties rather than going through the traditionally long journey of testing large samples of molecules. In this case, not only has AI significantly reduced the firms’ R&D costs but it also accelerated the time to market of their new products. Another example is machine learning capabilities that allow resource-constrained new ventures to automate routine tasks related to organizing such as accounting, financial control, document sorting and classification, and customer relationship management. Related to AI is the increasing availability of data that are collected through online platforms (social media and online markets) and connected objects (Internet of Things, IoT). Big data facilitate the identification and exploitation of business opportunities, and when combined with powerful AI algorithms, they can considerably reduce the uncertainty that is inherent to the entrepreneurial process (McMullen & Shepherd, 2006). At a strategic level, AI technologies may disrupt the strategy and business model of firms (Agrawal et al., 2018). Amazon’s current strategy is driven by “order and ship”, but as its business model is highly dependent upon always shorter shipping time, the predictive power of AI technologies can enable the company to anticipate what its customers might need and ship the item even before they express such a need. The company’s model would then shift from “order and ship” to “ship and return”. For entrepreneurs, AI can unlock more variations of business models in venture creation (Lee et al., 2019; Loebbecke & Picot, 2015, Gomez-Uribe & Hunt, 2015). Consider the case of Rushmix which provides post-production video services to users online: Its current business model is locked-up by considerable human work on raw video footage, but the founders are now developing AI to assist humans by automating some time-consuming post-production work such as the pre-identification of key moments in the footage. This improvement would enable the startup to reduce delivery time, lower costs, and process higher volumes of video footage, thus reordering the basic elements of its business model. Altogether, AI and big data can lower entry barriers for nascent entrepreneurs, facilitate the process of venture creation, accelerate growth, and increase the chances of survival. This special issue focuses on the conditions under which AI can serve as an enabler for entrepreneurs in the process of creation and growth. We encourage submissions of papers that examine the ways AI and its capabilities (e.g. visual recognition, predictive models, classification, machine learning algorithms, natural language processing, clustering, etc.) can be leveraged throughout the entrepreneurial process from opportunity recognition, organization creation, resource acquisition, product development and commercialization, and growth (Baron, 2008; Bhave, 1994; Shane, 2003). Potential authors should note that this special issue is not fit for papers that study AI-based new ventures (new ventures that incorporate AI capabilities in its offerings). For example, we are interested in how entrepreneurs can use AI capabilities to develop new products and services (by automating market studies or product testing) but not in how entrepreneurs develop AI capabilities in their products and services. The focus is on the entrepreneurial process rather than on products or services. Possible topics The Guest Editors encourage submissions of theoretical and empirical contributions that address the following list of possible topics. The list is non-exhaustive so we welcome submissions that broaden our understanding of the enabling role of AI for entrepreneurs. AI in the venture creation process - How can particular AI technologies (e.g. visual recognition, predictive models, machine learning algorithms, classification, natural language processing, clustering, etc.) be leveraged at each stage of the entrepreneurial process from opportunity recognition, organization creation, resource acquisition, product development and commercialization, and growth? AI and business model design in the nascent phase - How do AI technologies affect the current business model design of nascent entrepreneurs in terms of value proposition, strategic implementation, and maintenance? - How can AI unlock the current business model of established new ventures? AI and new venture growth and survival - What roles can AI technologies play in unlocking growth for established new ventures? - How can AI improve the survival of new ventures beyond the notorious 3-year threshold? Submissions: Papers should be submitted via the journal’s online submission system available through the journal homepage. When submitting please choose the special issue: “Artificial intelligence as an enabler for entrepreneurs” as the article type from the drop-down menu. All papers must follow the guidelines outlined by the journal for submission, available at: http://www.emeraldgrouppublishing.com/products/journals/author_guidelines.htm?id=ijebr#13 For any questions, interested authors can contact the corresponding guest editor: Yann TRUONG (yann.truong@bsb-education.com) Submission deadline: 30th of November 2020