nep-ino New Economics Papers
on Innovation
Issue of 2022‒04‒11
eight papers chosen by
Uwe Cantner
University of Jena

  1. Revisiting innovation typology: A systemic approach By Louis Knuepling; Colin Wessendorf; Stefano Basilico
  2. Tools and concepts for understanding disruptive technological change after Schumpeter By Mark Knell; Simone Vannuccini
  3. Risk-Sharing and Entrepreneurship By Kilström, Matilda; Roth, Paula
  4. Using big data for generating firm-level innovation indicators: A literature review By Rammer, Christian; Es-Sadki, Nordine
  5. Entrepreneurship and Regulatory Voids: The Case of Ridesharing By Deerfield, Amanda; Elert, Niklas
  6. From InnoMix to University-Industry Collaboration: Fostering Exchange at Eye Level By Hille, Carsten; Morcinczyk-Meier, Daria; Schneider, Sarah; Mietzner, Dana
  7. Innovation, Circular economy practices and organisational settings: empirical evidence from Italy By Davide Antonioli; Claudia Ghisetti; Stefano Pareglio; Marco Quatrosi
  8. Outcomes of Science-Industry Collaboration: Factors and Interdependencies By Uwe Cantner; Martin Kalthaus; Indira Yarullina

  1. By: Louis Knuepling (Institute of Economic and Cultural Geography, Leibniz University Hannover); Colin Wessendorf (Centre for Regional and Innovation Economics, University of Bremen); Stefano Basilico (Chair of Microeconomics, Friedrich-Schiller-Universität Jena & Faculty of Economics and Business Studies, University of Bremen)
    Abstract: Innovation studies use labels such as radical or disruptive to qualify innovation according to different concepts. Within the literature, these labels are frequently used interchangeably due to overlaps in their characteristics. These various definitions present challenges when the labels are operationalized in empirical studies. Based on a quantitative analysis of the most common innovation labels' definitions in 532 scientific papers, we find that novelty and impact, predominantly used for empirical operationalization, differentiate only between ordinary and more exceptional innovations. Based on our findings, a differentiation between the impact’s target and the consideration of positive versus negative effects enables better distinction between labels for more 'exceptional' innovations. We extend the existing literature and enable a more precise definition of (single) innovations by providing a novel, more nuanced description of innovations' different characteristics and a further distinction of their effects. Thereby, the relevant decisive aspects will be communicated more accurately.
    Keywords: radical, incremental, disruptive, breakthrough, innovation typology
    JEL: O31 O32 O33
    Date: 2022–03–02
  2. By: Mark Knell (NIFU); Simone Vannuccini (SPRU, University of Sussex)
    Abstract: This chapter is about radical innovation and disruptive technological change. Discovering the nature and mechanisms of disruptive technological change can help to understand the long-run dynamics of innovation and map profound transformation in socio-economic systems. The chapter considers four concepts essential for the understanding radical and disruptive technological change: long waves, techno-economic paradigms, general purpose technologies (GPTs), and disruptive technologies. We conclude with some insights on the emerging technologies in the latest techno-economic paradigm. The tools and concepts given here remain the cornerstone of a useful theory of innovation and change even in our current complex socio-technical landscape.
    Keywords: Radical innovation, Kondratiev, long wave cycle, Schumpeter, perennial gale of creative destruction, technological discontinuities, techno-economic paradigm, technological revolution, great surge of development, general purpose technology, disruptive technology, emerging technology
    JEL: O31
    Date: 2022–04–04
  3. By: Kilström, Matilda (Stockholm School of Economics); Roth, Paula (Research Institute of Industrial Economics (IFN))
    Abstract: In this paper, we study the role of risk-sharing in entrepreneurship-driven innovation. Studying entrepreneurship and innovation entails modeling an occupational choice and an effort choice. Risk-sharing may increase the number of individuals who become entrepreneurs by limiting the downside risk. The effort of entrepreneurs may, however, be hampered by high risk-sharing if this limits the returns faced by successful entrepreneurs relative to unsuccessful entrepreneurs. We construct a simple theoretical model where risk-sharing may be either private or provided through the welfare state by means of taxation. We show that, in addition to the occupational and effort choice dimensions, the level of public risk-sharing also matters for the characteristics of entrepreneurs.
    Keywords: Innovation; Institutions; Growth risk-sharing; Inequality; Incentives
    JEL: D64 E02 O30 O33 O43 O47
    Date: 2021–02–16
  4. By: Rammer, Christian; Es-Sadki, Nordine
    Abstract: Obtaining indicators on innovation activities of firms has been a challenge in economic research for a long time. The most frequently used indicators - R&D expenditure and patents - provide an incomplete picture as they represent inputs and throughputs in the innovation process. Output measurement of innovation has strongly been relying on survey data such as the Community Innovation Survey (CIS), but suffers from several short-comings typical to sample surveys, including incomplete coverage of the firm sector, low timeliness and limited comparability across industries and firms. The availability of big data sources has initiated new efforts to collect innovation data at the firm level. This paper discusses recent attempts of using digital big data sources on firms for generating firm-level innovation indicators, including Websites and social media. It summarises main challenges when using big data and proposes avenues for future research.
    Keywords: Big data,innovation indicators,CIS,literature review
    JEL: O30 C81
    Date: 2022
  5. By: Deerfield, Amanda (Economics Department); Elert, Niklas (Research Institute of Industrial Economics (IFN))
    Abstract: Formal institutions, e.g., regulations, are considered crucial determinants of entrepreneurship, but what enables regulatory change when there is a regulatory void, meaning entrepreneurship clashes with existing regulations? Drawing on public choice theory, we hypothesize that regulatory freedom facilitates the introduction of legislation to fill such voids. We test this hypothesis using unique data documenting the time for ridesharing to become legalized at the state level across the United States following its local (and often illegal) rollout. Results suggest states with greater regulatory freedom passed ridesharing legislation quicker, highlighting an underappreciated way that extant regulatory freedom facilitates the accommodation of entrepreneurship.
    Keywords: Entrepreneurship; Innovation; Regulation; Institutional change; Institutional voids; Institutional entrepreneurship; Sharing economy; Economic freedom; Survival analysis
    JEL: C21 O31 R49
    Date: 2022–03–24
  6. By: Hille, Carsten; Morcinczyk-Meier, Daria; Schneider, Sarah; Mietzner, Dana
    Abstract: In this paper, we address a specific tool-InnoMix-that is implemented to overcome the lack of university-industry interaction in a selected region facing structural change with its corresponding impact on the economy and society. InnoMix is facilitated and implemented by university-based transfer scouts who act as mediators and translators between the players of the regional innovation system. These transfer scouts are part of the Innovation Hub 13, in which the region's partners and stakeholders, infrastructures and competencies are systematically networked with each other to set new impulses for knowledge and technology transfer. These new impulses are brought into the region through new transfer approaches ranging from people and tools to infrastructure. InnoMix can be considered to be a highly interactive tool to overcome the weak, direct interaction between researchers and potential corporate partners in the region to foster strong collaboration between academia and industry. InnoMix especially aims to strengthen interdisciplinary exchange to shed light on cross-disciplinary perspectives. For that reason, transfer scouts focusing on transfer activities related to the life sciences, digitalisation and lightweight construction are involved in the implementation of InnoMix. Based on 11 InnoMix running since 2019, we provide insights into the planning and preparation phase of InnoMix and the selection of relevant topics and requirements for matching participants. Furthermore, we clearly indicate which formats of InnoMix work best and in which way university-industry interactions could be curated after InnoMix is implemented.
    Keywords: collaboration,transfer scouts,knowledge and technology transfer (KTT),innovation,innovation hub,networking/matchmaking
    Date: 2021
  7. By: Davide Antonioli (University of Ferrara); Claudia Ghisetti (Università degli Studi di Milano-Bicocca); Stefano Pareglio (Università Cattolica del Sacro Cuore); Marco Quatrosi (University of Ferrara)
    Abstract: This paper builds on the available knowledge on what drives firms’ production choices towards circular economy practices to shed new light on a so far quite neglected dimension: the role of organizational settings. Being the transition to a more circular economy systemic in nature, itdraws not only on technological but also on organizational changes and new set-ups. Coherently, the paper investigates how certain organizational settings (such as practices of communication to employees on critical aspects of the life of the company, the implementation of new performance evaluation mechanisms and incentive-based payment methods and the implementation of changes in recruitment and training of (new) employees affect the adoption of circular economy innovation. The work is empirical, and it draws on a newly collected dataset representative for Italian manufacturing firms in 2017-2018. Results show new light on the role of such organizational set-ups, which are found to be making the transition towards a circular economy more effective.
    Keywords: Circular Economy, Sustainable Production, Environmental Innovation, Organisational Change
    JEL: O30 O44 O55
    Date: 2022–02
  8. By: Uwe Cantner (Friedrich Schiller University Jena, Department of Economics, and University of Southern Denmark, Department of Marketing and Management); Martin Kalthaus (Friedrich Schiller University Jena, Department of Economics); Indira Yarullina (Friedrich Schiller University Jena, Department of Economics)
    Abstract: Science-industry collaboration is one of the major channels for transferring new scientific ideas into economic applications. Whereas the factors leading to collaboration are reasonably well understood, the determinants of the outcomes generated by such collaboration are unknown. This paper fills this gap by a new conceptualisation of collaboration outcomes and proposes factors that influence the generation of outcomes. We distinguish three different types of outcomes, namely scientific ones, commercialisable ones, and follow-up cooperation. We argue that scientific factors influence the generation of scientific outcomes, and economic factors the generation of commercialisable outcomes; interaction factors are proposed to influence the emergence of follow-up cooperation. We further propose that these outcomes depend on each other and hence are co-generated. We test our propositions with survey data from scientists in the German state of Thuringia. We asked scientists about characteristics of a particular collaboration and its outcomes. Multivariate probit estimations show that scientific factors positively related to scientific outcomes, and interaction factors are relevant for the follow-up cooperation. However, for economic factors, we find mixed evidence for their relation to commercialisable outcomes. As to outcome interdependence, we only find support for scientific outcomes to be co-generated with each of the other two types. Our results provide implications for policymakers and science managers on how to design funding policies and their evaluation.
    Keywords: Technology Transfer, Science-Industry collaboration, Scientific outcome, Commercialisable outcome, Follow-up cooperation, Mulitvariate probit
    JEL: I23 O31 O32
    Date: 2022–03–04

This nep-ino issue is ©2022 by Uwe Cantner. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.