Fri, 17 Dec|
Edward Lorenz | Jun Jin | Veronica Robert | Florencia Fiorentin | Muyang Liu | Jana Schmutzler | Michael Awoleye
Time & Location
17-Dec-2021, 1:00 pm – 3:00 pm GMT
About the Event
Innovation Systems-102- Practical application of the IS-theory
17th DECEMBER, 2021 | 01.00 PM LONDON TIME (GMT)
8 AM - New York | 10 AM – Brazil |2 pm - Denmark, France | 3 pm - South Africa | 4 PM - EAT | 6:30 PM - India | 9 PM China & Malaysia |10 PM - Tokyo
REGISTRATION IS MANDATORY: https://unu-edu.zoom.us/meeting/register/tJclcO6gpjkpGNOSHIGqHAKnea82iZlg55AD
Florencia Fiorentin [Argentina]: From literature on innovation systems to the evaluation of innovation policy. Challenges to address a systemic phenomenon.
MuyangLiu [China]: How Disruptive Innovation of Digital Platform Gains Legitimacy?—Based on Didi platform
Edward Lorenz | Jun Jin | Jana Schmutzler | Michael Awoleye
CHAIR: Veronica Robert
Only Registered participants can attend the programme.
Meeting ID: 938 7788 2327
Passcode : 623847
Registered participants who attend all lectures and respond to feedback form will receive a diploma signed by Bengt Ake Lundvall
MORE DETAILS & REGISTRATION: www.cris-is.org
Co organised by CRIS-IS.ORG, UNU-MERIT, CONCORDE, NIIM-CHINA and CICALICS
Edward Lorenz : Emeritus Professor of Economics at the University of Côte d’Azur, France. He also holds the posts of Distinguished Visiting Professor at the University of Johannesburg in South Africa, and Adjunct Professor at Aalborg University in Denmark. He is Vice President of Globelics, a worldwide community of scholars working on innovation and development. His research focuses on the relation between innovation, new technologies and sustainable development. He publishes in such journals as Industrial and Corporate Change, Industry and Innovation, and Research Policy. He contributes regularly to United Nations events related to the SDGs including the February 2019 workshop on ‘Science Technology and Innovation for the SDGs’ organised by UN DESA and ESCAP in Bangkok, Thailand and the 13 November 2019 meeting on ‘Structural Transformation, Industry 4.0 and Inequality’ organised by UNCTAD in Geneva.
JIN Jun is currently an Associate Professor on Innovation Management, at the Department of Innovation, Entrepreneurship and Strategy, School of Management, Zhejiang University, China. Her research interests are in the areas of global innovation, catching up strategies, open innovation, and eco-innovation. Her current research focuses on the strategy dealing with climate change and carbon neutrality, as well as digital transformation of firms. Her articles have been published in journals such as the Technovation, Journal of Cleaner Production, International Journal of Technology Management, Asian Journal of Technology Innovation, Sustainability, among others.
Veronica Robert: fellow researcher at National Council of Technical and Scientific Research (CONICET) and at National University of San Martin (UNSAM), in Argentina. She obtained her Doctoral Degree at Buenos Aires University. Her thesis was awarded the Raúl Prebicsh national prize given oy by the Central Bank of Rep. Argentina. The main topic of her research is the relation between innovation, development and structural change.
Verónica Robert heads the Research Office of the Institute of Advance Studies in Social Science at UNSAM, and she is the Director of the Centre for Studies on Economics and Development at IDEAS-UNSAM. She has led five research projects and has participated in more than 20. She also has been a consultant for ECLAC, ILO and WB.
Since 2005, she has published more than 50 publications as books, journal articles, and book chapters
More about Veronica R.
Florencia Fiorentin: Florencia Fiorentin is a CONICET PhD fellow at the Institute of Industry, National University of General Sarmiento (IdeI/UNGS). She is PhD candidate in Economics and a master candidate in Management of Science, Technology and Innovation. She has a bachelor’s degree in Political Economics (UNGS, Argentina). Her field of research is related to the analysis of micro innovation processes and the dynamic study of public innovation policy, in terms of design, allocation and impact. Also, to the analysis of gender gaps in academic activity and in the processes of allocation and impact of science and technology promotion policy. She teaches in undergraduate courses in political economics, economic growth and development fields. ORCID ID: https://orcid.org/0000-0001-5210-4715. https://www.facebook.com/ctidesarrolloidei
Jana Schmutzler: Jana Schmutzler de Uribe studied business administration at the Friedrich-Schiller- Universität Jena. She received a German degree in business administration with the specialization of international strategic management, marketing and intercultural communication studies in 2001. Jana Schmutzler gained international exposure as an exchange student at the “Ocean University Qingdao” in China and at “University of Cordoba” in Spain.
Jana Schmutzler worked as business consultant at Simon-Kucher & Partners in Bonn, Germany and Boston, USA. She was managing strategic projects for international clients from automotive and pharmaceutical industry. Moreover, Jana Schmutzler set up the “Market intelligence team” at Merck in Colombia. She was also the vice president of the business development section of a medium sized enterprise in Colombia.
In 2012 she got a Master degree in the field of Organizational Studies at the “Universidad de los Andes” in Bogotá. Afterwards she started working as an external doctoral student at the “Chair of Industrial Organization and Innovation” and “Jackstädt center of entrepreneurship and innovation research” in Wuppertal, Germany. In July 2016 she successfully defended her dissertation on the topic of “Influence of social environment on entrepreneurship and innovation”.
Since 2014, Jana Schmutzler is professor at the “Universidad del Norte” in Barranquilla, Colombia. She teaches organizational theory and entrepreneurship in the bachelor program. She is an active member of the Colombian “Global Entrepreneurship Monitor” team. More: https://www.jackstaedt.uni-wuppertal.de/en/team/research-fellows/jana-schmutzler-de-uribe-dr.html
michael Awoleye: is a Research Fellow at the African Institute for Science Policy and Innovation (AISPI), Obafemi Awolowo University (OAU), Nigeria. He earned a B.Sc. degree in Computer Science at the Olabisi Onabanjo University and M.Sc. degree in Information Engineering with Network Management at the Robert Gordon University, Aberdeen, UK in 2010. He obtained a Ph.D. degree in Technology Management at OAU in 2015. Dr Awoleye has previously worked at the National Centre for Technology Management (a Policy Research Institute of the Federal Government of Nigeria) for over 10 years in different academic/research cadres. He rose to the position of an Assistant Chief Research Officer in 2012 before he joined his services with OAU in March 2013. His area of research interest among others include but not limited to Innovation management, assessment firm’s performance, emerging technologies, digital sovereignty, Internet governance, big data analytics, among others.
I. Muyang Liu
How Disruptive Innovation of Digital Platform Gains Legitimacy? —Based on Didi platform
1. Research Question
Digital platform as a disruptive innovation, is a new kind of business model based on digital technology, with its advantage of resources integration, and rapid development, such as online car-hailing platform, online shopping platform, social platform etc. Disruptive innovation as the new market entrants facing serious legitimacy challenges, entrepreneurial success ratio is extremely low. The root cause is that the agents, agents collaboration and innovation disruption connected by digital technology making it more difficult and complex to gain legitimacy. Therefore, it is of great significance to explore digital platforms disruptive innovation legitimacy. However, despite the extensive research in this field, there are still some significant gaps in the understanding of innovation legitimacy judgment. One reason is a limited understanding of the differences between different audiences in judging innovation legitimacy. In the previous innovation legitimacy research, audience is all in the overall concept of organizational environment. On the one hand, no systematic differentiation innovation facing the different audience types of legitimacy judgment, on the other hand to earn legitimacy only focus on a specific type of audience (Überbacher, 2014). This defect in the research of innovation legitimacy makes it necessary for future research to explore the differences in legitimacy judgments among different audiences.
The process of obtaining digital platform disruptive innovation legitimacy is difficult and tortuous, facing increasingly complex and diverse innovation agents and constant conflicts of innovation values. Disruptive innovation legitimacy of digital platform is deeply influenced by the cognitive heterogeneity and innovation synergy of the relevant agents of the platform for innovation action, innovation goal and expectation, innovation value. The disruptive innovation of digital platform is an innovation ecosystem in which multiple entities share resources, complement technologies and create values around the innovation focus enterprises. Under this background, based on innovation ecosystem perspective to explore digital platform disruptive innovation legitimacy issues, to give new theory interpretation and innovation practice.
This study attempts to fill the existing research gap by answering the following two key questions: What is the rule of agents innovation value conflict and synergy in digital platform disruptive innovation? In the agents innovation value conflict and synergy, how does digital platform disruptive innovation gain legitimacy? In order to answer the above research questions, this study conducted a longitudinal case study on the ride-hailing platform of Didi. Didi is a typical digital platform disruptive innovation, which fundamentally changes the travel behavior pattern of consumers and market interests, and restructures the travel ecosystem. By analyzing agents innovation value conflict and synergy in different market development stages of Didi platform, this article hopes to open the black box of the mechanism of obtaining legitimacy of digital platform disruptive innovation, and provides targeted decision-making reference for the practice and policy improvement of digital platform disruptive innovation in China.
2. Literature Review
2.1 Disruptive Innovation Legitimacy
Disruptive innovation is a process in which new entrants with new technologies (business models) are positioned in new markets or marginal markets at the beginning. With the improvement of the performance of their products or services, they will eventually attract mainstream consumers and completely change the original technological paradigm and market competition pattern (Bower and Christensen, 1995; Christensen, 1997). As disruptors of existing institutional arrangements, disruptive innovation enterprises often face the threshold of legitimacy (Zimmerman and Zeitz, 2002), which leads to a high failure rate of disruptive innovation (Steve et al., 2020).
Innovation legitimacy refers to the innovation strategy of an enterprise that provides innovative products or services to gain competitive advantages under the institutional constraints of satisfying social norms. It refers to the degree to which agents accept the innovation actions of an enterprise, and emphasizes the judgment of acceptability, desirability or appropriateness of innovation by the innovation-related groups (Suchman, 1995; Suddaby and Greenwood, 2005). So far, scholars have mainly discussed the innovation legitimacy from three perspectives: institutional perspective, strategic perspective and evaluator perspective. This article is based on the evaluator perspective
2.2 Innovation value of digital platform innovation ecosystem
Digital transformation promote the business logic based on digital technology, digital platform innovation ecosystem is the important way to realize value creation and access. Digital platform innovation ecosystem is a system interaction in digital environment, and companies participating in this context must consider the impact of such relationships, as well as the network interdependence between different actors in general.
The evaluator's perspective of innovation legitimacy focuses on the research of material benefits, spiritual value and ethical innovation value. When innovation achieves self-defined goals and results, it is material benefit value (Lankoski et al., 2016; Tost, 2011), focuses on the satisfaction degree of innovation to the low level needs of the self. When innovation can affirm one's own identity, support the sense of self-worth, and ensure the maintenance of self-dignity, etc., it is spiritual value (Tost, 2011), focusing on the satisfaction degree of high level needs of oneself by innovation. When innovation is consistent with the ethical values of agents, it is the ethical values (Skitka et al., 2009; Leach et al., 2007) focuses on the degree to which innovation promotes social welfare.
2.3 Digital platform innovation ecosystem agents value conflicts
Compared with traditional two-sided market, the digital platform presents the characteristics of agents, fluid, strong interaction and so on. In the process of agents value co-creation of digital platform, the heterogeneous evaluators of digital platform interact with each other, and there will be different value judgments, which inevitably leads to contradictory and competitive value conflicts (Nissen et al., 2014; Barta and Neff, 2016), and a wider range of agents innovation value conflicts. Through an empirical study in the energy sector of Finland, Almpanopoulou et al. (2019) found the inertia of incumbents, ambiguity of regulation and policy making, cognitive limitations on opportunity recognition, and institutional complexity hinder the emergence of new technologies, are the institutional reaction force for the emergence of innovation.
However, digital platform innovation ecosystem caused by user, incumbent, suppliers and other agents value conflicts, guide entrepreneurs find consumer pain points, incumbent market value blank, contradiction of supply chain, etc., motivating entrepreneurs to improve, cooperation and alliance, to get users, incumbents, suppliers support.
3. Research Methods
3.1 Selection of research objects
The reasons to choose Didi as the sample are: First, Didi is a mobile digital platform for the development of disruptive innovation, starting from taxi business, quickly subverted the traditional taxi market and grew into a leading enterprise in the online car-hailing industry, which is an ideal research sample and meets the requirements of typical cases (Eisenhardt, 1989). Second, since the purpose of this paper is to investigate the mechanism of obtaining legitimacy of digital platform disruptive innovation through interaction between innovative agents behavior and values, it is necessary to select a case that has experienced the iconic innovative agents behavior and values in its core activities, so as to follow the targeted sampling strategy. Third, Didi is the most complete business one-stop travel platform in China, its development course has a lot of news report books, etc., increasing the availability of data. Fourth, Didi is the typical cases of digital platform disruptive innovation in China, choosing it as the research object is the embodiment of the Chinese context of research on digital platform disruptive innovation management.
3.2 Data collection
This paper collects data from multiple information sources such as interviews, commercial websites, archives, records, literatures, etc., aiming to form triangular verification in the research and improve reliability and validity of research. The specific data collection methods include: interview and secondary data. (1) Interview. The research team conducted in-depth interviews with drivers and passengers in the form of interviews, and recorded the evaluation and feedback of drivers and passengers on Didi, which was used to clarify driver and passenger behavior goals at different stages of Didi platform, obtain the response and action strategies such as the development of high-end technology, cooperation research and development, strategic alliance, etc. (2) Second-hand data collection. The research team collects secondary data of Didi platform through the following ways: First, enterprise records. Mainly comes from Didi website, company executives in corporate conference speech, related publication information, in order to understand the development of company. Such file data can provide Didi development strategy planning and development of the whole history of effective information, in order to obtain the enterprise behavior, goal expectation, value, etc. We obtained the establishment of research institute, user data analysis, big data system research and development platform, system improvement and other response, action and strategies. Third, Google Scholar and other literatures. We obtained the existing theoretical research on Didi. In this study, duplicate data and information lacking of multiple verification were deleted, and data sources with sufficient basis were retained.
3.3 Data analysis
This paper analyzes the legitimacy acquisition process of disruptive innovation on digital platforms based on paradigm of grounded management research in China (Jia Xudong, and Heng Liang, 2020). Firstly, the first-level open coding of innovation behavior is carried out for agents learning, response and search of innovation ecosystem. Secondly, the innovation behavior is the reaction of agents innovation goal, expectation, and the agents innovation goal, expectation is obtained through the second-level open coding. Thirdly, the agents innovation goal, expectation is the response of agents innovation value, and the agents innovation value is obtained by three-level open coding. Fourthly, the agents innovation value is selectively coded to analyze the agents innovation value conflict and collaboration. Finally, summarize the dynamic evolution law of digital platform disruptive innovation legitimacy (see appendix 1-6).
The dynamic evolution mechanism model of disruptive innovation legitimacy on digital platforms is summarized, see Figure 1.
First of all, the focus enterprise in digital platform innovation ecosystem interacts with agents innovation behaviors, resulting in goals, expectations and values conflicts of agents innovation, and forming the mechanism of market opportunity discovery, innovation motivation and innovation value source. Agents innovation value conflicts in digital platform innovation ecosystem promote innovator's discovery of market opportunity, technology opportunity and institutional opportunity, and inspire innovator's knowledge search, technology search and institutional entrepreneurship, so as to improve platform innovative products.
Secondly, the disruptive innovation strategy actions of focus enterprise in digital platform innovation ecosystem interacts with agents adaptive learning, innovation response and other behaviors, forming agents innovation value conflicts and coordination mechanism of digital platform innovation ecosystem. The disruptive innovation strategy action of focus enterprise leads to agents value conflicts, which promote the strategic action of focus enterprises to influence the value judgment of agents. The adaptive learning and innovation response behaviors of agents make the agents innovation value coordination, and promote the deepening of agents cognition, standard change, and institution innovation.
Finally, in the digital platform innovation ecosystem, agents innovation value conflict and synergy is the dynamic evolution mechanism of digital platform disruptive innovation legitimacy. In the digital platform innovation ecosystem, the agents innovation value conflicts drive innovator to discover market opportunities, innovation motivation and sources of innovation values, and promote the focus enterprises to take innovation strategy actions to coordinate agents innovation values. In the digital platform innovation ecosystem, the agents innovation value synergies motivate more players to enter, and agents will obtain more values from available resources and knowledge to realize value co-creation. In addition, in the digital platform innovation ecosystem, the agents innovation value conflicts evolution may make agents cannot get enough values from it, and agents may choose to withdraw from the digital platform innovation ecosystem. Therefore, in the digital platform innovation ecosystem, agents innovation value conflict and synergy will push digital platform innovation ecosystem upgrade, attract more innovation agents to participate in the platform to create value, promote positive circle of digital platform innovation ecosystem, realizing digital platform of disruptive innovation legitimacy dynamic evolution.
This study only discusses the legitimacy acquisition of disruptive innovation on digital platform in travel industry. The possible differences in agents value conflict and synergy in different industries need to be further explored.
II. Florencia Fiorentin
From literature on innovation systems to the evaluation of innovation policy. Challenges to address a systemic phenomenon.
The objective of this research is to reflect on the challenges that the complexity of innovation processes implies for the design, implementation and evaluation of innovation policy. There is an old consensus in the literature on National Innovation Systems (NIS) that sustains the need for public policy to foster innovation process at the firm level, that might lead to better economic performance at the more aggregated levels (Metcalfe 2005; Chaminade and Edquist 2010). However, after thirty years of implementation of these policies in Latin American countries, they have not been enough to impulse such processes (Castillo et al. 2013; Crespi et al. 2015). Empirical analysis based on non-linear strategies must be implemented to better address the complex and systemic process of innovation policy at the firm level. New evidence on this regard is needed to contribute to the original objective of innovation policy, namely the diversification of productive structures to increase productivity and competitivity.
Based on NIS approach, we sustain that firms innovate deliberately and emergently to survive the processes of Schumpeterian competition and to generate extraordinary benefits (Nelson 1991; Srholec and Verspagen 2012). Innovation is a complex, path dependence and systemic process that depends on the priors capabilities’ development and economic and innovative performance of the firm (Dodgson 2017). The systemic nature of innovation and firms’ microheterogeneity are starting and finishing points of the Schumpeterian competition processes.
Additionally, we have focused on the study of innovation policy at the firm level. Literature on innovation policy has reached consensus in terms of the relevance of fostering private innovation, given that innovation positively impacts on firm’s innovative and economic performance (Metcalfe 2005; Edler and Fagerberg 2017; Chaminade and Edquist 2010). The focus is on the existence of systemic problems that must been addressed by innovative processes, that are possible thanks to the innovation policy at the firm level.
Nevertheless, neither the design nor the policy evaluations have addressed the issue of microheterogeneity. Nor have they adequately addressed the systemic nature of innovation processes. Innovation policy has not been fully analyzed and recognized as a systemic process (Chaminade and Edquist 2010). Firm’s decision about funding an innovative project is part of a complex, path dependence and emergent innovation process. However, innovation policy instruments tend to be designed based on an horizontal perspective (Borrás and Edquist 2013), ignoring that some firms’ characteristics make them more likely to be granted, given microheterogeneity, as well as the fact that firms apply for funding as part of their innovative strategy.
In this regard, evidence for Latin American countries suggests innovation policies tends to benefit sectors and activities that were already the most dynamic ones, and reinforces the strong heterogeneity that characterizes less developed countries (Suárez and Erbes 2021). Then, innovation policy is not strong enough to trigger processes of structural change.
Literature on evaluation policy has been mostly centered on the ex-post impacts of instruments on several dimensions of firms (mainly innovative and economic performance) (see Jugend et al. 2020). These evaluations ignore that innovation funding is a necessary stage of a dynamic process within the firm, and between the firm and its environment. In order to be aware of the availability of public funding, the firm must have accumulated some level of absorptive capabilities to recognise sources of financing and to develop an innovative project to apply (Cohen and Levinthal 1990). Therefore, once the firm has applied and been granted, the impact of public funding will depend on its capabilities to carry on the innovative project that was funded and the rest of technological and institutional factors that affect the search for innovations.
To the best of our knowledge, none policy evaluation has focused on looking at the entire process, from the ex-ante allocation to the ex-post impacts. In this respect, evaluations about the allocation process are mostly based on probitmodels for panel databases, both based on random or fixed effects. Impact evaluations imply contrafactual analysis, which are mostly addressed by Difference in Differences (DID) models and Propensity Score Matching (PSM) techniques. The problem here is the use of linear strategies to approach a systemic phenomenon. This impedes the analysis of innovation policy in the terms we have defined it.
To address -to some extent- these problems, our recent research has been focused on analyzing the entire process of innovation policy at the firm level. On the one hand, we have surveyed and analyzed literature on NIS and innovation policy, and have published some reflections (F Fiorentin, Pereira, and Suarez 2019; Suárez and Erbes 2021; Florencia Fiorentin, Suarez, and Pereira, n.d.; Erbes and Suarez 2016). On the other hand, we have made empirical evaluations centered on the allocation process of innovation policy at the firm level, to address that less studied stage of the process (Pereira and Suárez 2017; F. A. Fiorentin, Pereira, and Suarez 2019; Florencia Fiorentin, Suárez, and Yoguel 2021; Suarez, Fiorentin, and Pereira 2021). For empirical analysis, we have mostly run random effect dynamic probit models for panel databases. This selection has allowed us to control microheterogeneity by means of the inclusion of unobserved effects using the solutions proposed by Mundlak (1978), Chamberlin (1948) and Wooldridge (2005). The rationale behind the selection of a random model is because we generally have binary variables, and there are no standardized estimations for a fixed effect dynamic probit model. Random effect models also better suit the characteristics of the expected dynamics of firms under analysis, which means that some non-observable attributes of firms will vary over time while others will remain invariant.
Our empirical analysis (based on the Argentinean case) permitted us to conclude that allocation process is an important stage of innovation policy. Firms’ productive, innovation, and connectivity capabilities impact on the probability of knowing about and accessing to innovation funds (Florencia Fiorentin, Suárez, and Yoguel 2021). Formulation skills also impact their probabilities of first accessing (Suarez, Fiorentin, and Pereira 2021), as well as innovation capabilities impact their probabilities of being granted more than once (Pereira and Suárez 2017; Suarez, Fiorentin, and Pereira 2021).
Some empirical challenges have emerged. On the one hand, databases only contain information about firms that applied for funds, whether they were granted or not. We have not information about all firms. This might bias our results and limit the study on policy allocation. Another limitation is empirical models, which are based on linear assumptions, so we cannot ask systemic research questions and/or make systemic evaluations.
Given the above, there is a theoretical vacancy in literature on NIS on analyzing innovation policy. Very important issues highlighted by the literature have not yet been properly addressed by empirical analysis: the systemic nature of the innovation process, meaning the innovation policy, and firms’ microheterogeneity. New theoretical and empirical studies are needed to enhance our knowledge on the systemic process of innovation policy, which includes the allocation process of funds and how this determines the subsequent impacts on firms. Such analysis would provide a better understanding of the innovation policy phenomenon at the firm level. This would also allow us to maximize the scope and impacts of intervention and improve the use of public resources. Governments from both developed and developing countries must invest on fostering innovation, while they have limited resources. Improving intervention is therefore a debt to democracy and to the commitment to include a greater diversity of firms in innovation processes.