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The Handbook of Systemic Family Therapy Volume 4 : Systemic Family Therapy and Global Health Issues Mudita Rastogi
Innovation in Engineering and Technology Set coordinated by Dimitri Uzunidis
Systemic Innovation
Entrepreneurial Strategies and Market Dynamics
Edited by
Dimitri Uzunidis
First published 2020 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
John Wiley & Sons, Inc.
27-37 St George’s Road 111 River Street London SW19 4EU Hoboken, NJ 07030 UK USA
The rights of Dimitri Uzunidis to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2020938717
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-658-6
1.1.
2.1.
2.2.
2.2.1.
2.2.2.
2.3.
2.3.1.
2.4. Towards a theory of
2.4.1. Utopian technologies and the technological utopianism of American culture
2.5. Conclusion
Chapter 3. The Management of Inventive Knowledge: From Inventive Intellectual Corpus to Innovation
Pierre SAULAIS and Jean-Louis ERMINE
3.1.
3.2.
3.4.1.
3.4.2. The inventive intellectual corpus and the dematerialized knowledge
3.4.3. The inventive intellectual corpus at the heart
3.5. The virtuous cycle
3.6. The MASK
3.6.1. MASK II:
3.6.2. MASK I: Capitalization of
3.6.3. MASK III: Sharing the knowledge capital
3.6.4. MASK IV: Evolution of the knowledge capital
3.7. Illustrations with real cases from “economic reality”
3.7.1. Strategic analysis and capitalization: the case of IRSN
3.7.2. Transfer: the case of Sonatrach
3.7.3. Innovation: the case of ONERA
3.8. Conclusion
3.9.
Chapter 4. Evolution of Firms Trajectories and Innovation: Knowledge Capital and Financial Opportunities
Blandine LAPERCHE
4.1. Introduction
4.2. Technological and firms trajectories
4.2.1. Technological paradigms and trajectories: first definitions
4.2.2. Paradigms, regimes and trajectories: empirical studies
4.2.3. The firm’s trajectory or evolutionary path
4.3. The formation of trajectories: knowledge capital and financial opportunities ..................................
4.3.1. Dynamic capabilities and knowledge capital .................
4.3.2. The collective dimension of trajectories and its consequences ....... 75
4.3.3. Financial opportunities, firm evolution and technical change ........
Chapter 7. Technological Change and Environmental Transition: Lessons from the Case of the Automobile 143
Smaïl AÏT-EL-HADJ
7.1. Introduction 143
7.2. Encountering a major technological limit: the environmental limitation ...............................
7.2.1. Technological system dynamics ........................
7.2.2. Nature and forms of the environmental limit ................. 145
7.3. The irruption of the environmental limit as a determining/dominant factor in technological change: the case of automotive system technology .......... 146
7.3.1. The environmental limit of the automotive system .............
7.3.2. Corrective action of a social, fiscal and regulatory nature .......... 149
7.3.3. Forms and stages of technological change in road transport ........ 153
7.4. The environmental limit as a factor of a major technological change ...............................
7.4.1. Nature and actions of environmental limits ..................
7.4.2. Generation of a new change regime ......................
Chapter 10. When Innovation Innovates: How Artificial Intelligence Challenges the Patent System
Marc
BAUDRY and Beatrice DUMONT
10.1. Introduction
10.2. Definitions and evolution over time of AI technologies
10.3. The difficult issue of the patentability of AI
10.3.1. The patent subject-matter eligibility of AI technologies
10.3.2. Who should be listed as the inventor?
10.3.3. Liability for patent infringement by AI ...................
10.4. AI patents in light of economic theory
10.4.1. The rationale for granting patents
10.4.2. AI patents, incremental inventions and legal implications
10.5. Conclusion
10.6. References
Chapter 11. Conflicting Standards and Innovation in Energy Transition
Stéphane
CALLENS
11.1. Introduction: a change of culture
11.2. Green innovations and standardization ......................
11.2.1. Regulatory quality defined on the basis of a relationship between standards and innovation
11.2.2. Another multi-level management: sovereignty and innovation
Systemic Innovation
11.3. The conflict of standards: globalization, sovereignty and democracy .....
11.3.1. Acting solely through taxation?
11.3.2. Acting solely through local and regional authorities?
11.3.3. The conflict of standards: Europe and the United States ..........
11.4. The energy transition: a natural experiment ...................
11.5. Conclusion
11.6. References
General Introduction: Systemic Innovations and Transformation of Organizational Models
Innovation is at the heart of the dynamic growth model based on uncertainty, risk and profit. In the current context of crisis and globalization, entrepreneurs, companies and public institutions for economic action are challenged by the need to renew technologies, organizational patterns and production and consumption modes as quickly as possible.
Whether gradual or radical, innovation is embedded in a complex process characterized by a large amount of feedback and interaction. The innovative organization is a dynamic system composed of specific and diversified competencies. By acquiring, combining and mobilizing these competencies, the innovation actor (entrepreneur or organization) can create technological resources and change the relationships it maintains with its environment. This explains the importance of design, application and development management in the implementation of an innovation process.
An innovation system mobilizes a set of knowledge and skills that are generated by learning processes and embedded in its memory. This knowledge must be enriched to be valued through the development, use and commercial launch of new goods, services and technologies. The survival of the system depends on its ability to innovate, which enables it to face external aggression, to transform itself and to endure. External stimuli (competition, product substitutability, consumer incentives, innovation policies, etc.) are generated by the economic context and act on entrepreneurs and companies as a means of selection. Selection procedures are
General Introduction written by Dimitri UZUNIDIS
Systemic Innovation
shaped by the business climate: the nature of the product market, the availability of capital and labor, the pace of innovation, the effects of public policies, the diverse demands of producers and consumers, etc. They can therefore create alternatives to the mode of operation, management and production of a given enterprise (or a particular innovation system) (Uzunidis and Saulais 2017).
The business climate, in any case, creates barriers or opportunities for the organization. The unpredictability of results and the possible existence of alternatives are the main inherent uncertainties in innovation activities. Moreover, since the innovation process is a learning and knowledge-enhancement process, the innovative organization must constantly make internal adjustments and review its relationships with its environment. It also goes without saying that, through their actions, the enterprise and the entrepreneur transform the socio-technical context and shape the surrounding economy.
In short, innovation can only be apprehended if the economist and the manager use a multifaceted analysis of the evolutionary relationships that are established between the actor and the system. Innovation unquestionably contributes to growth and, above all, to economic and social change. The success of its multiplier effects depends as much on the effectiveness of the economic policies applied by public decision-makers to support the act of entrepreneurship and innovation as on the strategies implemented by companies in the field of production, acquisition, application or dissemination of knowledge and new ideas. The most difficult element, however, is to predict its direction: areas of application of science in production, combinatory effects of knowledge and techniques, needs revealed and/or satisfied, etc. It is therefore hard to say with certainty where the trajectory of innovations is heading, although, given the immense volume of scientific and technical knowledge accumulated, diverse calls for change and the strategic orientations of large companies – pivots in particular sectors – some indications of the evolution of the socio-technical paradigm may emerge.
Innovation is inseparable from an ongoing questioning of the social relations and institutional structures that characterize, at a given time, a given economy. Innovation is the result of close relationships between producers and consumers, asymmetric information or asymptomatic micro- and macroeconomic growth processes. It arises from imperfection or imbalance and contributes, in turn, to making the economic field perfectible but also unbalanced. The “flaws” that characterize an economic system are nevertheless important sources of opportunities for investment, production and dissemination of new market values. However, in order to achieve this, it is necessary for the economic mechanisms to find themselves, at one time or another, in line with one another.
Time intervenes in the preparation, organization and, quite simply, in the seizing of the opportunities that the market offers to the agents who are supposed to realize new productive combinations. The synchronization phase of the socio-economic and technical processes leading to innovation thus becomes crucial. Synchronization means that several processes take place at the same time. It is an action of coordination of several operations that take place (or should take place) simultaneously. This coordination can take place spontaneously, opportunely or inopportunely, but more often than not the voluntarist strategies of economic and political agents set in motion at the same time actions to increase the value of capital, reorganize production structures and entities, create new markets and reveal new needs. The synchronization of innovation processes depends on the norms, rules, traditions and institutions through which economic functions (including power relations) are organized and through which the choices and activities, in our case, of innovation are compatible with each other in time and space. Moreover, the synchronization of these processes must be considered through the interaction abilities and strategies of companies, consumers and public authorities, as well as through ways of coordinating their actions with the aim of creating or organizing a market, which must amortize investments, mitigate risks and guarantee, for a certain period of time, the vitality of business.
The synchronization of innovation processes can be studied through the prism, among others, of the entrepreneurship, enterprise, work organization, expansion strategy, choice of location and macroeconomic contexts (Uzunidis 2018). How do entrepreneurs and companies mobilize their networks to innovate? What are the success factors from which an idea can become an innovation and create value? How is the diffusion of innovations organized? How does the company transform itself? How does the economic and socio-technical environment influence the evolution of global or specific innovation systems? How does a particular innovation articulate with other innovations (ancillary or related) to lead to the emergence of innovation clusters?
Novelty disturbs routines, especially since, before its appearance and generalization, a whole set of processes are triggered that may not be socially and strategically controllable. Innovation is a result; a result of human work, of a process that brings together individuals with different statuses, different roles and particular social functions. Individual work is defined as much by the task performed as by the organization that frames it, that implements it. However, this work is only partial in terms of the result (of the innovation). The necessary knowledge, skill, information, creativity, funding, etc. that contribute to the choice and decision come from different time and geographical horizons. Moreover, they are transmitted and grouped together following explicit and implicit cooperation procedures that are not always immediately visible. In addition to the difficulty of studying all the processes
Systemic Innovation
leading to the realization of an economic novelty, there is also the fear of the unknown to which innovation leads the company and the economy as a whole.
Systemic innovation is born and spreads from a disruptive innovation that stems from the socio-technical system of innovation, which itself is “naturally” unstable. This innovation is multiplied by the interplay of actors, thanks to incremental intra- and inter-sectoral innovations, and also thanks to disruptive subsidiary, ancillary and related innovations, leading to the creation of new combinations of innovations, known as “systemic innovations” (product, process, organization, management or commercial innovations). In their turn, systemic innovations (sets of innovations from new combinations allowed through the diffusion/adaptation of the disruptive innovation) again disrupt the socio-technical system whose robustness depends on the stability of the routines it generates.
In the complex economy, systemic innovation is largely due to the fact that a company tends to make greater use of the resources of its environment (education, environment, health, finance, inter-industrial links, communication, needs and aspirations, etc.) than to invest, for example, in all the phases of achieving the combination of knowledge, skills, ideas, technologies, capital, etc. This can surely be explained by the fact that investments in the acquisition of production resources are less costly than those devoted to the training of these resources. Under these conditions, the collective profitability of capital may be high, while individual profitability may be insufficient; this leads to the reorganization of market structures through the acquisition or disappearance of those companies that do not have the financial means to maintain their place in the innovation system. The ensuing concentration may create routines that (1) place the system on a “path of dependence” and (2) predefine the evolutionary trajectories of systemic innovations. The innovative actor (entrepreneur or company), in a system of real or latent competition as well as of de facto cooperation, is led to appropriate these strategic resources or at least to control their impact on their performance, or, even better, to appropriate the non-market logics that generate and reproduce these resources (the “commons”) by making them marketable. The level of external savings that the company is able to achieve conditions its success in terms of innovation. This is how the act of innovating is currently defined: the realization of new combinations of codified knowledge, the dissemination of this knowledge, as well as its appropriation and integration into a broader combination of productive resources. Systemic innovation results and at the same time intensifies the socialization of the production, distribution and consumption of goods and services integrated into a broad socio-technical system.
The authors of this book undertake the theorization of systemic innovations based on concrete sectoral and entrepreneurial examples. If the creative spirit is at the basis of the design of new products and technologies, new modes and models or
General Introduction xv
new organizations and activities, the company is the implementation node of the collective processes of knowledge development, processes that lead to the emergence of systemic innovations that modify or renew particular or global innovation systems.
Let us begin the analysis of these processes and their results in terms of innovation by following the reasoning of the authors. Systemic innovation is now the result of an organization’s ability to be agile in generating and appropriating knowledge useful to its adaptation and development process: agility primarily refers to “the ability and dynamism that an organization can demonstrate, relative to others, to quickly identify, adapt to and seize opportunities for environmental change. (…) agility applied to the design process requires intelligence and creativity” (Chapter 1; see also Goria et al . 2019). Creativity and imagination stimulate traditional research and development (R&D) which “(…) is based on a technical imagination in which science fiction plays a central role. Indeed, it generates a form of collec tive belief as well as a synthesis of the most unbridled fantasies about the future of technosciences” (Chapter 2; see also Michaud 2017).
Training, development and transmission of the asset of “inventive knowledge” come from the technical imaginary of work collectives in an organization. “The transition from knowledge in general to inventive knowledge leads to the management of inventive knowledge centered on the inventive intellectual asset, which is itself at the heart of the first part of the innovation process” (Chapter 3; see also Saulais and Ermine 2019). The ultimate goal of knowledge management is to maximize the profits of the company, which, by investing, enhances its “knowledge capital”: “the current challenge for companies is to develop the dynamic abilities (…) that make it possible to accumulate the organizational routines specific to the control and direction of technological change. The constant enrichment of the ‘knowledge capital’ (…), all the scientific and technical information and knowledge produced, acquired and systematized by one or more companies with a productive objective and, more broadly, in a value production process, is for us the essential pillar” (Chapter 4; see also Laperche 2017). The more agile and financially powerful the company is, the more its frontier becomes blurred and the greater its capacity to acquire (and add value to) production resources.
Organizational agility as a collective mode of “realizing” systemic innovations is explored in the context of automation, which questions the links between machines, work and employment. Automation “has developed in different ways during two particular periods: that of the rigid conception of activities and production based on reproductions of the actions, gestures and attitudes of employees creating inventions of machines, networks of inventors and new conceptions of jobs and work; and that
Systemic Innovation
of the flexibility of techniques and work based on the different social status of employees within flexible companies with innovations based on different sets of machines, networks and organizations of work and jobs” (Chapter 5; see also Vigezzi 2019).
Whether at the microeconomic or sectoral level, systemic innovation takes shape and spreads by the extension of the firm’s frontier and the extension of the intersections that are created by inter-firm cooperation agreements. When considering inter-industry technology collaborations on a global scale, they occur primarily between partners with complementary technological skills. They are thus at the heart of the relationship between technology systems and sectoral innovation systems (SIS). “From this point of view, a multitude of issues can be addressed, whether they relate to (…) the way in which SISs are combined over time through technology according to the strategic choices of companies and the characteristics of the institutional environments in which they operate, or the way in which technologies redraw sectoral boundaries by promoting convergent or, on the contrary, fragmented logics” (Chapter 6; see also Lebert and El Younsi 2017).
Through agile entrepreneurial strategies for the valorization of inventive assets and knowledge capital, supported by financial considerations (profitability), focused on accelerating the rotation of committed capital (innovation) and aiming at increasing the density and intensity of intersections around nodal technologies (cost and risk control), techno-scientific pathways and sectoral innovation systems originate from the formation of product-services systems that trace the evolutionary trajectories (transition, bifurcation, disruption) of process technologies and final goods and services. Systemic innovation is thus formed and diffused by the grouping together, in the same system, of innovations based on the same (or related) knowledge bases. And all this under a double constraint: that of competition and that of the (relative or absolute) scarcity of production resources. For example, “the appearance of the new global environmental constraint and its consideration can disrupt the contemporary technological system by leading to the elimination of a whole series of technologies, due to their inability to comply with environmental requirements; putting them in a sort of state of ‘anticipated obsolescence’” (Aït-El-Hadj 2017) (Chapter 7). More explicitly, in the “broad” industrial and technological defense sector, the process of its transformation has multiple facets grouped into three dimensions: “(1) the evolution of the defence-related knowledge bases from which defence technologies are developed, (2) breakthrough technologies (such as artificial intelligence and digital technologies) and their implications for defence actors, and (3) the transformation of military capabilities” (Barbaroux 2019) (Chapter 8).
Innovation is thus the result of the accumulation of large volumes of information. The information collected and exploited enables individual companies or networks
General Introduction xvii
of companies to fuel their agility and responsiveness to adapt to changes in their economic and institutional environment. “The exploitation of collected data requires: sorting, verification, processing, analysis and synthesis. It is through this process that the raw data collected during the research will be transformed into strategic information. The interpretation of the information collected through its processing, analysis and synthesis represents the lever for the success of the economic intelligence process (…) Economic intelligence will make extensive use of nanotechnologies in the sharing and dissemination of information and to increase the computing power of objects that will be evanescently disseminated in our environment” (Monino and Sedkaoui 2016) (Chapter 9).
According to the above, systemic innovation becomes an inexorably complex process. Supported by precise institutional frameworks and arrangements, especially in the case of technologies that are intensive in scientific and technical knowledge, this process integrates within itself a set of incentives to innovate, as well as a set of guarantees of risk control and opportunity for return on capital employed. In the case of artificial intelligence (AI), “it is important to have the 'right' patent regime to shape the development and diffusion of AI-related innovations, and more importantly, to provide effective incentives for innovation. A long period of uncertainty in patent offices could hinder innovation, and thus productivity and growth” (Chapter 10; see also Baudry and Dumont 2017). The inadequacy of the regulatory framework in driving change and disseminating systemic innovations may indeed be due to the incompatibility and therefore conflict of standards. The difficult energy transition illustrates a set of obstacles created by the lack of institutional arrangements: “the normative framework of climate change can be situated today in an intermediate position between a fossil fuel mining law based on a principle of sovereignty and an environmental law. The need for an international consensus means that only some of the principles of environmental law are taken up as a basis for action to combat and adapt to climate change” (Callens 2018).
From a methodological point of view, the studies presented by the authors (specialists in economics, engineering and innovation management) of this volume illustrate the fact that the behavior of innovators in the face of general and specific constraints and profit opportunities generates individual and collective investment processes that lead to the implementation of new organizational models intimately linked to the design of systemic innovations. The latter, in turn, influence the performance of the collective work mobilized, the network of actors created and each stakeholder taken individually.
xviii Systemic Innovation
References
Aït-El-Hadj, S. (2017). The Ongoing Technological System. ISTE Ltd, London, and Wiley, New York.
Barbaroux, P. (ed.) (2019). Disruptive Technology and Defence Innovation Ecosystems. ISTE Ltd, London, and Wiley, New York.
Baudry, M. and Dumont, B. (2017). Patents: Prompting or Restricting Innovation? ISTE Ltd, London, and Wiley, New York.
Callens, S. (2018). Creative Globalization. ISTE Ltd, London, and Wiley, New York.
Goria, S., Humbert, P., and Roussel, B. (2019). Information, Knowledge and Agile Creativity ISTE Ltd, London, and Wiley, New York.
Laperche, B. (2017). Enterprise Knowledge Capital. ISTE Ltd, London, and Wiley, New York.
Lebert, D. and El Younsi, H. (2017). International Specialization Dynamics. ISTE Ltd, London, and Wiley, New York.
Michaud, T. (2017). Innovation, Between Science and Science Fiction. ISTE Ltd, London, and Wiley, New York.
Monino, J.-L. and Sedkaoui, S. (2016). Big Data, Open Data and Data Development. ISTE Ltd, London, and Wiley, New York.
Saulais, P. and Ermine J.-L. (2019). Knowledge Management in Innovative Companies 1: Understanding and Deploying a KM Plan within a Learning Organization. ISTE Ltd, London, and Wiley, New York.
Uzunidis, D. (ed.) (2018). Collective Innovation Processes: Principles and Practices ISTE Ltd, London, and Wiley, New York.
Uzunidis, D. and Saulais, P. (eds) (2017). Innovation Engines: Entrepreneurs and Enterprises in a Turbulent World. ISTE Ltd, London, and Wiley, New York.
Vigezzi, M. (2019). World Industrialization: Shared Inventions, Competitive Innovations and Social Dynamics. ISTE Ltd, London, and Wiley, New York.
Enterprise Through the Lens of Agility, Creativity and Monitoring Method Combinations
1.1. Introduction
The term agility is now very often used to describe innovative management methods or companies. In this sense, at the company level, agility is considered as an ability to adopt a “start-up” spirit to develop new products that will be successful very quickly. In a more cross-functional way, relating just as much to the individual, innovation or organization, agility evokes both an intellectual and a mechanical ability that qualifies an ease of adaptation to an increasingly turbulent and unpredictable environment (Sonntag 2019). It is not reducible to the application of a method that cannot, by itself, guarantee the effectiveness of the company or its teams to respond quickly and more effectively to the needs of its customers.
Agility is a complex concept which, if it is to be defined, must take into account its two aspects. It is the initiative that an organization can demonstrate in order to seize new opportunities and effectively guard against threats through its ability to detect and react quickly to environmental changes (Lim et al. 2015). However, this definition is not sufficient, as it does not provide information on what an initiative is and how it is implemented. Agility is also a set of values and beliefs to which its proponents are explicitly attached (Oswald 2018). It is therefore necessary to understand both of these aspects. Therefore, this chapter presents the origins of agility, the values and principles to which it refers and how it can be implemented or perceived in terms of decision-making initiatives.
Chapter written by Stéphane GORIA
1.2.
Agility and its manifesto
Agility is a buzzword, synonymous with the evolution of management methods for teams and organizations. After numerous proposals and trials in the 1980s and 1990s, new organizational options emerged to change the practices of teams, including those involved in IT development. Indeed, the latter were very often confronted with customer dissatisfaction at the time of the final delivery of the product for various reasons, including a certain lack of understanding of the real targeted need, changes in the perception of this need during the design phase and the management of deadlines. To this was added, and still is, the impact of changes in an environment that is constantly evolving and whose transformations can be very rapid and violent. Thus, in 1991, the first major gathering of researchers and professionals emerged to talk about agility: Agile Manufacturing (Barrand and Deglaine 2013). From then on, this approach to project and team management became an international concern. From that time on, even if they do not guarantee complete security in the face of change, agile methods have been proposed in various forms in order to provide a form of response to this type of problem.
That said, the first major agility success stories emerged in the field of software development, which prompted, in the early 2000s, a clearer promotion of agility. A working group of mainly IT agility specialists was set up to highlight the values and common elements of a set of methods that were among the most popular in this field at that time (Extreme Programming, SCRUM, Dynamic Systems Development Method, Adaptive Software Development, Crystal, etc.). This synthesis took the form of the Agile Manifesto, which now acts as the banner of this movement.
This manifesto is presented as a series of values and principles to follow in order to improve performance. In terms of values, it is a matter of giving more importance to people (and their interactions) than to processes and tools; developing solutions that are primarily operational rather than focusing on exhaustive documentation; collaborating rather than negotiating with clients; and adapting to change rather than sticking to a plan that is (in fact) older than the changes that have taken place. In addition, the principles are intended to provide guidance on how to maintain the above-mentioned values. To do this, each principle is supplemented by one or more corollaries in the following form1:
– first principle: “our highest priority is to satisfy the customer through early and continuous delivery of valuable software”;
– second principle: “welcome changing requirements, even late in development. Agile processes harness change for the customer’s competitive advantage”;
1 For a complete list of these principles, see: https://agilemanifesto.org/principles.html.
– third principle: “deliver working software frequently, from a couple of weeks to a couple of months, with a preference to the shorter timescale”;
– (…)
– twelfth principle: “at regular intervals, the team reflects on how to become more effective, then tunes and adjusts its behavior accordingly”.
There is clearly a fad in the current use of agile methods. But it is not the methods that constitute agility, but the implementation of the values and principles in which they are embedded. In this sense, all areas and processes can be reconsidered from the point of view of agility and a management adapted accordingly. Agile innovation has thus emerged as a set of approaches employing and adapting agile concepts to orient them towards an innovation objective (Frimousse and Peretti 2015). They are, for the most part, considered at the level of product or process innovation, but other agile methods exist to apply to many activities. Thus, all levels of innovation can now be rethought in terms of agility (Barrand and Deglaine 2013). At the company level, it is not enough to transform, by analogy, from the individual to the company. The agile capabilities expected of the individual become those that the structure must adopt. The subjectivity of the individual must be respected and managed as such, starting with its consideration at the level of the agile team (Unhelkar 2016, p. 25).
All these different adaptations of the agile spirit and the success of its diffusion are, in part, linked to the fact that the term “agility” can be conceived in an individual or collective way. From an individualistic perspective, agility can be understood as the ability of an individual to be able to adapt and demonstrate flexibility of mind as well as speed of interpretation, inference and execution to respond to changes in understanding of a need which must be addressed. Organizational agility can then be understood as an extension of team agility, which in turn comes from a respect for individual agility. It should preserve and, where possible, enhance the abilities of individuals by empowering them to respond promptly to a perceived environmental disruption that may affect the business. In this sense, it can also be understood as the ability of a group to anticipate, prepare, identify and then seize opportunities that will appear in its field of action (Charbonnier-Voirin 2011). It is a new way of approaching collective intelligence that can be considered as a four-stage process (reflection, understanding, decision and action), linked to four levers of action (cognitive, relational, social and managerial) with the collaborative project as its main instrument (Gréselle-Zaïbet 2019). These levers can be considered to resonate with the different levels of innovation analysis (product, process, strategic, managerial) (Hamel 2007). That said, their implementation requires an idea of how they can be accompanied, if only at the level of the design process.
1.3. Agility and the design process
Accelerating the design and feedback process from prototyping is one of the priorities in implementing an agile method. At the level of an agile project itself, this translates into the implementation of stages often referred to as sprints. This term, borrowed from athletics and the SCRUM method (Hundermark 2014), defines a predetermined period of time that the project team, if it wants to be agile, will not be able to postpone, extend or shift. As well as considering them in the literal sense, sprints should be considered as successive races of the same duration, in which the whole team participates. From this point of view, a project is experienced as a series of races/sprints that generally last from one to four weeks. Once the project is launched, the team is not supposed to stop and should focus on the different sprints to be run. During these periods, the team ideally lives in isolation from other company projects (except, of course, for crises). In this context, the agile team is self-managing and is, at the same time, the participant, manager and observer of its course. It uses dashboards that allow it to observe the following:
– the evolution of the content of the current sprint;
– everything that remains to be done, what has been completed, problems that have arisen or are being dealt with;
– feedback from stakeholders in special sessions called “sprint reviews”;
– team-specific debriefing feedback named “retrospectives”.
With agile methods of the SCRUM type or using the KANBAN method (Morisseau and Pernot 2019), these tables are organized from a “product backlog”. This is a list of tasks (to be carried out by the team) that are prioritized and then distributed, first as a whole, then by group to be carried out during a sprint called a “sprint backlog”. In each of these lists, the tasks to be carried out are placed on different columns of a table expressing a temporal positioning in relation to the project, depending on whether they are tasks at the proposal stage, accepted tasks to be carried out, tasks in progress, in the test phase or validated and completed. Traditionally, each task is associated with a user story (or simply a story) expressing an expected use of the future user/customer and that they will consider as a source of value for the product concerned. Of course, tasks can be broken down into sub-tasks in order to better determine what to develop. In the case of disagreement regarding the order of the tasks to be performed or requests for further information from future recipients and other users, one member of the team undertakes these responsibilities: the product owner. It is this person who should know the future users and other people concerned by the solution best, because if necessary, when there is a dilemma or when there is no unanimity on the choices to be made concerning development, it is this person who is responsible for deciding on the priority to be given to the tasks (Aubry and Appert 2019).
Enterprise Through the Lens of Agility, Creativity and Monitoring Method Combinations 5
Not surprisingly, these tables also serve an explicit and recurrent phase known as “sprint planning”, which therefore precedes each sprint. However, if this is the very first sprint, then this step may be preceded by an initial assessment, understanding of the problem/need and possibly a prototype draft called sprint zero. This sprint is used by the team to better define the problem and to estimate what will be feasible, to better understand what the team will be able to develop according to the time and means at its disposal. It allows the team to engage with more confidence and arguments, particularly concerning the methodology to be used (Qureshi et al. 2012). Beyond the zero sprint, this planning stage is a time when the team visualizes, discusses and ranks the priorities, the order of the elements (user stories) to be dealt with during the sprint that is about to start and the people who will be in charge of it. In theory, a sprint never stops, i.e. for the team concerned there is no project with higher priority. However, as the work is carried out over several consecutive days, there is a small phase of work and interruption during a sprint that is called scrum. This is a quick session (15–30 minutes) which is carried out at the beginning of the day by the whole team in front of the storyboard and possibly followed up by a second meeting to manage the problems encountered. These scrums should allow, in an agile spirit, a better discussion of the current elements that pose problems, bring reinforcement staff where necessary, etc.
Figure 1.1. An example of a “conventional” presentation of an agile SCRUM-type production system2 (source: author, inspired by https://expertprogrammanagement.com/2010/08/the-scrum-process/)
2 In this scheme, we consider sprints over full weeks of five days and do not count among the reviews the “final presentation” of the product when it is finished or when its delivery deadline is reached.
When a sprint is over, it is time to present the results to the various stakeholders and, above all, to the representatives of the future recipients; this is called a review. It is a matter of presenting at least one minimalist prototype or demonstrating what has been achieved. In terms of review management, there is no real rule, each sprint is usually followed by a review, but it is possible, depending on how the schedule is managed, to do only one every two sprints. However, this is not the recommended solution, because in this case, there will in fact be fewer exchanges with future users and other stakeholders (funders, associates, intermediaries, indirect users, etc.). The important thing is to maintain regular reviews throughout the development process so that there is a real implementation of agile development cycles nourished by exchanges between the team and the project stakeholders. These points often take the form of demonstrations and presentation of prototypes that the people affected by the product/service developed and not only future uses will react to. The review allows them to better project themselves and to clarify certain needs, constraints and implicit problems. It then allows the team to adjust its decisions and may even lead to the termination of the project (Hundermark 2014).
At the end of a sprint, the team meets during a phase called the “retrospective”. This serves to integrate the twelfth principle of the Agile Manifesto (see above). The team then devotes a few hours to analyzing what was done, how it was done and why it was done. It goes through the chronology of the last sprint, noting all the important and notable points, both negative and positive. Then, the team identifies good practices and looks for solutions to solve some of the problems that have occurred and are likely to occur again. As this phase is specific to the team, it cannot transfer the causes of problems and errors to members outside the team, since in this case, it would not be able to propose solutions to act on them. The key idea is to improve the management of its own practices between the different members, including a facilitator responsible for the proper keeping of deadlines and development in agile mode who in SCRUM teams has the title SCRUM master. This facilitator must accompany the members of the group in the implementation of agility by providing them with solutions, i.e. means to improve the management of ongoing processes. They are also responsible for ensuring that the time limits of the process steps, as set by the group, are respected. They must do this while being attentive to the needs and conditions of each party. They are not a project manager. They focus, above all, on how to make the team work so that it achieves its objectives. They are the guarantor of an agile mind while the product manager is the guarantor of the development of a product with added value for the client. In addition to these two roles (scrum master and product manager), it is possible to strengthen the agile team by assigning another of its members to be responsible for “why” questions (Ries 2015, p. 244). The holder of this role must allow the team to regularly step back, to integrate objective reasons into its developments and to recall them. They are especially active in the planning of sprints and retrospectives. They should not be the only one to ask these types of questions but should encourage the whole team to ask them.
This role is not to be neglected. It helps to indicate why certain elements are a priority or simply exist and to inform understanding (of methods) such as problem solving. Its function is also to strengthen the innovation process, in the sense of S. Sinek (2009) by first asking questions about objectives in the form of “why do we do it?”, “why do this?”, “why go in that direction?”, etc. Then, they are linked to new questions about the way to go about it (how?) and what can be done as a value-added answer (what?). In this way, it is the product/service developed, as well as the way of doing it, that must be the subject of a search for added value. In this respect, the integration of a why manager into the team is a possible reinforcement to increase the chances of complying with the first principle of the Agile Manifesto.
1.4. Agility and creativity
While agility is the result of empirical thinking and experience in product development and other solutions, in an innovative environment, it has an impact on the way the creativity process is approached within the organization. Among the notable changes in creative practices in recent years, design thinking has largely developed as a product/service innovation process borrowing from the principles of agility (Autissier et al. 2016). We find design thinking and other methods based on rapid prototyping cycles and dedicated spaces in companies such as fab labs or living labs. However, they are also found integrated into certain forms of third places, including co-working spaces, makerspaces and hackerspaces, specifically designed to stimulate creativity and innovation (Capdevila 2016, Liefooghe 2018).
Design thinking is part of the family of agile design processes. It includes the importance of short design cycles and rapid testing including enhanced interaction with future product recipients. The aim is to combine four dimensions of product design: feasibility, viability, desirability and time (Brown 2014). However, the cost criterion or financial dimension related to design takes a back seat, as this variable is not independent of the others and is therefore already taken into account from the other three dimensions. The feasibility of the product is dependent on what is attainable in terms of the means available and the design choices including a hierarchy of functions to be integrated into the solution. In terms of development, the direct approach via the creation of one or more “minimum viable product(s)”/“minimalist viable product(s)” largely reduces the costs incurred via rapid and targeted feedback (Biso and Le Naour 2017). It is a functional prototype corresponding to the development priorities of the moment, summarizing the essential customer requirements that are already testable and estimable as such. This allows an initial “handling” and thus a quick insight into the suitability of the prototype for the needs in context and the highlighting of practices or needs underlying its use, which were previously implicit or simply ignored.
These creation methods focus the attention of the developers on the development and its actual conformity with what has been imagined. In this, they must be systematically associated with a creative step back. The design can then be interpreted according to three types of spaces in which objects are thought about: (1) an enclosed space such as a fab lab or makerspace where we design a minimum viable product and directly test its feasibility and part of its conformity to the need(s) for which it is designed, (2) an enclosed space which can be a living lab where a practice is observed in a physical and life-size simulation of the real world concerning already-existing objects or minimum viable products, and (3) a space for real-life situations allowing tests, observations and feedback. Designers, inventors and innovators use each of these types of spaces in turn. The implementation of a classic design thinking process thus requires the observation of real practices in real situations before moving on/returning to the creativity and innovation stages. However, closed creative spaces based on spatial proximity can be as much a source of inspiration as they can be a source of technical blindness (Morel et al. 2016). Thus, their users constitute communities that have the advantage of improving the sharing of knowledge and new techniques and stimulating the development of new projects (Aubouin and Capdevila 2019). However, they can have the disadvantage in the medium term of trapping actors in an informational and creative bubble if the renewal of people and methods over time is low. Indeed, in order to better collaborate and innovate, especially in groups of collective co-creation, various methods and techniques are used, mastered and then preferred. Initially, they facilitate the implementation of collective creative processes (Capdevila 2016) but go on to promote a uniform, group-specific design form. If there is no effort to open up to new practices, the old ones persist without the group necessarily realizing it. Openness can be achieved in a variety of ways. For example, the generation of ideas specific to an innovation process does not necessarily lead to better results when working alone or in a group (Ashton 2016, Weisberg 1986). Thus, it can be very interesting to encourage the development of individual or team responses to a problem (Ashton 2016), to test them quickly and only then to confront them. More original ideas and approaches may thus emerge. In addition to these creative limits, there is another: the routinization of practices. It should not be forgotten that innovation means following a trajectory of change (Alter 2015). Prolonged practice transforms a creative practice into a design routine that is simple to implement and can be systematized. The same group can thus trap itself in an imaginative (via its favorite creative method(s)) and informational (via the sources of inspiration to which it refers) bubble.
To ensure that these risks and benefits are properly taken into account, it is necessary to consider the implementation of the various stages that make up the innovation process. A simple way to do this is to focus, step by step, on the evolution of the stages: of the profiles of the people involved in the process, of the resources used and of the reference models (including methods) combined to solve a
problem (Goria 2019). This step back applies equally well to observing the creative practices of an individual, a team or a company. If agility advocates the acceptance of changes requested by the sponsor or future users, it may be more difficult to consider changing our own practices without an external element pushing us to do so. In this way, the product leader of an agile team may find themselves faced with a creative dilemma similar to that of the innovator formulated by Christensen (2000). In this adaptation to creativity, the company prefers the use of mastered creative techniques that are at the origin of success in the face of new techniques whose effectiveness is less certain. Over time, gradual adaptations of mastered techniques can be made, but they may no longer be sufficient if society evolves in a different direction from these adaptations. The disruptive innovation that will endanger the company may thus come from a creative process that breaks with those it has developed, loved and retained without really considering alternatives to the latter.
1.5. Agility and decision-making
If agility is a key feature of today’s IT development teams, it can also be defined outside the agile manifesto, without betraying its semantics. This proposal was, for example, based on the transposition of a model of the decision-making process involved in aerial combat to the enterprise (Richards 1996). According to this perspective, agility is perceived as an estimate of the ability that an organization can demonstrate to change maneuvers in a relatively short period of time. With this in mind, since the 1980s, various adaptations and reinterpretations of the so-called “OODA” loop have been made at the organizational level, as it provides a very practical representation of what agility in one system can be in relation to another. This abbreviation “OODA” corresponds to the initials of four verbs: Observe, Orient, Decide, Act. It was proposed in the mid-1970s by John Boyd, a former American pilot and air combat instructor, who was once responsible for designing combat aircraft. He demonstrated that his methods and visions of product innovation could be successful in his flying practice as well as in the design and production of aircraft. Thus, as a pilot, then flight instructor, he was never defeated in an air battle despite his long experience and, as a designer, he was at the origin of the development of the F-16 Fighting Falcon, which was long in service in the armed forces (Coram 2002).
The OODA loop models a decision-making process that mixes several feedback loops (Figure 1.2). It can be interpreted at an operational and individual level, as well as at an organizational and strategic level, even if this was not the purpose of its development. The four verbs in this acronym focus on four points necessary to adapt to a changing environment, including the actions of adversaries. In fact, while the observation stage is self-explanatory, the orientation stage is to be interpreted as a system bringing together a set of factors specific to the individual or company that
will influence its decisions (Figure 1.2). It is in this space that a creative process must take place (Osinga 2018). The decision stage consists of the development and selection of a hypothesis for action based on the information acquisition and reflection that has taken place during the two previous stages. The aim is to find the best solution between a current position and a desired position in relation to a situation as anticipated. This solution is, at this stage, only a hypothesis selected among others, which is only verified when it is implemented during the action stage. If there is no rapid action, then the hypothesis put forward is quickly no longer valid since the environment has changed.
In this way, several decision sequences can be quickly formulated and compared, the best of which is assumed to be: O–O–D–A (observe, orient, decide, act). This is more relevant than O–O–D–O (observe, orient, decide, observe), because action will only take place after a much longer time or not at all (we just observe and think but do not act). In the same way, a decision-making process that lacks adaptation to changes and creativity in responding to them may result in the extreme case of an O–O–A chain of which the action would not depend on a decision (elaboration of a relevant action hypothesis based on the observations made) but on an automatism based on certain observed changes (a reaction by conditioned reflexes). In a confrontation, the O–O–A decision process can be very problematic. Once the sequence has been observed and understood by the opponent, they can adapt to it, anticipate their opponent’s choices and set traps for them.
Generally speaking, in a competitive situation in which the confrontation is not direct, these last two sequences are not as good as O–O–D–A. There are therefore at least two situations to be avoided from this point of view: first, a long decision-making process that usually requires new information (O–O–D–O).
Figure 1.2. OODA loop (source: Boyd 2018)
In this case, the decision-making chain is too long. There are too many opinions to be collected, too much time to circulate information or too many uncertainties to resolve. By the time a decision is made, it is no longer relevant. Second, the company acts quickly, but its responses are conditioned and repetitive. However, the competitive environment is changing and within it there are certainly many observers who will not refrain from developing strategies that break with yours.
That said, there is a second element to be taken into account in the decisionmaking system that is implied in the model of this loop: time. It is obvious that both the individual and the organization to which he or she belongs do not act outside of time. It is therefore the individual with the fastest ODDA loop who has, a priori, an advantage over their opponent. As the loops progress, the gap between the two opponents tends to widen, except in the case where a surprising and relevant action is carried out by the one who was the slowest to react, allowing him/her to (re)take the initiative. Likewise, the internal elements, which make up the orientation process, relating to experience, analytical capacities, the latest information obtained and genetic and cultural assets, require rapid adaptation to the environment. If there is a change in perception, it is necessary to adapt to it as quickly as possible, or if the environment does not change, it is an opportunity to carry out an action that has an impact on it and to initiate the change by founding it with a certain creativity. Otherwise, as we have just mentioned, if the process is reduced to an O–O–A (observe, orient, act) loop, the actions undertaken will always be the same despite the perception of changes in the environment. If an individual or a company always acts the same way regardless of the change, they will not be able to count on any element of surprise. At most, their only chance to gain an advantage over their opponents will be based on accelerating their action process and hoping that this will be enough. In fact, John Boyd has incorporated as his modus operandi an internal creation–destruction loop at the orient part. In a context of primarily military application, he drew inspiration from the writings of Sun Tzu and Liddel Hart, concerning the contribution of an indirect strategy to surprise and defeat one’s opponent (Osinga 2018), as well as from the work of Michael Polanyi with regard to the production and sharing of knowledge (Polanyi 1969). The proper functioning of an OODA loop requires that it be developed discreetly, quickly and result in actions and time frames that are as unpredictable as possible. In addition, the mobilization of the knowledge needed for decision-making must be easy. The implementation of this loop must allow for “repeated and unexpected penetration of vulnerabilities and weaknesses exposed by this or other efforts that retain, divert or drain adverse attention (and strength)” (Osinga 2018, p 174).
Adapted to the company level, the OODA loop can be replaced by an equivalent loop with slightly different verbs: WCDA (watch, create, decide, act). It can also be used to understand the success of agile methods such as design thinking workshops. We can thus compare a model of the design thinking process (Figure 1.3) with the
decision process of the OODA loop. However, in the case of design thinking, the orientation stage is implicit, and this is precisely one of the points that should deserve the attention of those who manage the implementation of such processes over a long period of time.
It therefore seems quite important to us that, in addition to external monitoring (including trends, inspiring ideas, new technologies, changes in regulations and competitors’ strategies), internal monitoring of the implementation of resources should be carried out.
1.6. Innovation-oriented agile monitoring
Agility requires regular observation of one’s environment and the ability to recognize, process and analyze information so that decisions and actions appropriate to the situation can be quickly implemented. This is the essence of a monitoring process, where the information provided is of interest only if it is used to inform decision-making or enable action (Bernat et al. 2008). In this sense, a monitoring process must be linked to the innovation process in order to provide it with useful information in terms of exploitation and time frame appropriate to the innovation. Agility is translated at the level of monitoring as an ability to provide such information quickly and to understand, or even anticipate, the related needs.
Monitoring is then understood as a key function of project management, participating in different phases of the project in the form of contributions such as monitoring and competitive positioning, detecting opportunities, identifying new competitors, spotting potential partnerships or suppliers and responding to specific requests for information depending on the evolution of the project (Perbal et al. 2009).
Figure 1.3. Design thinking process (source: Plattner et al. 2009)
Within this framework, monitoring follows its own iterative process known as the intelligence or information cycle (Huot de Saint Albin 2014), which, in its version in four main stages, takes place in the following order: (1) expression of needs, (2) data collection, (3) data analysis, and (4) dissemination of information. However, it should be understood that these stages involve many intermediate processes such as3: (1.5) planning and searching for sources of information for collection, (2.5) sorting and transforming data, (3.5) summarizing and formatting information, and (4.5) anticipating and identifying information needs. If we conceive monitoring as a support to an innovation process, we must then distinguish two cases/levels, according to its place in the innovation system: within an agile team (micro level) or in support of the company for all its processes (macro level).
At the micro level, projects directly involve monitoring. In this case, it is preferable to think of a project’s information management in terms of its participation in the OODA loop (Middelfart 2007, Moinet 2007). To do this, it seems that integrating a person with the role of information supply manager into the agile team is a good solution. In this way, this person can act alone or act as an interface with a monitoring unit. Their role is to anticipate needs and identify the form, quantity and relevant information for each team member. They therefore have their own list of information backlog to support the production of stories. They can rely on more complex tools dedicated to displaying information for an agile team (Stefanovic and Milosevic 2017). Their integration into the team should allow them to better understand the implicit elements, to discuss directly with other members by being in direct interaction with them (Houston 2014) and to participate in sprint reviews to have regular direct feedback on their contribution to the process. Among the responsibilities that a creative monitor can be entrusted with at this level are the identification of emerging trends (new trend, style and esthetic monitoring, etc.), the search for inspiring ideas (idea, concept and creation monitoring, etc.), overseeing the evolution of regulations and technologies (regulatory, legislative, technological and patent monitoring, etc.) or simply punctual searches for information needed by team members. This monitoring should include feedback on the team’s own practices in terms of self-critical analysis of its ability to be agile. At this level, it is carried out by the whole team. For example, an annual review can be done to take a step back, within the context of a retrospective analysis, about the elements deployed agilely and to check whether the team is still acting in an agile way, does not use the same tools systematically and has not abandoned the values that are at the very heart of agility. This last aspect is important to watch for as for some time now there have been warnings from agility promoters of interventions entitled “funerals of agile methods” (Boutin 2018).
3 We number these examples of intermediate steps by adding 0.5 to the step before it. Step (2.5) is therefore located between steps (2) and (3), and step (4.5) between steps (4) and (1).
