Collective innovation processes principles and practices

Innovation itself is a collective, cumulative and historical process defined by the following seven main characteristics: 1 the effects of innovation are difficult to predict; 2 the scal

coordinated by Dimitri Uzunidis

First published 2018 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc

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Contents

Introduction ix

Dimitri UZUNIDIS Chapter 1 Enterprise Knowledge Capital and Innovation: Definition, Roles and Challenges 1

Blandine LAPERCHE 1.1 Knowledge capital: definition and roles 3

1.1.1 Information and knowledge 3

1.1.2 Definition of knowledge capital 6

1.1.3 Knowledge capital and managing knowledge 8

1.2 Productive use of knowledge capital 11

1.2.1 Knowledge capital and the production of new goods and services 12

1.2.2 Knowledge capital and the cohesiveness of work collectives 16

1.2.3 The use of knowledge capital in the digital era: reduction of the production process completion time 17

1.3 Conclusion 21

1.4 Bibliography 22

Chapter 2 The Non-economic Values of Innovation 27

EdouardLE MARÉCHAL 2.1 Introduction 27

2.2 The development of business models caused by digitization 29

2.3 Extending the notion of value generation to include non-economic values 32

2.4 Putting forward a value system to be considered when creating

innovation business models 35

2.5 How values can be used in a systemic representation of innovation 39

2.6 Conclusion 41

2.7 Bibliography 43

Chapter 3 Long-term Survival of Innovative Organizations 47

Sophie MIGNON 3.1 Long-term survival: finding a balance between change and continuity 48

3.2 Multiple possibilities between change and continuity 50

3.2.1 A balance resulting from a structural, spatial and architectural separation of opposite forces: the theory of structural ambidexterity 50

3.2.2 Reaching an equilibrium by temporally alternating between the two dynamics: punctuated equilibrium theory 51

3.2.3 Finding a balance through ambidexterity in individuals and more generally in the organizational context: the contextual ambidexterity approach 52

3.3 Which innovation strategy should companies aiming for long-term survival adopt? The concept of prudent innovation 54

3.4 Conclusion 58

3.5 Bibliography 59

Chapter 4 The Resources Potential of the Innovative Entrepreneur 63

Sophie BOUTILLIER 4.1 The resources potential of innovative entrepreneurs 64

4.1.1 Defining innovative entrepreneurs 64

4.1.2 The resources potential of innovative entrepreneurs 69

4.2 The innovative entrepreneur’s resources: knowledge, finance and social networks 72

4.2.1 Knowledge and financial means, the indispensable resources for innovative entrepreneurs… 72

4.2.2 Mobility thanks to the networks of social relationships 76

4.3 Conclusion 81

4.4 Bibliography 82

Chapter 5 Innovation Spaces: New Places for

Collective Intelligence? 87

Laure MOREL, Laurent DUPONT and Marie-Reine BOUDAREL 5.1 Introduction 87

5.2 Innovation spaces: the spaces where all the new innovation trends coexist 89

5.3 Which types of spaces, to what innovating or innovative ends? 91

5.4 The innovation space: a design issue approached in the wrong way 94

5.5 Places in the service of collective intelligence? 97

5.6 Conclusion 102

5.7 Bibliography 103

Chapter 6 The Innovative Territory 109

Corinne TANGUY 6.1 Territory and innovation: a collective process of co-construction 110

6.2 Territorial proximities and cooperation networks 114

6.2.1 Challenging the predominant role of geographic proximity 114

6.2.2 Different forms of proximity 115

6.3 The complementary nature of local and distant collaborations 118

6.4 Conclusion: project territories and new governance systems 120

6.5 Bibliography 122

Chapter 7 The “Eco-innovative” Milieu: Industrial Ecology and Diversification of Territorial Economy 131

Fedoua KASMI 7.1 Industrial ecology and the “eco-innovative” milieu 132

7.1.1 Industrial ecology and industrial regions 132

7.1.2 Industrial ecology as an “eco-innovative” milieu 134

7.2 From specialization to “smart” diversification: altering the economic trajectory of a region 138

7.2.1 Specialization versus diversification 138

7.2.2 “Smart” diversification and a new territorial path 143

7.3 Conclusion 150

7.4 Bibliography 150

Chapter 8 Responsible Innovation 159

Leïla TEMRI 8.1 Foundations 160

8.1.1 Responsibility in science and technology 160

8.1.2 Technology assessment 161

8.2 Responsible research and innovation in European policies 163

8.3 Responsible innovation and companies 166

8.4 Conclusion 173

8.5 Bibliography 174

Chapter 9 Innovation Capacities as a Prerequisite for Forming a National Innovation System 177

Vanessa CASADELLA and Dimitri UZUNIDIS 9.1 Institutions and innovation capacities 179

9.1.1 Taking institutions into consideration in economic theory 179

9.1.2 Institutions and innovation capacities 182

9.2 Innovation capacities and national innovation systems 185

9.2.1 National innovation systems and their heterogeneity 186

9.2.2 Innovation capacities, the inseparable pillars of NIS 191

9.3 Conclusion 194

9.4 Bibliography 195

List of Authors 201

Index 203

of scale and scope lead to an increase in the size of companies and the creation of oligopolistic market structures, dominated by firms focusing on technological and financial power On the other hand, competition, the diffusion of new ways of producing, organizing the innovation process, marketing or consuming, as well as public policies, favor the creation of new actors, upsetting the existing rules These changes, which affect both sides of the market, contribute to the transformation of established structures and the institution of new entities and activities Innovation is now central to the analysis linking market structures, the actors’ strategies and performances However, innovation strategies refer to a broad environment that incorporates the market structure (the level of concentration of sellers and buyers, the degree of product differentiation, market entry conditions) and includes human, financial, material or immaterial resources that businesses can make use of to innovate and transform market structures with their strategies and performances Institutional characteristics (laws, rules, norms, conventions) also help structure the framework within which companies act Alteration of these structures may result from technological progress, the behaviors and strategies employed by companies, and the action of public policies on national, regional and even global levels Studying market structures then becomes pertinent only if the analysis of innovation strategies

Introduction written by Dimitri U ZUNIDIS

is associated with the use of performance criteria beyond merely their economic aspect

The innovation process is causally related to a problem – technological, economic or social – facing the market economy and consciously or unconsciously identified by its actors Thus, innovation is related to finding the best solution to this problem This implies the use of knowledge and information coming from practice, experience and science Innovation itself

is a collective, cumulative and historical process defined by the following seven main characteristics: (1) the effects of innovation are difficult to predict; (2) the scale of the dissemination of innovation is difficult to calculate; (3) innovative activities are asymmetrical and evolve at different paces over time; (4) learning, execution and diffusion time plays a major part

in the innovative act; (5) the business environment affects the time, scale, nature and effects of innovation; (6) the implementation space, in other words, the geographic and communication distances, favor or, on the contrary, hinder access to the information and strategic knowledge of the innovation process and (7) innovations are interconnected; due to the risk associated with cost and time, innovation is at times a collective act and

at other times – or simultaneously – the result of the collectivization of its inputs

In new approaches to innovation, entrepreneurs and companies are understood in relation to their skills and their function in the creation of resources Whether gradual or radical, innovation therefore becomes endogenous and it is incorporated in a complex process characterized by several types of feedback and interactions An innovative organization is presented as a dynamic system that includes specific and diversified skills

An innovative agent (entrepreneur, company, group or, in general, organization), by acquiring, combining and making use of these skills, can create technological resources and develop its relationship with its environment This accounts for the importance of managing design, application and development in the implementation of an innovation process

An innovation system mobilizes a set of bodies of knowledge and skills resulting from learning processes and that are incorporated in its memory These bodies of knowledge must be enriched to be developed by technological, organizational and business innovation The survival of the system depends on its ability to innovate, which allows it to face external attacks, evolve and persist External stimuli (competition, product substitutability, innovation policies, etc.) are generated by the economic

environment and affect entrepreneurs and companies as a means of selection Selection procedures are shaped by the business climate: the nature of the product market, availability of capital and work, innovation pace, effects of public policies, etc Consequently, they can create alternatives to the way in which a given company (an organization or, more generally, a specific innovation system) works, manages and produces The strategy of an

innovative organization is therefore based on the three-“A” model: analyze

one’s own strengths and weaknesses and those of the technological,

economic and social environment in order to anticipate change and act to

adapt to or, on the contrary, drive progress [BOU 13]

The methods employed for managing innovation have radically evolved

in the last two decades The progress observed in digital technologies and the increase in the pressure of competition have led companies to open up their research and development (R&D) activities, employing strategies that rely more and more on the combination of their internal capacities with a range

of external resources [UZU 12]

Decision and power are the two key words in business management The decision-making system of a company ensures the regulation of its activities It is built on the game for power and control of its owners and contributes to the establishment of the decision-making authority of its

“technostructure” A company is forced to grow in size and strengthen its power on the market to avoid disappearing In order to do so, it must reduce the uncertainty that characterizes how the market works by acquiring all means necessary to capture, sort, process and use the largest number of economic, technological, financial, business and political pieces of information A more changeable environment is associated with a more rapid capital turnover, leading to a faster innovation pace and higher business risks The constant expansion, integration and renewal of the market increases business and financial risks Therefore, a company must invest in the creation of a partnership network and/or in the involvement with existing innovation networks to ward off these risks thanks to access to rare skills and knowledge, the profit made from intra-network externalities or closer relationships with its customers and providers

The theoretical models that focus on opening the borders of a company include the open innovation paradigm, the user-led innovation theory, approaches to innovation such as the community of communities, or the approach involving business ecosystems and models All of these

approaches consider innovation as the result of the interaction and collaboration between organizations They involve researching, selecting, combining and integrating a wide range of tangible and intangible resources, incorporated in different organizational and technological contexts and distributed within and beyond the borders of an organization These interactive approaches to innovation involve the transformation of the processes and practices related to knowledge management implemented by innovative organizations Thus, the distribution of knowledge associated with performing tasks that involve invention and innovation consequently develops the way in which knowledge is created, applied and put to work Collective intelligence (multi-individual, multi-organizational) must outweigh singular intelligence (individual, mono-organizational)

A company “collectivizes” innovation processes by spreading out as a network and simultaneously setting up networks to share the costs and controlling when new technologies and production methods, as well as new products and marketing methods, are introduced internally and on the market In any case, an outline based on a collective effort is shaped by the companies’ decision to build up “knowledge capital” (and integrate it in its own assets, both tangible and intangible, material and immaterial, productive and financial) in order to guarantee access to and the creation of new resources necessary for continuous innovation (see Blandine Laperche’s chapter in this book (Chapter 1) and [LAP 17]) Large companies appropriate the useful scientific and technological information available in their environment to incorporate it in their own bodies of knowledge The production of knowledge and innovation are therefore considered as collective processes built within complex networks of interorganizational cooperation

For several years, the networking of individuals and organizations, the multiplication of data and format dematerialization have brought about a real change in human activities, leading us to reconsider uses and to satisfy them

in different ways All these changes also lead us to rethink companies and their business models The tension between increasingly more sophisticated innovations in a complex world and short-term profit motives draws attention to the economic assessment of an innovative project However, other values such as knowledge, trust and achievement are generated by the collective work mobilized by not only a company or business networks, but also innovative products or processes, which must be regarded as outputs in their own right rather than as positive consequences, and, on this basis,

we should build multidimensional business models that include unmeasurable parameters Non-economic values, resulting in most cases from the collectivization of innovation processes, may over time turn into economic values Moreover, they are as indispensable as economic flows for transforming an innovation project into a long-term success Finally, they make it possible to integrate in the process the issue of the hidden costs caused by the fact that innovation negatively affects relationships among the stakeholders (see Edouard Le Maréchal’s chapter in this book (Chapter 2)) These economic values, resulting from companies’ collective work and their creation of collectives of innovation, require the collective clusters to

be somewhat stable over time in order to develop with the goal of making a profit Immobility leads organizations to certain death, but excessive disruption also leads them to take some risks that may be fatal

Is it necessary to change constantly for everything to remain the same?

Or is it necessary to keep what is essential in order to develop? The issue of innovation makes it possible to consider the change necessary for a company, the paradoxical tensions within the processes of exploration required for development (searching for new investment and profit opportunities) and the capitalization processes based on invariants, which represent the DNA of the company (see Sophie Mignon’s chapter in this book (Chapter 3)), to remain stable over time A company must accumulate knowledge capital (instead of dispersed capital) to ensure constant innovation processes

The creation of collectives of functions related to innovation (through work, research institutions and companies) and the underlying collectivization of the processes producing “new productive combinations” can also apply to innovating entrepreneurs Mythical figures in the economic thought of capitalism, entrepreneurs play a key role in systemic evolution: they open new markets and update the existing ones As the product of organizations, an entrepreneur mobilizes the resources with which these organizations provide him; he combines them in different ways and offers the economy new growth benchmarks

Since the beginning of the 1980s, entrepreneurs have become a topical issue The direct support given to new entrepreneurs at the beginning of this period has been replaced by policies that are decidedly more liberal and aimed at creating institutional conditions favorable for the creation of companies In order to understand modern entrepreneurs, we have to

consider the “entrepreneurial function”: entrepreneur = f (incertitude + risk +

innovation + social capital + public policy) An entrepreneur, regardless of the merit we attribute to them, is created by their network He or she creates his or her company and innovates by appropriating and developing a set of economic, financial and social resources There is an “entrepreneurial milieu” that supports the project and enables the creation of the corresponding company This environment includes the entrepreneur themselves, the organization, the relational context and time Time explains why some entrepreneurs are more successful than others Those who make

it, arrive on time and not too early (to seize the opportunities at the right moment) The “milieu” provides information about the market conditions, the risks that should be taken and the production resources that should be combined Information, network and innovation are situated at the center of the entrepreneurial function An economy develops a dynamic industrial environment based on conventions and dynamic bonds that favor the emergence of new ideas and the sharing of resources (being cognitive, financial and social) Some of these resources are “useful” and “strategic” for carrying out the entrepreneurial project They constitute the entrepreneur’s resource potential (see Sophie Boutillier’s chapter in this book (Chapter 4) and [BOU 16b]) An entrepreneur gains access to, sorts, appropriates, combines and mobilizes different types of information, bodies

of knowledge, funding sources and social relationships that are necessary to reach his aims He or she creates, through his or her function, a collective of innovation whose main goal is entrepreneurship

On the contray, a type of effervescence – or enthusiasm – which stimulates all sides takes shape in the “entrepreneurial milieu”

If this “milieu” makes distance and time (as the source of transaction costs that, in most cases, cannot be reduced) disappear, as well as strengthens the relationships based on trust and mutual acknowledgment, then the creative innovation process turns into a collective innovation process This is the case for “innovation spaces” (see the chapter by Laure Morel, Laurent Dupont and Marie-Reine Boudarel in this book (Chapter 5)) that take shape as co-working spaces, third places, Living Labs, Open Labs, incubators, accelerators, hothouses, FabLabs, Makerspaces, Tech Shops, Hackerspaces, Design Factories, etc All these different places share means in a communal workspace, grouping producers, consumers and users to boost creativity, entrepreneurship and innovation This collectivization of localized processes

of innovation is supported by (1) the creation of innovation networks (collaborative digital manufacturing laboratories: FabLabs, Makerspaces,

Tech Shops, Hackerspaces, etc [MOR 16]); (2) creative groups aimed at innovating (third places, Living Labs, etc.) and (3) the networking and sharing of physical means commonly classed as “collaborative spaces”

Sharing means and services aim to collectivize the “spirit of enterprise” and favor the emergence of innovations through the cross-fertilization of ideas and knowledge and the reduction of time and distance in the relationships among stakeholders (designers, users, producers and consumers) The goal of these spaces is to bring about creative environments that boost innovations, making it possible to transform a basic idea into an innovative product, to perfect it and to fashion it based on the evolution of the customers or the markets’ needs Once again, the resource potential and knowledge capital of creative and entrepreneurial individuals develop through collaborations expected to take shape among the members of budding collectives and among the collectives themselves

The main feature of these collaborative spaces is the spatial, organizational and cognitive closeness between the members of a given collective and among the collectives of innovation themselves Spatial proximity is characterized by the shortening of the distances (and time) that physically separate the economic actors; this is the condition that allows the development of relationships of recognition and mutual acknowledgment among them What defines organizational proximity is belonging to the same organization (company, R&D laboratory, university, a team within the same company or an administration unit, etc.), the same network (intraorganizational and/or interorganizational) or, more broadly, the same collective (of innovation) Cognitive proximity refers to the different actors’ adherence to the same way of conceiving innovation, the same paradigm (technological and/or organizational), the same routines, the same conventions, the same traditions, the same beliefs, the same internal codes, the same languages and/or the same learning, decision-making and management procedures Thus, it is situated within the same organizations, networks and communities

The density of close relationships between collectives of innovation, for its part, strengthens the ability of a local economy to generate small independent companies The performances of the economy in question can improve thanks to an intensification of the activity of the entrepreneurial ecosystems present in the area [BOU 16a] These ecosystems, through their ability to reinforce the innovation potential, can turn into “innovative

milieus” They situate business activities in a given territorial framework where networks of complex relationships, between competition and collaboration, take shape and give rise to the reconstitution of work collectives whose members may belong to different actors but whose economic objectives may be the same (e.g creating the same value chain) The “productive alchemy” created between the innovative milieu on a territorial level (clusters, incubators, collaborative spaces, technopoles, etc.) and the close relationships that characterize it may bring about innovations This innovative milieu is defined by a way of organizing production and creating new and specific productive combinations where close relationships contribute to the creation and flow/appropriation of a set of resources embodied in types of knowledge, capital, means of production, etc The resulting “resource potential” is mobilized and developed by collectives

of innovation, which, for their part, produce a network of externalities identified by new innovation and business collectives Territories become the foundations and major actors for innovation and entrepreneurship thanks

to their cultural heritage, expertise, skills, resources and the generic and especially the specific assets that they have been able to create and promote (see Corinne Tanguy’s chapter in this book (Chapter 6) and [TAN 17])

To illustrate the force of collectives in the “innovative milieu” (logic of interaction and learning, articulation of proximity forms, agglomeration effects, innovation dynamics), let us consider the issue of “sustainable development” Applied to the protection of the environment (less waste of energy and material through the implementation of short supply chains in inter-industrial exchanges [GAL 16]), this concept becomes meaningful in relation to the environmental constraints that offer new opportunities of producing new goods and services In a given area, industrial symbiosis, defined as the concrete application of the concept of industrial ecology involving actors situated in a given geographical space, allows industrial ecology to become a catalyst for the development or redevelopment of

an area to the extent that it can become a “sustainable” innovative milieu or

an “eco-innovative milieu” (see Fedoua Kasmi’s chapter in this book (Chapter 7)) This happens by favoring the conglomeration of actors, which are scattered and yet generate eco-innovations (technological, organizational/institutional or business-related)

Industrial symbiosis may be an “eco-innovative milieu” to the extent that

it is based on: (1) a collective of actors that relies on the establishment of eco-industrial collaborations (exchanges of materials and energy) and is

characterized by its economic coherence and cohesiveness; (2) the ability to produce resources sustainably thanks to pooling and substitution flows; (3) a learning ability linked to the implementation of organizational and technological changes to face the complex enactment of industrial ecology measures; (4) a type of relational capital developed thanks to the creation of regionally differentiated dynamic networks (based on the relationships between matter and energy flows); (5) regulations that can devise specific norms and laws, founded on the knowledge of the actual matter, energy and issue flows as well as on a precise knowledge of the risks, stakes and challenges involved in the sustainable management of resources and (6) conventional collaborations that can not only build confidence through contracts and develop a clear type of communication and effective coordination, but also ensure the negotiation of conflicts among the actors The example of industrial ecology as a field where collectives of innovation take shape in a given geographical area should be considered alongside “responsible innovation”, which is becoming more and more significant The goal of innovation must be placed in relation to perspectives involving sustainable development, taking into consideration environmental and social issues The focus is also on “social desirability”, which makes managers and entrepreneurs face their societal responsibilities: they must willingly act in order to meet those objectives and values that are regarded as socially desirable The behavior of companies must be in keeping with the values of society as a whole (it must consequently evolve) for innovation to become meaningful (see Leïla Temri’s chapter in this book (Chapter 8)) The implication of a group of stakeholders present as early as possible during the innovation process leads to the creation of tacit or explicit collectives, driving scientific research, which is the foundation for “new productive combinations”

Therefore, collectives take shape by incorporating interactive dynamics among the actors governed by the same tacit and explicit rules that define competition and cooperation The collaborations among the collectives’ members and between specific collectives will be all the more significant, as the legal and institutional framework related to the promotion of skills able

to “produce” innovations is suitable and stable This framework, created by a coherent set of coercive rules, procedures, lines of action and ways of controlling and monitoring the markets, is used to organize collectives of innovation by training human resources, raising awareness among both producers and consumers, guiding entrepreneurship and consumption

patterns or strengthening the innovation capabilities of the national economy (see Vanessa Casadella and Dimitri Uzunidis’ chapter in this book (Chapter 9) and [CAS 15])

Innovation results from a set of learning processes that start with problems to be solved and involve individuals, structures, methods and bodies of knowledge in precise relationships We refer to innovation systems

to define this set of processes designed for the emergence and dissemination

of productive “new combinations” and their related new knowledge Learning, defined as a process inherent in the creation, transfer, absorption and improvement of techniques and practices, is the cornerstone of innovation capabilities The degree and speed of knowledge transfer allowed

by technological capabilities rely on very heterogeneous infrastructural and institutional foundations, so that each country is ultimately characterized by its specific technological and innovation framework Innovations do not spread with the same intensity and at the same level Although industrialized countries benefit from rich interactive learning spaces, these same spaces are poor in developing countries The weaker the collectives of innovation, the weaker the innovation capabilities (or potential) and the worse the national innovation system performs On the contrary, a rich national innovation potential indicates that the innovation capabilities and collectives can sustain

an efficient national innovation system

Innovation involves a significant organizational effort, but it also results from the organization Currently, the aforementioned organization of the innovation process is characterized by the significance of the strategies whereby the innovative and entrepreneurial act is collectivized: access, training, appropriation and dissemination of scientific, technological and business knowledge Investing in the acquisition of production resources is less costly than investing in their creation The collective return on capital may be high, whereas private profitability may become insufficient The reason behind the fact that the social productivity of investing in innovation is higher than the productivity of individual capital (the company’s or the entrepreneur’s) lies in the increased number of factors that become involved when trying to achieve financial results These factors (skills, abilities, finance, communication, needs and aspirations, etc.) of a collective type affect the trajectory of the marginal cost of a company or activity and, other things being equal, have an effect on the return of the capital invested A company, in a system of actual or latent competition, must appropriate these factors or at least control their impact on its

profitability, or even better make a profit (abundance of appropriable production resources, opening of new markets) from the non-market dynamics that generate and reproduce these factors In relation to how production is currently socialized, the innovative act involves creating new combinations of codified knowledge, disseminating these bodies of knowledge, as well as appropriating and integrating them in a broader combination of productive resources

Entrepreneurs and companies, through several partnerships, are situated

at the center of a network that includes a collective of actors mobilizing different types of productive capacity (material and cognitive) However, collectivizing innovation processes only become profitable when the actors ensure a certain organizational stability so that seizing opportunities can lead

to growth Companies and entrepreneurs, through their functions, not only create collectives of innovation, but also favor the emergence of collective innovations: clusters, co-working spaces, FabLabs, Living Labs, etc The “innovative milieu” favors the development of innovation networks

It emerges in those economies where knowledge resources (and consequently information, scientific, technological, industrial and financial resources) and technological learning abilities are significant enough for innovation to appear as a collective adventure On the contrary, under the pressure of “demand” and, even more importantly, the aspirations

of civil society, for example, in relation to health, environmental protection, education, etc., the collectivization of innovation processes also incorporates

in their spaces consumers or, more generally, citizens Therefore,

“responsible innovation” results from the deep socialization of the activity of both companies and entrepreneurs

Bibliography

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1

Enterprise Knowledge Capital and Innovation: Definition,

The economic analysis of innovation has significantly developed since the 1950s Classical economists had however already set down the essential foundations in the 18th Century For example, Adam Smith (1723–1790) observed and described the forms of division of labor in the first factories and highlighted the importance of divided and combined labor, as well as the significance of learning through practice and interaction in the emergence of technological ideas and artifacts Jean-Baptiste Say (1767–1832) emphasized the characteristics and role of entrepreneurs as well as the institutional conditions that could help or hinder the development and dissemination of knowledge Karl Marx (1818–1883) analyzed mechanization and its effects

on large-scale industry, and also underlined the importance of collective workers in their organization and the gradual integration and incorporation

of science at the service of capital These essential contributions were overshadowed by the increasing popularity of the neoclassical approach, based on the market sphere, in which technological progress did not figure prominently since the main factors of production, capital and labor, were regarded as homogeneous It was only in the 1950s that Robert Solow’s growth models integrated, although imperfectly, technological progress into the neoclassical analysis of economic growth [SOL 56, SOL 57] As a factor

Chapter written by Blandine L APERCHE

1 This chapter draws on excerpts from [LAP 17]

Collective Innovation Processes: Principles and Practices,

First Edition Edited by Dimitri Uzunidis.

© ISTE Ltd 2018 Published by ISTE Ltd and John Wiley & Sons, Inc.

external to the economic sphere, it constitutes “manna from heaven” representing the global productivity of the factors Technological progress, a residual factor of the production function, was an explanation for the differences observed between GDP growth and the growth in the quantities

of factors employed in the production process in the period of strong growth after the Second World War However, some authors, such as Schumpeter (1883–1950), regarded innovation as the main driver of the change inherent

in the capitalist production method, associating it with the adventurous spirit

of entrepreneurs Yet, the origins of technological progress remained murky

or, in other words, technological progress was still a black box, and no-one was sure what it contained

The developments of industrial economy, which first focused on researching and explaining the actors’ performances in relation to their behaviors, as well as the structures within which they evolve, made it possible to gradually open up the black box of technology, to use the title of Nathan Rosenberg’s 1982 work2 In the theory of the firm, theoretical approaches also diverge from the restrictive perspective of neoclassical economists, who limit a business to the rational and maximizing individual Behaviorist and management theories interpret a company as a complex organization with different goals, where innovation progressively becomes

an essential factor in differentiation and performance Since the 1980s, evolutionary theories and theoretical approaches based on resources have enriched the study of the origin of innovation in companies Knowledge, its characteristics as an economic good and the conditions for its production, accumulation and appropriation are at the center of current theoretical developments

This chapter is built on these theoretical foundations Its goal is to present the concept of enterprise knowledge capital, which makes it possible to study how a company combines resources made of knowledge and information It also contributes to the identification of the actors, inside and outside the company, involved in the innovation process The first part of this chapter (section 1.1) provides a general definition of knowledge capital, while also linking it to current and topical concepts The second part (section 1.2) presents the roles knowledge capital plays in the production process and the specific functions of information in this context

2 Inside the Black Box: Technology and Economics

1.1 Knowledge capital: definition and roles

1.1.1 Information and knowledge

To understand the concept of knowledge capital, it is useful to recall the difference and complementarity between information and knowledge In economics, and more generally in social sciences, Information and Knowledge were for a long time considered to be synonymous, then as being separate, but they can also be considered complementary

In the first case, economists have highlighted the common features of information and knowledge, often regarding them as synonymous According to Fritz Machlup [MAC 84], knowledge (like information) is characterized by high fixed production costs and zero or close to zero reproduction costs This can be explained in relation to the characteristics of these specific goods, especially their non-excludability (namely the inability

to exclude a user from using the goods, even if he does not help finance them) and non-rivalry (in other words, an individual consuming the goods does not decrease their availability for other users) This is the root of the issue involving the companies’ incentive to invest in the production of knowledge [ARR 62a]

Others have attempted to highlight the differences between information and knowledge and to separate the two ideas, correctly relying on the meaning that cybernetics gave to information, namely “a set of data” For example, according to Dominique Foray, “Knowledge is fundamentally a matter of cognitive capability Information, on the other hand, takes the shape of structured and formatted data that remain passive and inert until used by those with the knowledge needed to interpret and process them” [FOR 04, p 6] Finally, we can consider them as complementary A such, knowledge is traditionally associated with individuals and defined as a set of more systematized bodies of knowledge, acquired through consistent mental activity Knowledge is associated with individuals It is the product of intellectual understanding, learning and behavioral processes Knowledge is first incorporated in individuals and in the collective memory of the social community In the case of companies, scientific and technological knowledge is incorporated in individuals (researchers, engineers, workers) and in the collective memory of the company (the “routines”, if we use an evolutionary vocabulary, that are embodied, for example, in specific

production processes) Knowledge is also integrated in the machines, objects and products created by the company’s members and then employed in its scientific and technological activity

Information, as a set of data, can be considered a part of knowledge The

whole knowledge (“savoir” in French) may be subdivided taking into

account the degree of systematization or structuration which is linked to the degree – high or low – of mental activity involved in its construction In this case (Figure 1.1), the whole knowledge looks like a Russian nesting doll Knowledge appears as a set of structured information Information corresponds to a set of data, and the data correspond to a set of facts Information and knowledge thus appear to be complementary

Figure 1.1 Knowledge: a Russian nesting doll (source: author)

This complementarity may also be studied in terms of the way they are being accounted for, as a stock or as a flow In this view, knowledge can be viewed as a stock, and information as a flow This approach is important when we want to study the innovation process within a company, and the role that knowledge and information play in this process (and thus the concept of knowledge capital) In a company, innovation can be considered

as an endogenous process as it results from a motivated investment in human (researchers, engineers), material (scientific and technological tools, machines) and immaterial (databases, software, more or less applied research activities) resources However innovation is not only a matter of internal knowledge production (knowledge as a stock) This would conceal all the economic intelligence activity, which is essential Information as a flow is central in the building of our concept of knowledge capital, and in the understanding of the innovation process within firms The creation of what

we name “knowledge capital” in fact requires researching and acquiring scientific, technological and business information with the potential to enrich

as well as “structure” or “systematize” the bodies of knowledge produced within a company

Figure 1.2 Information, input and output of knowledge (source: author)

The set of scientific and technological knowledge of a company then constitutes a stock that the company can use This stock is constantly evolving in a changing economy, and this evolution tends to question the existence of a marginal cost close to zero, which goes hand in hand with the identical reproduction of the stock of knowledge Here the role of scientific and commercial information appears clearly Scientific and technological information, as a flow, then appears to be simultaneously an input and an output of knowledge (Figure 1.2) Therefore, information and knowledge are not synonymous, nor are they dissociable: they are complementary Information is a description, whether written, visual or sound-related, of codified or tacit knowledge It includes established, published and disseminated images of events, behaviors and facts of the physical, biological, natural and human world The words “inform” and “information”

come from the Middle English enforme or informe, meaning to “give form or

shape to” and also “form the mind of, teach”, as well as from the Old French

enfourmer, from the Latin informare, meaning “to give a form, a meaning”3 Therefore, information has a structuring power

3 References on “formation or molding of the mind or character, training, instruction, teaching” date from the 14th Century in both English (according to the Oxford English Dictionary) and other European languages In the transition from the Middle Ages to Modernity the use of the concept of information reflected a fundamental turn in epistemological basis – from “giving a (substantial) form to matter” to “communicating something to someone” [CAP 03]

Thus, knowledge and information are intrinsically linked: the information flows coming into a company have a structuring power on the accumulated bodies of knowledge (input) They can be organized in relation to a specific goal: for example, to create a new product However, knowledge, like information, results from work Knowledge implies a work that is theoretical

as well as practical, aiming to improve the understanding of natural and social facts Information describes and disseminates this knowledge produced by work and involves a supplementary selection of the most pertinent elements of knowledge Thus, information is also the disseminated result of knowledge (output)

Not every body of knowledge will become information, either because it does not reach a sufficient degree of formalization to be able to lead to a better understanding of natural and social facts (knowledge is still only a series of hypotheses) or because it is not immediately useful in terms of market or non-market value and individual or collective knowledge

1.1.2 Definition of knowledge capital

To get to the notion of knowledge capital, it is necessary to focus on how

a company specifically uses knowledge In which case can a resource be regarded as capital? When it is employed in a production process This is the case, for example, for science, which, integrated in the production, has become a productive force of capital [MAR 57, UZU 03]

Figure 1.3 Knowledge capital (source: author) For a color

version of this figure, see www.iste.co.uk/uzunidis/processes.zip

We can define knowledge capital as “the set of scientific and technological information and knowledge produced, acquired, combined and systematized by one or several firms within a particular productive objective and, more broadly, within a process of value creation” Knowledge capital (Figure 1.3) refers to the knowledge accumulated by one or several linked companies

It is embedded in the individuals (know-how), machines, technologies and routines of the enterprise It is continuously enriched by information flows Knowledge capital represents more than the sum of its parts: a cross-fertilization process between all these sources of information and knowledge makes it so that the return from the use of this combined set of information and knowledge is higher than the return from the use of the pieces of information and knowledge taken separately Therefore, knowledge capital is a dynamic concept – a process – that defines the knowledge accumulated by one or several companies and constantly enriched or combined in different ways This productive goal – generation of value – is the main feature that turns knowledge into “capital”

Thus, the concept is in line with a dynamic way of conceiving the notion

of capital, which can be clearly discerned in the process of capital accumulation analyzed by Marx Here, a sum of money M is invested in a productive process in which a commodity C is transformed through capital and labor (K and L) into a commodity of greater value C’, which, by being sold on the market, will turn into a larger sum of money M’, destined in turn

to be reinvested

In this approach, capital is not just a stock of resources available or productive activities It is mostly presented as a process that illustrates the constant renewal and productive use of this stock Therefore, knowledge capital is not a passive stock, but it integrates the generation of value as a key element of its definition This way of conceiving the generation of value determines the incorporation of new pieces of information, their combination

as well as the combination of knowledge and the double process of dissemination/protection By emphasizing the goal – the generation of value – we can reintroduce in the analysis the tensions linked to the power relationships in place among companies of different sizes and strengths Studying the companies’ knowledge capital makes it possible to understand how they generate new knowledge and turn it into technological,

organizational and business innovations Information is collected on the markets through economic intelligence strategies, access to patent documents and the purchase of technologies, and by signing license and other cooperation agreements It is incorporated in the company’s stock of knowledge thanks to learning processes that constitute the foundations for turning information (as a flow) into knowledge (as stock) Using this stock

of knowledge depends on the market’s opportunities and on the degree of development of the technologies designed

A company can use knowledge capital to generate value in the following two ways:

– by simply selling this knowledge capital to another company (e.g selling software) In this case, the knowledge capital is transferred to another company (or several other companies), which will use it in its production process;

– by using this knowledge capital in the production process In the latter case, knowledge capital may be considered as a means of producing goods, a tool for the cohesiveness of work collectives and an instrument that can reduce the time necessary to complete a production process We will focus

on these types of productive uses later on in this chapter (section 1.2)

First, we show how our concept of knowledge capital is linked to other recent concepts used to study in particular how companies manage knowledge

1.1.3 Knowledge capital and managing knowledge

The concept of knowledge capital is analytical in nature and its goal is to improve the understanding of the content of the “black box” represented by the companies’ innovation process Despite being distinct, it supplements the notion of “knowledge-based capital (KBC)” recently proposed by the OECD [OEC 13] The goal of the KBC concept is to more accurately list and measure the intangible assets in which companies invest, such as data, software, patents, designs, new organizational processes and specific skills

of firms They are classed into three groups: computerized information,

innovative property and economic competencies (see also Corrado et al

[COR 05]) Another division is also considered if we analyze “intellectual capital”, broadly defined as all the useful knowledge that may be converted

into value [EDV 97] This includes human capital (knowledge, know-how, human skills), relational capital (external relationships with customers and providers) and structural capital (databases, organizational routines, culture) [MIG 15]

The ultimate goal of the concepts of “knowledge-based capital” and intellectual capital is to illustrate the economic value of intangible assets and therefore to study their effect on growth and productivity, as well as on the competitiveness of companies, so as to promote public policy measures suitable for this broader vision of innovation Nevertheless, according to Zambon and Monciardini [ZAM 15], most of the studies centered on this topic focus on measuring and accounting for intangible assets, neglecting the study of their specific role in the process of value generation

However, these remain useful concepts, as they make it possible to assess quantitatively the contribution of intangible assets In this sense, they supplement our approach to a company’s knowledge capital, since they list with greater precision the intangible assets that contribute to innovation However, they must certainly be associated with other concepts in order to lead to a dynamic vision of the innovation process, as Užiené [UŽI 15] also suggests Moreover, even if intangible assets are central elements to a company’s innovation strategy, tangible assets also contain knowledge in the shape of the “dead labor” included in production tools, machines and processes According to us, these tangible assets are as essential as intangible assets for the innovation process From this standpoint, our approach to knowledge capital is more complete

The importance of tacit knowledge and its interactions with explicit knowledge4 is also one of the key topics of management studies focusing on how knowledge is created within an organization For example, this is the

case for Ikujiro Nonaka et al.’s works on the generation and dissemination of

knowledge within an organization What they put forward is the SECI (socialization, exteriorization, combination, interiorization) model [NON 95], where innovation in an organization emerges from the interaction

4 Knowledge can be “codified” and “explicit” or “tacit” Codified or explicit knowledge can

be written, categorized and made available in a report for example or sent as an electronic message Tacit knowledge is different It can be defined using the famous sentence of Karl Polanyi, saying that “we know more than we can tell” [POL 66] It is part of the individuals’ know-how and transferred through a learning activity, practice [ARR 62b], use (of advanced technology [ROS 82]) or interaction [LUN 92]

between the explicit and tacit, and is associated with the dissemination of bodies of knowledge from an individual to an interorganizational level (for a

detailed presentation, see Barbaroux et al [BAR 16, p 43 et seq.] and Lièvre

et al [LIÈ 16]) We can see that this process involving the creation of

knowledge allows us to explain in detail the activities at work in the stock of knowledge central to our structure of knowledge capital Similarly, the C–K theory (concept and knowledge) emphasizes issues of creativity during the design phase and makes it possible to elaborate more on the development of knowledge within organizations [HAT 09, LEM 16]

These works are in line with resource theory, which, ever since it was put forward, has followed on from Penrose Their authors specifically emphasize the role of competences (especially key competences; Prahalad and Hamel [PRA 90]) and capabilities in the explanation of the competitive advantage

of companies David J Teece et al [TEE 97] named capabilities, which have

the potential to develop new specific assets gathered in organizational routines, “dynamic capabilities” These capabilities refer to the “the firm’s ability to integrate, build and reconfigure internal and external competences

to rapidly address changing environments” The study of how dynamic capabilities are developed is the focus of the cognitive theory of the firm, according to which “knowledge constitutes the most crucial asset and hence, the ability to develop and employ knowledge is the most crucial organizational capability” [NOO 09, p 11] Among these dynamic capabilities, the absorptive capability turns out to be essential when analyzing the formation of the companies’ knowledge capital Wesley M Cohen and Daniel A Levinthal [COH 90] first defined this capability as the ability of a firm to recognize the value of new information, turn it into knowledge, assimilate it and apply it to commercial ends The absorptive capability is generally identified to include the following four aspects: acquisition, assimilation, transformation and exploitation [ZAH 02]

“Acquisition refers to a firm’s capability to identify and acquire externally generated knowledge that is critical to its operations”; “assimilation refers to the firm’s routines and processes that allow it to analyze, process, interpret and understand the information obtained from external sources”;

“transformation denotes a firm’s capability to develop and refine the routines that facilitate combining existing knowledge and the newly acquired and assimilated knowledge” and “exploitation reflects a firm’s ability to harvest and incorporate knowledge into its operations” [ZAH 02, pp 189–190] These four dimensions of the absorptive capability are regarded as essential

for creating and maintaining competitive advantage over competitors, especially in an open innovation context

In our opinion, the absorptive capability concerns the central part of our concept of knowledge capital (Figure 1.4):

Figure 1.4 Absorptive capability and knowledge capital For a color

version of this figure, see www.iste.co.uk/uzunidis/processes.zip

The learning process is a means of integrating (assimilation) the information flows coming from outside the company (acquisition) These flows are converted (transformation) into knowledge and integrated into the company’s stock of knowledge This knowledge is then exploited and embodied in various forms of innovations or becomes incorporated as such into another production process (exploitation)

In the second part of this chapter, we focus on the specific use by firms of knowledge capital

1.2 Productive use of knowledge capital

Our approach to knowledge capital may be qualified as dynamic Knowledge capital is constantly changing, and this continuous transformation justifies the significance that companies attribute to it Information, especially scientific and technological, as well as business-related, is at the root of the dynamic process Thanks to the structuring power of information, knowledge capital becomes a means of

producing new goods Thanks to information, it also constitutes a way of increasing cohesiveness among work groups or “collectives” Finally, the acquiring, processing and disseminating part of the information contained in the knowledge capital can reduce the time it takes during the process to produce the goods and launch them on to the market, especially in the digital age

1.2.1 Knowledge capital and the production of new goods and

services

Ever since the beginning of industrial capitalism, knowledge capital has been an essential input in the production of new commodities This is the first aspect of its role in the production process Information has a structuring role: integrated into a stock of knowledge, information can orient it toward a different application or strengthen it

The creation of knowledge capital requires the gathering of various inputs, namely human (researchers, engineers), material (machines, tools) and informational (patents, software, databases, free information) resources

A company tries to integrate new information and scientific or technological knowledge, which will enrich the knowledge it has already accumulated in different ways: by paying employees, relying on the economic intelligence activity, cooperating with other firms and external institutions and carrying

out intra muros and extra muros R&D In other words, the constitution of

knowledge capital takes place within the ecosystem built by the company Multi-partner innovation (open innovation) is here regarded as a generic model that incorporates all the ways in which a company opens up to its environment with the aim of innovating It suggests that a company’s way of managing innovation activities has evolved over time, turning from a

“closed” process into an “open” process, where “valuable ideas can come from inside or outside the company and can go to market from inside or outside the company as well” [CHE 03, p 47]

According to this author, the open innovation paradigm started replacing the former closed-innovation paradigm in the late 20th Century The logic of the closed-innovation paradigm relied on internal dynamics, according to which companies financed, generated, developed, built and marketed their inventions We can link these dynamics to those of the linear innovation model, which became predominant after the Second World War, where

innovation resulted from a series of successive stages (over time, but also institutionally) from the development of science to the dissemination of new products and services The closed-innovation paradigm started being questioned in the late 20th Century by the convergence of several factors such as the increased mobility of highly qualified workers, the more substantial presence of private funding for companies, unprecedented possibilities for marketing new ideas and the more specialized skills of external providers The increasing role of collaborations in the construction

of knowledge capital can also be explained by the economic context Since the 1980s, innovation performance has become the engine of competition and replaced the "Fordist model” where competition was mainly based on prices and where goods where undifferentiated The new context where products and services differentiation is the basis of business models requires

us to develop and have access to new sources of knowledge Collaborations are a path to these new sources of knowledge They prevent firms investing

on their own in the whole process of knowledge creation as the investment is shared between the partners Collaborations are therefore adapted to the profitability imperatives which nowadays constrain the investment policy of firms These profitability imperatives are linked to the financing of their activity which rely more and more on shareholders demanding high levels of return on investments The complexity of technological products and services (and of product-service systems) also justify collaborations, which facilitates the access to complementary resources

While Henry Chesbrough emphasizes the novelty of the open innovation model by pitching it against a closed-innovation model, according to some other works open innovation is not such a new phenomenon and other authors, long before the publication of his books, had already highlighted it Michel Callon [CAL 99] for example highlighted the role played by the associations of parents of sick children in scientific research Mowery in his historical study on the organizational structures of innovation shed light on open innovation processes whose first steps could already be detected in the United States in the early 20th Century [MOW 09] However, the literature

on scientific and technological collaborations clearly shows that the 1980s marked a turning point not only in the strengthening of interfirm cooperation [CHE 88, COL 96], but also in other forms of partner-based innovations (especially the companies’ relationships with universities and research institutions)

Figure 1.5 Mechanisms at work in open innovation (source: [PIC 14])

According to the open innovation model, firms collaborate at every step

of the “interactive” innovation process (design, production and marketing) and with several partners The analysis of collaborative innovation according

to an open innovation model incorporates two main open innovation processes [CHE 10] The first is known as inbound or outside-in (OI) and refers to research and the integration of external resources that drive the internal development of bodies of knowledge The second, named outbound

or inside-out (IO), exploits external technological capabilities by using different ways of marketing, such as licenses, transfer agreements or the creation of companies A third process is also mentioned This is a mixed process that associates outside-in and inside-out processes, while also grouping different partners in the same R&D project

If we return to our knowledge capital scheme, then we can see that open innovation concerns the two poles of knowledge capital (see Figure 1.6)

Figure 1.6 Open innovation and knowledge capital For a color

version of this figure, see www.iste.co.uk/uzunidis/processes.zip

Through economic intelligence strategies and partnerships with different actors (competitors, providers, customers, start-ups, communities, crowd, research institutions), a firm adopts an outside-in strategy that helps it increase its stock of knowledge On the opposite side, through the transfer to other companies via, for example, intellectual property rights licensing or spin-offs (inside-out), a firm commercializes and gives value to its knowledge stock

As such, open innovation strategies demonstrate the major role played by networks regarded as knowledge factories or boosters [LAP 10] The companies’ knowledge capital is built within networks A new concept is currently used to study the importance of the environment into which the firm acts and the networks it builds: this is the “ecosystem” concept Authors refer to “business ecosystems” which consist of inter-organization networks that involve collaboration and competitive interactions [MOO, 93] This concept mainly stresses the interdependency of actors for the capture of value and the co-evolution that binds them together over time The

“innovation ecosystem” puts forward the variety of actors that contribute to the innovation process and to the creation of value [DEV 16] It opens up new pathways for managerial strategies Indeed, the latter should not only be focused on internal management, but also has to be open to opportunities in terms of access to knowledge sources and to new markets It also constitutes

a basis for the design of new policy agendas aimed to spur on innovation

We should note that the concept of innovation ecosystem first appeared in policy and business debates before academics began to use it One question

raised by some authors is what does the eco- prefix add to an analysis in

terms of “systems of innovation”? According to these authors [OH 16] the natural/ecological metaphor is quite loose and not well explained and as such does not really consist of a rigorous construct As a response, Ritala and Almpanopoulou [RIT 2017] recognize the lack of consistency in the many definitions found in the literature and suggest ways to increase its academic rigor Whatever the outcome of this debate, the proliferation of terms like networks, systems, openness, etc demonstrates the socialization of the constitution of knowledge capital Such socialization means that a large variety of actors (firms of different size, public and private institutions and organizations) are involved in the production of knowledge with the aim of

increasing value (and not always knowledge per se and/or knowledge aimed

at answering more social aims, which are not directly associated with value creation)

1.2.2 Knowledge capital and the cohesiveness of work

collectives

Knowledge capital also plays a key role in the cohesiveness of work groups Emile Durkheim, in the late 19th Century, mentioned the “organic solidarity”, resulting from learning processes, which gives to a work group its truly collective character A work collective, or “collective worker” if we employ a Marxist term, is at the source of the processing of scientific information acquired outside a company and, therefore, of the development and (re)production of knowledge capital This work collective also ensures the productive use of knowledge capital

A work collective results from the interconnection between the fragmentary scientific and technological bodies of knowledge of employees The dissemination of bodies of knowledge as well as of scientific and technological information within a work collective affects its existence, operations and cohesiveness This work collective, once clearly contained within the boundaries of the company, has spread in the era of network firms and innovation (eco)systems beyond its borders Thus, the dissemination of knowledge capital beyond the borders of a company is fundamental in ensuring the teams’ cohesiveness Considering the terms developed by information theory and cybernetics, scientific and technological information can thus be regarded as a means of controlling, directing and guiding work collectives toward clearly defined goals, just like information in general plays this role in relation to machines or society at large [WIE 48]

Another example that demonstrates the scope of work collectives concerns the current importance of “knowledge communities” [AMI 04], an umbrella term used to label different types of epistemic communities of practice At the source of the concept [LAV 91, BRO 91], communities of practice appear as informal groups of individuals who exchange their procedures within the context of interaction norms built by practice, without any visible hierarchy, and produce knowledge in specific fields A typical

example involves open-source software communities As Barbaroux et al

[BAR 16] explain, companies are now fully aware of the significant role of these knowledge communities as a means of developing their knowledge capital and therefore driving innovation The example considered in the literature involves IBM, and it has given rise to the notion of pilot communities of practice, where a company “looks for the alignment between the activity of a community and its strategic orientations, while preserving

the self-organized and spontaneous character of the community” [BAR 16,

p 77] In other words, the goal is to take advantage of the creativity resulting from the organizational flexibility, in the shape of a community, in order to strengthen the firm’s knowledge capital

Multi-partner relationships also contribute to the blurred lines demarcating work collectives On the one hand, disseminating the elements that constitute knowledge capital among the members of a company as well

as externally enables them to remain cohesive beyond the boundaries of the company On the other hand, however, this dissemination increases the risks involved in the uncontrolled disclosure of elements essential for the firm’s competitiveness Thus, the challenge for the company is to control the information it decides to disseminate or not, and the degree of access of the various members of work collectives to the most sensitive elements The information systems of a digital company can therefore rely on more or less controlled access rights Non-disclosure agreements included in contracts (employment or partnership contracts) reduce, but do not completely prevent, the risks of information leaking out Moreover, smart and connected products

and equipment increase the need for robust security management systems

1.2.3 The use of knowledge capital in the digital era: reduction

of the production process completion time

The multiple roles played by knowledge capital account for the fact that companies invest in its creation and protection However, we have recently experienced a growing dissemination of scientific and technological information incorporated in advertisements and through sophisticated and rapid means (the Internet, smart and connected products) In other words, modern means of communication speed up the dissemination of information The search for information, as well as its growing dissemination, can be explained by the fact that knowledge capital is not only involved as a means of generating value during the production process, even if this is its primary role, but it is also employed in reducing the duration of the whole production process, either at the investing stage, during the actual production, or when goods are marketed

The work involved in acquiring and collecting new information available on the market, as well as incorporating it into the knowledge capital, corresponds to business intelligence and technology watch activities: searching patent databases, shows, specialized press, data processing This allows companies to

increase the rapidity of their technological, productive and business choices, and also enables them to avoid mistakes and redundancies

This activity, which involves researching and processing information, has always been carried out by companies Large trading companies in the 17th Century were already sending informers on horseback across Europe to find the latest economic developments and consumer tastes However, this activity has taken on a new dimension in the age of Big Data and digitization5 According to M Porter and J.E Heppelmann [POR 14], we can nowadays witness a third wave of IT-driven competition The first wave, in the 1960s and 1970s, corresponded to the automation of activities in the value chain The second wave in the 1980s and 1990s was based on the rise

of the Internet which has increased coordination and integration across individual activities In the third wave, “IT is becoming an integral part of the product itself” which takes the form of smart and connected products, with embedded sensors, processors, software and connectivity According to the authors, this “third wave of IT driven transformation thus has the potential to be the biggest yet, triggering even more innovation, productivity gains and economic growth than the previous two”

Smart and connected products have several capabilities: monitoring, control, optimization and autonomy, and as such they become themselves a source of data6 [POR 14, POR 15] ; they are used at each step of the value-chain As a matter of fact within the firm, the smart products and tools

5 Big Data often involves a reference to the four Vs – volume, variety, velocity and value – used to characterize it A fifth ‘V’ may be added: veracity With the Internet and new media (tablets, mobile phones, connected objects), the quantity of data a company must manage has become very significant and requires new approaches and tools (such as data mining and text mining, profiling and visual analysis techniques) in order to be stored, processed and used The expression “Big Data” refers then to a “set of methods and tools used to process and interpret large quantities of data that are generated by the increasing digitization of content, the monitoring of human activities, and the disseminating of the Internet of Things” [OEC 15] These are processes and techniques that allow an organization to create, manipulate and manage data on a large scale [HOP 11] and to extract new knowledge in order

to convert it into economic value [MON 16, p 47] Big Data is also giving rise to new positions, such as data scientists, whose mission is to sort data and turn them into information

so as to boost the companies’ stock of knowledge

6 Monitoring refers to the fact that products can report on their own conditions of use which facilitates improvements; Operation can be controlled by users who can customize the

production according to the use and thanks to multiple available functions As a consequence

many opportunities of optimizations appear due to algorithms Autonomy in use also increases

[POR 14, 15]

give the possibility of choosing more rapidly the means of production that it must acquire to implement the production A more rapid dissemination of information also reduces the delivery time for the means of production, especially the progressively more numerous immaterial ones (software, databases, etc.) Moreover, the organization of companies, which is by now globalized and whose units are interconnected, enables an international technological watch, as well as the recruitment of qualified staff, rich in

“human capital” and therefore in scientific and technological knowledge, regardless of location Choosing future employees is made easier by data collection and processing in the different locations

During the production process, times are reduced thanks to the use of updated technological means (the Internet, intranet, databases, smart and connected tools and machines), which multiply the flows of scientific and technological information Therefore, the internal dissemination of scientific and technological information consolidates work collectives and makes it possible to increase productivity, optimize work Smart and connected tools and machines give the possibility to constantly adapt to external conditions and lead to an optimization of their use For example, as stated by Porter and Heppelmann [POR 15], “In a farm setting, data from humidity sensors can

be combined with weather forecasts to optimize irrigation equipment and reduce water use In fleets of vehicles, information about the pending service needs of each car or truck, and its location, allows service departments to stage parts, schedule maintenance, and increase the efficiency of repairs (…)” In other words, connected equipment, functioning as a system of products, machines and tools, can improve overall equipment performance The external dissemination of part of the information that constitutes knowledge capital can reduce the time necessary to market the goods and services produced by a company To sell goods, a company now disseminates scientific and technological information (in addition to the more traditional information concerning price, form, etc.) These pieces of information help “lend credibility” to the product, educate the consumers (or define and make fundamental the use of the goods) and win their loyalty (in highly uncertain periods, the investment risk will be proportionally reduced) Advertising that conveys scientific and technological information was used early on by manufacturers to speed up the sale of goods and establish their hold on the market However, the current technological means of communication are strengthening this influence Smart and connected products give the possibility to customers to adapt the products they buy to

their own needs and large-scale processing of the data collected in turn facilitate the adaptation of the supply to the consumers’ needs (customization of products, development of product-service systems [LAP 13]) In this way, they can increase buyer loyalty and raise switching costs Let us consider the example of Amazon, which, by gathering and processing the traces left on its page by consumers (purchase and browsing history), offers them a targeted range of books [MON 16, p 44] Nike offers its customers a complete ecosystem in order to manage their physical activity; this also makes it possible to recommend specific products to them Tennis racket manufacturer Babolat with the “Babolat Play Pure Driven System”, which consists of including sensors and connectivity in the racket handle, offers a whole service to customers in terms of analysis of the way they play tennis and makes recommendations in terms of improvements (and associated products and services) [POR 14]

The dissemination of scientific and technological information is followed

by data collection and the analysis of the effect on consumers Opinion surveys, polls, etc help orient the following cycle of productive capital development They guide not only productive work (design, production) but also, earlier on, the choice regulating the means of production and the most suitable employees for developing them For example, data on product usage and performance comes back to product design, and as a consequence firms can adapt the new series of tailored products, reduce failures and offer more adapted services associated to products The innovation process, which is by now interactive and no longer linear, explains the growing overlap among the stages in the production process and the set of activities carried out (scientific research, production, marketing) The collection and dissemination of information, traditionally regarded as characteristic of the marketing stage and used as foundations for the new production process, takes place from the investing stage onward, namely before the actual production of goods, with the goal of shortening even more the difficult step that involves converting a good into actual jingling cash

The time to complete the production process will depend, of course, on market prospects: supply and demand However, it can be technically reduced with the voluntary dissemination of scientific and technological information, which encourages a more rapid resumption of the production process Of course, the efficiency in the use of knowledge capital will also depend on the firm ability to build a new technology infrastructure, a

“technology stack” in the words of Porter and Heppelmann [POR 15], which

constitutes a gateway for data exchange and a platform for data storage and analytics Its building requires an important change in the firm’s organization, new competences, new business models, and asks for more openness, thus giving a further incentive to the deployment of (eco)systems

In this chapter, we have also highlighted the significance of information and the way information is processed in relation to knowledge capital Information has a structuring power on the stock of knowledge accumulated and orients it toward new uses Its regulated dissemination allows work collectives to be cohesive and favors investment decisions It also helps shape and guide consumers Therefore, our work underlines the complementary relationship between information and knowledge, which are either treated synonymously or pitted one against the other in economic analysis One of the current great issues faced by companies, namely the protection of their knowledge capital, always concerns information and its dissemination and control

According to several analysts, all business sectors are progressively experiencing the age of digitization and industry 4.0 The latter, which follows in the wake of three previous revolutions (the introduction of steam, electricity and then automated machines), involves, according to the consulting company McKinsey, the digitization of the manufacturing industry, based on sensors built into virtually every component and machine, omnipresent cyber-physical systems and the analysis of all relevant data [MCK 15] In this new context, information processing, its conversion into knowledge and its controlled or uncontrolled dissemination constitute the core of business management A “digital thread” runs across the whole value chain of a company: “This digital thread starts with the digital design of the product, passes on through the digitally steered and controlled manufacturing process, leads to the digital monitoring of the end product in operation (e.g., for maintenance purposes), and finally ends in the recycling