The second major problem that slows or hinders emerging technologies adoption and deployment is good data. Granted, there is data everywhere and coming torrentially from various sources. However, AI/ML/DL, IoT, and BlockChain deployment require good data. Good Data is paramount to any decision-making. To be trusted, measures of data include accuracy, completeness, timeliness, consistency, and uniqueness. Today, a large majority of organizations (up to 80%) believe their data cannot be trusted to deploy AI/ML/DL. Managers across industries use analytics for visualizing trends, status of their finance, sales, productivity, and more in the form of graphs, charts, or other representations. They assume the data used is a good fit to business reality. Hence, they take decisions based on these assumptions.

     Management of emerging supply chain technologies is completely different from management of “old” technologies of the past such as EDI, ERP or even Cloud-ERP. They are deployed as running complete systems. True, they were in many initiatives implemented as pilot projects to experiment and learn. However, they come as a complete system.

The emerging technologies is inherently dependent on good data. They are deployed to take advantage of the torrent of data and Big Data. Machine Learning algorithms are useless if trained on corrupt data. Experimentation is key to create a kind of analytics culture. ML (supervised and unsupervised) algorithms can be used to clean the data and allow to exploit and experiment rapidly. Therefore, ML algorithms should be deployed and used to automatically clean data and make data quality available for “trainable” ML applications. For example, to develop a reliable classification and clustering models, there are various supervised machine learning algorithms to classify and unsupervised algorithms to cluster for data cleansing. Data scientists can use clustering algorithms to detect outliers (an outlier is a data point that differs significantly from other observations) and “dirty” data. Classification algorithms (which are supervised ML) can be applied to assign the correct class label or detect duplicate data. Likewise, deep learning Convolutional Neural Network (CNN) can be deployed to generate potential domains. Cleaning is not highlighted too often. Yet, it is critical for quality analytics and prediction. Powerful algorithms without quality data training will yield wrong prediction and prescription. The bottleneck for organizations is data scientists’ talent. Data scientists are hard to find and to train. New emerging technology-based supply chain requires the training of scientists with a new culture that think beyond “cost and efficiency metrics.” 

Summing-up:  Measures and ROI can slow or hinder supply chain emerging technologies. The scarcity of finding good people to experiment and exploit these technologies is a major barrier for technology adoption. Considering the challenges ahead, organizations should hire and start training data scientists capable of creating a new culture, that think beyond “cost and efficiency metrics.” Moreover, organizations would consider applications that will speed-up decision-making, increase asset velocity, and create new performance analytics in real-time that enhance visibility, trust and mutual understanding between trading partners, and other new capabilities that are of important value to customers.

Technologies are Revolutionize eLearning

Today, many technologies integrating many characteristics of hypertext/multimedia/ hypermedia systems (HmH) and Web-based learning are revolutionizing eLearning. For example, intelligent mobile phones embody those characteristics such as digital sound, images, text, graphics, video, and music.

The rapid development of functionality and capability of Learning Management System (LMS – e.g., Moodle) is a major driver of Massive Open Online Courses (MOOCs)’ popularity and eLearning providers (for-profit and non-profit organizations).

Hypertext, multimedia, hypermedia, and new emerging learning technologies will accelerate eLearning adoption and deployment. LMS technology trends are exploding. All these technologies combine both visual and auditory channels. Combining visual and auditory channels increases knowledge retention. Multimedia allows the stimulation of learners’ both visual and auditory channels, resulting in better understanding and learning. Emerging technologies embody more innovations, helping the development of personalized tools.

Driven by mobile intelligent phones, tablets, bandwidth (5G is coming soon), and social networking, learning can be achieved everywhere through a multitude of expert-authored videos, expert blogs, tweets, and webcasts. The huge growth of internal and external content is encouraging LMS providers to design and deliver a learning system with enhanced functionality and capability. In this environment, content is sliced into small segments with more relevant information and knowledge. That is why mobile-based learning (mLearning) and MicroLearning (learning method that fits anytime and anyplace). Smartphones with their enhanced features allow learners to share content, images, video, audio, documents and so forth, hence fostering P2P–peer to peer- learning. P2P is an effective collaborative method that facilitates the transfer of explicit knowledge (codified and easy to transfer). Because of enhanced functionality of technology, part of tacit knowledge (sticky knowledge and costly to transfer) can be transferred through the P2P mechanism making it an effective way of learning. British Telecom saved 12 million dollars per year by using an open-source learning solution, encouraging peer teaching.

eLearning, and its driver MOOCs, is the early development of an open participatory learning infrastructure that may, in the future, become a global learning ecosystem, which includes a focus on peer-learning. Peer learning will evolve to group formation that shares an area of interest where members develop shared understanding and trust, allowing them to work, innovate, and develop entrepreneurial projects together. P2P based learning will foster a kind of peer-to-peer society, where everyone is able to teach and learn from others. This kind of P2P learning will enhance collaboration and develop a distributed shared knowledge, resulting in life- long learning.

Social Learning (web 2.0) is rising as a way of learning. “Social learning theories help us to understand how people learn in social contexts” (Vygotsky, 1978). Indeed, when people interact with each other, learning will result. It is a powerful form of learning, particularly in the workplace. Discussion forums, note sharing, and other forms of interaction using collaborative platforms help people generate and share ideas. Social learning is becoming a feature of LMSs. Social learning is different from social media, such as Facebook and Twitter. Social learning is designed for the professional environment and not for ad-hoc interaction.

ICT continues to develop rapidly (e.g., 5G) along with awareness of the need to increase accessibility for learning, reskilling, and training. Emerging technologies are opening-up huge learning opportunities for large masses of individuals.

eLearning in Cloud computing Environment

Cloud computing refers to the delivery of computing services such as storing, accessing data and programs, networking, databases, business intelligence, learning, and more over the Internet. Cloud Computing provides flexibility, an easy adaptation to eLearning’s value proposition, computational requirements, and easy network accessibility allowing learning institutions to respond promptly to the exponential growth of eLearning. Cloud computing offers eLearning institutions scalability, accommodating a large number of students enrolling in a MOOC which cannot be handled by a conventional web server. Moreover, pay-per-use and realized savings in new hardware and additional software licences make cloud computing a kind of computation paradigm shift.

Cloud Computing provides faster and universal access, flexible use of resources and economies of scale, and lower operating costs. The development of cloud computing allows business enterprises to scale exponentially. Likewise, cloud computing enables eLearning platforms to develop and become responsive, fast, and easy to scale-up, reaching everybody worldwide who has access to Internet. The progress of cloud computing technologies can provide quality education and enhance quality learning experiences on a large scale all over the world. All these characteristics confer to cloud computing the role of a sound alternative to eLearning.

Game-Based Learning:

Game-based learning is on the rise. The emergence of the gamification concept shows the importance of this phenomenon. Gamification refers to a process of taking an existing website, an online community or a learning application and integrating games into it in order to “increase motivation” (Dichev & Dicheva, 2017). The ultimate goal is to combine enjoyment and involvement by attracting students’ attention and encouraging them to improve their learning and understanding.

Adaptive learning:

Adaptive learning is rising as an effective method of learning, training or reskilling. Learners have different backgrounds, experiences, and skill levels. Consequently, their needs are different from one group to another and from one individual to another. Adaptive learning is an automatic process that filters and recommends content to learners based on their backgrounds, education levels, skill levels, and experiences. An Automated intelligent tool that learns, adapts, and somewhat behaves and acts autonomously, allows users, based on their profile data – revealed choices – interests, to display needed content. Consequently, adaptive learning intelligent software allows learners to move rapidly through materials they already know and focus on those that they need to enhance their learning and improve their skills. Adaptive technologies, therefore, can reduce the time it takes learners to become knowledgeable. This improves the effectiveness of training and learning programs for business enterprises and academic learning as well. Looking ahead, adaptive learning can potentially improve dramatically learning by relieving learners from information overload and useless information, making future learning very promising.

MicroLearning:

The rise of smartphones and ubiquitous Internet (Anywhere, Anytime, Any Device) is fostering MicroLearning. Courses are designed in small learning units and short-term assignment activities giving learners the perception of being in control of what and when they are learning. This method of learning is particularly appropriate for specific easy problems,

leveraging easy search and the use of entering keywords. Mobile applications are not costly to design and maintain. Moreover, they are ubiquitous and embody the capabilities to adapt to a full-scale platform or a website. In the near future, mobile applications will revolutionize the entire learning industry.

Content Curation:

Content curation is increasingly used as a learning tool. Curation services are used both by business enterprises and end-users. Content curation is a process of collecting, organizing, and presenting relevant information from reliable sources related to domain areas or specific topics in a manner that enhances understanding. Curation allows organizations to customize eLearning and deliver it through mLearning.

Leveraging the Extended Business Enterprise:

LMS’s capability allows business enterprises, or any organization, to use a central system that deploys unique learning experiences for various teams of learners throughout the supply chain. This will help to interface and develop a kind of shared understanding of business processes and enhance visibility. Moreover, each learner of any teamwork will be able to view content curated to his/her specific needs. Content curation as a process of gathering information relevant to a particular area of interest can be valuable for all actors along the supply chain.

Advanced Learning Devices:

Advanced learning devices play a major role in eLearning. The new emerging devices for learning allow students to learn in a way that meets their needs. Advanced learning devices are making learning more inclusive, helping learners with certain disabilities such as dyslexia. Audiobooks, dictation software, and reading applications are already helping many students. The spread of AI devices, that speak and interact with the learner, is facilitating greater understanding. Moreover, it presents an opportunity for instructors to encourage learner involvement, thereby creating more participation and deep learning environments for all students.

Authur: Mokhtar Amami

Excerpt from from Amami, M., “Open Innovation and Web technologies”. Mediterranean Conference on Information Systems, Samos (Greece), 3-5 September 2015. AIS Library

The Global and dynamic competitive business environment is forcing private and public organizations to look beyond their traditional boundaries for new ideas, new innovations, and reliable and innovative network of suppliers. During the last decade Open Innovation (OI) has been the focus of academia and professionals due to their ever-increasing role in time-based competition, customer satisfaction of tomorrow needs, shrinking innovation cycles and rising customer expectations.

OI is a relatively a new paradigm shift for business enterprises (Chesbrough, 2003; 2007a, 2007b) that aims to seize the potentialities of the web and abandon innovation secrecy paradigm to a kind of paradigm of knowledge sharing. The ultimate goal is to expand markets for external use of innovation (Chesbrough, 2006, 2011), reduce costs of internal innovation and capture the widely distributed knowledge in order to buy/license inventions/Innovations or processes from other businesses.

Although open innovation is attracting more and more researchers, the main problems, however, of creating ideas and sharing them along the supply chain are hard to resolve. In the same token, scanning and capturing new technologies through a web of inventors and startups, or any other channels that can be used as the basis for technology transfer, internal development and joint development exploration and exploitation are even much harder. Granted, a vertical and stable supply chain “à la Toyota, BMW or Walmart” makes the transfer and joint development relatively easy. In fact, the pivot (Toyota, etc.) in this kind of vertical and stable supply chain control the microcosm of process innovation from suppliers to consumers. Suppliers’ involvement in new product development in this type of supply chain is well known and well documented. However, they are still underused, and their innovation process is far from integrated in an open innovation process model.

The open innovation process model is much more challenging than the ecosystem of the vertical and stable supply chain. The pitfalls of this rising model are somewhat numerous.
The new open innovation process model requires new management competencies for managing fuzzy boundaries, inter-organizational relationships, inter-organizational constraints, and bottlenecks. Second, it requires the development of web based OI strategies to scan and channel ideas, knowledge, and innovations from varieties of actors distributed around the globe and integrate them in an open innovation process that expand markets through Licensing/Buying, Spin-off and Joint Development.

There are key central problems that must be analyzed, evaluated, and harnessed in order to seize the opportunities of OI strategies. These are absorptive capacity, complementary assets, intellectual property (IP) and control, networks and the rise of Peer-Production and Network Externalities and Dominant Design.

Absorptive capacity

Absorptive capacity is critical to organizations to open their internal R&D and start transacting ideas, scientific and technological knowledge across their frontiers. Organizations must have the

capability to scan, identify technological opportunities, assimilate, and integrate them in a profitable business model. Cohen & Levinthal (1990) already underlined this capability of absorptive capacity for firms to generate gains from recognizing, assimilating, and adopting external knowledge. Recently other researchers have arrived at similar conclusion. Networked companies with high scientific and technical competencies tend to favor OI. Silicon Valley (SV) is an illustration of this phenomenon. The performance of SV cluster is the concentration of high scientific and technological skills that materialize in intense knowledge exchange and fast innovations and adoption (Saxenian, 1999; Kenny & Burg, 1999; Castilla et al., 2000). Absorptive Capacity can be developed by focusing on the following dimensions:

Awareness: Awareness requires the development of specific processes by which an organization scans for weak signal and uncover knowledge and emerging technologies.

Association: Association requires processes by which an organization uncovers the value of new ideas, prototypes, products, or emerging technologies

Assimilation: Assimilation requires the design of processes that fit organizational design by which the organization dissimulate and debate these new ideas, prototypes, products, or emerging technologies to create, capture and deliver value to customers.

Adoption and Implementation: Adoption and implementation requires the development of processes by which an organization transacts ideas, products, or technologies for competitive sustainability.

Complementary Assets

Complementary assets (CA) are those capabilities (apart from those underpinning the technology) that firms need to exploit the technology (e.g., manufacturing, marketing, reputation, complementary technologies, brand names, supply chain networks, etc.).
The main idea, Teece asserts, that the more complementary assets a company possesses, the more likelihood the company will take advantage of sourcing external scientific and technological knowledge. Teece (1998) distinguished two types of complementary assets: freely available and unimportant and tightly held (kept proprietary) and important. The appropriability regime determines the importance of gains that companies can derive from external knowledge sourcing. Large companies with large assets pool of complementary assets (CA) and tightly held are more oriented toward sourcing external knowledge. The large pool of CA provides potentialities of integrating external knowledge or buying start-ups or buying even large companies. CA, therefore, are important to commercialize innovations and collaborate with other companies.

Intellectual Property: Appropriability and control

Under the traditional innovation paradigm, secrecy, and control of intellectual property (IP) are the driving force for a company’s ability to capture innovation value. Under the new paradigm of organizing Innovation (Open Innovation), the risk for loss of IP control increases particularly when innovation exchange flows are with competitors (Greenhalgh & Rogers, 2010). The dilemma of OI is not easy to overcome if the firm avoids developing a kind of understanding and trust relationships based on adaptability to market and technology shifts and alignment of business interests.
IP control and protection refers to the extent to which technology can be protected from imitation. Control and protection depend on the degree to which scientific and technological

knowledge is tacit (non-codified). IP control and protection depend on the kind of appropriability regimes: “tight” (e.g., Coca-Cola recipe) vs “weak” (e.g., standard consumer electronic).

Networks and the Rise of Peer-Production

Metcalf’s Law states a network’s value grows proportionately to the square of the number of nodes within the network. Although one can dispute the accuracy of this law, intuitively it can provide the most casual observer with insight into the value of networks. Today, all organizations and individuals recognize the value of networks. In the domain of OI the power of networks to source scientific and technological knowledge (buying and selling) is paramount. The creation and thoroughly designed networks can support the development of a kind of understanding and ultimately a community of trust.

Web technologies-based networks enhance inter-organizational relationships and interpersonal relationships (Johnson & Duxbury, 2010). These relationships are fundamental to cross- organizational frontiers resources exchange. This environment of web technologies-based networks facilitates the strategic roles of OI community drivers. They are external brokers, internal brokers, technology entrepreneurs (P&G), champions (e.g., CEO of P&G) and Capital Venture/Angel entrepreneurs, embodying competencies that allow them to manage cross- organizational relationships to facilitate bidirectional flows of scientific and technological knowledge. These drivers/leaders are key in diffusing technology, mobilizing volunteers, organizing social networks (Castella et al., 2000) and in managing issues such as technology path divergence (forking problems) and fragmentation of innovation process (balkanization problems) (Fleming & Waguespack, 2007). OI environment requires reputation and trust from OI community leaders to create and sustain an Open Innovation Ecosystem. The key roles of these leaders allow them to resolve issues through social brokerage in connecting OI actors and technological boundary spanning through a process of scanning, identifying, translating, and relaying scientific and technological knowledge across organizational frontiers (Fleming & Waguespack, 2007). Because of their key roles as organizational frontiers spanners, OI community hold a higher degree of trust. Because they have an early access to information and knowledge, they have enormous impact on technology diffusion and control. They can develop and implement varieties of strategies to different groups in an effort to hedge on alternative development. Case studies (e.g., P&G, Peugeot, PG, Coca Cola, etc.) and literature review seem to suggest companies that invest in technology gatekeepers not only enhance the flows of scientific and technological knowledge but can immerse themselves in a OI ecosystem that support strategic technological sustainability.

Web technologies fostered a new mode of production system that helped the emergence of the rise of the commons and new mode of producing and channeling information and knowledge. The old alternative modes of production, Markets and Hierarchies, are characterized by either high coordination costs or production costs. Web technologies support groups of individuals collaborate on large-scale projects following a diverse cluster of motivational drives and social signals. This web based emerging collaboration is characterized by both low coordination costs and low production particularly when the object of production is information or culture. Because of web technologies and the wide diffusion of computers, communications capabilities, and increasingly mobile technologies, OI community will play in the future a major role in scientific and technological knowledge exchange among organizations.

The rise of the commons favor peer-participation where community membership is characterized by anti-credentials and there is no a priori selection criteria for participation (Bauwens, 2006).

Low barriers to participate increase the pool of participants and increase the probability of solutions to problems and particularly the new flow of ideas. Moreover, the lower costs (almost null) associated with peer production allow OI community to play fully their roles without organizational financial constraints.
Web technologies-based peer production foster diversity of theories, ideas, and perspectives and consequently improve quality outcomes through interactions in innovation open ecosystem. Peer production system can be characterized by independence, pluralism, representation, decentralized decision-making, and autonomous participation (O’Mahony, 2007). This environment of production can provide scientific and technological contribution to firms that invest in gatekeepers.

Network Externalities and Dominant Design

Network externalities effect occurs when open innovation increases the participation of users which increases the value of products for more users. On the demand side, the telephone service illustrates the network externalities. The benefit that people get from the telephone service depends on the extent to which other people also use this service. In other words, network externalities effect happens when “the attractiveness of a product to customers increases with the use of that product by others” (Fisher & Rubinfeld, 2000). Firms with dominant design (standard system) tend to gain enormously from direct network externalities (when an increase in the size of a network increases the number of others). They tend also to gain from indirect network externalities (when an increase in the size of a network expands the range of complementary products available to the users of the network). The more people who adopt the same standard system, the more services, and applications the user can access, and so the greater the value of that system to each individual user. On the supply side the firm with the largest network tend to achieve increasing returns to scale because the cost of developing and maintaining the network can be spread over a large and increasing growth of the system or product.

In OI environment network externalities or network effects has a profound impact. The dominant design which is the source of network effects tend to favor an open innovation ecosystem to increase the direct and indirect effects. The more users (organizations or individuals) interact with each other, the more ideas and knowledge creation to improve the system and consequently the more the network will increase in value. The increase in the size of network will tend to create an environment of creation of new ideas and knowledge that benefits the creation of more complementary products.

References

Bauwens, M. (2006). The Political Economy of Peer Production. Thailand: Payap University and Chiang Mai University.
Castilla,EJ., Hwang, H., Granovetter, E. & Granovetter, M. (2000). Social networks in Silicon Valley.
Chapter 11 in The Silicon Valley Edge: A Habitat for Innovation and Entrepreneurship, edited by Chong-Moon Lee, William F. Miller, Henry Rowen, and Marguerite Hancock. Stanford: Stanford University Press.
Chesbrough, H. W. (2011). “The Era of Open Innovation”. Sloan Management Review(Winter), 35-41.
Chesbrough, H.W. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Cambridge, MA: Harvard Business School Publishing.

Chesbrough, H.W. (2006). Open Business Models: How to Thrive in the New Innovation Landscape. Cambridge, MA: Harvard Business School Publishing.
Chesbrough, H.W. (2007a). “Why companies should have open business models”. Sloan Management Review, 48, 2, 22–28.

Chesbrough, H.W. (2007b). “Business model innovation: It’s not just about technology anymore”. Strategy and Leadership, 35, 6, 12-17.
Cohen, W. and Levinthal, D. (1990), “Absorptive capacity: A new perspective on learning and innovation”, Administrative Science Quarterly, pp. 128-152.

Fleming, L., & Waguespack, D. M. (2007). “Brokerage, Boundary Spanning, and Leadership in Open Innovation Communities”. Organization Science, 18(2), 165-180.
Greenhalgh, C., & Rogers, M. (2010). The Nature and Importance of Innovation. In Innovation, intellectual property and economic growth. Princeton: Princeton University Press.

Johnson, K. L., & Duxbury, L. (2010). “The view from the field: A case study of the expatriate boundary-spanning role”. Journal of World Business , 45, 29-40.
Kenny, M. & Burg, U.V. (1999). Technology, entrepreneurship and path dependence: industrial clustering in Silicon Valley and route 128, Industrial and Corporate Change. Vol. 8, No. 1 O’Mahony, S. (2007). “The governance of open source initiatives: what does it mean to be community managed?”. Journal of Management and Governance, 11, 139-150.

Saxenian, A. (1999). “Comment on Kenney and von Burg: Technology, entrepreneurship and path dependence: industrial clustering in Silicon Valley and Route 128”. Industrial Corporate Change. Vol. 8, p 105-110
Teece, D. (1998). “Capturing Value from Knowledge Assets: The New Economy, Markets for Know-how, and Intangible Assets”. California Management Review, Vol. 40, No. 3, pp. 55-79.

Fisher, Franklin M. and Rubinfeld, Daniel L., United States V. Microsoft: An Economic

Analysis. Available at

SSRN: https://ssrn.com/abstract=247520 or http://dx.doi.org/10.2139/ssrn.247520

(Extrait de l’article : Amami & Rowe (2000). Les Opportunités de recherche en commerce électronique sur Internet. Congrès AIM, Montpellier. Novembre 2000.)

La lenteur des grandes entreprises à se lancer dans une stratégie Internet reflète une certaine sagesse. Au-delà d’un effet de communication et d’annonce destiné à réguler le cours des marchés boursiers, les grandes entreprises se sont vites rendu compte qu’une stratégie Internet n’était viable vis-à-vis du client que si l’organisation interne était capable de suivre l’offre. L’enjeu de la coordination interne et de la capacité à transformer l’organisation pour maintenir ou développer une relation de service de qualité avec le client et le fidéliser est donc considérable. Théoriquement la baisse des coûts d’information entraîne une baisse de coûts de coordination c’est à dire une baisse de coûts de communication et de contrôle. Malone (1997) a fait l’observation suivante : lorsque les coûts de coordination sont élevés, les décisions se prennent localement et inversement lorsque les coûts de coordination baissent, la tendance est à la centralisation. Lorsque les coûts baissent encore plus, il y a tendance à une prise de décision à la fois décentralisée et connectée.

Avec le commerce électronique sur Internet la baisse du coût des télécommunications doit donc avoir des effets sur la participation au processus décisionnel et sur la qualité des décisions des entreprises. Plus généralement le commerce électronique sur Internet se traduit par des effets sur la conception et la transformation de l’organisation interne, mais aussi sur la taille des unités et les systèmes d’incitation dans le partage de la connaissance et de l’information créée. Finalement c’est la question de la communauté de référence et d’une alternative à la quête de la culture d’entreprise qui émerge avec les communautés (professionnelles) virtuelles.

Impact du CE sur la participation et la qualité des décisions

La baisse des coûts de communication de l’information et la diffusion des mobiles fait augmenter le nombre de participants à la prise de décision et rend moins constant le niveau hiérarchique qui prend effectivement la décision. Un domaine de recherche prometteur consiste à vérifier si le type de support de communication influence le nombre de participants et la qualité de la décision. Il s’agit d’étudier comment le nombre de participants peut varier selon le type de décision et d’autres facteurs tels que la confiance et la motivation. En particulier, il faut étudier l’impact d’Internet sur les coûts de coordination dans des organisations ayant des unités dispersées, interdépendantes et spécialisées. En outre il faut étudier comment une structure de prise de décision connectée et décentralisée utilisant le support Internet favorise la diffusion et le partage de l’information et des connaissances.

Impact de la technologie Internet sur la taille des unités des décisions

La taille des unités de décision implique le sens suivant : des unités où le contrôle des actifs est concentré entre les mains de ceux qui possèdent l’information et les connaissances, des unités qui possèdent une autonomie de gestion mais qui sont contrôlées par des grandes unités où les actifs sont entre les mains du public, et des unités qui possèdent une autonomie de gestion et où les actifs sont contrôlés par un partenariat entrepreneur/entrepreneur-capital-risques (capital venture). La technologie Internet et le développement du commerce électronique semble favoriser les tailles réduites (Brynjolfsson et al., 1994) L’émergence des nouvelles entreprises Internet donne à penser que la tendance des tailles est vers la baisse. Il existe actuellement Une entreprise des entreprises relativement de taille réduite (e.g., Schwab) mais dont la capitalisation boursière peut dépasser plusieurs celles des entreprises qui sont établies depuis longtemps. Partant de l’hypothèse que la technologie Internet et le commerce électronique ‘libèrent’ le consommateur, les nouvelles entreprises développent les affaires non en bâtissant des murs autour du consommateur mais plutôt en offrant le meilleur service. Or, la prestation d’un bon service nécessite une interaction avec le consommateur et des agents qui comprennent et répondent à ses besoins. Il est difficile d’établir un contrat qui compensera les efforts et le temps d’un agent pour livrer un excellent service. L’usage du commerce électronique (CE) combiné aux structures de taille petite (animées par des agents motivés et de confiance) représente un atout pour être concurrentiel et survivre. Étant donné la difficulté de déterminer les contributions de ces agents, une unité de grande taille ne peut pas livrer les prestations attendues. Enfin, la réduction des tailles

des unités peut être rendue possible dû à l’existence des experts électroniques. L’exemple le plus souvent cité est celui des agents intelligents qui sont capables d’éliminer les courtiers traditionnels et donc permettre à des unités de taille modeste de donner un excellent service. De ce qui précède, il semble que l’impact de la technologie Internet et du CE sur la taille des unités de décision représente un domaine de recherche intéressant.

Impact de la technologie Internet et du CE sur l’appropriation des actifs

Peut-on séparer contrôle et appropriation? Voilà une vieille question qui refait surface et devient de plus en plus pertinente à fur et à mesure que la technologie de l’information se développe. La théorie du principal-agent soutient que lorsqu’une organisation (principal) embauche un employé (agent), il est recommandé d’offrir des récompenses sous forme de bénéfices marginaux afin de le motiver à produire un rendement optimum. Étant donné que l’entreprise (principal) est incapable de mesurer l’effort de l’employé (agent), il est conseiller de lier une partie des récompenses à la performance (exemple : commission sur les ventes totales). Le développement de la technologie de l’information a rendu la tâche encore plus ardue. En effet, dans une économie numérisée les connaissances sont devenues la ressource fondamentale alors que les facteurs traditionnels comme le capital et le travail sont devenues des ressources secondaires. La ressource principale la plus productive sont des programmes et des compétences qui sont partiellement gelés dans le design des machines et largement stockés dans les cerveaux des humains. Dans une économie informationnelle, les connaissances jouent un rôle fondamental. Dans ces conditions comment évaluer les récompenses et mesurer les performances. Tant qu’une action menée par l’agent au profit du principal est mesurable, elle peut être contractée. Or les actions des agents dans une économie informationnelle sont difficiles à mesurer et donc difficiles à contracter. La théorie des contrats incomplets (Hart & Moore, 1990) peut aider à résoudre le dilemme de contrat principal-agent. Hart et Moore suggèrent que lorsqu’un agent devient indispensable au développement d’un actif, le principal doit le posséder. On peut extrapoler en soutenant que l’agent le plus informé peut exploiter ses connaissances seulement quand il prend le contrôle total de l’unité de production. Les réseaux électroniques et en particulier Internet permettent, de façon non discriminatoire, à l’agent et au principal d’accéder à l’information. Cependant, un agent peut devenir plus informé par sa capacité supérieure aux autres (agent ou principal) à extraire une connaissance de la masse d’information. Cette capacité d’attention, de détection et de traitement est fondamentale pour déterminer la contribution d’un agent dans une économie informationnelle. Par conséquent un domaine de recherche prometteur serait d’explorer la théorie des contrats incomplets (Hart & Moore, 1990) et donc la question de l’appropriation des actifs et de contrôle dans une économie numérisée.

Design organisationnel et transformation des organisations

L’avènement de la technologie Internet et du CE a induit les managers à penser que l’organisation est une arme pour gagner un avantage concurrentiel et non un mal nécessaire. Il est admis aujourd’hui que les organisations gagnantes sont celles qui font participer leurs employés au processus de création, de prise de décision, et aux profits réalisés. Pour faciliter le travail et les innovations de ces employés, ils doivent être organisés. Par conséquent, le design organisationnel doit être considéré comme une variable stratégique importante difficile à imiter. La propagation de l’usage du CE dans la gestion des affaires va favoriser un design basé sur la communication

latérale, la gestion par projets et un système de récompenses équilibrant les contributions et les rétributions. Une opportunité de recherche consiste à étudier l’impact de la technologie Internet et du CE sur le design des organisations et leur performance. Les études d’impact peuvent donc, évaluer les effets de la technologie Internet: 1) sur la coordination latérale, la transformation des processus de décisions, le système de récompenses et le style de leadership; et 2) sur la performance telle que mesurée par des indicateurs financiers, opérationnels et autres.

La technologie Internet transforme les marchés, les organisations et les relations intra et inter-organisationnelles. Les technologies émergentes (e.g., Intranet, Extranet, RFID, ERP) vont transformer les fonctions de l’entreprise telles que le développement du produit, approvisionnement, contrôle des inventaires, marketing et ventes. Ces transformations auront un impact sur les relations entre les individus, les groupes et les unités d’affaires et par conséquent sur le leadership et la culture de l’entreprise. Les organisations qui adoptent ces technologies tendent à réduire leurs hiérarchies et éliminer les murs entre les fonctions managériales. Par conséquent les entreprises tendent à adopter une structure plus horizontale où les employés et les équipes jouissent d’une plus grande autonomie et d’une marge plus grande dans la prise de décision.

L’impact de ces technologies sur les organisations est immense. Elles tendent à modifier les stratégies, les structures et la performance des organisations. Le commerce électronique sur Internet va forcer les organisations à reconsidérer les mécanismes de coordination (communication et contrôle) c’est à dire leur design organisationnel. La littérature est volumineuse mettant de l’avant de nouveaux concepts et paradigmes relatifs à l’organisation du travail dans les nouvelles entreprises : le paradigme de la remise à plat ou re-engineering, le concept de l’entreprise en réseau et la notion des entreprises inter-réseaux.

En empruntant des notions à la théorie de la complexité, certains théoriciens ont soutenu que les entreprises doivent « émuler » les systèmes biologiques comme celui du réseau des neurones et éliminer les hiérarchies rigides. Les employés peuvent ainsi jouir d’une certaine autonomie qui à la fois augmente leur productivité mais surtout le sens des responsabilités. Le défi majeur est de restructurer les processus et les relations de manière à faciliter le flux d’information vers ceux qui y ont besoin. Les questions de recherche suivantes semblent pertinentes pour les chercheurs qui s’intéressent à étudier les impacts de la technologie Internet sur la transformation des organisations. Quelles formes ces organisations prennent-elles ? Quelles sont les frontières de ces nouvelles organisations ? Comment gérer à la fois l’indépendance et l’interdépendance ? Comment identifier et développer les compétences uniques ? Comment à la fois gérer les innovations et respecter les propriétés intellectuelles ?

Internet et communauté virtuelle

La technologie Internet a attiré une nouvelle classe d’entrepreneurs et d’entreprises qui ont transformé le commerce. En effet, des nouveaux entrepreneurs avec une imagination qui sait prévoir les besoins des consommateurs de demain ont pu bâtir et développé des nouvelles entreprises avec un nouveau modèle d’affaires et une nouvelle approche de marketing. Résultat, des nouvelles entreprises comme eBay, Amazon.com, etc., ont émergé et pourrait transformer le monde du commerce qu’on a toujours connu. L’environnement Internet peut augmenter le pouvoir de négociation des consommateurs qui sont capables d’identifier les produits répondant à des besoins spécifiques, comparer la qualité, le prix et d’autres caractéristiques du produit. L’émergence de la «communauté virtuelle» peut augmenter le pouvoir du consommateur. Une

communauté virtuelle est un groupe d’individus éparpillés à travers le globe ayant des intérêts et des besoins similaires et qui sont liés par le réseau Internet. Pourquoi l’émergence de la communauté virtuelle est-elle un phénomène important ? L’individu est attiré par l’opportunité d’appartenir à un groupe d’individus avec qui il veut partager sa vision du monde, son modèle de consommation, ses désirs et ses craintes. Ces nouveaux groupes représentent à la fois un nouveau phénomène social et un nouveau pouvoir économique. Parce que les membres de la communauté sont informés sur la qualité, le prix et d’autres caractéristiques, les nouvelles communautés virtuelles sont capables d’influencer les décisions des entreprises en matière des offres possibles et des prix (Cucchi, 1999).

Références

Brynjolfsson, E., Malone, T., Gurbaxani, V. & Kambil, A. (1994), « Does IT Lead to Smaller Firms? », Management Science, Vol. 40, No12, pp. 1628-1644.
Hart, O. and Moore, J. (1990), “ Property Rights and the Nature of the Firm,” Journal of Political Economy, 98, 4, pp.1119-1158.
Malone, T.W. (1997), « Is empowerment Just a Fad? Control, Decision Making and IT », Sloan Management Review, Winter, pp.23-35.

Cucchi, A. (1999). “Les communautés cognitives : l’information et la décision au coeur des relations partenariales.” Systèmes d’Information et Management- SIM. Vol.4, No 3.