IS Design Principles for Empowering Domain Experts in Innovation: Findings From Three Case Studies

Today a significant part of innovation activities in firms is carried out within innovation networks of cooperating enterprises. In such networks, one key challenge is to provide software that enables to systematically share and adaptively integrate knowledge between the partners’ domains of expertise. One potential answer is to apply application software that allows for end-user or domain expert configuration. We provide preliminary empiric evidence from a field test of an expert-configurable collaborative information system in three innovation networks. In a three-year qualitative study, we have identified challenges to software support originating from knowledge, methodical and relational diversity in the networks. We formulate design challenges and design principles relevant for developing and applying domain expert-configurable software. We provide insights into the significance of related user roles in cooperative innovation projects, and offer the role of ‘facilitators’ as mediating agents in application configuration

The coevolution of knowledge networks and knowledge creation

Trabajo presentado al 8th European Meeting on Applied Evolutionary Economics celebrado en Sophia Antipolis (Francia) del 10 al 12 de Junio de 2013.Previous research has modeled the evolution of either knowledge creation or knowledge networks, but not their co-evolution. This work presents an agent-based model to cover this gap and challenge the intuition that both phenomena are mutually re-enforcing. The model consists on the rules of partner selection and the rules of knowledge creation by the agents. Agents in the knowledge network choose their partners depending on their previous collaboration history and on their attractiveness. Similarly, the amount of knowledge created by each agent depends on his number of partners and the knowledge he has created earlier. The simulations of the model show a wide variety of scenarios with different policy strategies suitable for each.Peer Reviewe

Service Cooperation in Alliance: A Shapley Value Perspective

[[abstract]]The globalization stimulates the revolution of market since 1980 in terms of politics, society, culture, technology, and economics. A firm may ally with others owing to strategic reasons and attempt to turn competitors to partners. This research proposes the following research questions: (1) can companies merely focus on service exchange and resource sharing? and (2) how to allocate the generated benefit in alliance? We propose a novel concept of service alliance that allows the company which is efficient and has great ability in a particular service. In this situation, the synergy of service alliance will be enlarged. Shapley value also helps companies identify the contribution fairly. In addition to fairly distribution the profit from Shapley value viewpoint, resource-based theory can be also used to consider the synergy of service alliance. In summary, we consider increasing the usability of recourses may result in a competitive environment.[[conferencetype]]國際[[conferencedate]]20130815~20130817[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Chicago, IL, US

The Joint Effect of Technological Distance and Market Distance on Strategic Alliances.

The literature on strategic alliances has deepened our understanding of the mechanisms behind their formation. This literature has given a central role to complementarities between firms, whereby complementarities are usually measured by technological overlap. An established result tells us that, there is an inverted-u relationship between technological distance and learning by firms. In this paper, we argue that technological distance is only one aspect of complementarities. Equally important is the market distance, which we define as the extent to which the value generated by the alliance depends on the synergies between firms’ products. These synergies may occur because of the complementarities between products, or the possibilities to apply similar knowledge fields in different product domains. Through an agent based simulation study, we show that when firms consider both distances jointly, an alliance strategy which favours being close in at least one dimension yields the highest payoff, rather than being at the intermediate distance in both dimensions.

Using Information Systems in Innovation Networks: Uncovering Network Resources

In order to innovate, firms progressively combine complementary abilities through forming networks. Such innovation networks represent temporary assemblages of partners that, in collaboration, pursue new product developments. Existing theories suggest that successful participation in such networks depends on firms’ having certain firm-level dynamic capabilities (i.e., skill in sensing the network and its environment, learning about the network, and coordinating and integrating individual resources across the network). In this paper, we argue that firms also have to develop particular networking capabilities (i.e., they have to understand who they are partnering with, what each partner can contribute, and how exactly each partner can cooperate with others across the network). We show that inter-organizational information systems (IS) are vital for facilitating the development of these networking capabilities. IS are also vital in developing unique constellations of resources (i.e., physical, human, and organizational resources) that we term IS-embedded network resources. These resources are manifested in the IS and are unique to the innovation network because they go beyond resources at the firm level. Using three innovation networks as case studies, we provide empiric evidence on how IS support networking capabilities to arrive at unique resource constellations embedded in IS and how the set of IS-embedded network resources is a determining factor for competitive advantage in innovation networks

Knowledge transfer dynamics : how to model knowledge in the first place?

In this paper, we study both processes of direct and indirect knowledge transfer, from a modelling perspective, using agent-based models. In fact, there are several ways to model knowledge. We choose to study three different representations, and try to determine which one allows to better capture the dynamics of knowledge diffusion within a social network. Results show that when knowledge is modelled as a binary vector, and not cumulated, this enables us to observe some heterogeneity in agents' learning and interactions, in both types of knowledge transfer

How do Clusters/Pipelines and Core/Periphery Structures Work Together in Knowledge Processes?

This paper contributes to the empirical identification of geographical and structural properties of innovative networks, focusing on the particular case of Global Navigation Satellite Systems (GNSS) at the European level. We show that knowledge bases of organizations and knowledge phases of the innovation process are the critical factors in determining the nature of the interplay between structural and geographical features of knowledge networks. Developing a database of R&D collaborative projects of the 5th and 6th European Framework Programs, we propose a methodology based on social network analysis. Its originality consists in starting from a bimodal network, in order to deduce two affiliation matrixes that allow us to study both the properties of the organization network and the properties of the project network. The results are discussed in the light of the mutual influence of the cognitive, structural and geographical dimensions on knowledge production and diffusion, and in the light of the knowledge drivers that give rise to the coexistence of a relational core-periphery structure with a geographical cluster and pipeline structure.Economic Geography, Knowledge networks, Social network analysis, EU Framework Programs, GNSS

A REVIEW AND DISCUSSION ON KNOWLEDGE SHARING, INNOVATION AND BUSINESS GROUP AFFILIATION

Firms’ knowledge sharing activities include utilization of existing knowledge and creation of new knowledge with other firms. These knowledge flows refer to the exploitation and exploration of knowledge, which are defined as the different modes of organizational learning. Both types of knowledge sharing enhance existing innovations and allow for the development of new products or processes. However, explorative and exploitative knowledge sharing may have different impacts on innovation in various organizational settings. In developing economies, one of the important factors that may condition the role of knowledge sharing in innovation is the business group. Previous studies have investigated the knowledge sharing and innovation relations considering various settings and moderating factors; however, few have addressed the role of business groups. Therefore, this study discusses the effects of explorative, exploitative knowledge sharing on innovation and the role of business group affiliation in this relationship within the framework of the relevant literature. Accordingly, this study puts forward propositions which require further investigation. The propositions suggest that firms benefit from explorative and exploitative knowledge sharing in terms of innovation; however, business group affiliation might have positive or negative moderating role in this relationshi

Quantifying knowledge exchange in R&D networks: A data-driven model

We propose a model that reflects two important processes in R&D activities of firms, the formation of R&D alliances and the exchange of knowledge as a result of these collaborations. In a data-driven approach, we analyze two large-scale data sets extracting unique information about 7500 R&D alliances and 5200 patent portfolios of firms. This data is used to calibrate the model parameters for network formation and knowledge exchange. We obtain probabilities for incumbent and newcomer firms to link to other incumbents or newcomers which are able to reproduce the topology of the empirical R&D network. The position of firms in a knowledge space is obtained from their patents using two different classification schemes, IPC in 8 dimensions and ISI-OST-INPI in 35 dimensions. Our dynamics of knowledge exchange assumes that collaborating firms approach each other in knowledge space at a rate $\mu$ for an alliance duration $\tau$. Both parameters are obtained in two different ways, by comparing knowledge distances from simulations and empirics and by analyzing the collaboration efficiency $\mathcal{\hat{C}}_{n}$. This is a new measure, that takes also in account the effort of firms to maintain concurrent alliances, and is evaluated via extensive computer simulations. We find that R&D alliances have a duration of around two years and that the subsequent knowledge exchange occurs at a very low rate. Hence, a firm's position in the knowledge space is rather a determinant than a consequence of its R&D alliances. From our data-driven approach we also find model configurations that can be both realistic and optimized with respect to the collaboration efficiency $\mathcal{\hat{C}}_{n}$. Effective policies, as suggested by our model, would incentivize shorter R&D alliances and higher knowledge exchange rates.Comment: 35 pages, 10 figure