scholarly journals Stakeholder influence on teaming and absorptive capacity in innovation networks

2021 ◽  
Author(s):  
Arnold Hoosbeek ◽  
Jan Vries
Keyword(s):  
Absorptive Capacity ◽  
Innovation Networks ◽  
Stakeholder Influence

Absorptive capacity and network orchestration in innovation communities – promoting service innovation

2014 ◽  
Vol 29 (2) ◽  
pp. 173-184 ◽  
Author(s):  
Satu Nätti ◽  
Pia Hurmelinna-Laukkanen ◽  
Wesley J. Johnston
Keyword(s):  
Absorptive Capacity ◽  
Service Innovation ◽  
Innovation Networks ◽  
Content Type ◽  
Distinctive Features ◽  
Loosely Coupled ◽  
Firm Competition ◽  
Study Approach ◽  
Traditional Sense

Purpose – The purpose of this study is to increase understanding of service innovation in networks. Especially the most loosely coupled forms of innovation networks, innovation communities, can be valuable in service innovation, but may not be manageable in the traditional sense. Rather, they may require orchestration characterized by discreet guidance that also accommodates the specific nature of services. Through informed orchestration, it is possible to deal with several contingencies, and influence the absorptive capacity at the network level to generate new service innovations. Design/methodology/approach – These issues are examined through literature review and a case study. Findings – The findings suggest that individual orchestration mechanisms may be more closely connected to certain contingencies than others, and that both orchestration mechanisms and contingency factors have a role in absorptive capacity development within service innovation networks. Research limitations/implications – While the case study approach limits the possibility to make wide generalizations, the in-depth insights provide valuable knowledge. Practical implications – There has been a shift from inter-firm competition towards competition between networks of organizations, increasing relevance of absorptive capacity at the network level. Originality/value – Despite the recent increase in service innovation literature, research on service innovation taking place in networks is scant. Knowledge on some aspects can be derived from more traditional notions on technological innovation, but both the distinctive features of services and central characteristics of innovation networks make it necessary to reconsider some of the established views. In particular, managing – or rather orchestrating – service innovation is still a challenging area.

Absorptive capacity, innovation networks and products: SME in the Basque Country

2010 ◽  
Vol 2 (3) ◽  
pp. 182 ◽  
Author(s):  
Javier Castro Spila ◽  
Liliana Rocca ◽  
Andoni Ibarra ◽  
Imanol Pradales ◽  
Nagore Perez Vega ◽  
...  
Keyword(s):  
Absorptive Capacity ◽  
Basque Country ◽  
Innovation Networks

scholarly journals SMEs embedded in collaborative innovation networks: How to measure their absorptive capacity?

2020 ◽  
Vol 159 ◽  
pp. 120196
Author(s):  
Lamiae Benhayoun ◽  
Marie-Anne Le Dain ◽  
Carine Dominguez-Péry ◽  
Andrew C. Lyons
Keyword(s):  
Absorptive Capacity ◽  
Innovation Networks ◽  
Collaborative Innovation

Information Sharing in Innovation Networks

2011 ◽  
pp. 1979-1984
Author(s):  
Jennifer Lewis Priestley ◽  
Subhashish Samaddar
Keyword(s):  
Absorptive Capacity ◽  
Environmental Uncertainty ◽  
Organizational Networks ◽  
Innovation Networks ◽  
Innovation Network ◽  
Network Characteristics ◽  
Emerging Trends ◽  
Causal Ambiguity ◽  
Similar Product ◽  
Transfer Of Information

Innovation networks help members develop new products at a faster rate with lower investment commitments. The R&D consortium named Semiconductor Manufacturing Technology (SEMATECH), with member firms such as Motorola, Texas Instruments, and others, is an example of such a network. In a study of this network, Lim (2000) found that the network members were able to develop an innovative copper-based semiconductor that rivaled a similar product developed by (at the time) an independently operating IBM. The SEMATECH consortium experienced a significantly abbreviated time line and collectively invested significantly less money than did IBM with almost identical results. Lim attributed the innovative success of SEMATECH to the “connectedness” of the firms. Researchers engaged in studies examining interorganizational alliances generally agree with the findings of Lim and others that innovation network alliances represent a potential solution to mitigate environmental uncertainty, in part through the sharing of information (e.g., Gulati & Gargiulo, 1999). Van de Ven (2005) refers to this strategy for dealing with environmental uncertainty as “Running In Packs.” The basic logic is that as a network grows in membership, the amount of information any individual firm can access grows, and the value of membership in that network grows. Consequently, firms engaged in networks typically realize superior economic gains from their increased access to information relative to independent or nonaligned firms (e.g., Carlsson, 2002; Van de Ven, 2005). Since organizations join networks to mitigate costs and uncertainties, the question of how network characteristics affect (or not) the transfer of information is relevant to both practitioners as well as researchers in knowledge management and/or organizational learning. For instance, some innovation networks are composed of members engaged in similar activities while other networks are composed of members engaged in very different activities. Some networks tolerate more competition among their members than others. Finally, some networks are more centrally governed than others. These differences in how an innovation network is formed and governed raises an important question—Given that firms embedded within organizational networks experience greater exchange of information relative to firms operating outside of a network, how do the different characteristics of these networks impact the movement of that information? In this chapter, we will first review the two primary factors that have been demonstrated to influence the transfer of information—absorptive capacity and causal ambiguity. We then review three characteristics of multi-organizational networks—governance structure, scope of operations, and intensity of competition—with particular attention to the issue of information transfer. We develop six testable propositions regarding how these network characteristics would be expected to affect absorptive capacity and causal ambiguity among networked firms. Finally, we discuss future and emerging trends related to the transfer of information among networked firms.

Macroeconomic uncertainty and the spatiotemporal evolution of innovation networks: evidence from China's urban technology supply chain

2021 ◽  
Vol 34 (1) ◽  
pp. 598-623
Author(s):  
Peizhen Jin ◽  
Hongyi Wu ◽  
Desheng Yin ◽  
Yupeng Zhang
Keyword(s):  
Supply Chain ◽  
Absorptive Capacity ◽  
Advanced Technology ◽  
Innovation Networks ◽  
Innovation Network ◽  
Spatiotemporal Evolution ◽  
Content Type ◽  
Node Importance ◽  
Macroeconomic Uncertainty ◽  
Negative Effect

PurposeBased on the perspective of technology supply chain, this study explores the effect of macroeconomic uncertainty regarding the spatiotemporal evolution of urban innovation networks to establish causality.Design/methodology/approachIt collects patent trading data for 283 cities in China (2005–2017) and employs the spatial econometric model to investigate the causal relationship.FindingsThe regional transfer of advanced technology in China is rising sharply, and the innovation network based on patent trading is typically high-density, multi-direction and wide-spreading. Further, macroeconomic uncertainty has a negative effect on the scale of innovation flows and the absorptive capacity in eastern cities. However, it has no significant impact on the innovation network characteristics in developed cities. In contrast, macroeconomic uncertainty is detrimental for the absorptive capacity and node importance in inland and undeveloped cities.Practical implicationsAs macroeconomic uncertainty increases, it is important to improve the quality of the urban innovation network with a better understanding of heterogeneity to promote further suitability innovation at the region-level.Originality/valueThis study highlights a clear and distinctive view that macroeconomic uncertainty not only directly affects the evolution of the urban innovation network but also indirectly affects the characteristics of other city nodes via the spatial spillover mechanism.

CHARACTERISING ABSORPTIVE CAPACITY SUPPORTING SMEs’ LEARNINGS WITHIN COLLABORATIVE INNOVATION NETWORKS: INSIGHTS FROM MULTI-LEVEL CASE STUDIES

2020 ◽  
pp. 2150047
Author(s):  
LAMIAE BENHAYOUN ◽  
MARIE-ANNE LE-DAIN ◽  
CARINE DOMINGUEZ-PÉRY
Keyword(s):  
Focus Groups ◽  
Case Studies ◽  
Absorptive Capacity ◽  
Innovation Networks ◽  
Reciprocal Learning ◽  
Small And Medium Enterprise ◽  
Collaborative Innovation ◽  
Medium Enterprise ◽  
Multi Level ◽  
Contextual Setting

Through collaborative innovation networks (CINs), SMEs access valuable knowledge which requires the deployment of their absorptive capacity (ACAP) to be efficiently used. A small and medium enterprise (SME) absorbs this knowledge to fulfil reciprocal learning for achieving the network innovation goal, but also one-way learning to individually improve its performance. Nevertheless, no study explains how these opposing learnings unfold for SMEs in CINs to guide their ACAP deployment. Based on three cases of CINs including SMEs and two focus groups, we propose a characterisation of ACAP supporting SMEs’ learnings within CINs. As a result, an SME deploys acquisition, assimilation and application actions and attitudes to contribute to the CIN’s setting-up and operational stages, while fulfilling one-way learning. These practices have different focuses and intensity according to the network stage and the SME’s contextual setting. Hence, this study highlights the peculiarities of ACAP that enable SMEs in CINs manage the interplay between reciprocal and one-way learnings.

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