Search results

The purpose of this paper is to attempt to calculate the trust degree between two enterprises in an industrial network using grey correlation degree algorithm for exploring characteristics of community structure and evolution rules of cluster cooperation networks in axle‐type and satellite‐type clusters.

Starting from analysis of trust formation mechanism of inter‐enterprise in industrial networks, adjacency of inter‐enterprise relationship, their information acquisition ability, their influence power in network and their past interaction experience are chosen as influencing factors of the trust between two enterprises. Grey correlation degree algorithm was chosen to calculate the trust degree between two enterprises in an industrial network. According to the rules of dynamic adjustment of trust degree originated from thoughts of the prisoners' dilemma model, computer simulation is applied to explore characteristics of community structure and evolution rules of cluster cooperation network in axle‐type and satellite‐type clusters.

With the dynamic adjustment of enterprises' trust degree, the network density of axle‐type and satellite‐type cluster networks was decreasing as the cluster scale was enlarging, and eventually tended to be stable; community structure was emerged in axle‐type and satellite‐type industrial clusters as the cluster scale was enlarging; community characteristics were obviously stronger in axle‐type cluster networks than in satellite‐type; communities were overlapped in axle‐type cluster networks, that is, bridge nodes emerged between communities.

This paper is the first to apply the grey correlation degree algorithm to calculate the trust degree between two enterprises in cluster networks for designing the rules of dynamic adjustment of trust degree.

The purpose of this paper is to identify types of professionalization in Swiss national sport federations (NSFs) and analyze organizational characteristics associated with specific types of professionalization. Such types reveal common patterns among the increasingly complex organizational designs of NSFs and thus contribute to the understanding of professionalization in NSFs.

An online survey of all Swiss NSFs was conducted to identify types of professionalization in these organizations using hierarchical cluster analysis, based on a multi-dimensional framework of professionalization.

The analysis revealed four types of professionalization: formalized NSFs managed by paid staff, NSFs managed by volunteers and a few paid staff off the field, NSFs with differing formalization and paid staff on the field, and moderately formalized NSFs managed by volunteers. The types differ in terms of the NSFs’ organizational characteristics, in particular, size, financial resources, Olympic status, and performance.

Applying factor and cluster analysis is a new approach to analyzing professionalization in NSFs that makes uncovering distinctive organizational patterns among a large number of NSFs possible. These results lay the foundation for understanding the professionalization of NSFs, counseling NSFs on their organizational development, and conducting future research on the design types of sport organizations.

The purpose of this study is to test the usefulness of the literature-based innovation output (LBIO) approach to identify innovation types from press releases of hospitality firms and to evaluate if the typology captures the effect of innovation on firm value.

The LBIO approach was applied to three years of press releases from two publicly traded lodging firms in the USA announcing innovations. A database of lodging and innovation relevant terms was compiled. Starting with classifications found in the innovation literature, the researchers coded each announcement. Coded announcements were clustered into innovation types using pairwise similarity analysis. Event study analysis assessed the efficacy of the overall method to find types that were useful to measure the impact on firm value from the company’s adoption of an innovation.

Cluster analysis identified four lodging innovation types: property and location, marketing, strategic development and guest experiences. These types corresponded closely with the innovation classification suggested by the Oslo Manual. The event study found that the typology was useful in determining the market value effects of an innovation.

This study focused on innovation; future studies might test other organizational factors. The study uses data from two large, publicly traded hospitality firms and may not extend to smaller, privately held businesses. A key implication is that human coding is sufficient to identify innovation types that correspond closely with existing classifications and affect firm value.

This study successful learns from hospitality press releases to identify a hospitality innovation typology and tests type impact on firm value.

This paper aims at investigating the multifaceted nature of innovation networks by focusing on two research questions: Do cluster actors exchange only one type of innovation-related knowledge? Do cluster actors play different roles in innovation-related knowledge exchange?

This paper builds on data collected at the firm level in an Italian aerospace cluster, that is a technology-intensive industry where innovation is at the base of local competitiveness. A questionnaire was used to collect both attribute data and relational data concerning collaboration and the flows of knowledge in innovation networks. The authors distinguished among three types of knowledge (technological, managerial and market knowledge) and five types of brokerage roles (coordinator, gatekeeper, liaison, representative and consultant). Data analysis relied on social network analysis techniques and software.

Concerning the first research question, the findings show that different types of knowledge flow in different ways in innovation networks. The different types of knowledge are unevenly exchanged. The exchange of technological knowledge is open to everyone in the cluster. The exchange of market and managerial knowledge is selective. Concerning the second research question, the authors suggest that different types of cluster actors (large firms, small- and medium-sized enterprises, research centers and universities and institutions for collaboration) do play different roles in innovation networks, especially with reference to the three types of knowledge considered in this study.

The present paper has some limitations. First of all, the analysis focuses on just one cluster (one industry in one specific location), cross- and comparative analyses with other clusters may illuminate the findings better, eliminating industry and geographical biases. Second, the paper focuses only on innovation-related knowledge exchanges within the cluster and not across it.

The results have practical implications both for policy makers and for managers. First, this research stresses how innovation often originates from a combination of different knowledge types acquired through the collaboration with heterogeneous cluster actors. Further, the analysis of brokerage roles in innovation-driven collaborations may help policy makers in designing programs for knowledge-transfer partnerships among the various actors of a cluster.

The paper suggests a clear need of developing professional figures capable of operating at the interface of different knowledge domains.

The data illuminate several aspects of how innovation takes place in a cluster opening up intriguing aspects that have been overlooked by extant literature. The authors believe that this may trigger several lines of further research on the topic.

Little is known, about the role played by start-ups in open innovation networks. Start-ups – due to their nature of new and emerging companies – can largely benefit from the knowledge that can flow intentionally or unintentionally from external partners during open innovation practices. When open innovation networks are not set among peers on both sides the authors expect to have more unintended knowledge flows. Such knowledge “leaks” – as the authors named them – in open innovation networks are totally unexplored in literature. Hence, the purpose of this paper is to focus “whether and how knowledge leaks occur in open innovation networks with start-ups”.

The research design of this study relies on social network analysis methods and techniques to disentangle the role of start-ups in open innovation networks – in a major Italian aerospace cluster – vis-à-vis the three types of knowledge considered in this study. Then the authors confirmed knowledge leaks to occur through a multiplexity analysis. In the second stage of the research, the authors decided to strengthen the results, making them more vivid and thorough, relying on four case studies.

The paper sheds light on a totally unexplored phenomenon, theorizing on the role of start-ups in open innovation networks and suggesting intriguing implications both for theory and managers on whether and how knowledge leaks occur.

The main limitations arise from the specific research context, in fact the study has been conducted in an aerospace cluster. So future studies might consider to explore knowledge leaks in non-cluster settings and in low tech industries.

The results have practical implications both for policy makers and for managers. First of all, the research confirms how open innovation often originates from a combination of different knowledge types acquired through the collaboration with heterogeneous players, start-ups included. Hence, managers may design open innovation strategies balancing their portfolio of collaborations to maximize the absorption of relevant knowledge and start-uppers may consider to engage in open innovation practices to accelerate knowledge absorption. Nevertheless, the study warns managers against the risk of knowledge leaks, especially in cases like start-ups where the eagerness to participate or the prestige associated with participating in open innovation networks with key players may hamper the control over knowledge leaks.

This opens up for possible interventions for policy makers too. First of all, policy makers may consider incorporating the concept of knowledge leaks in their campaign in favour of open innovation. Second, the study may help policy makers in designing programmes for knowledge transfer partnerships amongst the various players of a cluster in a more conscious way, especially warning new to business companies, like start-ups, about possible leaks. Finally, there is also the need of developing professional figures like consultants capable of supporting start-ups in their open innovation practices.

Findings reported in the paper confirm multiplexity and heteromorphism in knowledge exchanges and shed the light on a completely unexplored field (i.e. open innovation and start-ups), focussing on knowledge leaks. Relevant implications for policy makers and managers are included in the study.

The purpose of this study is to explore the early stage of development of a cluster. The literature on early stage of cluster development shows that there are often random effects such as an entrepreneur and spin-off companies, and in this study, a coordinated approach for cluster development is described.

A single exploratory case study approach is followed. The aerospace cluster in the Spokane region, State of Washington, is described. Data from a variety of sources are triangulated to enhance the credibility of the case study findings.

It was found that although there are many types of collaborations occurring in the region, which involve policy and government organizations, the main driver of the early-stage cluster development is manufacturers-led coordinating mechanism. Individual manufacturers are too small to be successful in the aerospace industry, and they are collaborating to present a united “front” to out-of-the-region customers. Once customers place an order, then within this coordinating mechanism, the work is divided among different manufacturers.

The research has two main limitations. First, it is a single case study, and therefore, the results may not be generalizable. Second, the cluster is in an early stage of development, so it is not (yet) clear whether this manufacturers-led coordinated approach will have long-term success.

The studies offer potential for cluster development that go beyond relying on a single entrepreneur or on mostly government- or policy-driven initiatives. Instead, this is an approach that can be used by industry to lift the overall competitiveness of their region.

This cluster development approach offers potential for economic development of smaller regions which mainly consist of small- and medium-sized companies without endowment benefits or a large local customer base.

This study adds to the existing knowledge on clusters and cluster types. The identified cluster approach does not fit with the main types of clusters that have been identified in the literature. The companies involved are mainly small- to medium-sized companies, but by coordinating their capabilities, they are able to present core capabilities in a much more attractive manner to customers. This cluster development approach is not driven by or achieved through advantages in innovation, vertical or horizontal supply chain competition and advantages, creation of spin-off firms, or a regional demand base as customers are located outside the region. It deviates in terms of the types of companies involved and, mostly, in a sense that it acts as one unit to customers who are located outside the region.

The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study.

To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with 35 features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study’s sample.

The taxonomy derived from the research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework.

A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox.

This paper develops a basis for understanding how start-ups build business models capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.

Location decisions are among the most costly decisions that organizations make. This research aims to examine location decisions from a macro perspective and to utilize findings for the development of a typology.

County level source information from the US Census Bureau, the United States (US) Department of Commerce: Bureau of Economic Analysis (BEA), National Association of Counties (NACO), and Fedstats is used in this analysis. Discriminant analysis as a profile analysis is utilized as an objective assessment of differences between the cluster concentrations.

The resulting typology of clusters concentrations is based on four constructs identified in the literature: innovation, specialization, complementariness and transfer of knowledge. This typology can serve as an aid in making these critical location decisions for practitioners as well as identifying future research topics for academia.

The research is an exploratory study and limited by its nature; therefore cause and effect cannot be definitively stated. Variables such as politics, environment, geography and cultural differences could have confounding effects on the study. The generalizability of the study could be affected because of the geographic location in relationship to national differences based on these and other variables.

This typology of cluster concentrations can be used as a tool for managers when making crucial location decisions.

The research is original in that it takes a more holistic approach to developing a typology of cluster concentrations. Rather than looking at specific industries and focusing on industry clusters, the research focuses on concentrations of industry clusters.

The purpose of this paper is to find out the different types of business networks formed by firms with the stakeholders present in a cluster, i.e. how firms in a cluster interact with the cluster stakeholders?

To answer the research question, this study uses an exploratory research design, which is carried out in two stages, Stage 1 involves use of primary data, which was collected through semi-structured personal face-to-face interview mode and Stage 2 involves survey research method where data was collected through a survey questionnaire. Data for interviews and questionnaires were collected from managers and owners of firms operating in the cluster at their offices.

The study has identified four types of business networks between a firm and its buyers, only one type of business network with the suppliers and educational institutes, finally two types of business networks with government agencies and local associations. However, with respect to network with other stakeholders such as research institutes and competitors, the study shows that the interaction between a firm and these stakeholders is not strong i.e. the linkages between them remain largely unfilled.

The study has been limited to only one cluster thus it might not be appropriate to generalise the findings. Further research in this area needs to be done by taking other clusters to generalise the findings.

The study has tried to answer the research gap of lack of literature on types of business networks formed by firms with the stakeholders present in an industrial cluster, and thus, contributed to the existing literature of business networks. The identified business networks provide a much deeper understanding of how firms connect with its buyers, its suppliers, government agencies and educational institutes operating in an auto-component cluster.