Search results

This chapter aims to identify the main determinants that define the architectural properties of network emergence and significantly influence the dynamics underlying network evolution in time. The identification and analysis of these determinants, as well as the dynamic processes tied to them, allows to appreciate the competitive bases and consequences of network morphology. To this purpose, using a complex systems perspective as an integrative conceptual approach, we represent networks as complex dynamic systems of knowledge and capabilities. We perform a comparative in-depth analysis of the processes underlying the emergence and evolution of STMicroelectronic's global network and of Toyota's supplier network in the US so as to allow an elucidatory empirical assessment of the theoretical representation elaborated in the article.

The purpose of this paper is to examine the effect of changes at the manufacturing plant level on other plants in the manufacturing network and also investigate the role of manufacturing plants on the evolution of a manufacturing network.

The research questions are developed by identifying the gaps in the reviewed literature. The paper is based on three case studies undertaken in Danish manufacturing companies to explore in detail their manufacturing plants and networks. The cases provide a sound basis for developing the research questions and explaining the interaction between different manufacturing plants in the network and their impact on network transformation.

The paper highlights the dominant role of manufacturing plants in the continuously changing shape of a manufacturing network. The paper demonstrates that a product or process change at one manufacturing plant affects the other plants in the same network by altering their strategic roles, which leads to the subsequent transformation of the manufacturing network.

A review of the existing literature investigated different elements of a manufacturing network independently. In this paper, the complex phenomenon of a manufacturing network evolution is observed by combining the analysis of a manufacturing plant and network level. The historical trajectories of manufacturing networks that are presented in the case studies are examined in order to understand and determine the future shape of the networks. This study will help industrial managers make more knowledgeable decisions regarding manufacturing network management.

This paper aims to investigate how the relationships among the different actors within the business networks affect the evolution of innovation along the different steps of technology life cycle.

The study is supported by a longitudinal case study referred to a mechanical company operating in the knitting machineries industry. A triangulation method integrating multiple data sources in a multiple method design was used to gather data.

The longitudinal study supports the idea that interactions among the actors within the business networks heavily influence the technology evolution.

Although the longitudinal case study reported in this paper refers to a period of time of about 10 years, our findings refer only to a single case study in a specific sector and so they cannot be generalized.

This paper provides important guidelines on how to manage the relationships emerging within a business network to influence the new technologies development.

The paper contributes to business networks literature, filling the gaps about how the relationships among the actors involved in a network can evolve over time and influence the evolution of technology in itself.

This chapter addresses ambidexterity at the individual level. Ambidexterity is defined as a company's ability to guarantee both short- and long-term successes by simultaneously exploring new market or new technological paths and improving existing products. We demonstrate that this ability can result from the evolution of social networks linking individuals involved in idea development. We used a longitudinal approach that combined case study and social network structure analysis of the R&D center of a semiconductor company. Six cases have been selected according to the level of disruption of the first idea generated and the end result in terms of exploration and exploitation. For these six cases, data have been gathered from monthly project reviews, press articles and listings of patents. Seventy-four interviews with key actors in the idea-development process have also been conducted.We mapped the relationships between actors who have contributed to the development of the idea through creative thinking and/or helped it to be accepted both internally and externally over three-year windows. Consequently, two network pictures are drawn for each case, and network structure indicators are computed for these two representations. We created a description of network evolution and the consequences of this process on the level of disruption of the ideas involved. This research demonstrated that different network structures and types of connections are relied upon depending on the explorative or exploitative objectives of teams of individuals.

The chapter presents a theoretical framework that deals with the basic question of how networks and industries coevolve. We draw upon the structural and relational perspectives of networks to theorize about changes occurring in interfirm networks over time and the coevolutionary linkage of these changes to the industry life cycle. We further extend the widely accepted industry life cycle model by claiming that industry-specific evolutionary patterns impact the structure of the network’s relations, which in turn lead to diversification in the sources of innovation and to variation in the patterns of industrial evolution.

The Moldoveanu, Baum & Rowley approach highlights the importance of developing a multi-level theory of network evolution that explicitly accounts for strategic agency and (bounded) rationality. I discuss intriguing questions raised by their model about structure vs. topology, feedback loops in evolution, and network resources other than information.

The research on social media-based academic communication has made great progress with the development of the mobile Internet era, and while a large number of research results have emerged, clarifying the topology of the knowledge label network (KLN) in this field and showing the development of its knowledge labels and related concepts is one of the issues that must be faced. This study aims to discuss the aforementioned issue.

From a bibliometric perspective, 5,217 research papers in this field from CNKI from 2011 to 2021 are selected, and the title and abstract of each paper are subjected to subword processing and topic model analysis, and the extended labels are obtained by taking the merged set with the original keywords, so as to construct a conceptually expanded KLN. At the same time, appropriate time window slicing is performed to observe the temporal evolution of the network topology. Specifically, the basic network topological parameters and the complex modal structure are analyzed empirically to explore the evolution pattern and inner mechanism of the KLN in this domain. In addition, the ARIMA time series prediction model is used to further predict and compare the changing trend of network structure among different disciplines, so as to compare the differences among different disciplines.

The results show that the degree sequence distribution of the KLN is power-law distributed during the growth process, and it performs better in the mature stage of network development, and the network shows more stable scale-free characteristics. At the same time, the network has the characteristics of “short path and high clustering” throughout the time series, which is a typical small-world network. The KLN consists of a small number of hub nodes occupying the core position of the network, while a large number of label nodes are distributed at the periphery of the network and formed around these hub nodes, and its knowledge expansion pattern has a certain retrospective nature. More knowledge label nodes expand from the center to the periphery and have a gradual and stable trend. In addition, there are certain differences between different disciplines, and the research direction or topic of library and information science (LIS) is more refined and deeper than that of journalism and media and computer science. The LIS discipline has shown better development momentum in this field.

KLN is constructed by using extended labels and empirically analyzed by using network frontier conceptual motifs, which reflects the innovation of the study to a certain extent. In future research, the influence of larger-scale network motifs on the structural features and evolutionary mechanisms of KLNs will be further explored.

The purpose of this paper is to investigate entrepreneurs’ network evolution in the start-up phase.

Based on the case studies of six fashion start-up firms, this study uses a three-dimensional perspective on social capital (structural, relational, cognitive) to investigate entrepreneurs’ network evolution (i.e. initiation of new relationships) in the start-up phase so as to acquire resources and support for firms’ goals. The study focuses particularly on the understudied cognitive dimension of social capital. The fashion industry provides a relevant research setting because it is characterised by changes in demand, which generate opportunities for entrepreneurship.

The findings show that the display of cognitive attributes is important for the creation of structural social capital (the establishment of new relationships). The findings also indicate that relationships initiated based on the cognitive dimension have a high probability of developing into embedded relationships, thereby becoming high in the relational dimension and providing access to private information containing referrals to other actors. Thus, these relationships also promote the continued development of the structural dimension.

The findings imply that the entrepreneurs’ sets of cognitive attributes constitute an important asset in the creation of social capital. They also point to the importance of signalling these values to potential resource holders. Relationships initiated through the display of cognitive attributes can provide resources without requiring immediate economic remuneration.

This paper aims to study the characteristics and evolution rules of tagging knowledge network for users with different activity levels in question-and-answer (Q&A) community represented by Zhihu.

A random sample of issue tag data generated by topics in the Zhihu network environment is selected. By defining user quality and selecting the top 20% and bottom 20% of users to focus on, i.e. top users and bot users, the authors apply time slicing for both types of data to construct label knowledge networks, use Q-Q diagrams and ARIMA models to analyze network indicators and introduce the theory and methods of network motif.

This study shows that when the power index of degree distribution is less than or equal to 3.1, the ARIMA model with rank index of label network has a higher fitting degree. With the development of the community, the correlation between tags in the tagging knowledge network is very weak.

It is not comprehensive and sufficient to classify users only according to their activity levels. And traditional statistical analysis is not applicable to large data sets. In the follow-up work, the authors will further explore the characteristics of the network at a larger scale and longer timescale and consider adding more node features, including some edge features. Then, users are statistically classified according to the attributes of nodes and edges to construct complex networks, and algorithms such as machine learning and deep learning are used to calculate large-scale data sets to deeply study the evolution of knowledge networks.

This paper uses the real data of the Zhihu community to divide users according to user activity and combines the theoretical methods of statistical testing, time series and network motifs to carry out the time series evolution of the knowledge network of the Q&A community. And these research methods provide other network problems with some new ideas. Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates.

Research has found that user activity has a certain impact on the evolution of the tagging network. The tagging network followed by users with high activity level tends to be stable, and the tagging network followed by users with low activity level gradually fluctuates. For the community, understanding the formation mechanism of its network structure and key nodes in the network is conducive to improving the knowledge system of the content, finding user behavior preferences and improving user experience. Future research work will focus on identifying outbreak points from a large number of topics, predicting topical trends and conducting timely public opinion guidance and control.

In terms of data selection, the user quality is defined; the Zhihu tags are divided into two categories for time slicing; and network indicators and network motifs are compared and analyzed. In addition, statistical tests, time series analysis and network modality theory are used to analyze the tags.

Within the context of a sustainable supply chain, the purpose of this paper is to report on empirical longitudinal research on supply chain network evolution and dynamics of governance in a multi‐stakeholder supply chain sustainability initiative led by Nespresso, the speciality coffee division of Nestlé.

The paper proposes a framework to study the creation and evolution of governance mechanisms over a five‐year period. Data from 48 semi‐structured interviews and 15 recent and historic documents were also analysed. The interviews were conducted among current and past representatives of all the organisations concerned including coffee traders, NGOs and farmers.

In contrast with literature on the subject, governance mechanisms initially relied mostly on informal mechanisms. Formal governance mechanisms were incorporated into the relationships to enable the supply chain network to grow and to provide clarity to all actors. Relational quality processes that increased trust were critical elements in the early phase, and were explicitly built into a second phase of development.

Being a single case study, some caution needs to be applied to generalise findings beyond the specific context studied. An additional methodological limitation is the limited number of actors involved in the overall supply chain network.

The paper shows the importance of treating governance mechanisms within the supply chain not as a fixed variable to be determined once and for all in the beginning of a relationship, but rather to adapt the coordination mechanisms of the relationships.

The evolution of relationships over time has been “often called for but rarely chosen”. The case highlights the potential benefits of this type of research to develop an understanding of the evolution of relationships in a supply chain network.