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We introduce a multi-level model of the dependence of interfirm network topologies on the distribution and commonality of information in a network and the information strategies pursued by its member firms. Network topology, information properties of the network, and firm-level action within the network form dynamic, recursive, cross-level relationships – information properties in the network determine firm-level action, which in turn impacts the network topology and information properties. We derive predictions about the kinds of information strategies that firms are likely to adopt and succeed with in different information regimes, and about the kinds and short- and long-run dynamics of network topologies expected under different information regimes. Our model sheds new light on network topologies as a dependent variable that can be explained by network-level information regimes and firm-level information strategies.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

Purpose – This chapter proposes three main objectives in relation to understanding customer involvement in business networks. First, to identify important aspects of the network structure and environment and how the actions of the customer and other network participants create and maintain these. Second, to identify and explore the mechanisms and processes of resource integration in the network. Third, to identify the capabilities and competencies that customers bring to the network, and to understand how these are enhanced and developed.

Methodology/approach – Conceptual.

Research implications – We recognize that aspects of the resources themselves are important and that the characteristics of the resource and the way in which partners align them were key components of resource analysis.

Practical implications – We note that the interaction of different operant and operand resource combinations opens new doors to customer knowledgeability and involvement, where power over either authoritative or allocative resources in itself will not guarantee value creation.

Social implications – We support the call for the development of more sociologically enriched and complex models of interagent resource exchange. In particular, we would advise the need for a better understanding of how different network structures and environments are created and maintained through domination, legitimation, and signification processes.

Originality/value of chapter – This chapter addresses the gap in our understanding of how customer involvement in business-to-business networks may influence learning, value cocreation, and innovation. This chapter makes an important contribution to research in the field in that it investigates how the inclusion of the customer in business networks alters current assumptions and practices.

Wonders whether companies actually have employees best interests at heart across physical, mental and spiritual spheres. Posits that most organizations ignore their workforce – not even, in many cases, describing workers as assets! Describes many studies to back up this claim in theis work based on the 2002 Employment Research Unit Annual Conference, in Cardiff, Wales.

The experience of successful firms has proven that one of the most important ways to promote co-learning and create successful networked innovations is the proper application of inter-organizational knowledge mechanisms. This study aims to use a resource-action-performance framework to open the black box on the relationship between networking capability and innovation performance. The research population embraces companies in the Iranian automotive industry.

Due to the latent nature of the variables studied, the required data are collected through a web-based cross-sectional survey. First, the content validity of the measurement tool is evaluated by experts. Then, a pre-test is conducted to assess the reliability of the measurement tool. All data are gathered by the Iranian Vehicle Manufacturers Association (IVMA) and Iranian Auto Parts Manufacturers Association (IAPMA) samples. The power analysis method and G*Power software are used to determine the sample size. Moreover, SmartPLS 3 and IBM SPSS 25 software are used for data analysis of the conceptual model and relating hypotheses.

The results of this study indicated that the relationships between networking capability, inter-organizational knowledge mechanisms and inter-organizational learning result in a self-reinforcing loop, with a marked impact on firm innovation performance.

Since there is little understanding of the interdependencies of networking capability, inter-organizational knowledge mechanisms, co-learning and their effect on firm innovation performance, most previous research studies have focused on only one or two of the above-mentioned variables. Thus, their cumulative effect has not examined yet. Looking at inter-organizational relationships from a network perspective and knowledge-based view (KBV), and to consider the simultaneous effect of knowledge mechanisms and learning as intermediary actions alongside, to consider the performance effect of the capability-building process, are the main advantages of this research.

The below-average innovative capacity of the tourism sector raises the question on the potentials of digital business ecosystems (DBEs) to overcome these shortages at a destination level – especially within a smart city environment. Using the example of the German Capital Berlin, this article aims to discuss both the possibilities and inhibitors of innovative knowledge-creation by building scenarios on one specific design option: the integration of digital deep learning (DL) functionalities and traditional organizational learning (OL) processes.

Using the qualitative GABEK-method, major characteristics of a DBE as resource-, platform- and innovation systems are analyzed toward their interactions with the construction of basic action models (as the basic building blocks of knowledge).

Against the background of the research findings, two scenarios are discussed for future evolution of the Berlin DBE, one building on cultural emulation as a trigger for optimized DL functionalities and one following the idea of cultural engineering supported by DL functionalities. Both scenarios focus specifically on the identified systemic inhibitors of innovative capabilities.

While this study highlights the potential of the GABEK method to analyze mental models, separation of explicit and latent models still remains challenging – so does the reconstruction of higher order mental models which require a combined take on interview techniques in the future.

The resulting scenarios innovatively combine concepts from OL theory with the concept of DBE, thus indicating possible pathways into a tourism future where the limitations of human learning capacities could be compensated through the targeted support of general artificial intelligence (AI).

The purpose of the research is to study the set‐up processes of formal networks in a multinational high‐tech corporation consisting of multiple business units. The aim is to learn more about what management can do in order to support the fragile process of setting up new knowledge networks.

The research project had a problem‐solving approach, which is why action research was selected as the method.

Based on the research the following findings are discussed: identify and support knowledge activists, put knowledge networks on the strategic agenda, formal networks being vulnerable to major organizational changes, create an understanding for how formal networks can coexist with the line organization.

Limitations of the study relate to the fact that the knowledge networks were set up within the same company. That the knowledge networks were set up during a turbulent time when cost reductions were a top priority may also limit the generalizability of the findings.

The findings of the research can help managers to set up a knowledge network successfully. If management is aware of the issues that could be encountered in a knowledge network set‐up, the chances for a successful knowledge network implementation will be enhanced.

Systematic research regarding the actual process of a knowledge network set‐up is lacking. This research fills this gap by the practical characteristics of the research project and its practical implications for management.

Networks are natural tools for understanding social and economic phenomena. For example, all markets are characterized by agents connected by complex, multilateral information networks, and the network structure influences economic outcomes. In an earlier study, we undertook an experimental investigation of learning in various three-person networks, each of which gives rise to its own learning patterns. In the laboratory, learning in networks is challenging and the difficulty of solving the decision problem is sometimes massive even in the case of three persons. We found that the theory can account surprisingly well for the behavior observed in the laboratory. The aim of the present paper is to investigate important and interesting questions about individual and group behavior, including comparisons across networks and information treatments. We find that in order to explain subjects’ behavior, it is necessary to take into account the details of the network architecture as well as the information structure. We also identify some “black spots” where the theory does least well in interpreting the data.

Reflecting on 25 years of collaborating in action learning research initiatives in interorganizational settings, the authors have framed three key theoretical contributions: (1) a formula for action learning in networks, (2) the notion of action learning research, and (3) the application of action learning research in networks. This chapter reviews how each of these three key theoretical contributions emerged as insights and were developed over time through three large-scale funded interorganizational action learning projects. The chapter provides insights into the process of theorizing as the authors show how these frameworks emerged through inquiry into experience and were consolidated through collaborative action as practice-based research, research as practice, and practice as research toward designed-in impact.