Internationalisation is the expansion of a firms operations to foreign markets and includes not only import and export but also foreign direct investments and international cooperation. Today’s globalising economy has resulted in a growing number of small and medium enterprises (SMEs) undertaking international activities. Internationalisation has been shown to be very beneficial for firms. Cross-border activities are an important means through which SMEs are able to create value, generate growth and access new knowledge and technologies. A strong relation has also been found between innovation and internationalisation: innovation may both be necessary to enter foreign markets as well as be a consequence of a firm’s foreign market activities. In addition to value creation at the firmlevel, crossborder entrepreneurship is assumed to create wealth at an economy wide level. With so many evident benefits to internationalisation, why don’t more SMEs internationalise? In her inaugural lecture Anne van Delft will illustrate the importance of “cooperation within networks” in international business. In today’s “network economy” it is important for firms to leverage their networks. Managing the interplay between networks and knowledge will be one of the key challenges for the 21st century. Cooperation with other firms is especially important for SMEs because it allows firms to utilise their limited resources in the most efficient way. Some of the sectors in the Rotterdam region are world leaders but nevertheless their main competitor might soon come from an emerging market rather than form within the regional cluster. The benefits of cooperation and knowledge sharing should therefore be exploited fully by SMEs in the Rotterdam region as global competition increases.
Social network analysis can be a powerful tool to better understand the social context of terrorist activities, and it may also offer potential leads for agencies to intervene. Our access to Dutch police information allows us to analyse the relational features of two networks that include actors who planned acts of terrorism and were active in the dissemination of a Salafi-Jihadi interpretation of Islam (n = 57; n = 26). Based on a mixed-method approach that combines qualitative and more formal statistical analysis (exponential random graph models), we analyse the structural characteristics of these networks, individual positions and the extent to which radical leaders, pre-existing family and friendship ties and radicalizing settings affect actors to form ties. We find that both networks resemble a core–periphery structure, with cores formed by a densely interconnected group of actors who frequently meet in radicalizing settings. Based on our findings, we discuss the potential effects of preventive and repressive measures developed within the Dutch counterterrorism framework.
Our study elucidates collaborative value creation and private value capture in collaborative networks in a context of sustainability. Collaborative networks that focus on innovative solutions for grand societal challenges are characterized by a multiplicity and diversity of actors that increase the complexity and coordination costs of collective action. These types of inter-organizational arrangements have underlying tensions as partners cooperate to create collaborative value and compete to capture or appropriate value on a private or organizational level, resulting in potential and actual value flows that are highly diffuse and uncertain among actors. We also observe that network participants capture value differentially, often citing the pro-social (e.g. community, belonging, importance) and extrinsic benefits of learning and reputation as valuable, but found it difficult to appropriate economic or social benefits from that value. Differential and asymmetric value appropriation among participants threatens continued network engagement and the potential collective value creation of collaborative networks. Our data indicates that networked value creation and capture requires maintaining resource complementarity and interdependency among network participants as the network evolves. We develop a framework to assess the relational value of collaborative networks and contribute to literature by unpacking the complexities of networked value creation and private value capture in collaborative networks for sustainability.
The AR in Staged Entertainment project focuses on utilizing immersive technologies to strengthen performances and create resiliency in live events. In this project The Experiencelab at BUas explores this by comparing live as well as pre-recorded events that utilize Augmented Reality technology to provide an added layer to the experience of the user. Experiences will be measured among others through observational measurements using biometrics. This projects runs in the Experience lab of BUas with partners The Effenaar and 4DR Studio and is connected to the networks and goals related to Chronosphere, Digireal and Makerspace. Project is powered by Fieldlab Events (PPS / ClickNL)..
Digital innovations in the field of immersive Augmented Reality (AR) can be a solution to offer adults who are mentally, physically or financially unable to attend sporting events such as premier league football a stadium and match experience. This allows them to continue to connect with their social networks. In the intended project, AR content will be further developed with the aim of evoking the stadium experience of home matches as much as possible. The extent to which AR enriches the experience is then tested in an experiment, in which the experience of a football match with and without AR enrichment is measured in a stadium setting and in a home setting. The experience is measured with physiological signals. In addition, a subjective experience measure is also being developed and benchmarked (the experience impact score). Societal issueInclusion and health: The joint experience of (top) sports competitions forms a platform for vulnerable adults, with a limited social capital, to build up and maintain the social networks that are so necessary for them. AR to fight against social isolation and loneliness.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
