Influence of Trust Factors on Shared Laboratory Resources in a Distributed Enviroment

Influence of Trust Factors on Shared Laboratory Resources in a Distributed Enviroment

WP 1: How to measure trust

Sharing with strangers? Why not? In DigiLab4U, sharing is part of the concept: a university or company shares laboratory equipment, professors share their knowledge, and students share their learning records. Workpackage 1 of the research project therefore focuses on this crucial aspect of interorganizational cooperation: trust.

No network without cooperation, no cooperation without trust

First, research was carried out into the factors that influence the relationship of trust between cooperation partners, especially when it comes to shared resources. For this purpose, various models were researched, which were then tested for applicability in DigiLab4U. Confidence factors such as the legal framework, reputation or certifications were considered, but behavioral criteria such as sharing information or creating mutual incentives were also taken into account.

These incentives in particular played a major role. In the course of a workshop, the research team identified six main groups of stakeholders in DigiLab4U: students, professors, universities, laboratory providers, companies (as users) and researchers. Since each of these groups sees different incentives in a shared laboratory environment, a differentiation was made as to which trust factors are relevant for which stakeholders.

Visualization with Social Network Analysis (SNA)

In a next step, the focus was on the representation of trust relationships. Five modeling methods were researched as candidates and evaluated and compared according to predetermined criteria. For example, it was considered how entities and their relationships can be represented explicitly with a model, and the usability and availability of suitable software were also considered.

In the end, we have chosen “Social Network Analysis” (SNA), which is now to be used for the project. In this model, social relationships are characterized both by a qualitative description of the connection and by means of quantitative weighting. With SNA, a network can be analyzed mathematically with tools from graph theory.

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Image I: Example graphic SNA

A visualization of a network could look like this, for example. The different colors of the nodes represent the different organizations. The strength of the edges symbolizes the strength of the relationship of trust between two people. The example shows that if Ms. Apfel ever leaves the company, the network will become unstable.

Trust as a cognitive model

To avoid such constellations, the research project was followed by the creation of a cognitive model based on the results of the previous research. An approach comes to help that was actually developed for supply chain management: Supply Chain Operations Reference (SCOR). There are five performance attributes in SCOR: reliability, receptivity, flexibility, costs and efficiency of asset management. The determined trust factors could now be sorted into these categories.

With the help of the performance attributes from the SCOR area, the quality of trust can now be quantified to a certain extent. At least relatively, a relationship can now be assessed dynamically (Have expectations been met / exceeded / disappointed?) and visually displayed in narrower or wider edges.

The next step for the research team is to operationalize and improve the model.

by Marlies Goes

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