Background: Correct technical decision-making is crucial for a software development company to maintain its competitive advantage in the market. However, despite the criticality, decision-making is often performed ad hoc and with flawed information, implying a need for more formal models, guidelines and tools. Decision-making models exist for asset selection with varying properties that characterize them, such as the type of activities included, the decision-making methods used, or the complexity of the decision-making models.

Objective: In this work, a rigorous model for industrial decision-making is compared against a light-weight model to elicit properties that practitioners require in such a model. The goal is to synthesise a list of properties that an industrial decision model should exhibit and that can be used for future research within this area.

Method: A static evaluation of the two models has been conducted with the goal of evaluating their representativeness of an ideal decision-making model. For this reason a survey was performed that was sent to 52 respondents from 14 companies in Scandinavia, including Saab, TetraPak and ABB. Viewpoints were thereby gathered from practitioners in broad range of different roles and contexts to enable result analysis with both qualitative and quantitative approaches.

Results: Results from the survey, and subsequent analysis, showed preference towards a lightweight decision-making model and its properties. Though, despite exhibiting different properties, we observe that none of the models are considered suitable in all contexts and synthesize 6 properties that are important for a favorable decision-model to exhibit.

Conclusion: We therefore conclude that there is no “one” model that can satisfy the plethora of different needs for decision-making performed in practice. Instead, a new approach is required, one which we outline on a conceptual level based on the inputs from this study. In addition, this work showcases the implications of mismatched requirements between industry and academia and the positive learnings that can be drawn from such a scenario.