Årbok 1964

To date, only one study by Strick and Volbeda (2018), titled ‘When the valence of unconditioned stimuli evolves over time: Evaluative conditioning with good-ending and bad-ending stories’, investigated stories in the context of evaluative conditioning to change brand attitudes. To find additional support for stories as unconditioned stimuli, we performed a partial replication of this study. As an extension, we also investigated the role of the need for affect as a mediator in this conditioning process. Our study had a within-subject design, in which MTurk workers (N = 66) participated in both our good- and bad-ending story conditions. In line with the original study and our hypothesis, our results suggest that the valence of the story ending determines the direction of the conditioning effect. Brands presented after good-ending stories have a stronger brand liking than brands presented after bad-ending stories. In practice, this would imply that advertisements should always end positively to induce a positive brand evaluation. Furthermore, as we hypothesized, our results indicate that the need for affect mediates this conditioning effect as people with a high need for affect rate brands more emotionally and strongly according to the story-ending valence than people with a low need for affect. Future research may distinguish other characteristics that mediate this effect to identify separate groups for targeted advertisements. To conclude, the ending of dramatic stories is determinative in brand evaluation when the brand is presented directly after, and the effect of these story endings is mediated by the need for affect

Systematic Development of Miniaturized (Bio)Processes using Process Systems Engineering (PSE) Methods and Tools

The focus of this work is on process systems engineering (PSE) methods and tools, and especially on how such PSE methods and tools can be used to accelerate and support systematic bioprocess development at a miniature scale. After a short presentation of the PSE methods and the bioprocess development drivers, three case studies are presented. In the first example it is demonstrated how experimental investigations of the bi-enzymatic production of lactobionic acid can be modeled with help of a new mechanistic mathematical model. The reaction was performed at lab scale and the prediction quality analyzed. In the second example a computational fluid dynamic (CFD) model is used to study mass transfer phenomena in a microreactor. In this example the model is not only used to predict the transient dynamics of the reactor system but also to extract material properties like the diffusion velocities of substrate and product, which is otherwise difficult to access. In the last example, a new approach to the design of microbioreactor layouts using topology optimization is presented and discussed. Finally, the PSE methods are carefully discussed with respect to the complexity of the presented approaches, the applicability with respect to practical considerations and the opportunity to analyze experimental results and transfer the knowledge between different scales