Assertive Ads for Want or Should? It Depends on Consumers’ Power

Advertisements using assertive language are commonly seen in marketing communications, yet assertive language has often been shown to be ineffective in communication and even decrease compliance. Recent research began to examine factors that influence the effectiveness of assertive advertising messages, but little research has studied the potential moderating role of consumer‐related factors. The current research fills this gap by investigating the moderating role of consumers’ power. Across five studies, the authors find that for high‐power consumers, assertive ads are effective in promoting want products but ineffective in promoting should products. For low‐power consumers, however, assertive ads are effective in promoting should products but ineffective in promoting want products. Moreover, the authors show that this pattern of results is driven (mediated) by consumers’ perception of the fit between the advertising message and the advertised product. Theoretical and practical implications of the findings are discussed.     

Mapping differential responses to cognitive training using machine learning

We used two simple unsupervised machine learning techniques to identify differential trajectories of change in children who undergo intensive working memory (WM) training. We used self‐organizing maps (SOMs)—a type of simple artificial neural network—to represent multivariate cognitive training data, and then tested whether the way tasks are represented changed as a result of training. The patterns of change we observed in the SOM weight matrices implied that the processes drawn upon to perform WM tasks changed following training. This was then combined with K‐means clustering to identify distinct groups of children who respond to the training in different ways. Firstly, the K‐means clustering was applied to an independent large sample (N = 616, M_age = 9.16 years, range = 5.16–17.91 years) to identify subgroups. We then allocated children who had been through cognitive training (N = 179, M_age = 9.00 years, range = 7.08–11.50 years) to these same four subgroups, both before and after their training. In doing so, we were able to map their improvement trajectories. Scores on a separate measure of fluid intelligence were predictive of a child's improvement trajectory. This paper provides an alternative approach to analysing cognitive training data that go beyond considering changes in individual tasks. This proof‐of‐principle demonstrates a potentially powerful way of distinguishing task‐specific from domain‐general changes following training and of establishing different profiles of response to training.