Social media is valuable when searching for information or to ease communication but what about the health risks, loss of productivity and physical activity, imaginary ideals and addiction? One third of women 16-29 years old suffer from a lower mental health and according to a large number of studies, social media has a big part in it. This study investigates how Instagram can be modified in order to reduce the nega- tive effects on young women caused by the app. Through interviews and a workshop, five concepts were produced and tested. The preferred solution for the users was to add a symbol if the image has been retouched, this was made to be able to separate the fake from the authentic uploads in Instagram. Totally removing the phone from the process was not an option since it is used in more situations like communication. Another concept that was instantly popular was the option to keep follow or unfollow recently added accounts. This makes the user aware of the content and is given the possibility to evaluate the accounts. Along with these concepts, it could be suggested that people need to be more aware of the problems caused by social media. It could also be suggested for users to work on their self-compassion, to not critique oneself too much, since fitspo-accounts affect users substantially. It is not the time spent on social media that matters but what people are actually engaging with.

The theme of the diploma thesis is called "On-line Communications Strategy Plan for the Cinoherni klub theatre". The theoretical part of the thesis deals mainly with internet marketing and communications strategies in a business. A method called Search Engine Marketing (SEM) is described in details in this part. The thesis deals also with some possibilities of how to effectively use internet advertising to drive traffic to company's websites. In the practical part of the thesis a specific internet communications strategy for Cinoherni club theatre is designed. Free Google AdWords and Sklik PPC advertising systems are used in this strategy. The data from a banner (3 options) concept test research are the main fundament for an appropriate graphic and communication tools selection.

Designers do not merely develop concepts; they are increasingly involved in testing product concepts and learning consumer preference. However, designers’ decision making processes in these tasks have been little studied. In the two essays, I apply decision making frameworks to concept testing and preference learning to study consumer’s and designer’s biases. In my first essay, I study consumer bias in concept testing. When consumers test new products, they are often asked to choose which product they prefer. However, a choice question can elicit biased preference because consumers simply choose the product that is superior on the attribute serving their purchase purpose. My studies show that when consumers are asked to predict which product they will enjoy more, they are more likely to prefer the product that actually reflects their consumption utility. These findings suggest that making trade-offs is avoided in the choice question, but is encouraged in the enjoyment prediction question. Thus, a simple change of question format, in otherwise identical product comparisons, elicits different answers. This holds true when product attributes are easy to evaluate; when product attributes are hard to evaluate, changing question format does not affect consumer choice. My second essay examines designer bias in preference learning. When designers predict consumer preference for a product, they often base their predictions on consumer preference for similar products. However, this categorization-based strategy can result in biased predictions because categorical similarity is not diagnostic for preference prediction. I conducted two studies by applying a Multiple Cue Probability Learning experiment to a designer’s prediction task. I found that when subjects used a sequential learning strategy, making a sequence of predictions and receiving feedback, they increased prediction accuracy by 14% on average. When they made predictions with multiple sets, with a break between each set during which they reflected on what they had learned, their prediction accuracy further improved by 7% on average. In sum, I demonstrate bias and propose approaches to avoid them in two design tasks. My two essays show that the decision making frameworks are crucial in understanding and improving the successful outcome of the design process.

Designers do not merely develop concepts; they are increasingly involved in testing product concepts and learning consumer preference. However, designers’ decision making processes in these tasks have been little studied. In the two essays, I apply decision making frameworks to concept testing and preference learning to study consumer’s and designer’s biases. In my first essay, I study consumer bias in concept testing. When consumers test new products, they are often asked to choose which product they prefer. However, a choice question can elicit biased preference because consumers simply choose the product that is superior on the attribute serving their purchase purpose. My studies show that when consumers are asked to predict which product they will enjoy more, they are more likely to prefer the product that actually reflects their consumption utility. These findings suggest that making trade-offs is avoided in the choice question, but is encouraged in the enjoyment prediction question. Thus, a simple change of question format, in otherwise identical product comparisons, elicits different answers. This holds true when product attributes are easy to evaluate; when product attributes are hard to evaluate, changing question format does not affect consumer choice. My second essay examines designer bias in preference learning. When designers predict consumer preference for a product, they often base their predictions on consumer preference for similar products. However, this categorization-based strategy can result in biased predictions because categorical similarity is not diagnostic for preference prediction. I conducted two studies by applying a Multiple Cue Probability Learning experiment to a designer’s prediction task. I found that when subjects used a sequential learning strategy, making a sequence of predictions and receiving feedback, they increased prediction accuracy by 14% on average. When they made predictions with multiple sets, with a break between each set during which they reflected on what they had learned, their prediction accuracy further improved by 7% on average. In sum, I demonstrate bias and propose approaches to avoid them in two design tasks. My two essays show that the decision making frameworks are crucial in understanding and improving the successful outcome of the design process.