This paper aims to investigate the relationship between advergame design, advergame experience and consumer behaviour across cultures. For this purpose, a study was designed to compare and contrast behaviour patterns across Brazilian and British cultures. An advergame (Colheita de Café (CC)), featuring the Fairtrade mark was developed through a semiotics approach. Participants from both Brazil (N=30) and the UK (N=34) were invited to play the advergame and answer a questionnaire before and after gameplay. The results indicate that Brazilians felt more familiar with the visual elements incorporated by the advergame when compared to British consumers. Brazilians also had more favourable attitudes towards the brand, suggesting that visual familiarity could influence consumer behaviour. Contrary to our expectations, both Brazilian and British respondents had the same attitudes towards the advergame, showing that visual familiarity did not particularly influence the advergame experience. The main contribution of this paper is the suggestion that visual familiarity could influence consumer behaviour across cultures. We expect that our findings can be used in future research that examines cultural nuances in advergame design.
AI is often looked at as a logical rational way to develop a games agent that methodically looks at options and delivers rational solutions. This paper is based on developing an AI agent that plays a game with a similar emotive content like a human. The purpose of the study was to see if the incorporation of this emotive content would influence the outcomes within the game Love Letter. In order to do this an AI agent with an emotive layer was developed to paly the game over a million times. A lower win/loss ratio demonstrates that to some extent this methodology was vindicated and a 100 per cent win for the AI agent did not happen. Machine learning techniques were modelled purposely so as to match extreme models of behavioural change. The results demonstrated a win/loss ration of 0.67 for the AI agent and in many ways reflectd the frustration that a normal player would exhibit during game play. As was hypothesised the final agent investment value was, on average, lower after matchplay than its initial value.
AI (Artificial Intelligence) is often looked at as a logical way to develop a game agent that methodically looks at options and delivers rational or irrational solutions. This paper is based on developing an AI agent that plays a game with a similar emotive content like a human. The purpose of the study was to see if the incorporation of this emotive content would influence the outcomes within the game Love Letter. In order to do this an AI agent with an emotive layer was developed to play the game over a million times. A lower win/loss ratio demonstrates that, to some extent, this methodology was vindicated and a 100 per cent win for the AI agent did not happen. Machine learning techniques were modelled purposely so as to match extreme models of behavioural change. The results demonstrated a win/loss ratio of 0.67 for the AI agent and, in many ways, reflected the frustration that a normal player would exhibit during game play. As was hypothesised, the final agent investment value was, on average, lower after match play than its initial value.
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