To fully understand the reasons why players return to certain games there needs to be sufficient understanding as to why people play games in the first place.
Games are often researched with same theories as other information systems and while this arrangement works, it has some problems (Hamari, Keronen, 2017).
Mostly information systems are used solely for their utilitarian values, such as the value of doing a specific task or helping solve a problem. This is a different matter in games where primarily the reason for playing games is the enjoyment of the game itself. (Salen & Zimmerman, 2003.) This is a hedonist reason and while hedonism is the primary reason to play games, there are many games where utilitarian reasons are just as valid. Many studies have been created to find the reasons why people play games, researchers are still not unanimous about why games are used. (Hamari, Keronen, 2017.)
Hamari and Keronen found 48 research articles in 2017 in their meta-anal-ysis of the currently available research on the subject. Many of the theories relied on older theories of technology and software acceptance and the most used the-ory was the Technology acceptance model (TAM) which focuses on attitude to-wards technology and in this case the attitude toto-wards games. This has been brought up again in other theories like the theory of planned behaviour by Ajzen
(1991). In the Hamari and Keronen meta-analysis, attitude towards games and gaming had the strongest relationship to playing games. Enjoyment and per-ceived usefulness also played a significant role in intentions to playing games.
Other variables that had impacts on players playing intentions were: satisfaction (How the game meets expectations of perceived enjoyment), perceived ease of use, perceived playfulness (what it feels like interacting with a game), subjective norms (social influence), critical mass (players perception of peers playing) and Csikszentmihalyi’s flow (1990). These variables are listed from a strong correla-tion to a weak one. Interestingly, while many studies that were included in the meta-analysis included gender as one variable that would have an impact on playing intentions, the analysis showed that there was no correlation.
From a designer’s perspective, studies on why people play are a bit more focused on the hedonic reasons and why previously mentioned enjoyment and other variables work the way they do. Salen and Zimmerman in their book, Rules of Play: game design fundamentals (2003) have stated that pleasure is the most distinctive characteristic of games. More about pleasure under the chapter 2.4 pleasure.
Players intention to play and return to play are correlated but are not syno-nyms. Just like a person loving a movie may never see it again, a person can love a game and never return to it. Salen and Zimmerman (2003) state that there is no single answer as to why players start to play a game and why do they return.
This can happen for many reasons, one of them being the fact that some games are more linear or short than other. If a game is designed to be beaten in one sitting and it so linear that other playthroughs would be for the most part be just identical, there is not much incentives for the player to return. This happens less in more arcade focused games than heavily narrative games and most freemium games are situated in the arcade side of the spectrum.
It is important to know how and why people act the way they do. Coming back to a mobile game is a decision so it is important to understand the motiva-tion for the people. This study aims to find game elements that lead to people
wanting to come back and even getting addicted. Designing the mechanics that facilitate this behaviour also requires decision making. The Cynefin framework which was created by Snowden and his colleagues in IBM, was originally created for management level decision making. Since its creation it has been used in many different fields and it has been found to be a useful tool even in academic research. The framework is based around domains of order (Complicated & Sim-ple) and un-order (Complex & Chaotic). (Kurtz & Snowden 2003.) Figure 7 ex-plores the five decision making domains of Cynefin framework.
Figure 2. Cynefin framework (Kurtz & Snowden 2003).
Simple domain consists of decisions in which cause and effect relations exist, they are predictable and they can be repeated with same results (Kurtz & Snow-den 2003). There should always be optimal decision or best practise.
Complicated domain contains decisions where there is a cause and effect, but it is not as clear and requires analysing before it can be determined. Problems often
have multiple solutions and therefore instead of singular best practise there are multiple good practises (Kurtz & Snowden 2003).
Complex domain consists of decisions where cause and effect are only clear after the action has occurred. Decision making is based on tests and trial and error (Kurtz & Snowden 2003). This is the domain of most game designers as results from playtests can only be observed after the test.
Chaotic domain is when there are no cause and effect, or they are so complicated that nobody will understand the logic behind them even after they happened (Kurtz & Snowden 2003).
Disorder is the domain of not knowing where the decision takes place (Kurtz &
Snowden 2003). In this domain, decision making should be halted until the right domain has been identified with gathered information.