What dynamics are in play when we examine the path to user engagement?
In visualizing how engagement flows from extrinsic game mechanics to intrinsic motivators, let’s look first at a smaller simplified model exploring the various stages a player experiences along the way.
The image below shows 5 basic elements.
To illustrate by way of example, let’s choose a quest with location check-ins:
1. The Game Mechanic is a quest, with an achievement delivered after check-in. The game mechanic facilitates a mode of play.
2. Play is freely chosen and associated with fun, pleasure and enjoyment. The idea of a scavenger hunt is playful and may initiate certain behaviors.
3. Behaviors refer to actions related to the game mechanics such as going to a retail store, checking in for an event, challenging friends to compete, or announcing check-ins on Facebook. These behaviors may evoke a player’s engagement.
4. Engagement in this example is when a player becomes absorbed and the game activities, subject or brand consumes one’s attention or time. For example, the player checks in every time they pass through a location and shares this with friends. It creates an increased involvement and participation that produces mastery.
5. Mastery is an intrinsic motivator that stimulates a player to independently and persistently overcome a challenging skill or task. Once the player has checked in at the quest locations, they obtain an achievement and look to the next challenge or motivator.
At this point, in order to keep the player further engaged, the game mechanic must either evolve and scale in difficultly, or shift to address another motivator. This dynamic balance between boredom and frustration is described in Mihaly Csikszentmihalyi book “Flow: The Psychology of Optimal Experience”:
Flow activities lead to growth and discovery. One cannot enjoy doing the same thing at the same level for long. We grow either bored or frustrated; and then the desire to enjoy ourselves again pushes us to stretch our skills, or to discover new opportunities for using them.
To evolve the simple model to accommodate more complex situations, we need to introduce other dynamics, leading to 2 questions:
1. How does Self-Direction influence this model?
2. How does direct feedback relate to each area?
In the next figure, the linear engagement flow is now a circle reflecting how the game mechanic shifts or scales to accommodate the growth of the player as they seek new motivations. This highlights the relationship between the engagement flow and Self-Direction.
Self-Direction, or independently guided actions occur by making choices in the game resulting in direct feedback. In an optimal scenario, direct feedback should take place throughout the entire experience, giving us an ever-evolving and adapting engagement model. It is important to note that each of the elements of the flow influence the other elements creating local feedback loops and are contributors to the dynamics of the game play.
The model also aligns with Jenova Chen’s variant of Csikszentmihalyi’s flow diagram. In his thesis “Flow in Games”, Chen describes active flow adjustments through self-directed choices, represented by the red path of arrows (see figure below). The Gamification Continuous Engagement Flow accounts for this meandering path through the introduction of direct user feedback. In Chen’s drawing, the white regions represent anxiety (upper) and boredom (lower):
The significance of self direction along with varying and often changing motivators make game design with a single unvarying game mechanic impractical. Successful designs will incorporate evolving and scaling mechanics suitable to a wide range of personalities, moods, player skill levels and interests.
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