Motivation Of Playing Video Games

Motivations for Play in Online Games

NICK YEE

ABSTRACT

An empirical model of player motivations in online games provides the foundation to understand

and assess how players differ from one another and how motivations of play relate to

age, gender, usage patterns, and in-game behaviors. In the current study, a factor analytic approach

was used to create an empirical model of player motivations. The analysis revealed 10

motivation subcomponents that grouped into three overarching components (achievement,

social, and immersion). Relationships between motivations and demographic variables (age,

gender, and usage patterns) are also presented.

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CYBERPSYCHOLOGY & BEHAVIOR

Volume 9, Number 6, 2006

© Mary Ann Liebert, Inc.

INTRODUCTION

EVERY DAY, millions of people1 interact with each

other in online environments known as Massively-

Multiplayer Online Role-Playing Games

(MMORPGs). MMORPG players, who on average

are 26 years old, typically spend 22 h per week in

these environments.2 Asking MMORPG players

why they play reveals a wide variation of motives:

Currently, I am trying to establish a working corporation

within the economic boundaries of the virtual

world—primarily, to learn more about how

real world social theories play out in a virtual economy

[male, age 30].

The fact that I was able to immerse myself in the

game and relate to other people or just listen in to

the “chatter” was appealing [female, age 34].

Indeed, the variation suggests that MMORPGs

may appeal to many players because they are able to

cater to many different kinds of play styles. Being able

to articulate and quantify these motivations provides

the foundation to explore whether different sections

of the player demographic are motivated differently,

and whether certain motivations are more highly correlated

with usage patterns or other in-game behaviors.

Such a model has value for both researchers and

game designers. For researchers, findings may clarify

whether certain kinds of players are more susceptible

to problematic usage, for example. For game developers,

findings may clarify how certain game mechanics

may attract or alienate certain kinds of players.

While Bartle’s Player Types3 is a well-known

player taxonomy of Multi-User Dungeon (MUD)

users, the underlying assumptions of the model

have never been empirically tested. For example,

Bartle assumed that preference for one type of play

(e.g., achievement) suppressed other types of play

(e.g., socializing or exploring). Also, it has never

been empirically shown that the four player types

are indeed independent types. In other words, several

of the types may correlate to a high degree. In

essence, it would be hard to use Bartle’s model on a

practical basis unless it was validated with and

grounded in empirical data. In the following work,

I describe a factor analytic approach to creating an

empirically grounded player motivation model.

Department of Communication, Stanford University, Palo Alto, California.

Rapid Communication

METHODS

A list of 40 questions that related to player motivations

was generated based on Bartle’s Player

Types3 and qualitative information from earlier

surveys of MMORPG players. Players used a fivepoint

fully labeled construct-specific scale to respond.

For example, respondents were asked,

“How important is it you to level up as fast as possible?.”

After the inventory of items was prepared,

data was then collected from 3,000 MMORPG players

through online surveys publicized at online

portals that catered to MMORPG players from several

popular MMORPGs—EverQuest, Dark Age of

Camelot, Ultima Online, and Star Wars Galaxies. A

factor analysis was then performed on this data to

detect the relationships among the inventory items

in order to reveal its underlying structure.

RESULTS

A principle components analysis was used to arrive

at a more parsimonious representation of the

40-item inventory set. Ten components emerged

with eigenvalues greater than 1. Together, these 10

components accounted for 60% of the overall variance.

An oblique rotation (Promax, kappa = 4) was

used to reflect the inherent correlations between

the components. Most loadings were in excess of

0.55, and no secondary loadings exceeded 30% of

the primary loadings. Almost all components had a

Cronbach’s alpha of over 0.70. Due to the high

number of components, an additional PCA was performed

on the 10 components in order to explore

whether certain components should be grouped together.

Three main components emerged with

eigenvalues greater than 1. Together, these three

main components accounted for 55% of the overall

variance. Again, an oblique rotation was used. The

10 components are shown here grouped according

to the second PCA (Table 1). The components will

now be described briefly:

Achievement component

Advancement—The desire to gain power, progress

rapidly, and accumulate in-game symbols

of wealth or status

Mechanics—Having an interest in analyzing

the underlying rules and system in order to

optimize character performance

Competition—The desire to challenge and

compete with others

Social component

Socializing—Having an interest in helping and

chatting with other players

Relationship—The desire to form long-term

meaningful relationships with others

Teamwork—Deriving satisfaction from being

part of a group effort.

Immersion component

Discovery—Finding and knowing things that

most other players don’t know about

Role-Playing—Creating a persona with a background

story and interacting with other

players to create an improvised story

Customization—Having an interest in customizing

the appearance of their character

MOTIVATIONS FOR PLAY IN ONLINE GAMES 773

TABLE 1. SUBCOMPONENTS REVEALED BY THE FACTOR ANALYSIS GROUPED BY THE

MAIN COMPONENT THEY FALL UNDER

...