MORAL DISENGAGEMENT OF VIOLENT AND NONVIOLENT ANTISOCIAL BEHAVIOR IN VIDEO GAMES
Michael H. Bailey
A Thesis
Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of
MASTER OF ARTS
December 2020
Committee:
Richard Anderson, Advisor
Eric Dubow
Laura Leventhal © 2020
Enter your Michael H. Bailey
All Rights Reserved iii
ABSTRACT
Richard B. Anderson, Advisor
Studies on moral disengagement have helped to explain how people who play violent video games are able to overcome or even ignore the questionable moral choices they often make while playing. While video game research often looks at the differences between morally questionable violence and nonviolence in video games, the nonviolent condition is usually in the form of a game that has little to no moral choices required of the player. There is an absence of research that looked at how immoral or antisocial violence, often used in mainstream video games, compares to antisocial but nonviolent (such as theft, lying, cheating, or other non- physically harmful actions) behavior in its ability to elicit guilt and prompt moral disengagement.
Prior researchers may have overestimated the effect that violence has on video game participants that could be partially explained by the antisocial nature of the choices made in the video game.
Participants played through Fallout 3 behaving in either an antisocial or prosocial fashion, and either using as little or as much violence as possible. The results found that violence had no significant effect on guilt, and had little to no effect on other affective outcomes. However, main effects of sociality were observed, with guilt being higher in the antisocial condition and moral disengagement being higher in the prosocial condition. These findings suggest that the prosocial and antisocial nature of in-game behaviors are the driving force of moral disengagement and guilt within games, rather than violence. iv
ACKNOWLEDGMENTS
I would like to express my sincere gratitude to my research advisor, Richard Anderson, for providing guidance and feedback throughout this project, as well as my committee members:
Laura Leventhal and Eric Dubow.
I would also like to thank the following people for their contributions in data collection:
Sam Beery, Charis Hoard, Riley Hessel, Jacqueline Mader, and Michael Spiegel who were instrumental in getting the project done in a timely manner. v
TABLE OF CONTENTS
Page
INTRODUCTION ...... 1
LITERATURE REVIEW ...... 4
Social Cognitive Theory ...... 4
Moral Agency ...... 5
Moral Disengagement ...... 7
Mechanisms ...... 7
Moral Justification ...... 7
Euphemistic Language ...... 8
Diffusion and Displacement of Responsibility ...... 8
Advantageous Comparison ...... 8
Distortion of Consequences ...... 9
Other Mechanisms ...... 10
Implications...... 11
Moral Disengagement in Video Games ...... 12
Empathy ...... 13
Guilt ...... 14
Violence in Video Games: Are Games to Blame? ...... 14
Alternative Explanations ...... 16
Social Context of Video Games ...... 17
The Present Research ...... 19
Prediction 1 ...... 20 vi
Prediction 2 ...... 21
Prediction 3 ...... 21
Prediction 4 ...... 21
METHOD ...... 23
Participants ...... 23
Design ...... 23
Principle Dependent Variables ...... 23
Other Measures ...... 24
Materials ...... 24
Procedure ...... 25
Coding ...... 26
RESULTS ...... 22
Manipulation Check ...... 28
Sociality ...... 28
Violence ...... 28
Tests of Predictions ...... 31
Other Results ...... 32
DISCUSSION ...... 39
Violence ...... 39
Sociality ...... 40
Conclusions ...... 41
Limitations ...... 42
REFERENCES ...... 45 vii
APPENDIX A. MODIFIED DIFFERENTIAL EMOTIONS SCALE ...... 55
APPENDIX B. MECHANISMS OF MORAL DISENGAGEMENT ...... 56
APPENDIX C. IMMERSION SCALE...... 58 viii
LIST OF FIGURES
Figure Page
1 Effect of Violence and Sociality on Guilt as a Function of Moral Disengagement ..... 35 ix
LIST OF TABLES
Table Page
1 Correlations of Self-rated Measure and Number of Dialog Choices ...... 30
2 Correlations of Moral Disengagement, Self-rated Measures, and Affective
Outcomes ...... 34
3 Sample Sizes for Effect of Violence and Sociality on Guilt as a Function of
Moral Disengagement ...... 36 1
INTRODUCTION Over the past 25 years the level of detail and realism presented in video games has grown significantly. Where video games once used block-shaped images on the screen, today, games are becoming less and less distinguishable from reality. With these graphical changes have come changes in the degree to which people empathize with virtual characters and view them as social entities rather than simply geometric shapes (Morrison & Ziemke, 2005; Yee et al., 2007).
Many experiments have been done examining the link between video games and certain social behaviors (Anderson & Bushman, 2001; Bushman & Anderson, 2015; Carnagey et al.,
2007; Ferguson et al., 2008; Greitemeyer & Mügge, 2014). Despite being a virtual world, the
actions players take can lead to emotional changes. Depending on the game and genre, those
behaviors can lead to feelings of pride (Granic et al., 2014), empathy (Greitemeyer et al., 2010),
or even guilt (Weaver & Lewis, 2012).
Regarding the mechanism by which media violence leads to increase in aggression and
violence, there are competing models. Based on the General Aggression Model, Anderson and
colleagues argue that violent video games act as an antecedent to aggressive behavior (Anderson
& Bushman, 2001; Anderson & Carnagey, 2014; Bushman and Anderson, 2015. The General
Aggression Model suggests that as people are exposed to violence and aggression, they
experience a short-term increase in aggression through the effects the violent media has on
cognitive, affective, and arousal states. Playing video games may prime aggressive scripts and
schemata that increase arousal and lead to a more aggressive affect. This eventually creates a
feedback loop where these modified internal states lead to decisions and actions that further
reinforce the view of the world as being dangerous and that aggressive responses are appropriate. 2
Over time, the continued activation of aggressive and violent cognitions creates a hostile expectation bias and may lead to a more aggressive personality.
In contrast to the General Aggression Model, the Catalyst Model proposed by Ferguson et al., (2008) argues that a genetic predisposition (particularly in males) leads to aggressive temperament in children and an aggressive personality in adulthood. Environmental factors moderate the influence of biology, particularly through exposure to family violence. This model suggests that those with aggressive personalities are more likely to engage in violent behavior when under stress. Thus, the environment does not cause a propensity towards violence but may act as a catalyst for violence in predisposed individuals. This means that people biologically predisposed to aggressive personalities may be more likely to play violent video games, but people who play violent video games are not more likely to develop aggressive personalities.
While there has been substantial research done on the relationship that violent video games have with cognition and prosocial behaviors (Anderson & Bushman, 2001; Carnagey et al., 2007; Sestir & Bartholow, 2010; Tear & Nielsen, 2014) I was unable to find research that has compared violent behavior to nonviolent, but still antisocial behavior. The bulk of the literature has revolved around the presence or absence of violence. This raises the question of whether violent antisocial behaviors and nonviolent antisocial behaviors lead to significantly different affective outcomes. If they do not, it is possible the antisocial nature of the choices made may partially explain the effects seen in past research, rather than the violence alone. Determining whether antisocial nonviolent choices produce a different affective outcome than do antisocial violent choices will be the first step in determining how important the antisocial nature of the 3
choices are (rather than just the violence) to the effects of these games, both short-term and long-term.
For the purposes of this thesis, antisocial behavior is defined as actions that break typical social norms or that are considered disruptive to others. Examples of antisocial nonviolent behavior include verbal abuse, threatening, bullying, lying, or theft. While antisocial violent behavior would include examples such as hitting, shooting, or killing. Prosocial behavior is defined as actions intended to benefit others, such as helping, sharing, kindness and respect, or altruism. Prosocial nonviolent behavior would include all of those, while prosocial violent behavior is defined as instrumental violence that has socially constructive or desirable consequences, such as intervening to prevent an assault, defending those being attacked, or acting against an unjust regime.
Additionally, while aggression and violence are often used interchangeably, this paper aims to keep them distinct in that violence expressly involves extreme forms of aggression, such as physical assault and/or murder. Aggression is behavior that carries the intent to harm another person, either emotionally, physically, or socially. All violence is aggressive, but not all aggression is considered violent. In the same vein, many aggressive behaviors are antisocial, but not all antisocial behaviors are violent.
This thesis aims to examine how violent and nonviolent antisocial/prosocial behaviors in a video game world differ from each other, specifically in degree of moral disengagement of the player. The level of guilt the player experiences from each condition will be measured and is expected to depend on sociality and the presence of violence. 4
LITERATURE REVIEW
Social Cognitive Theory
According to Social Cognitive Theory (Bandura, 1991), human behavior is extensively
motivated and regulated internally. External influences can still have an effect, but they are
mediated by internal systems of self-regulation. Most human behavior is purposive, having
required forethought. People plan, make predictions about what is likely to occur, what actions
they can take, and what outcomes are most desirable. Through the use of forethought, people
motivate themselves and guide their actions. People exercise some control over their thoughts,
feelings, motivation, and actions through self-reflective (thinking about one’s behavior) and
self-reactive (intentionally changing one’s behavior) influences. If behavior was not internally regulated, external circumstances would constantly shift people’s motivation and behavior based on the social environment currently present. Human behavior is regulated by a combination of self-generated and external sources of influence.
This internal self-regulation can be broken down into three sub-functions: self-monitoring, judgement, and self-reactive (Bandura, 1991). Successful self-regulation
requires an awareness of actions, emotions, and the effects of those actions. Through
self-monitoring people become aware of what they have done and what they are capable of
doing, and can determine how they are progressing towards goals. Effective self-monitoring can
highlight patterns of behaviors and outcomes. For example, people may notice that when staying
up in bed reading on a phone or tablet, their sleep is negatively impacted. Additionally, paying
close attention to thought patterns and behavior in different social settings can contribute to self- directed change. 5
Only after a person becomes aware of their actions and behavior, can self-evaluation occur. This is where personal standards come into place. Personal standards develop based partially on how other people have reacted to the individual’s behavior. Significant people in a person’s life can pass on moral standards based on the positive and negative reactions to that person’s behavior. Eventually, people may come to judge themselves based on the social acceptability of others.
Additionally, individuals often come to judge their actions by how those actions compare to those of their peers. Behavior is easier to regulate when there is a clear indication of adequacy.
Comparing performance to that of others can result in self-satisfaction if, for example, a person achieved the goal of scoring top 10% in the class. If that person did not meet the goal and fell short in relation to others’ performance, motivation to behave differently to attain a different, more beneficial outcome, might occur.
Judgements of an individual’s performance allows for self-reactive influence.
Self-regulatory control is achieved by creating incentives for behaviors and anticipating the affective reaction to behaviors, based on how those behaviors measure up to an internal standard.
People pursue behaviors that result in positive self-reactions and outcomes and avoid negative outcomes that result in self-censure. By making benefits or rewards conditional on certain accomplishments, people can motivate themselves to attain the desired reward: “If I make an A on my exam, I’ll treat myself to that new game I wanted.”
Moral Agency
Moral agency can be understood through Social Cognitive Theory (Bandura, 1991;
Bandura, Barbaranelli, & Caprara, 1996; Bandura, 2002). People monitor their actions and 6 behaviors, judge those actions in comparison to their moral standards (which were attained through social and internal factors), and then self-regulate their behavior against those standards to determine feelings such as self-worth and satisfaction or self-condemnation and guilt.
Therefore, morality is primarily a self-regulated process, in which individuals constantly evaluate their actions against internal standards and assess their positive or negative reaction to those standards.
The role of self-regulation in moral agency can be highlighted by research that found moral judgements can better be predicted from affective reactions rather than from appraisals of harm or deliberate reasoning (Haidt, Koller, & Dias, 1993; Haidt, 2001; Green & Haidt, 2002).
Haidt, Koller, and Dias (1993) presented people with scenarios that were harmless but that involved disrespect, disobedience, or unconventional food or sexual practices that were designed to trigger disgust. For example, “A family's dog was killed by a car in front of their house. They had heard that dog meat was delicious, so they cut up the dog's body and cooked it and ate it for dinner.” Those scenarios, despite not resulting in any harm, participants largely viewed the scenarios to be immoral, due to the negative emotional response that were experienced.
Miller, Hannikainen, and Cushman (2014) found that moral condemnation of harmful behavior in the context of moral dilemmas is better predicted by the participant’s aversion to the action (how the person would feel to perform the action) than the affective response of the victim’s suffering (the consequences of the action). This further supports the argument that moral agency is based primarily on regulating actions based on the internal affective response one experiences, rather than empathy, where actions are regulated based on the external consequences they have for others. 7
Moral Disengagement
Mechanisms
Normally, when people behave in ways that violate their moral standards, they experience self-condemnation. Moral disengagement argues that people can avoid self-condemnation by disengaging the moral process (Bandura, 1999). Moral standards can be circumvented, and immoral behavior can occur without guilt or distress, by utilizing eight interrelated cognitive mechanism: moral justification, euphemistic labelling, diffusion of responsibility, displacement of responsibility, advantageous comparison, distortion of consequences, dehumanization, and attribution of blame.
The simplest way to achieve moral justification is for people to Moral Justification. redefine the behavior to fall in line with their moral standard. By redefining the behavior as desirable or justified, they can behave in ways that would otherwise be unacceptable. Killing is bad, but killing to save a loved one, or to defend innocent people is less violating to the moral standard. This kind of behavior can be seen most clearly in military pursuits (Moore, 2015).
Ordinary, decent people can become dedicated soldiers who fight and kill by redefining the morality of killing so that it does not violate their moral standard and avoids self-censure. Killing a terrorist, or killing someone to prevent an attack, both become justified actions. Furthermore, moral justification has been used alongside the politicisation of religion to justify some of the most heinous acts in history. Pope Urban II launched the Crusades with the moral proclamation that “Christ commands it.” Islamic extremists mount jihad, construed as self-defense against tyrannical infidels, viewing their actions as a “religious duty.” 8
Language shapes how people perceive the world and their place Euphemistic Language. in it. To change language is to change how people perceive the world (Lutz, 1987). By using euphemisms in language, severity and personal responsibility for actions can be reduced. Klimmt et al., (2006) found respondents used euphemistic labeling in describing killing enemies in a game. The participants used terms such as “switch off the enemy,” “floor or finish,” or
“eliminate” instead of “killing.” Through the use of euphemistic language (along with other moral disengagement mechanisms) players were able to ‘manage’ their moral concerns. In the military, unintended civilian deaths are considered “collateral damage.” By changing the word usage or using euphemisms the behavior becomes harsh, less emotional, and thus less immoral.
Moral disengagement can also be Diffusion and Displacement of Responsibility. obtained by minimizing or obscuring the self’s role in the harm (Bandura, 2002). By placing the responsibility for one’s choices on another person (displacement of responsibility), or by believing that everyone else does the same (diffusion of responsibility), people can avoid self-condemning reactions. Many atrocities have been committed with the excuse of, “I was just following orders” (displacement of responsibility). Milgram (1974) demonstrated the extent to which normal people would perceivably harm others simply from an authority figure telling them to. Under displaced responsibility, the authority figure is the culpable party and the responsibility for actions would fall to the person in charge rather than the individual carrying out those orders. Nazi officers and staff relieved themselves of the moral burden of their actions by displacing the blame to their superiors (Andrus, 1969).
Andrus (1969) also recounted the fact that some Nazi Advantageous Comparison. soldiers rationalized that their behavior wasn’t as bad as some of the other soldiers. By utilizing 9 contrast, awful acts can be made to appear more benevolent. Terrorists see their behavior as righteous, selfless, martyrdom in comparison to the cruelties inflicted upon the people they identify with (Bandura, 1990). Within politics, it is often the case that choice comes down to the
“lesser of two evils,” and through comparisons of deeds, stances, and viewing the politician as
“better than the alternative”, people can justify voting for even a racist and sexist candidate.
It can be difficult to justify actions when the harm of such Distortion of Consequences. behavior is directly and easily observed. When people can see and hear the harm they inflict, people become more self-restrained in their actions (Bandura, 1992). As such, to be able to commit horrible acts requires more than just absolving personal responsibility. Thus, another way of morally disengaging involves minimizing, ignoring, or distorting the outcomes of such behavior (distortion of consequences). It becomes easier to cause harm to others when their suffering is not visible or when the outcomes are physically or temporally distant from the behavior itself (Tilker, 1970).
In many organizations today, there is a clear hierarchy, with upper levels creating the plans, that get passed down the chain to executors who carry them out. The further away an individual is from the end result, the easier it is to dismiss the consequences. Middle management in particular can avoid accountability for their actions as they avoid the responsibility of both creating the plan and enacting the plan (Bandura, 1992).
Distortion of consequences is also readily visible in our military, where technology has progressed to the point where mechanized weapons and explosive devices can be controlled from dozens to thousands of miles away. A soldier pushing a button and seeing a flash on a screen is far removed from the actual destruction, pain, and suffering of those affected by the explosion. 10
As such, it is substantially easier for such soldiers to minimize or disregard the harm their actions had.
The final set of disengagement practices depend on how the Other Mechanisms. perpetrators regard those they mistreat (Bandura, 2002). The degree to which the other is humanized (a mechanism referred to as "dehumanization") can determine how they are treated. It is more difficult to mistreat those humanized individuals--with whom one identifies--without risking self-condemnation. The dehumanization of others can be a powerful mechanism of moral disengagement. This behavior can be seen in war, with enemies being compared to animals,
“savages’, “gooks”, and other names meant to degrade. People are more willing to torture prisoners of war of a dehumanized group (Viki, Osgood, & Philips, 2013). This is likewise done in racism and sexism. To regard another person as sub-human removes that person’s relatability, perception changes from that of a person, to that of an animal, in which the same set of moral standards no longer apply. In Nazi camps, extreme lengths were gone to to degrade the victims, to dehumanized them. It was done purposefully to ease the burden of those who operated the gas chambers (Levi, 1989).
Additionally, due to a person’s beliefs or rationalization, blame may be placed on the victim. Perpetrators may believe that the victim deserved the punishment or behavior, that the victim was “asking for it,” or “made me do it” (the "attribution of blame" mechanism). This type of moral disengagement is especially common in cases of sexual assault, where the victim is blamed for the actions of the perpetrator in an attempt to lessen the severity of the crime or to frame it as not a crime at all. In these instances, attribution of blame can explain why rape victims are often seen as deserving of the assault (Grubb & Turner, 2012). 11
One of the most effective ways in which to disengage moral control involves combining moral justification, euphemistic labelling, and advantageous comparison. By giving high moral purpose to harmful conduct, by changing the language, and by comparing the actions against some other more heinous act, not only can the person avoid self-censure and personal distress, but what was once morally condemnable becomes a source of self-valuation and pride.
Implications
The study or moral disengagement theory has garnered research interest across a wide variety of disciplines and domains, including child and adolescent development (Gini, Pozzoli, &
Hymel, 2014), organizational behavior (Duffy, Scott, Shaw, Tepper, & Aquino 2012), criminology (Cardwell et al., 2015), military psychology (Beu & Buckley, 2004), and sports psychology (Boardley & Kavussanu, 2011). Through these various studies, predispositions towards moral disengagement have been associated with various negative behaviors, such as bullying, misconduct in the workplace, increased aggression, criminal behavior, as well as increases in dehumanization and endorsements of violence towards those dehumanized (Moore,
2015).
Additional work has examined whether moral disengagement is stable over the life course and how it may be triggered or engaged. Broad findings suggest that while there is a predispositional aspect to moral disengagement, contextual factors, especially during childhood are predictive of moral disengagement as a teenager (Moore, 2015). Additionally, while moral disengagement is somewhat stable over the lifespan, it tends to be higher in early adolescence and decreases as the person reaches adulthood. Evidence has indicated that as moral 12 disengagement decreases so does antisocial behavior (Bandura et al., 1996). However, age cannot be the only factor as adults still commit crimes and act in antisocial fashions.
Bandura et al., (1996) argues that moral disengagement operates similarly, and uses the same paths of influence as aggressiveness. Frequent moral disengagements can reduce prosociality and guilt, which in turn makes it easier to continue to morally disengage. Similar to the previously mentioned models of aggression, habitual disengagement may shape a person’s personality and result in permanent changes to moral standards over time, as well as make it easier to engage in aggressive action. It might be possible that the process of moral disengagement when encountering antisocial violence in video games is partially responsible for the affective outcomes previously found.
Moral Disengagement in Video Games
Some of the most interesting research on moral disengagement examines the role of video games in manipulating levels of moral disengagement and the moral implications of behaviors within a game world.
Current video games often feature complex systems that allow for a wide variety of actions to be taken. Included in these well-written worlds are characters that feature depth, nuance, and conflicts. This is further enhanced by professional voice acting that makes both the characters and the world feel alive and immersive. However, despite the ever-narrowing gap distinguishing virtual world and reality, game developers are often deliberate in how they design their games to maximize enjoyment and minimize negative feelings. Players are able to enjoy the virtual violence not because of dysfunctional personality traits, but because the game provides specific cues that make moral disengagement more likely (Bandura, 2002; Haidt, 2001). Cues 13 such as: fighting to save the world (moral justification), fighting creatures that are difficult to anthropomorphize (dehumanization), being commanded to do a task or quest (displacement of responsibility), using particular terminology (euphemistic language), or featuring villains that are vile or morally extreme (moral justification, and advantageous comparison) are all designed to make it easier for the play to enjoy the game without feeling guilty. Such game design decisions as those allow for players to often feel justified in their violence.
However, that is not to say that all games are designed like that. Many games allow for the player to intentionally or unintentionally to act in ways that are not justified or moral. Games such as GTA V and Fallout 3 allow the player to attack and kill peaceful non-player characters, and steal from or intentionally lie and betray those characters.
Empathy
There is a strong body of literature that suggests humans routinely and automatically assign social meaning to stimuli that are not social, based on cues and signals received
(Heberlein & Adolphs, 2004; Nass & Moon, 2000). By merely detecting biological motion or a simple action-sequence, people identify artificial objects as social entities (Heider & Simmel,
1944; Oatley & Yuill, 1985). People easily anthropomorphize nonhuman characters (Epley,
Waytz, & Cacioppo, 2007; Mar & Macrae, 2006), and are inclined to treat computer generated characters as if they were real social entities in which empathy, and moral and ethical standards should apply (Morrison & Ziemke, 2005; Hartmann, 2008). This effect can become especially strong given technological advances in computer-generated imagery (CGI), motion capture
(where the physical movements of real people are acted out and then digitized into the game or movie), and performance capture (which includes recording the face and fingers and captures 14
subtle expressions) that create movement and animations that are nearly indistinguishable from
live actors. Given the above, people who play video games are susceptible to the emotional
outcomes associated with violations of their moral standards within a game.
Guilt
Video games are capable of eliciting a wide variety of emotions, both positive and
negative (Ravaja et al., 2004; Schneider, 2004; Hazlett, 2006). What is particularly interesting
about video games, and what sets them apart from other forms of media, is their ability to elicit
guilt. While moral disengagement may reduce or remove self-condemnation of immoral behavior
in video games (Hartmann & Vorderer, 2010), that may not always be the case. When engaging
in unjustified virtual violence, some players, especially empathetic players, report feelings of
guilt (Hartmann et al., 2010; Grizzard et al., 2014). While the game mechanics, storyline, and
content of the game may lend itself towards moral disengagement, players can still feel guilt for
their actions. This implies that some actions are more or less justifiable and result in different
degrees of moral disengagement.
While there is evidence to suggest that violent video games lead to a desensitization of
real-life violence (Bartholow et al., 2006; Carnagey et al., 2007), Grizzard et al., (2014) found
that when players commit immoral virtual behaviors in games, rather than becoming less
sensitive to future moral violations, if the players who committed these behaviors felt guilt, they
became more sensitive to moral violations.
Violence in Video Games: Are Games to Blame?
Have violent video games actually made people more aggressive and more violent?
Since the 1990’s when video games rose in popularity, this question has become the source of 15 substantial controversy. A substantial portion of research involving the effects of video games has examined the influence of video games featuring violence on violence, aggression, and prosocial behaviors (Anderson & Bushman, 2001; Bushman & Anderson, 2015; Carnagey,
Anderson, & Bushman 2007; Ferguson et al., 2008; Greitemeyer & Mügge, 2014; Tear &
Nielsen, 2014; Wiegman & Van Schie, 1998). Considering the countless wars and atrocities people have committed prior to the invention of video games, it seems unlikely that games are to blame for modern day violence. However, it can’t be denied that a large body of evidence has found a link between increased aggression and violent video games. In a meta-analysis by
Greitemeyer and Mügge (2014) examining the effects of violent and prosocial video game play, they found a significant association with social outcomes. Across 98 independent studies with
36,965 participants, violent video games were found to increase aggression and decrease prosocial outcomes, while prosocial games had the opposite effect. These results were reliable across the studies and indicated a causal link between video game exposure and social outcomes.
Some of these studies argue that violent video games have a negative effect on children and youth by increasing levels of aggression (Anderson & Bushman, 2001; Carnagy et al., 2007).
Anderson and Bushman (2001) did a meta-analytic review of video game violence and their role in aggression, following in the wake of the Columbine school shooting in 1999. Their analysis examined 35 research reports that totaled 4,262 participants. Looking at prosocial behavior, aggressive cognition, physiological arousal and aggressive affect, they report negative correlations between violent game exposure and prosocial behavior. The results of their research, as well as others, has contributed to the societal view that video games are harmful, and that games such as Grand Theft Auto V are a cause of the apparent rise of school shootings in the 16
U.S. In 2019, President Trump blamed violent video games in the wake of multiple mass shootings within a 24-hour time span.
However, there is a lack of research linking school shootings to video games. Markey et al., (2019) did multiple studies examining potential stereotypes regarding school shootings. They found evidence of racial bias and stereotypes: when participants read a mock news story about a shooting, they were more likely to blame video games when the perpetrator was White, rather than Black. Study 2 found that, across 204,796 news stories of 204 mass shootings in the US, video games were 8 times more likely to be discussed when the shooter was White than when the shooter was Black. The researchers suggest that due to there being a stereotypical association between minorities and violent crime, when the crime is committed by White perpetrators, people look for video games as the cause since that individual doesn’t fit the “stereotype.”
Alternative Explanations
While there seems to be a large body of evidence suggesting a causal link between violent video games increasing aggression, a growing number of studies have found no evidence of causation, or in some cases, the reverse. Ferguson et al. (2008), examined whether the link between violent video games and aggression was causal or if it might be a byproduct of family violence and intrinsic motivation. In Study 1, participants were randomly assigned to one of three groups. The first group was assigned to the violent game condition and the second group was assigned to the nonviolent game. Participants in the third group, after a brief description, were allowed to choose between a violent and nonviolent game. While males were more aggressive than females, exposure to the violent game did not cause any difference in aggression, neither did a history of playing violent video games. In Study 2 they looked at trait aggression, 17 violent criminal acts, and exposure to violent video games and family violence. Results showed that violent video game exposure was correlated with trait aggression, but not with violent crime.
However, once gender and family violence were controlled for, violent video games were no longer a significant predictor of aggression, however, being male, and experiencing physical and verbal abuse were predictive of aggression. In summary, researchers state that violent behavior is best predicted by trait aggression, being male, and a history of family violence, with those individuals actively seeking media that includes violence rather violent video games leading to increased aggression.
Social Context of Video Game Violence
A further complication of this debate is that, depending on the game, the moral nature of the violence may differ. Many modern games include morality both explicitly or implicitly within the game. Players often can respond to non-player characters in a variety of ways. Players can be kind, helpful, and benevolent, they can lie, cheat, and steal, or they can be aggressive, rude, and violent. As games have gotten more sophisticated, the complexity of choices within the game and the nuance allowed has also increased. Likewise, violence can be committed for multiple reasons. Within some games, players can kill the bad guys, but do not even have the option to attack allies or innocent civilians. While others, such as GTA V, gives players the freedom to attack whomever they wish.
Hartmann et al., (2010) compared the effects of violence by examining justified violence verses unjustified violence in a modified game. In the unjustified condition, players acted as terrorists within a torture camp fighting off U.N. soldiers send to rescue the hostages, while the justified condition had players act as U.N. soldiers attacking the camp. Participants in the 18 unjustified condition experienced more guilt. As such, the motivations of the violence within the game are prone to change and depend on the game itself.
Critics of the idea that video games significantly affect social behaviors (e.g. Furguson,
2007; Ferguson, 2010; Ferguson & Kilburn, 2010) argue that much of the current literature regarding violent video games suffer from multiple confounds. Video game experiments often suffer from poorly matched video games, with multiple variables differing between the conditions such as difficulty, frustration, and style. This makes it more difficult to isolate the violence or social conditions and threatens construct validity. Ferguson (2007) proposed that publication bias (or file drawer effect) had also implications for the effects of violent video games, and after publication bias adjustment, related studies could not support the hypothesis of violent video games being highly correlated with increased aggression.
There has been push-back on Ferguson and colleagues' arguments. A meta-analysis by
Anderson et al., (2010) argue that such meta-studies (Ferguson, 2007; Ferguson & Kilburn,
2009) used flawed methodology, such as only including published articles and misinterpreting procedures to assess publication bias, which calls into question their results and conclusions.
Including a more restrictive methodological quality inclusion criteria, cross-cultural comparisons, longitudinal studies, and multiple moderator analyses, Anderson et al., (2010) found evidence to suggest that exposure to violent video games is a causal risk factor for increased aggressive behavior, cognition, and affect (r = .15). Additionally, sensitivity analyses revealed the effects to be robust, with little evidence of publication bias.
In reply to Anderson et al., (2010), Ferguson and Kilburn (2010) discussed the methodological flaws of the meta-analysis, specifically arguing that Anderson et al., (2010) did 19 not rigidly apply their own standards. The meta-analysis included unpublished manuscripts primarily drawn from their own research group, and seemed to miss unpublished and published works by research groups that, like Ferguson and Kilburn, have found results at odds with
Anderson et al.,’s (2010) findings. Ferguson and Kilburn (2010) also raise concern of the impact of unstandardized measures of aggression, as well as a reliance on bivariate correlations used in many papers that may overestimate the effect size.
Breuer et al., (2015) looked at the social context and game outcomes of playing a competitive colocated multiplayer sports game. Results found that losing, but not trash-talking by an opponent, can increase aggression and this effect was mediated by negative affect. This suggests that the frustration-aggression hypothesis (that aggressive behavior and frustration and link and that frustration always leads to increased aggression) can be applied to digital games.
Further evidence that violence alone may not be sufficient to explain the effects on aggression. Przybylski et al., (2014) conducted seven studies examining the degree to which elements of games that impede a player’s feeling of competence, are associated with aggression. Researchers found that independent of the presence or absence of violence, perceived competence was negatively related to player aggression and positively related to gaming motivation. The less competent the players felt, the higher the level of aggressive feelings, ease of aggressive thoughts, and likelihood of enacting aggressive behavior.
The Present Research While playing violent video games may lead to moral disengagement, violent actions are not the only negative behaviors that people use in games. Do nonviolent antisocial actions lead to similar levels of moral disengagement? If they do, is the violence within certain video games the 20
primary force driving the short-term effects seen in past research, or could it be the antisocial nature of the choices made? While past research has demonstrated that violent video games result in moral disengagement of the player’s antisocial actions, it has not addressed the question of how that compares to antisocial yet nonviolent actions. The present study addresses this question by examining how affective outcomes of making antisocial violent or antisocial nonviolent choices in a video game differ from prosocial violent and prosocial nonviolent and to what degree those choices lead to moral disengagement.
Based on moral disengagement theory, video games that feature violence and antisocial behaviors should encourage players to morally disengage to avoid self-condemnation from violating their moral standards. Prior research has demonstrated increased rates of moral disengagement for violent video games (Hartmann & Vorderer, 2010), as well as showing that antisocial behavior (that would violate moral standards) leads to increased moral disengagement.
Given the above, the following predictions were proposed:
Prediction 1
Players who engage in violence will have higher levels of moral disengagement compared to those who are less violent.
Previous research has demonstrated an association between exposure to violent video games and moral disengagement (Teng et al., 2017; Hartmann & Vorderer, 2010). When decent people perform activities that result in injury and harm to others, they tend to exonerate themselves to prevent feelings of self-censure or a loss in self-esteem. 21
Prediction 2
Players who are antisocial within the game will show higher levels of moral disengagement compared to those who act prosocially.
Most people tend to view video game characters as social entities in which empathy and morality applies (Morrison & Ziemke, 2005; Hartmann, 2008). Within video games, the majority of players make moral decisions towards non-player characters and behave as if they were actual interpersonal interactions (Weaver & Lewis, 2012). When encouraged to behave antisocially, participants would be expected to morally disengage to avoid self-condemnation for violating their moral standards.
Prediction 3
There will be an interaction between Violence and Sociality, such that the effect of sociality will
be greater in the violent condition compared to the nonviolent condition.
It is easier to justify causing harm to another when reconstructing the behavior to be prosocial, by using moral justification, than harming another out of an antisocial drive (Bandura,
2002). To violate moral standards in multiple degrees (violence and antisocial behavior) should result in a heightened affective outcome compared to prosocial violence.
Prediction 4
Players who behave in an antisocial fashion will have higher levels of guilt compared to those who act prosocially.
Players tend to view characters within a video game as social entities (Morrison &
Ziemke, 2005; Hartmann, 2008). Prior research has also demonstrated that immoral behavior towards such characters can produce guilt in players, as it violates the players’ moral standards
(Hartmann et al., 2010). 22
Hartmann et al. (2010) found that players who violate their moral standards and engage in unjustified harm experience increased feelings of guilt. However, there is an argument to be made that moral disengagement will inhibit or reduce feelings of guilt for players who violate their moral standards. Research regarding video game design and the intentional moral disengagement cues the game provide may frame the violence against game characters as justified, allowing the game to be enjoyed guilt-free (Bandura, 1990, 2002; Opotow, 1990; Dill,
Gentile, Richter, and Dill, 2005). For this reason, making a prediction about guilt outcomes as a result of violence alone would not be very informative as multiple outcomes could be predicted based on whether the participant views the violence as justified. As justification was not experimentally manipulated, it would be difficult to draw clear conclusions from the results. 23
METHOD
Participants
Undergraduate students at Bowling Green State University (Bowling Green, Ohio) were recruited as participants in this study in exchange for course credit (instructors decided how participation affected the student’s grade, some used extra credit). A total of 107 students participated in the experiment (25 women, 77 men, 5 non-binary) and were between the ages of
18-26 (M = 19.25, SD = 1.47). Due to being unable to complete the experiment, the data from two participants were excluded from analysis, along with an additional three participants due to technical errors in recording their data. As a result, analysis was conducted on 103 participants.
To qualify, participants were required to have played video games for at least an average of 2 hours a week over the past year and to have never played Fallout 3 before. Most participants reported playing between 5 and 10 hours a week.
Design
This study utilized a 2 x 2 x 2 factorial design, with sociality (prosocial or antisocial) and violence (violent or nonviolent) as between-subject factors as well as gender. However, because gender differences have been found for reasons for playing games, the amount of time spent, motivation towards playing competitive games, and tendency to morally justify physical violence
(Hartmann et al., 2006; Hartmann et al., 2015; Lucas et al., 2004), it was decided that the analyses would include gender as a factor, when possible.
Principal Dependent Variables
Guilt was measured using a modified 14-item questionnaire (See Appendix A) based on
Differential Emotions Scale (DES-IV; Izard, 1977; Kotsch, Gerbing, & Schwartz, 1982). The 24
original scale asked people how often in their daily lives they “feel regret, sorry about something
you did,” “feel glad about something,” and similar questions on a 5-point Likert scale from
“Rarely or Never” to “Very Often.” These were rephrased to be “While playing the game, how
often did you feel…” Multiple emotions were included in the scale to mask the focus on guilt.
Moral disengagement was measured using a 16-item version of the scale (See Appendix
B) formed by Bandura, Barbaranelli, and Caprara (1996). Participants indicated their agreement
or disagreement to moral statements on a 7-point Likert scale from “Completely Disagree” to
“Completely Agree.”
Other Measures
Participants reported demographic information (age, sex, and race), general media use,
and video game use.
A manipulation check was conducted by recording the number of violent, as well as antisocial, neutral, and prosocial dialog choices were made. This served to measure the participants' actual behavior in game and to ensure it matched their assigned condition.
Additionally, a subjective scale from 1-10 (10 being "A Great Deal") was used to measure the degree participants self-rated their level of violence, antisocial behavior, and prosocial behavior during play.
Finally, a 6-item immersion scale (see Appendix C) was included based on Jennett et al.
(2008) in an attempt to assess attention and immersion in the game world.
Materials
The game Fallout 3 for Microsoft Windows was used for this study. Fallout 3 was originally released in 2008 by Bethesda Game Studios. This game has been used in previous 25 research involving morality and moral choices (Weaver & Lewis, 2012) as the game presents the player with several explicit and implicit moral choices the player must make. Participants played through the first act of the game, using an Xbox One controller plugged into the PC, that guided them through character creation, training of the controls (so external training would not be necessary), and then through a somewhat linear story-line that provided backstory and set up the premise of the game. The players encountered many situations where they were free to act as they wished and engaged with non-playable characters in which they chose their responses from a list presented on screen. Responses available to the player included options for prosocial (e.g. polite or kind), antisocial (e.g. rude or hostile), and neutral. Some events had an uneven distribution of options, in that there may have been two prosocial responses, one neutral response, and three antisocial responses. Finally, some dialog options led to combat with the non-playable character following selection (e.g. when you insult one of the non-playable character's mother).
A modification (“mod”) was created for Fallout 3 and was used for this study. The mod caused each dialog line selected by the player to be added to a log file to facilitate coding and analysis of the data.
Procedure
An a priori power analysis was conducted using WebPower (Zhang & Yuan, 2018). For a
2 (non-violent versus violent) by 2 (pro-social versus anti-social) ANOVA, with α = .05, a medium effect size of Cohen’s f = .25, and a power of .65, the required sample size was 90. We therefore endeavored to collect data from about 100 participants. 26
Participants were told this was a study about emotional responses to violent video games.
An initial questionnaire was given in which they were asked their prior game experience in average play-time per week over the last year, as well as if they have played Fallout 3 previously. If participants reported less than an average of 2 hours a week playing games or have played Fallout 3, they were informed they did not qualify for the experiment. Upon arriving at the lab the participant gave informed consent, was randomly assigned to a group (antisocial nonviolent, prosocial nonviolent, prosocial violent, or antisocial nonviolent), and was then given scripted instructions from the researcher in the form of encouragement to behave in either an antisocial or prosocial fashion and either encouraged to use violence or discouraged from using violence. Encouragement rather than more explicit direction was used to mitigate the chance of participants placing responsibility for their in-game choices on the researcher (i.e., “I was just doing as I was told.”) Participants were then seated in front of a computer running Fallout 3, with an attached Xbox One controller and headset and gameplay footage began recording as they started the game. Participants played through the first act at their leisure starting with character creation and ending when they escape the Vault. The researcher was present during the play through if they struggled or had questions. Once they completed the experiment, they were asked to fill out a questionnaire that measured emotional responses, moral disengagement, and immersion.
Coding
Due to the small size of non-binary people (n = 5) within the sample, gender was recoded to be male/non-male, in which female and non-binary were grouped together. 27
Five researchers, four of which were undergraduate students, were trained to code the recorded gameplay. There were a number of interactions through the first act in which players had an explicit choice in their responses between social (polite, kind), antisocial (rude, hostile, insulting), and neutral. The choices the players made were clearly visible on screen, and were recorded. The game was modified to record in a text file a list of all the dialog choices the player makes to ease analysis. Additionally, the participants had the opportunity to engage in unprovoked violence and theft, though this option was not explicitly stated or instructed. Within the game the participants could attack non-hostile non-playable characters as well as steal items from containers and dead bodies. This was recorded as well. Researchers pre-coded all potential dialog choices available to the participants as being prosocial, neutral, or antisocial and whether it threatened or led to violence. As such, Krippendorff's Alpha was computed as a reliability estimate (훼 = .729). Final categorization of each dialog choice was decided based on the assessment given by a majority of the coders. 28
RESULTS
The data were analyzed using 2 x 2 x 2 factorial designs, with sociality (prosocial or antisocial), (violent or nonviolent), and gender (male or other than male) as between-subject factors. Note that there were too few non-binary participants to include non-binary as a factor level, and so the non-binary and female participants were combined into a single, other-than-male category for purposes of analysis. ANCOVA were also performed with gender as a covariate rather than a factor.
Manipulation Check
Sociality
There was a main effect of Sociality on prosocial behavior such that participants in the prosocial condition (M = 15.73, SD = 3.69) selected significantly more prosocial dialog choices
2 than those in the antisocial condition (M = 4.40, SD = 4.89), F(1, 93) = 154.33, p < .001, ηp = .63). There was a main effect of Sociality on antisocial behavior such that participants in the antisocial condition (M = 14.66, SD = 6.32) selected significantly more antisocial dialog choices
2 than those in the prosocial condition (M = 3.16, SD = 3.06), F(1, 93) = 125.19, p < .001, ηp = .58). There was a main effect of Sociality on violence such that participants in the antisocial condition (M = 2.52, SD = 1.69) selected significantly more violent dialog choices than those in
2 the a prosocial condition (M = 1.04, SD = 1.33), F(1, 93) = 30.31, p < .001, ηp = .25. The manipulation for Sociality was successful.
Violence
There was a main effect of Violence on aggression such that participants in the violent condition (M = 2.5, SD = 1.84) selected significantly more violent dialog choices than those in 29
2 the nonviolent condition (M = 1.19, SD = 1.27), F(1, 102) = 16.41, p < .001, ηp = .15. Additionally, participants in the violent condition (M = 5.70, SD = 3.07) reported acting significantly more aggressive than those in the a nonviolent condition (M = 3.70, SD = 2.35),
2 F(1, 102) = 8.50, p = .004, ηp = .086). The manipulation for violence was successful. Finally, all self-rated measures were significantly correlated with their respective in-game dialog choices (See Table 1). 30
Table 1
Correlations of Moral Disengagement, Self-rated Measures, and Affective Outcomes Variable n M SD 1 2 3 4 5 6 7 1. Self-rated aggression 102 4.59 2.90 — 2. Self-rated antisocial 102 4.82 3.54 .491** — 3. Self-rated prosocial 102 4.63 3.17 -.389** -.746** — 4. Prosocial dialog count 94 9.70 7.15 -.326** -.808** .702** — 5. Neutral dialog count 94 12.68 4.49 -.245* -.610** .471** .755** — 6. Antisocial dialog count 94 9.28 7.66 .333** .794** -.685** -.898** -.679** — 7. Violent dialog count 94 1.83 1.70 .383** .448** -.467** -.489** -.307** .677** — *p < .05. **p < .01. ***p < .001 31
Note. Self-rated measures and dialog count were recorded separately. Some participants (N = 8), had missing data due to technical difficulties with the recording software.
Tests of Predictions
To test the first three predictions, a 2 x 2 x 2 ANOVA was conducted on moral disengagement, with sociality (prosocial/antisocial), violence (violent/nonviolent), as well as gender, as factors. Prediction 1 was that there would be a main effect of violence on level of moral disengagement, however, no main effect was found: Mean moral disengagement for the violent condition (M = 1.95, SD = .30) was not significantly different from the nonviolent
2 condition (M = 1.93, SD = .35), F(1, 102) = .41, p = .523, ηp = .004. Additionally, an ANCOVA
2 was run with gender as a covariate, resulting in similar results: F(1, 101) = .017, p = .90, ηp = .00. There was no significant interaction. Thus, Prediction 1 was not supported.
Prediction 2 was that moral disengagement would be higher in the antisocial than in the prosocial condition, while Prediction 3 was that there would be an interactive effect of violence and sociality on moral disengagement. The results indicated a main effect of sociality such that those in the prosocial condition (M = 2.00, SD = .26) had significantly higher moral disengagement compared to the antisocial condition (M = 1.88, SD = .36), F(1, 102) = 3.96, p =
2 .05, ηp = .04. Note that this is marginal and is the opposite direction of what was predicted. However, this was no longer significant when an ANCOVA was run with gender as a covariate:
2 F(1, 101) = 3.6, p = .061, ηp = .036. Additionally, there was no significant interaction. Given the above data, neither Prediction 2 nor Prediction 3 were supported.
Prediction 4 assessed whether the social nature of the in-game behaviors differed in its ability to induce feelings of guilt. It was predicted that participants in the antisocial condition 32 would report higher levels of guilt compared to those in the prosocial condition. Results found a main effect of sociality, such that those who were in the antisocial condition (M = 9.00, SD = 3.46) had significantly higher levels of guilt than participants in the prosocial condition (M =
2 7.15, SD = 2.33), F(1,102) = 11.94, p = .001, ηp = .11. Additionally, an ANCOVA was run with
2 gender as a covariate, resulting in similar results: F(1, 101) = 9.03, p = .003, ηp = .086. No main effect of gender was observed, nor any interactions. Thus, Prediction 4 was supported.
Other Results
Following analysis for my principal findings, additional exploratory tests were run to examine correlations between variables of interests and look at gender differences.
No additional main effects were observed for gender, nor any interactions.
Self-rated prosocial and moral disengagement were significantly correlated (r(100) = .313, p < .001). Speculation as to why this occurred is discussed in the conclusion. Furthermore, moral disengagement was positively correlated with several other measures: Anger (r(100) = .331, p < .001), interest (r(100) = .45, p < .001), disgust (r(100) = .266, p < .017), and immersion (r(100) = .313, p < .001), (See Table 2). Following analysis of the data, it was identified that the prediction involving the moral disengagement variable was flawed. The Moral Disengagement Scale (Bandura et al., 1996) was designed to measure proneness to morally disengage, rather than a measure of state moral disengagement. As such, an additional analysis was conducted to determine if proneness to morally disengage moderates the relationship between sociality and violence, and guilt. An
ANOVA was conducted to assess a potential three-way interaction. Moral disengagement scores were binned, by partitioning the range of scores into three intervals (Low MD:19 - 28, Medium 33
MD: 29 - 34, and High MD: 35 - 45). Results showed the three categories of moral disengagement: low MD (M = 8.06, SD = 3.70), medium MD (M = 7.93, SD = 2.93), and high MD (M = 8.56, SD = 2.62), did not differ significantly on feelings of mean guilt, F(2, 90) =
2 1.063, p = .35, ηp = .023. Additionally, there was no significant three-way interaction, F(2, 90) =
2 2.78, p = .067, ηp = .058, (see Figure 1). 34
Table 2
Correlations of Moral Disengagement, Self-rated Measures, and Affective Outcomes Variable n M SD 1 2 3 4 5 6 7 8 9 1. Moral disengagement 102 1.94 0.32 — 2. Self-rate aggression 102 4.58 2.90 .003 — 3. Self-rated antisocial 102 4.82 3.54 -.076 .491*** — 4. Self-rated prosocial 102 4.63 3.17 .312*** -.389*** -.746*** — 5. Total immersion 102 21.33 4.34 .313** .011 .051 .160 — 6. Guilt 102 8.13 3.11 -.090 .273** .386*** -.285*** .174 — 7. Anger 102 8.12 2.53 -.331*** .354*** .287** -.109 -.227* .542** — 8. Interest 102 10.4 2.53 -.449*** -.043 -.097 -.272** .452*** .130 .215* — 9. Disgust 102 4.81 2.11 -.266** .152 .182 -.044 .174 .471*** .549*** .203* — *p < .05. **p < .01. ***p < .001 35
Figure 1
Effect of Violence and Sociality on Guilt as a Function of Moral Disengagement
Note. This figure illustrates the three-way interaction between violence, sociality, and moral disengagement. Error bars represent 95% CI. 36
Table 3
Samples Sizes for Effect of Violence and Sociality on Guilt as a Function of Moral
Disengagement
Descriptives Violence Sociality Disengagement Mean Guilt N Non-V. Antisocial Low Diseng. 13 Medium Diseng. 12 High Diseng. 5 Prosocial Low Diseng. 5 Medium Diseng. 11 High Diseng. 8 Violent Antisocial Low Diseng. 9 Medium Diseng. 10 High Diseng. 5 Prosocial Low Diseng. 7 Medium Diseng. 10 High Diseng. 7 37
Regarding analysis of a possible impact of in-game violence on feelings of guilt, there were two potential outcomes that depended on whether the participants viewed their actions as justified or not. As justification was not experimentally manipulated, this was harder to meaningfully interpret. If participants viewed their violence as justified, lower levels of guilt in the violent condition compared to the nonviolent condition would have been observed. However, if the participants viewed their violence as unjustified, higher levels of guilt in the violent condition compared to the nonviolent condition would have been observed. To perform post-hoc comparisons in SPSS, I re-coded the data so that the four sociality-by-violence conditions constituted four levels of a single factor and performed a one-way ANOVA with guilt as the dependent variable. The results found that there was significant variation across the four
2 conditions, F(3, 102) = 4.06, p = .009, ηp =.118. The mean guilt for the antisocial non-violent condition, the antisocial violent condition, the prosocial non-violent condition, and the prosocial violent condition was 9.40 (SD = 3.00), 8.50 (SD = 3.92), 7.29 (SD = 2.39), and 7.00 (SD = 2.32), respectively. Post hoc Tukey’s t tests found a significant difference between antisocial nonviolent and prosocial violent (p = .02), and a marginally significant difference between antisocial nonviolent and prosocial nonviolent (p = .051). There was no significant difference between antisocial nonviolent and antisocial violent (p = .68), antisocial violent and prosocial nonviolent (p = .49), antisocial violent and prosocial violent (p = .30), and prosocial violent and prosocial nonviolent (p = .99). A correlation was run to see if those higher in moral disengagement had lower levels of guilt, which might suggest those participants viewed their behavior as justified, found no significant relationship (r(100) = .09, p = .37). Additionally, a moderate correlation was found 38 for guilt and self-rated aggression (r(100) = .273, p = .006). Implications of these results are covered in the Discussion section. 39
DISCUSSION
As video games continue to increase in popularity, and sophistication, moral choices are increasingly being utilized at plot devices. As technology increases, the ability for players to differentiate between the real world and a game world might shrink. As games become more sophisticated, the effort required to maintain suspension of disbelief, is likely to decrease as well.
There is plenty of evidence that suggests people view video game characters as if they were real people and such interactions can lead to experiencing real emotions. Video games are already being used in clinical settings as a therapeutic tool for the prevention, treatment, and rehabilitation of mental and other health issues (Carras et al., 2018). A better understanding of how people interact with video game characters, and how (im)moral behaviors with the game affect the player are necessary.
This study aimed to answer the question “How does prosocial versus antisocial violence differ from prosocial versus antisocial nonviolence?” To that end, this study looked at sociality and violence within the game Fallout 3 in an effort to individually parse out the effects of violence/nonviolence and prosocial/antisocial behaviors on proneness to morally disengage and guilt.
Violence
Regarding the violence manipulation, the data seems to suggest that on its own, violence within the game had little to no effect on guilt. There were theoretical reasons to support multiple predictions for guilt that depended on whether the participants viewed the behavior as justified or not. If participants viewed their violence as unjustified, higher guilt would be expected. If they viewed their violence as justified, lower guilt would be expected. Instead, no support for 40
Prediction 1 nor an effect of violence on guilt were found. The significant correlation between self-rated aggression and guilt (r(100) = .273, p = .006) was of interest, as it suggests that the more violent and aggressive they viewed their behavior, the more guilt they felt. However, this is still at odds with there being no observed main effect of violence.
This finding has important implications for the field, as it suggests that from a moral and affective stand point, violence on its own may have a far less significant role than previously thought. Further research will be needed to verify and replicate this finding.
Sociality
While the effect of violence had little to no impact, sociality played a larger role in both moral disengagement and guilt. Results of this study found support for Prediction 4: Players engaging in antisocial behavior reported higher levels of guilt. This evidence supports previous findings that show players view non-player characters as if they were entities in which morality applies. If players did not identify and react to non-player characters as if they were real, there would be little reason for the players to feel guilty.
Given the support of Prediction 4, that antisocial players showed higher levels of guilt,, could imply these participants viewed their antisocial actions to be unjustified. Based on moral disengagement theory, if participants used moral disengagement to justify their antisocial behavior, lower levels of guilt should have been found. Instead, results found higher levels of guilt in the antisocial condition compared to the prosocial condition. This explanation could be supported by prior research, such as Grizzard et al., (2014) who found that following antisocial behavior within a game that produces guilt, players showed increased moral sensitivity. As indicated earlier in the manuscript, the present research did not assess participants' states of 41 moral disengagement since the Moral Disengagement scale is a trait measure rather than a state measure. While McAlister (2001) found moral disengagement could change following exposure to certain types of information regarding military use, resulting in an increase of moral disengagement in one group and a decrease in another, the researcher used a scale specifically developed to measure moral disengagement in support of military action. The author is unaware of any experimental research that has shown a similar change in moral disengagement following use of video games.
Finally, given the effect of sociality on the moral disengagement personality trait was marginal when gender was a factor, p = .05 (and non-significant when treated as a covariate, p = .06), it is possible this occurred by chance and within a larger sample size, with more balanced gender proportions, the relationship would no longer reach significance.
Conclusions
Taken together, these results suggest whether players engage in antisocial or prosocial behavior within the game is the primary factor driving affective and moral disengagement outcomes, not violence. Given that there was no significant difference between the nonviolent and violent antisocial condition, prior research that has examined and found playing violent games to be associated with guilt and higher moral disengagement, may be partially misattributing those associations to be the result of violence featured in the game, when the antisocial nature of the choices may be the partially responsible.
While much research on video games has focused on the effects of violence within the game on outcomes such as aggression, moral disengagement, guilt, and many other factors, these results suggest that the antisocial and prosocial nature of the choices may be driving the 42 outcomes more so than the violence itself. It is possible that the violence used in prior research is having an effect due in part to it being antisocial. More research that breaks down behavior into prosocial or antisocial and attempts to separate the violence may help delineate some of the effects previously observed and help us better understand the mechanisms at play.
The present study attempted to tackle some of the issues raised by previous researchers regarding confounds found within recent video game studies. Confounds regarding the use of multiple video games that differ in style, or difficulty was addressed by using a single game.
With a single, linear experience, this helped ensure that the differences between participants was primarily due to experimental factors. The present study was also successful in isolating antisocial behavior from violence. While there are many other scenarios and situations for which this isolation can and should be applied, this study can hopefully be a stepping stone for further research, and evidence that it can be done.
Limitations
A number of limitations are present in the study that may have impacted results. First, the study had relatively weak power (0.6). This was further impacted by data failures that resulted in missing data for participants, however, given the significant main effect observed in the opposite direction this seems less likely. Second, the degree to which behaviors were justified was not directly measured. The moral disengagement scale used was informative on the participants general level of moral disengagement and combined with the measure of guilt we can infer whether participants viewed their behavior as justified. However, a specific measure of the extent participants justified their behavior within the game was not used. Additionally, the degree to which each behavior was justified may have varied. As a result, it is possible whether 43 they viewed their behaviors as justified or not may have acted as a partial mediator between the conditions and moral disengagement and guilt.
While the emotional scale was reframed to ask how participants felt while playing the game, the moral disengagement scale measured proneness to morally disengage, rather than moral disengagement as a state. As such, the dependent variable was flawed. Additional analysis was done in an attempt to account for this, however, as this was a post-hoc analysis added after the fact to counter a flaw in the measures, the analysis may be less reliable.
A further limitation is the game used. While Fallout 3 has been used previously in research involving moral disengagement, the game released in 2008 on a prior generation of consoles. As such, the graphics and quality of the game could have had an effect on players who are used to more recent advancements in video game quality and technology. Additionally, the game does not allow for a purely pacifistic style of gameplay. Players were able to complete the portion of the game played without killing any humans, however, this required them to actively flee from hostile entities. Other games such as Undertale which allow you to complete the game without harming or killing anyone are worth considering for future research.
It is also worth noting the sample was limited to 18- to 26-year-old (M = 19.24) college students. Moral decision making is something that could change over a person’s lifespan. Joeckel et al. (2011) found that in adolescents, moral salience was not predictive of decision making within a video game for American adolescents (however, it was significant for German adolescents).
Finally, given the nature of video games and the complexity of virtual environments combined with availability of choice in this style of game, it can be difficult to maintain full 44 experimental control. While games can be made or modified to be more restrictive and linear, doing so may have implications on affective outcomes as well as moral disengagement, leading to potential responses as:“the game made me do it,” or “I had no other choice.” As such, there is an inherent limitation for replicating results given the variability between subjects in their in-game behaviors and responses. Despite this limitation, it is still important to examine the potential games have to impact cognition, moral reasoning, and other psychological factors. 45
REFERENCES
Anderson, C. A., & Bushman, B. J. (2001). Effects of violent video games on aggressive
behavior, aggressive cognition, aggressive affect, physiological arousal and prosocial
behavior: A meta-analytic review of the scientific literature. Psychological Science, 12(5),
353–359. https://doi.org/10.1111/1467-9280.00366
Anderson, C. A., and Carnagey, N. L. (2014). The role of theory in the study of media violence:
The general aggression model. In Gentile, D. A. (Ed). Media violence and children. (pp.
103–133). Praeger.
Anderson, C. A., Sakamoto, A., Gentile, D. A., Ihori, N., Shibuya, A., Yukawa, S., & Kobayashi,
K. (2008). Longitudinal effects of violent video games on aggression in japan and the united
states. Pediatrics, 122(5), e1067–e1072. https://doi.org/10.1542/peds.2008-1425
Andrus, B. C. (1969).The infamous of Nuremberg. Leslie Frewin.
Bandura, A. (1991). Social cognitive theory of self-regulation. Organizational Behavior and
Human Decision Processes, 50(2), 248–287. https://doi.org/10.1016/0749-5978(91)90022-L
Bandura, A. (1999). Moral Disengagement in the Perpetration of Inhumanities. Personality and
Social Psychology Review, 3(3), 193–209. https://doi.org/10.1207/s15327957pspr0303_3
Bandura, A. (2002). Selective moral disengagement in the exercise of moral agency. Journal of
Moral Education, 31 (2), 101–119. https://doi.org/10.1080/0305724022014322 46
Bandura, A., Barbaranelli, C., & Caprara, G. V. (1996). Mechanism of moral disengagement in
the exercise of moral agency. Journal of Personality and Social Psychology, 71 (2),
364–374. https://doi.org/10.1037/0022-3514.71.2.364
Bartholow, B. D., Bushman, B. J., & Sestir, M. A. (2006). Chronic violent video game exposure
and desensitization to violence: Behavioral and event-related brain potential data. Journal of
Experimental Social Psychology, 42(2006), 532–539.
Beu, D. S., & Buckley, M. R. (2004). This is war: How the politically astute achieve crimes of
obedience through the use of moral disengagement. Leadership Quarterly, 15(4), 551–568.
https://doi.org/10.1016/j.leaqua.2004.05.007
Boardley, D., & Kavussanu, M. (2011). Moral disengagement in sport. International Review of
Sport and Exercise Psychology, 4(2), 93-108,
https://doi.org/10.1080/1750984X.2011.570361
Boyle, G. J. (1986). Higher-order factors in the differential emotions scale (DES-III). Personality
and Individual Differences, 7(3), 305–310. https://doi.org/10.1016/0191-8869(86)90005-X
Bushman, B. J., & Anderson, C. A. (2015). Understanding Causality in the Effects of Media
Violence. American Behavioral Scientist, 59(14), 1807–1821.
https://doi.org/10.1177/0002764215596554
Breuer, J., Scharkow, M., & Quandt, T. (2015). Sore losers? A reexamination of the frustration–
aggression hypothesis for colocated video game play. Psychology of Popular Media Culture
, 4(2), 126–137. https://doi.org/10.1037/ppm0000020 47
Carras, M. C., Van Rooij, A. J., Spruijt-Metz, D., Kvedar, J., Griffiths, M. D., Carabas, Y., &
Labrique, A. (2018). Commercial video games as therapy: A new research agenda to unlock
the potential of a global pastime. Frontiers in Psychiatry, 8, 1–7.
https://doi.org/10.3389/fpsyt.2017.00300
Cardwell, S. M., Piquero, A. R., Jennings, W. G., Copes, H., Schubert, C. A., & Mulvey, E. P.
(2015). Variability in moral disengagement and its relation to offending in a sample of
serious youthful offenders. Criminal Justice and Behavior, 42(8), 819–839.
https://doi.org/10.1177/0093854814567472
Carnagey, N. L., Anderson, C. A., & Bushman, B. J. (2007). The effect of video game violence
on physiological desensitization to real-life violence. Journal of Experimental Social
Psychology, 43(3), 489–496. https://doi.org/10.1016/j.jesp.2006.05.003
Duffy, M. K., Scott, K. L., Shaw, J. D., Tepper, B. J., & Aquino, K. (2012). A Social context
model of envy and social undermining. Academy of Management Journal, 55(3), 643–666.
https://doi.org/10.5465/amj.2009.0804
Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical
power analysis program for the social, behavioral, and biomedical sciences. Behavior
Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
Ferguson, C. J. (2007). Evidence for publication bias in video game violence effects literature: a
meta-analytic review. Aggression and Violent Behavior 12(4), 470–482. doi:
10.1016/j.avb.2007.01.001 48
Ferguson, C. J. (2010). Blazing angels or resident evil? Can violent video games be a force for
good? Review of General Psychology, 14(2), 68-81. https://doi.org/10.1037/a0018941
Ferguson, C. J., & Kilburn, J. (2010). Much ado about nothing: The misestimation and
overinterpretation of violent video game effects in eastern and western nations: Comment
on Anderson et al. (2010). Psychological Bulletin, 136(2), 174–178.
https://doi.org/10.1037/a0018566
Ferguson, C. J., Rueda, S. M., Cruz, A. M., Ferguson, D. E., Fritz, S., & Smith, S. M. (2008).
Violent video games and aggression: Causal relationship or byproduct of family violence
and intrinsic violence motivation? Criminal Justice and Behavior, 35 (3), 311–332.
https://doi.org/10.1177/0093854807311719
Fox, T. (2015). Undertale [Computer software]. Toby Fox.
Gabbiadini, A., Andrighetto, L., & Volpato, C. (2012). Brief report: Does exposure to violent
video games increase moral disengagement among adolescents? Journal of Adolescence, 35
(5), 1403–1406. https://doi.org/10.1016/j.adolescence.2012.06.001
Gini, G., Pozzoli, T., & Hauser, M. (2011). Bullies have enhanced moral competence to judge
relative to victims, but lack moral compassion. Personality and Individual Differences,
50(5), 603–608. https://doi.org/10.1016/j.paid.2010.12.002
Granic, I., Lobel, A., & Engels, R. C. M. E. (2014). The benefits of playing video games.
American Psychologist, 69(1), 66–78. https://doi.org/10.1037/a0034857 49
Greitemeyer, T., & Mügge, D. O. (2014). Video games do affect social outcomes: a meta-
analytic review of the effects of violent and prosocial video game play. Personality and
Social Psychology Bulletin, 40(5), 578–589. https://doi.org/10.1177/0146167213520459
Greitemeyer, T., Osswald, S., & Brauer, M. (2010). Playing prosocial video games increases
empathy and decreases schadenfreude. Emotion, 10(6), 796–802.
https://doi.org/10.1037/a0020194
Greene, J., & Haidt, J. (2002). How (and where) does moral judgment work? Trends in Cognitive
Sciences, 6(12), 517–523. https://doi.org/10.1016/S1364-6613(02)02011-9
Grizzard, M., Tamborini, R., Lewis, R. J., Wang, L., & Prabhu, S. (2014). Being bad in a video
game can make us morally sensitive. Cyberpsychology, Behavior, and Social Networking,
17(8), 499–504. https://doi.org/10.1089/cyber.2013.0658
Grubb, A., & Turner, E. (2012). Attribution of blame in rape cases: A review of the impact of
rape myth acceptance, gender role conformity and substance use on victim blaming.
Aggression and Violent Behavior, 17(5), 443–452. https://doi.org/10.1016/j.avb.2012.06.002
Haidt, J. (2001). The emotional dog and its rational tail: A social intuitionist approach to moral
judgment. Psychological Review, 108(4), 814–834. https://doi.org/10.1037/0033-
295x.108.4.814
Haidt, J., Roller, S. H., Dias, M. G., Kurtz, S., Marin, L., Mccauley, C., & Wright, A. (1993). 50
Affect, culture, and morality, or is it wrong to eat your dog? Journal of Personality and
Social Psychology, 65(4), 613–628. https://doi.org/10.1037/0022-3514.65.4.613
Hartmann, T. (2008). Parasocial interactions and paracommunication with new media characters.
In Koniji, E. A., Utz, S., Tanis, M., & Barnes, S. B., (Eds.), Mediated
Interpersonal Communication (1st ed.). Routledge. 177–199. https://
doi.org/10.4324/9780203926864
Hartmann, T., & Klimmt, C. (2006). Gender and computer games: Exploring females’ dislikes.
Journal of Computer-Mediated Communication, 11(4), 910–931.
https://doi.org/10.1111/j.1083-6101.2006.00301.x
Hartmann, T., Möller, I., & Krause, C. (2015). Factors underlying male and female use of violent
video games. New Media and Society, 17(11), 1777–1794.
https://doi.org/10.1177/1461444814533067
Hartmann, T., Toz, E., & Brandon, M. (2010). Just a game? Unjustified virtual violence produces
guilt in empathetic players. Media Psychology, 13(4), 339–363.
https://doi.org/10.1080/15213269.2010.524912
Hartmann, T., & Vorderer, P. (2010). It’s okay to shoot a character: Moral disengagement in
violent video games. Journal of Communication, 60 (1), 94–119.
https://doi.org/10.1111/j.1460-2466.2009.01459.x 51
Hazlett, R. L. (2006). Measuring emotional valence during interactive experiences. Proceedings
of the SIGCHI Conference on Human Factors in Computing Systems - CHI ’06, 1023.
https://doi.org/10.1145/1124772.1124925
Izard C. E. (1977). Human Emotions . Plenum Press.
Jennett, C., Cox, A. L., Cairns, P., Dhoparee, S., Epps, A., Tijs, T., & Walton, A. (2008).
Measuring and defining the experience of immersion in games. International Journal of Human-
Computer Studies, 66 (9), 641–661. https://doi.org/10.1016/j.ijhcs.2008.04.004
Joeckel, S., Bowman, N. D., & Dogruel, L. (2013). The Influence of Adolescents’ Moral
Salience on Actions and Entertainment Experience in Interactive Media. Journal of Children
and Media, 7 (4), 480–506. https://doi.org/10.1080/17482798.2013.781513
Klimmt, C., Schmid, H., Nosper, A., Hartmann, T., & Vorderer, P. (2006). How players manage
moral concerns to make video game violence enjoyable. Communications, 31 (3), 309–328.
https://doi.org/10.1515/COMMUN.2006.020
Kotsch W. E., Gerbing D. W., & Schwartz G. E. (1982). The construct validity of the Differential
Emotions Scale as adapted for children and adolescents. In Izard C. E. (Ed.), In Measuring
Emotions in Infants and Children (pp. 251-278). Cambridge University Press.
Lucas, K., & Sherry, J. L. (2004). Sex differences in video game play: A communication-based
explanation. Communication Research, 31(5), 499–523.
https://doi.org/10.1177/0093650204267930
Levi, P. (1993). Survival in Auschwitz: The Nazi assault on humanity . Collier Books Lutz, W. 52
D. (1987). Language, appearance, and reality: Doublespeak in 1984. ETC: A Review of General
Semantic 44 (4), 382-391. Institute of General Semantics.
Markey, P. M., Ivory, J. D., Slotter, E. B., Oliver, M. B., & Maglalang, O. (2020). He does not
look like video games made him do it: Racial stereotypes and school shootings. Psychology
of Popular Media, 9(4), 493–498. https://doi.org/10.1037/ppm0000255
Milgram, S. (1974). Obedience to authority: An experimental view . Harper & Row.
Miller, R. M., Hannikainen, I. A., & Cushman, F. A. (2014). Bad actions or bad outcomes?
Differentiating affective contributions to the moral condemnation of harm. Emotion , 14
(3), 573–587. https://doi.org/10.1037/a003536 1
Moore, C. (2015). Moral disengagement. Current Opinion in Psychology, 6 (2015):199–204.
https://doi.org/10.1016/j.copsyc.2015.07.018
Mori, M., MacDorman, K. F., & Kageki, N,. (2012). The uncanny valley. IEEE Robotics and
Automation, 19(2), 98–100. https://doi.org/10.1109/MRA.2012.2192811
Morrison, I. & Ziemke, T. (2005). Empathy with computer game characters: A cognitive
neuroscience perspective. In L. Halle, D. K. J. Heylen, S. Marsella, C. Pelachaud, P. Wallis,
& S. Woods (Eds.), Proceedings of the Joint Symposium on Virtual Social Agents (pp.
153-154). The Society for the Study of AI and the Simulation of Behaviour 53
Przybylski, A. K., Rigby, C. S., Deci, E. L., & Ryan, R. M. (2014). Competence-impeding
electronic games and players’ aggressive feelings, thoughts, and behaviors. Journal of
Personality and Social Psychology, 106(3), 441–457. https://doi.org/10.1037/
a0034820
Tear, M. J., & Nielsen, M. (2014). Video games and prosocial behavior: A study of the effects of
nonviolent, violent and ultra-violent gameplay. Computers in Human Behavior, 41 , 8–13.
https://doi.org/10.1016/j.chb.2014.09.002
Tilker, H. A. (1970). Socially responsible behavior as a function of observer responsibility and
victim feedback. Journal of Personality and Social Psychology, 14(2), 95–100.
https://doi.org/10.1037/h0028773
Teng, Z., Nie, Q., Guo, C., & Liu, Y. (2017). Violent video game exposure and moral
disengagement in early adolescence: The moderating effect of moral identity. Computers in
Human Behavior, 77, 54–62. https://doi.org/10.1016/j.chb.2017.08.031
Ravaja, N., Salminen, M., Holopainen, J., Saari, T., Laarni, J., & Järvinen, A. (2004). Emotional
response patterns and sense of presence during video games. Proceedings of the Third
Nordic Conference on Human-Computer Interaction - NordiCHI ’04, 339–347.
https://doi.org/10.1145/1028014.1028068
Schneider, E. F. (2004). Death with a story. Human Communication Research, 30(3), 361–375.
https://doi.org/10.1111/j.1468-2958.2004.tb00736.x 54
Viki, G. T., Osgood, D., & Phillips, S. (2013). Dehumanization and self-reported proclivity to
torture prisoners of war. Journal of Experimental Social Psychology, 49(3), 325–328.
https://doi.org/10.1016/j.jesp.2012.11.006
Weaver, A. J., & Lewis, N. (2012). Mirrored morality: An exploration of moral choice in video
games. Cyberpsychology, Behavior, and Social Networking, 15(11), 610–614.
https://doi.org/10.1089/cyber.2012.0235
Wiegman, O., & Van Schie, E. G. M. (1998). Video game playing and its relations with
aggressive and prosocial behaviour. British Journal of Social Psychology, 37(3), 367–378.
https://doi.org/10.1111/j.2044-8309.1998.tb01177.x
Willoughby, T., Adachi, P. J. C., & Good, M. (2012). A longitudinal study of the association
between violent video game play and aggression among adolescents. Developmental
Psychology, 48(4), 1044–1057. https://doi.org/10.1037/a0026046
Yee, N., Bailenson, J. N., Urbanek, M., Chang, F., & Merget, D. (2007). The unbearable likeness
of being digital: the persistence of nonverbal social norms in online virtual environments.
CyberPsychology & Behavior, 10(1), 115–121. https://doi.org/10.1089/cpb.2006.9984 55
APPENDIX A. MODIFIED EMOTIONAL DIFFERENTIAL SCALE
While playing the game, how often did you feel...
1. Interested in what you're doing, caught up in it
2. Like you ought to be blamed for something
3. Like somebody is a "good-for-nothing"
4. Regret or sorry about something you did
5. Angry, irritated, or annoyed
6. Unhappy, blue, downhearted
7. Glad about something
8. Like you did something wrong
9. Surprised, like when something suddenly happens you had no idea would happen
10. Mad at somebody
11. Like you did something aggressive
12. Alert, curious, kind of excited about something
13. Like somebody is a low-life, not worth the time of day
14. Like you were danger
Adapted from Differential Emotions Scale (DES-IV; Izard, 1977; Kotsch, Gerbing, & Schwartz,
1982). Scale used a 5-point Likert scale measured as: “Rarely or Never,” “Hardly ever,”
“Sometimes,” “Often,” and “Very Often.” 56
APPENDIX B. MECHANISMS OF MORAL DISENGAGEMENT
1. It is alright to fight to protect your friends.
2. To hit obnoxious classmates is just giving them "a lesson."
3. It is okay to insult a classmate because beating him/her is worse.
4. A kid in a gang should not be blamed for the trouble the gang causes.
5. Kids cannot be blamed for misbehaving if their friends pressured them to do it.
6. It is okay to tell small lies because they don't really do any harm.
7. Someone who is obnoxious does not deserve to be treated like a human being.
8. If people are careless where they leave their things it is their own fault if they get stolen.
9. It is alright to lie to keep your friends out of trouble.
10. Slapping and shoving someone is just a way of joking.
11. Stealing some money is not too serious compared to those who steal a lot of money.
12. If a group decides together to do something harmful it is unfair to blame any kid in the group
for it.
13. If kids are not disciplined they should not be blamed for misbehaving.
14. Teasing someone does not really hurt them.
15. Some people have to be treated roughly because they lack feelings that can be hurt.
16. Kids who get mistreated usually do things that deserve it.
Note. The following items correspond to the various mechanisms of moral disengagement. Moral justification: 1,9. Euphemistic language: 2,10. Advantageous comparison: 3,11. Diffusion of responsibility: 4, 12. Displacement of responsibility: 5, 13. Distorting consequences: 6, 14. 57
Dehumanization: 7, 15. Attribution of blame: 8, 16. Adapted from Bandura et al., (1996). Scale used a 4-point Likert scale that measured: “Completely Disagree,” ``Somewhat Disagree,”
Somewhat Agree,” “Completely Agree.” 58
APPENDIX C. IMMERSION SCALE
Please answer the following questions regarding how you felt at the END of the game.
1. To what extent did the game hold your attention?
2. How much effort did you put into playing the game?
3. To what extent did you feel like the characters in the game were realistic?
4. To what extent did you lose track of time?
5. To what extent did you feel as though you were separated from your real-world
environment?
6. To what extent did you feel like you were in the game world?
Note. Scale used a 5-point Likert scale that ranged from “None at all” to “A great deal.”