Online Game Addiction among Adolescents: Motivation and prevention Factors Online Game Addiction among Adolescents: Motivation and Prevention Factors ABSTRACT ndividuals and societies. In this study we rely on the functionalist perspective ofh behavior and propose and test a balanced model of the antecedents of online game addiction among adolescents, which simultaneously focuses on moti revention and harm reduction forces. First, a pilot survey of 163 adolescents was used for validating and refining a instrument. Second, formal survey data c from 623 adolescents were analyzed with Partial Least Squares techniques. The findings point to several functional needs (eg-, need for relationship and need for drive online game playing and addiction, as well as to several prevention and harm reduction factors(e.g, education, attention switching activities)that reduce game nd alleviate online game addiction. The effects of motivation and prevention factors on online game addiction are partially mediated by online game playing. Implic d practice are discussed Keywords: Addiction, technology addiction, computer games, motivation, prevention, harm reduction. INTRODUCTION The driving forces behind individuals'informed decisions to keep on using information technologies(IT) have been widely studied in the MIS literature(Bhatta al., 2008; Kim, 2009). The basic assumptions of such studies were that information systems are beneficial in terms of hedonic or utilitarian gains, and that people are decision makers. However, information systems are not always beneficial, and users may develop distorted rationales(turel et al, Forthcoming). When being used in lay also have negative impact on individuals as well as on the society(Block, 2008). One such potential negative outcome is technology addictio Building on the definition of drug addiction( Robinson Berridge, 2003), technology addiction is defined as a users psychological state of dependency on the I manifested through the compulsive pattern of IT-seeking and IT-use behaviors that take place at the expense of other important activities. This phenomenon has bee in numerous IT contexts, including, for example, online video games( Charlton Danforth, 2007), mobile email (turel Serenko, 2010), and online gambling(Mch erevensky, 2009). It is imperative to study these addictions because they can negatively influence many facets of life, including personal, school, social, financial, ar relationship aspects(Billieux et al., 2008; Caplan, 2002; Turel Serenko, 2010) One of the prominent and worrisome technology related addictions is online game addiction( Charlton Danforth, 2010; Huh Bowman, 2008). Building on c definition of general technology addictions, online game addiction is defined as a state of dependency on online games which is manifested through the compulsive p eking and use behaviors that take place at the expense of other important activities. Online video games have become a popular form of electronic entertainment, es among children and adolescents( Choi Kim, 2004; Griffiths et al., 2003; Griffiths et al., 2004). While not all online games are addictive or harmful, some games, EverQuest", and the"Dark Age of Camelot", and many massively multiplayer online games can be addiction- prone due to their interactive and collaborative/compet Barnett Coulson, 2010; Liu& Peng, 2009). These features give such games a social aspect which may be missing from offline activities, and make it difficult to st (Young, 2010b). As such, excessive use of online video games and addiction to them have become common, and may result in many negative psychological and phys damages, including social isolation, suicide, lack of sleep, hypertension, and death(Bruner Bruner, 2006). These phenomena may be more prevalent among adoles group which tends to engage in more risk behaviors than adults(Nelson et al., 1997; Quadrel et al, 1993), including in the context of video games(Anderson et al., 2 In this study we therefore focus on online game addiction, and try to explain its formation among adolescents. By understanding the antecedents of this psycholo better prevention and intervention techniques can be developed. This also represents a gap in the literature that we intend to fill. Specifically, much research so far has devoted to the drivers of online game playing( Choi& Kim, 2004; Yee, 2006), but these antecedents may have different roles in forming addictions. Other studies dea efinition and measurement of online game addiction(e.g, Byun et al, 2009, Charlton& Danforth, 2007), but only a limited set of potential correlates with online ga diction have been identified(e. g, Charlton Danforth, 2010; Huh& Bowman, 2008) To advance our understanding of the formation of online game addiction among adolescents, a model explicating the links between two families of predictors an phenomenon is proposed. We take the functionalist approach(Katz, 1960, Smith et al., 1956), and build on research on game playing motivations(Yee, 2006), in chor nore holistic view of the phenomenon we study. After all, game players are often exposed simultaneously to both types of factors(e. g, a social need that drives them mbined with parental monitoring that inhibits playing)
Online Game Addiction among Adolescents: Motivation and Prevention Factors Online Game Addiction among Adolescents: Motivation and Prevention Factors ABSTRACT Online game addiction has become a common phenomenon that affects many individuals and societies. In this study we rely on the functionalist perspective of h behavior and propose and test a balanced model of the antecedents of online game addiction among adolescents, which simultaneously focuses on motivating forces, prevention and harm reduction forces. First, a pilot survey of 163 adolescents was used for validating and refining a survey instrument. Second, formal survey data c from 623 adolescents were analyzed with Partial Least Squares techniques. The findings point to several functional needs (e.g., need for relationship and need for esc drive online game playing and addiction, as well as to several prevention and harm reduction factors (e.g., education, attention switching activities) that reduce game and alleviate online game addiction. The effects of motivation and prevention factors on online game addiction are partially mediated by online game playing. Implic research and practice are discussed. Keywords : Addiction, technology addiction, computer games, motivation, prevention, harm reduction. INTRODUCTION The driving forces behind individuals’ informed decisions to keep on using information technologies (IT) have been widely studied in the MIS literature (Bhatta al., 2008; Kim, 2009). The basic assumptions of such studies were that information systems are beneficial in terms of hedonic or utilitarian gains, and that people are decision makers. However, information systems are not always beneficial, and users may develop distorted rationales (Turel et al., Forthcoming). When being used im may also have negative impact on individuals as well as on the society (Block, 2008). One such potential negative outcome is technology addiction. Building on the definition of drug addiction (Robinson & Berridge, 2003), technology addiction is defined as a user’s psychological state of dependency on the I is manifested through the compulsive pattern of IT-seeking and IT-use behaviors that take place at the expense of other important activities. This phenomenon has bee in numerous IT contexts, including, for example, online video games (Charlton & Danforth, 2007), mobile email (Turel & Serenko, 2010), and online gambling (McB Derevensky, 2009). It is imperative to study these addictions because they can negatively influence many facets of life, including personal, school, social, financial, an relationship aspects (Billieux et al., 2008; Caplan, 2002; Turel & Serenko, 2010). One of the prominent and worrisome technology related addictions is online game addiction (Charlton & Danforth, 2010; Huh & Bowman, 2008). Building on o definition of general technology addictions, online game addiction is defined as a state of dependency on online games which is manifested through the compulsive p seeking and use behaviors that take place at the expense of other important activities. Online video games have become a popular form of electronic entertainment, es among children and adolescents (Choi & Kim, 2004; Griffiths et al., 2003; Griffiths et al., 2004). While not all online games are addictive or harmful, some games, su “EverQuest”, and the “Dark Age of Camelot”, and many massively multiplayer online games can be addiction-prone due to their interactive and collaborative/compet (Barnett & Coulson, 2010; Liu & Peng, 2009). These features give such games a social aspect which may be missing from offline activities, and make it difficult to st (Young, 2010b). As such, excessive use of online video games and addiction to them have become common, and may result in many negative psychological and phys damages, including social isolation, suicide, lack of sleep, hypertension, and death (Bruner & Bruner, 2006). These phenomena may be more prevalent among adolesc group which tends to engage in more risk behaviors than adults (Nelson et al., 1997; Quadrel et al., 1993), including in the context of video games (Anderson et al., 2 In this study we therefore focus on online game addiction, and try to explain its formation among adolescents. By understanding the antecedents of this psycholo better prevention and intervention techniques can be developed. This also represents a gap in the literature that we intend to fill. Specifically, much research so far has devoted to the drivers of online game playing (Choi & Kim, 2004; Yee, 2006), but these antecedents may have different roles in forming addictions. Other studies dea definition and measurement of online game addiction (e.g., Byun et al., 2009; Charlton & Danforth, 2007), but only a limited set of potential correlates with online ga addiction have been identified (e.g., Charlton & Danforth, 2010; Huh & Bowman, 2008). To advance our understanding of the formation of online game addiction among adolescents, a model explicating the links between two families of predictors an phenomenon is proposed. We take the functionalist approach (Katz, 1960; Smith et al., 1956), and build on research on game playing motivations (Yee, 2006), in chor research on problematic behavior prevention (Dickson et al., 2002; van Hamel et al., 2007). The joint focus on motivating and preventing factors is advantageous as i more holistic view of the phenomenon we study. After all, game players are often exposed simultaneously to both types of factors (e.g., a social need that drives them combined with parental monitoring that inhibits playing)
that online nline games, key functional motives may include the advancement, need to master the mechanics of games, need for relationship, and need for escapism (Y While these are intemally formulated motivations, the prevention factors among adolescents are often external to the person and fall into ones environment. These parental monitoring, education, the cost of conducting the activity(playing online games), resources shortage, and alternative, attention switching, activities(Dickson 2002) To test our model, two surveys were conducted. First, data from a sample of 163 Chinese adolescents were utilized for developing the measurement instruments not found in the literature. Second, using the developed measurement scales, data from a sample of 623 Chinese adolescents were collected and subjected to Partial L (PLS)analysis. The results point to several motivation and prevention factors that explain variation in game playing and addiction, and also show that some preventio ay not have merit. functionalist approach. The Hypotheses section presents the development of our research model. The Methods section describes the approaches taken for survey 3 The remainder of this paper is organized as follows. The Theoretical Background section reviews the literature operationalization and data collection. The Analysis and Results section provides information on, and the results of the analyses performed. In the Discussion section summarize the results, outline implications for research and practice, and acknowledge limitations that point to future research directions, Concluding remarks are pi the Conclusion section Online Game Addiction Game addiction is one type of Internet addiction that encapsulates the dependency on a specific family of f artifacts-online games. To capture it, many research dopted the criteria of Internet addiction(Young, 1998b; Young, 2010a)or a broader set for diagnosing problematic non-substance related compulsions, i.e., behavior (Brown, 1997). These works largely build on a closely related disorder- problem pathological gambling, which is described in DSM-IV(Diagnostic and Statistics M ental Disorders- Fourth Edition; American Psychiatric Association, 1994). Because no official cutoff points to classify a person as addicted exist(Block, 2008),m search on this topic so far has treated online game and other technology addictions as continuous concepts -ranging from low to high levels of addiction Ferraro et al., 2007, Hur, 2006), and hence assumed that all users may have a certain level of addiction, though low for most users(Turel et al. Forthcomi perspective in this stud ike other behavioral addictions, online game addiction can be manifested through six core symptoms described by Brown(1997). These include conflict (i.e, pl games meaningfully conflicts with other important tasks), withdrawal (ie, negative emotions arise if one cannot play online games), relapse and reinstatement (i.e, i voluntarily reduce the time spent on online games), and behavioral salience (i. e, playing online games dominates ones life and takes over other tasks). Other less sev symptoms are indicative of high engagement, and serve as a precondition for the more severe addiction symptoms( Charlton Danforth, 2007; Charlton Danforth include tolerance(need to increase the time spent on online games ), euphoria(a buzz of excitement from playing online games), and cognitive salience(frequently th online games) How is online game addiction formed? While not all reasons are clear, evidence so far suggests that it has to do with at least two families of factors: Internal psyc factors and socio-environmental factors(Shi, 2008), as well as potentially with neurobehavioral deficiencies(Ko et al., 2009a). For example, social-behaviorally inac ersonality traits, such as shyness, dependence, depression, aggression, low self-esteem, low self-control, and narcissism, may predispose some individuals to play ex nd become addicted to online games(Ho Lee, 2001; Kim et al., 2008; Yang Tung, 2007, Dominick, 1984). Socio-environmental factors can include one's work (e.g, working night shifts), socio economic status(Hur, 2006), and demographics(e.g, age and gender)(Ko et al., 2005). Game design is another external factor that fluence online game addiction. Role playing games, action games, adventure games, strategy games, fighting games, and shooting games are more addictive than ot 2008)plausibly due to their interactive, collaborative and competitive nature( Barnett Coulson, 2010, Liu& Peng, 2009)which serves social needs of individuals t hissing in their real lives( Young, 2010b) While focusing on such variables is important and fruitful, the functionalist perspective which we take in this study suggests that these factors inform a set of funct motives, which in turn, influence behavioral outcomes( Clary et al., 1998; Mowen, 2000; Mowen et al., 2007). Thus, one's functional motives are different from traits hore proximal determinants of his or her program of behavior(Mowen Sujan, 2005). Accordingly, in this study we focus on functional motives and inhibitors The Functional Approach for Online Game Playing Most if not all activities humans perform are driven by purposeful striving toward social and personal goals( Snyder, 1993)such as to increase ones social status become healthy. People's actions hence cater to various functional needs( Clary et al., 1998). These needs often evolve around key themes, such as serving knowle oneself), and utilitarian functions(need for obtaining rewards and avoiding punishment)(Katz, 1960). This perspective has been utilized and validated in numerous st Cooper et al., 1998: De Cremer Tyler, 2005; Rioux Penner, 2001)
Specifically, in line with functionalist approach of motivation we argue that online gamers follow a program of behavior to serve their functional motives. In the online games, key functional motives may include the need for advancement, need to master the mechanics of games, need for relationship, and need for escapism (Y While these are internally formulated motivations, the prevention factors among adolescents are often external to the person and fall into one’s environment. These ca parental monitoring, education, the cost of conducting the activity (playing online games), resources shortage, and alternative, attention switching, activities (Dickson 2002). To test our model, two surveys were conducted. First, data from a sample of 163 Chinese adolescents were utilized for developing the measurement instruments not found in the literature. Second, using the developed measurement scales, data from a sample of 623 Chinese adolescents were collected and subjected to Partial L (PLS) analysis. The results point to several motivation and prevention factors that explain variation in game playing and addiction, and also show that some preventio may not have merit. The remainder of this paper is organized as follows. The Theoretical Background section reviews the literature on online game addiction, some of its antecedents functionalist approach. The Hypotheses section presents the development of our research model. The Methods section describes the approaches taken for survey operationalization and data collection. The Analysis and Results section provides information on, and the results of the analyses performed. In the Discussion section summarize the results, outline implications for research and practice, and acknowledge limitations that point to future research directions. Concluding remarks are pr the Conclusion section. Theoretical background Online Game Addiction Game addiction is one type of Internet addiction that encapsulates the dependency on a specific family of IT artifacts – online games. To capture it, many research adopted the criteria of Internet addiction (Young, 1998b; Young, 2010a) or a broader set for diagnosing problematic non-substance related compulsions, i.e., behavior (Brown, 1997). These works largely build on a closely related disorder – problem pathological gambling, which is described in DSM-IV (Diagnostic and Statistics M Mental Disorders - Fourth Edition; American Psychiatric Association, 1994). Because no official cutoff points to classify a person as addicted exist (Block, 2008), m research on this topic so far has treated online game and other technology addictions as continuous concepts – ranging from low to high levels of addiction (Byun et a Ferraro et al., 2007; Hur, 2006), and hence assumed that all users may have a certain level of addiction, though low for most users (Turel et al., Forthcoming). We ado perspective in this study. Like other behavioral addictions, online game addiction can be manifested through six core symptoms described by Brown (1997). These include conflict (i.e., pla games meaningfully conflicts with other important tasks), withdrawal (i.e., negative emotions arise if one cannot play online games), relapse and reinstatement (i.e., i voluntarily reduce the time spent on online games), and behavioral salience (i.e., playing online games dominates one’s life and takes over other tasks). Other less sev symptoms are indicative of high engagement, and serve as a precondition for the more severe addiction symptoms (Charlton & Danforth, 2007; Charlton & Danforth, include tolerance (need to increase the time spent on online games), euphoria (a buzz of excitement from playing online games), and cognitive salience (frequently th online games). How is online game addiction formed? While not all reasons are clear, evidence so far suggests that it has to do with at least two families of factors: Internal psyc factors and socio-environmental factors (Shi, 2008), as well as potentially with neurobehavioral deficiencies (Ko et al., 2009a). For example, social-behaviorally inac personality traits, such as shyness, dependence, depression, aggression, low self-esteem, low self-control, and narcissism, may predispose some individuals to play ex and become addicted to online games (Ho & Lee, 2001; Kim et al., 2008; Yang & Tung, 2007; Dominick, 1984). Socio-environmental factors can include one’s work (e.g., working night shifts), socio economic status (Hur, 2006), and demographics (e.g., age and gender) (Ko et al., 2005). Game design is another external factor that influence online game addiction. Role playing games, action games, adventure games, strategy games, fighting games, and shooting games are more addictive than ot 2008) plausibly due to their interactive, collaborative and competitive nature (Barnett & Coulson, 2010; Liu & Peng, 2009) which serves social needs of individuals th missing in their real lives (Young, 2010b) While focusing on such variables is important and fruitful, the functionalist perspective which we take in this study suggests that these factors inform a set of funct motives, which in turn, influence behavioral outcomes (Clary et al., 1998; Mowen, 2000; Mowen et al., 2007). Thus, one’s functional motives are different from traits more proximal determinants of his or her program of behavior (Mowen & Sujan, 2005). Accordingly, in this study we focus on functional motives and inhibitors. The Functional Approach for Online Game Playing Most if not all activities humans perform are driven by purposeful striving toward social and personal goals (Snyder, 1993) such as to increase one’s social status, or become healthy. People’s actions hence cater to various functional needs (Clary et al., 1998). These needs often evolve around key themes, such as serving knowle (better understanding a concept or object), value expressive function (need to express one’s values), ego-defense needs (need to protect a person from threatening trut oneself), and utilitarian functions (need for obtaining rewards and avoiding punishment) (Katz, 1960). This perspective has been utilized and validated in numerous st (Cooper et al., 1998; De Cremer & Tyler, 2005; Rioux & Penner, 2001)
Given the broad range of needs that this perspective covers in the abovementioned themes, it is fair to ask what the more specific needs human behaviors serve are many elemental needs that are being served by human behaviors(food, sleep, etc )and other sometimes less prominent psychological and physiological needs(Brugh Dietch, 1978; Watson, 1996)that mostly fall into Maslow's hierarchy of needs(Maslow, 1943). But, each behavior serves different needs( Clary et al., 1998; Mowen, Mowen Sujan, 2005). For example, we may conduct research to gain utilitarian benefits(e.g, merit based salary increase), to increase knowledge, and to express b pic. Other activities, such as writing software code, may be motivated by other needs, say only utilitarian needs. Thus, it is imperative to focus on the specific functi by online game playing, which is the phenomenon of interest in this study, to understand people's decision to play online games everal studies have addressed this issue. It has been suggested that the motivation to play online games stems from three types of needs: sense of achievement, so visibility, and feeling of immersion(Wan& Chiou, 2006). Other studies further decompose these needs. The sense of achievement includes need to advance in game master the mechanics of game( to be an expert, and need to challenge others(Yee, 2006). All these factors relate to a broader need for sense of control that drives pe online games( Chou& Tsai, 2007). The social visibility need includes both social and emotional needs, and the need to develop ones social skills; and the immersion encapsulates a desire to become virtually a part of the experience itself in the game(Yee, 2006), 1.e, to experience the fascination of a temporary escape into another Castell Jenson, 2007) Note that the functionalist perspective assumes calculated decision making. It is reasonable to expect game players in general to apply rational decision making I umans want to maximize their subjective utility, and game players are not different Even in cases of addiction, where strong psychological dependency develops, ga by turel et al. (Forthcoming). It means that addicted individuals will sti best serves their functional needs. Their belief system may be distorted, but they will employ calculated considerations to make informed decisions regarding online playing Online Game Addiction Prevention and Harm Reduction Motivating game playing through the functional needs lens is one side of the coin. The other side in many problematic programs of behavior situations (e.g set of extemal inhibitors that operate to prevent and later reduce the harm of ones program of behavior(Benowitz, 2008: Hatsukami et al., 2004; Marlatt, 1996).We elements in our model as well. Note that the term prevention refers to a-priori acts (i.e, before a person engages in a harmful pattern of behavior and develops addict arm reduction refers to acts that take place throughout, and potentially after, the execution of the potentially problematic program of behavior. Because there are no medical or academic criteria for determining when the problematic program of behavior begins and when addiction develops(Block, 2008; Turel Serenko, 20 distinguish between prevention and harm reduction strategies, and treat them interchangeably. For er lucational efforts to teach adolescents about the risks of playing can take place before someone starts playing games, throughout the development of a game though little research has been conducted on Internet addiction and specifically online game addiction prevention, there has been substantial research on the pr ubstance abuse and problematic pathological behaviors, such as gambling(Dickson et al, 2002, Stockwell et al., 1996). We argue that it is reasonable to draw on the work because there are many similarities between technology addictions and alcohol or gambling addictions(Young, 1998b ). In fact, they even share similar neurobe ays(Ko et al., 2009a)and symptoms( Charlton Danforth, 2007). Building on typical gambling prevention and harm-reduction tactics, we argue that four key ay be relevant in the context of online game playing. These include education to mold and/or correct one's belief system(e.g, talking with a child regarding the risk excessive online game playing), behavioral interventions(e.g, keeping a person busy with other activities such as sports), resource restrictions(e.g, limiting ones or time), and social environment improvement(e.g, providing parental support)( Flay Petraitis, 1991; Hwang et al., 2004; Stice et al., 2006: Wiehe et al, 2005). We c detail in the hypotheses section The influences of motivating factors on online game addictio Tyler, 2005; Rioux Penner, 2001), and focusing on the needs that a program of behavio game playing fulfils (Yee, 2006), we argue that the stronger these needs, the higher ones game playing time will be. The needs represent a gap between ones current physical and psychological well being and social status, to his or her desired state. A calculated program of behavior is presumed to be able to help a person reach the lary et al., 1998). Yee(2006) provided the Player Motivation Factors Model, which describes three groups of game-playing functional needs(i.e, motivating factors) similar to the hievement, social visibil ity, and immersion motivation described by Wan and Chiou(2006): need for achievement, need for socialization, and need for immersion these needs includes several sub-dimensions, or more specific needs. For example, the need for achievement can be expressed through a need to advance in a game. E research(Lu& Wang, 2008, Seay Kraut, 2007: Yee, 2006)we propose that the needs for advancement and mastering the mechanics(sub dimensions of a broader lievement), need for relationship(a sub dimension of a broader need for social visibility), and need for escapism(a sub dimension of immersion motivation)are im otivation factors that may have positive effects not only on game playing, but also on the formation of technology addiction. These factors represent all the facets de Yee(2006)and by Wan and Chiou(2006). Their definitions are listed in Table I Table 1. Key Functional Needs
Given the broad range of needs that this perspective covers in the abovementioned themes, it is fair to ask what the more specific needs human behaviors serve are many elemental needs that are being served by human behaviors (food, sleep, etc.) and other sometimes less prominent psychological and physiological needs (Brugh Dietch, 1978; Watson, 1996) that mostly fall into Maslow’s hierarchy of needs (Maslow, 1943). But, each behavior serves different needs (Clary et al., 1998; Mowen, Mowen & Sujan, 2005). For example, we may conduct research to gain utilitarian benefits (e.g., merit based salary increase), to increase knowledge, and to express b topic. Other activities, such as writing software code, may be motivated by other needs, say only utilitarian needs. Thus, it is imperative to focus on the specific functi by online game playing, which is the phenomenon of interest in this study, to understand people’s decision to play online games. Several studies have addressed this issue. It has been suggested that the motivation to play online games stems from three types of needs: sense of achievement, so visibility, and feeling of immersion (Wan & Chiou, 2006). Other studies further decompose these needs. The sense of achievement includes need to advance in game master the mechanics of game (to be an expert), and need to challenge others (Yee, 2006). All these factors relate to a broader need for sense of control that drives peo online games (Chou & Tsai, 2007). The social visibility need includes both social and emotional needs, and the need to develop one’s social skills; and the immersion encapsulates a desire to become virtually a part of the experience itself in the game (Yee, 2006), i.e., to experience the fascination of a temporary escape into another w Castell & Jenson, 2007). Note that the functionalist perspective assumes calculated decision making. It is reasonable to expect game players in general to apply rational decision making pro humans want to maximize their subjective utility, and game players are not different. Even in cases of addiction, where strong psychological dependency develops, ga can follow the distorted-rationality processes described by Turel et al. (Forthcoming). It means that addicted individuals will still follow a program of behavior that in best serves their functional needs. Their belief system may be distorted, but they will employ calculated considerations to make informed decisions regarding online g playing. Online Game Addiction Prevention and Harm Reduction Motivating game playing through the functional needs lens is one side of the coin. The other side in many problematic programs of behavior situations (e.g., sm set of external inhibitors that operate to prevent and later reduce the harm of one’s program of behavior (Benowitz, 2008; Hatsukami et al., 2004; Marlatt, 1996). We i elements in our model as well. Note that the term prevention refers to a-priori acts (i.e., before a person engages in a harmful pattern of behavior and develops addicti harm reduction refers to acts that take place throughout, and potentially after, the execution of the potentially problematic program of behavior. Because there are no a medical or academic criteria for determining when the problematic program of behavior begins and when addiction develops (Block, 2008; Turel & Serenko, 2010), w distinguish between prevention and harm reduction strategies, and treat them interchangeably. For example educational efforts to teach adolescents about the risks of playing can take place before someone starts playing games, throughout the development of a game playing pattern, and after a person has presented strong addiction Although little research has been conducted on Internet addiction and specifically online game addiction prevention, there has been substantial research on the pr substance abuse and problematic pathological behaviors, such as gambling (Dickson et al., 2002; Stockwell et al., 1996). We argue that it is reasonable to draw on the work because there are many similarities between technology addictions and alcohol or gambling addictions (Young, 1998b). In fact, they even share similar neurobe pathways (Ko et al., 2009a) and symptoms (Charlton & Danforth, 2007). Building on typical gambling prevention and harm-reduction tactics, we argue that four key may be relevant in the context of online game playing. These include education to mold and/or correct one’s belief system (e.g., talking with a child regarding the risk excessive online game playing), behavioral interventions (e.g., keeping a person busy with other activities such as sports), resource restrictions (e.g., limiting one’s on time), and social environment improvement (e.g., providing parental support) (Flay & Petraitis, 1991; Hwang et al., 2004; Stice et al., 2006; Wiehe et al., 2005). We d in detail in the hypotheses section. hypotheses The influences of motivating factors on online game addiction Taking the functionalist perspective (Cooper et al., 1998; De Cremer & Tyler, 2005; Rioux & Penner, 2001), and focusing on the needs that a program of behavio game playing fulfils (Yee, 2006), we argue that the stronger these needs, the higher one’s game playing time will be. The needs represent a gap between one’s current physical and psychological well being and social status, to his or her desired state. A calculated program of behavior is presumed to be able to help a person reach the objectives and bridge the gaps (Clary et al., 1998). Yee (2006) provided the Player Motivation Factors Model, which describes three groups of game-playing functional needs (i.e., motivating factors) similar to the achievement, social visibility, and immersion motivation described by Wan and Chiou (2006): need for achievement, need for socialization, and need for immersion. E these needs includes several sub-dimensions, or more specific needs. For example, the need for achievement can be expressed through a need to advance in a game. B research (Lu & Wang, 2008; Seay & Kraut, 2007; Yee, 2006) we propose that the needs for advancement and mastering the mechanics (sub dimensions of a broader n achievement), need for relationship (a sub dimension of a broader need for social visibility), and need for escapism (a sub dimension of immersion motivation) are im motivation factors that may have positive effects not only on game playing, but also on the formation of technology addiction. These factors represent all the facets de Yee (2006) and by Wan and Chiou (2006) . Their definitions are listed in Table 1. Table 1. Key Functional Needs
Definition Advancement The desire to gain power, progress rapidly, and accumulate in-game symbols of wealth or status ing an interest in analyzing the underlying rules and system in order to character performan L Relationship he need to form long-term relationships with others Escapism Need to avoid thinking about real life problems through immersion in the People with strong needs for advancement and interest in the mechanics of the game are likely to spend more time playing online games. The same goes for thos strong need for forming relationships and escaping the reality through online games. These functional objectives are often unattainable without practice, repeated atte perseverance, and hence are expected to be positively related to game playing. Anecdotal evidence suggests that these needs are associated with continued game play Hla: Need for advancement increases the extent of online game playing HIb: Need for mastering a game s mechanics increases the extent of online game playing HId: Need for escapism increases the extent of online game playing We argue that the functional needs that motivate game playing can also cater to the formation of high levels of online game playing addiction. When ones abovement become constant, frequent, strong and automatic, he or she can develop a compulsive pattern of game-seeking and use behaviors which is a manifestation of addiction Berridge, 2003). The competitive aspects of games cater to ones need for achievement and mastering the game mechanics, which in turn, help the formation of add et al., 2009). The social and interactive aspect of games caters to the social and escapism needs of game players, and can also inform the formation of online game ad (Klimmt et al., 2009 Specifically, the need for competition and mastering the mechanics of the game may cause users to be more engaged in the games, which is a manifestation of flow st et al., 1993). Flow states often drive computer users to further and repeatedly use information systems (Koufaris, 2002), and can lead to online game addiction( Chou Ting, 2003). Moreover, online game playing can become a substitute for real life social interaction( riffiths, 2009). When one's needs for virtual social interactions and escaping real-life interactions are high, he or she can also engage in excessive game playing whi diction Lo et al., 2005). Indeed, it has been shown that at least the need for escapism, and need for advancement predict online game addiction (Yee, 2006). There H2a: Need for advancement increases the level of online game addiction Ime 's mechanics increases the level of online game addiction H2c: Need for relationship increases the level of online game addiction. H2d: Need for escapism increases the level of online game addiction Addiction often develops through excessive and repetitive use, which re-wires people's brains and makes them develop a somewhat unrealistic set of positive expecta the IT artifact(Turel et al., Forthcoming). In substance abuse settings, people's brains become hypersensitive to cues from the addictive substance and overemphasize salience of the thrill, until it forms a pathological state of"wanting"(Robinson Berridge, 1993; Robinson Berridge, 2001). Given the neurobehavioral similarities substance addictions and online game addiction(Ko et al., 2009a), it is reasonable to expect that the same holds in the context of online games. Taken together, we ex hore a user plays online games, the stronger the psychological dependency he or she develops, and the stronger his or her addiction symptoms(e. g, conflict with oth will be. Hence H3: The extent of online game playing increases the level of online game addiction. The influences of prevention and harm reduction factors on online addiction Based on a review of the addiction prevention and harm reduction literature(Dickson et al., 2002; Echeburua& de corral, 2010: Eissenberg, 2004: Flay petra Hatsukami et al., 2004; Hwang et al., 2004; Marlatt, 1996, Stice et al., 2006, van Hamel et al., 2007, wiehe et al., 2005), we identified six prevention and harm reduc that can reduce online game playing, ease some of the symptoms of online game addiction(e.g, conflict with other activities), and ultimately alleviate one's level of a These factors and their definitions are listed in Table 2
Construct Definition 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. Relationship The need to form long-term relationships with others. Escapism Need to avoid thinking about real life problems through immersion in the game. People with strong needs for advancement and interest in the mechanics of the game are likely to spend more time playing online games. The same goes for thos strong need for forming relationships and escaping the reality through online games. These functional objectives are often unattainable without practice, repeated atte perseverance; and hence are expected to be positively related to game playing. Anecdotal evidence suggests that these needs are associated with continued game play 2010; Joe & Chiu, 2009; Lin, 2010), and that many online game players engage in this activity to escape reality (Hussain & Griffiths, 2009), socialize (Blais et al., 20 a need to excel, at least in the virtual world, as a potential alternative to deficiencies in some aspects of the real world (Yee, 2006; Young, 2010a; Young, 2010b). Hen H1a: Need for advancement increases the extent of online game playing. H1b: Need for mastering a game’s mechanics increases the extent of online game playing. H1c: Need for relationship increases the extent of online game playing. H1d: Need for escapism increases the extent of online game playing. We argue that the functional needs that motivate game playing can also cater to the formation of high levels of online game playing addiction. When one’s abovement become constant, frequent, strong and automatic, he or she can develop a compulsive pattern of game-seeking and use behaviors which is a manifestation of addiction & Berridge, 2003). The competitive aspects of games cater to ones need for achievement and mastering the game mechanics, which in turn, help the formation of add et al., 2009). The social and interactive aspect of games caters to the social and escapism needs of game players, and can also inform the formation of online game ad (Klimmt et al., 2009). Specifically, the need for competition and mastering the mechanics of the game may cause users to be more engaged in the games, which is a manifestation of flow st et al., 1993). Flow states often drive computer users to further and repeatedly use information systems (Koufaris, 2002), and can lead to online game addiction (Chou & Ting, 2003). Moreover, online game playing can become a substitute for real life social interaction ( Griffiths, 2009). When one’s needs for virtual social interactions and escaping real-life interactions are high, he or she can also engage in excessive game playing whi addiction (Lo et al., 2005). Indeed, it has been shown that at least the need for escapism, and need for advancement predict online game addiction (Yee, 2006). There H2a: Need for advancement increases the level of online game addiction. H2b: Need for mastering a game’s mechanics increases the level of online game addiction. H2c: Need for relationship increases the level of online game addiction. H2d: Need for escapism increases the level of online game addiction. Addiction often develops through excessive and repetitive use, which re-wires people’s brains and makes them develop a somewhat unrealistic set of positive expecta the IT artifact (Turel et al., Forthcoming). In substance abuse settings, people’s brains become hypersensitive to cues from the addictive substance and overemphasize salience of the thrill, until it forms a pathological state of “wanting” (Robinson & Berridge, 1993; Robinson & Berridge, 2001). Given the neurobehavioral similaritie substance addictions and online game addiction (Ko et al., 2009a), it is reasonable to expect that the same holds in the context of online games. Taken together, we ex more a user plays online games, the stronger the psychological dependency he or she develops, and the stronger his or her addiction symptoms (e.g., conflict with oth will be. Hence: H3: The extent of online game playing increases the level of online game addiction. The influences of prevention and harm reduction factors on online game addiction Based on a review of the addiction prevention and harm reduction literature (Dickson et al., 2002; Echeburua & de Corral, 2010; Eissenberg, 2004; Flay & Petra Hatsukami et al., 2004; Hwang et al., 2004; Marlatt, 1996; Stice et al., 2006; van Hamel et al., 2007; Wiehe et al., 2005), we identified six prevention and harm reduc that can reduce online game playing, ease some of the symptoms of online game addiction (e.g., conflict with other activities), and ultimately alleviate one’s level of a These factors and their definitions are listed in Table 2
Table 2. Prevention and Harm Reduction Factors Construct Definition Dissuasion The extent to which an individual per ers efforts to prevent playing online game by means of exhortation, ng, browbeating Rationalization/ The degree to which an individual is trained to understand the Education ociated with a problematic program of behavior ention to and track his or her whereabouts, activities, and outcomes Resource h he degree to which a player perceives that he or she is being restricted by Restriction the constraining of game playing resources such time, money, equipment Perceived Cost The ex which a player perceives the financial cost of playing online We believe that general extracurricular activities(e.g, sports)may make adolescents focus less on game playing and thus reduce the extent of game playing and one's level of online game addiction. Even though motivating factors such as a strong need for advancement may push individuals towards excessive use and potentia addiction, the constant occupation with other activities(e.g. school, sports, and social events)will plausibly reduce ones online game playing time, shift his or her att from online games, and ultimately alleviate his or her addiction levels. Indeed, it has been reported that attention switching can stop or reduce participation in online g which is an attitude-discrepant behavior for addicts and excessive users(Wan& Chiou, 2006).Hence H4a: Attention switching reduces online game playing H4b: Attention switching reduces the level of addiction to online games To dissuade means to advise against something by means of exhortation or argument. The term implies coaxing rather than browbeating, using reason instead of coer ommon practice exercised by external forces(regulators, parents, teachers, friends) for the prevention of undesirable behaviors such as smoking, alcohol abuse, and (e.g, De Brouwere et al., 1998; Eiser Vanderpligt, 1986). Studies have demonstrated that dissuasion can make a difference, at least in the case of alcohol abuse(Ba In the same vein, we believe that dissuasion is a common tactic employed by parents and social circles, which may be effective in reducing online game use and ultim addiction. Turel et al(Forthcoming ) has shown that technology addicts have a distorted rationality, and dissuasion is one potential means for shaping and fixing or stem. Therefore H5a: Dissuasion reduces online game playing H5b: Dissuasion reduces the level of addiction to online games ognitions, rationalization is aimed mostly at building good cognitive foundations, and can be self-managed. That is, it does not have to come from an external source parents and teachers. Rather, a person can educate herself regarding the risks of a certain program of behavior (e. g, by reading newspaper articles or watching TV ner Some online game players may not naturally realize the potential negative consequences of online game-playing, and may have distorted beliefs and attitudes tow games(Yang& Tung, 2007). Exposure to appropriate education and guidance may encourage rational thinking so as to reduce the chance of excessive use and potent preventing addiction(Faggiano et al., 2008). This mechanism operates, like dissuasion, against the potential distorted rationality of online game players and addicts Forthcoming). Thus H6a: Rationalization/education reduces online game playing. H6b: Rationalicationeducation reduces the level of addiction to online games Parental influence is an important risk factor or protective factor of youth problem behaviors( Chen et al., 2008), and especially addictions( Loke Wong, 201 Wilson, 2008). Specifically, lack of parental monitoring is correlated with risky behavior of young children, leading to accidental injury, antisocial and delinquent beh abstance use in adolescence(Dishion et al., 2003; Kiesner et al., 2009). Greater parental monitoring or parents'knowledge of their childrens daily activities and wh are associated with less deviant behavior. I Monitoring in the case of online games is an effective strategy preventing users from engaging in seemingly unsupervised acts of excessive or inappropriate use Bruner, 2006: Young, 1998a; Young, 2010b). Even the location of one's computer(how observable it is in one's house)can affect online game playing and addiction
Table 2. Prevention and Harm Reduction Factors Construct Definition Attention Switching The extent to which other meaningful activities are offered to distract addict’s attention from engaging in the problematic behavior. Dissuasion The extent to which an individual perceives others’ efforts to prevent playing online game by means of exhortation, argument, coaxing, browbeating or coercion. Rationalization/ Education The degree to which an individual is trained to understand the issues associated with a problematic program of behavior. Parental Monitoring The extent to which an individual perceives his or her parents or guardians to pay attention to and track his or her whereabouts, activities, and outcomes. Resource Restriction The degree to which a player perceives that he or she is being restricted by the constraining of game playing resources such time, money, equipment, regulation and guidance. Perceived Cost The extent to which a player perceives the financial cost of playing online games to be high. We believe that general extracurricular activities (e.g., sports) may make adolescents focus less on game playing and thus reduce the extent of game playing and one’s level of online game addiction. Even though motivating factors such as a strong need for advancement may push individuals towards excessive use and potentia addiction, the constant occupation with other activities (e.g. school, sports, and social events) will plausibly reduce one’s online game playing time, shift his or her att from online games, and ultimately alleviate his or her addiction levels. Indeed, it has been reported that attention switching can stop or reduce participation in online g which is an attitude-discrepant behavior for addicts and excessive users (Wan & Chiou, 2006). Hence: H4a: Attention switching reduces online game playing. H4b: Attention switching reduces the level of addiction to online games. To dissuade means to advise against something by means of exhortation or argument. The term implies coaxing rather than browbeating, using reason instead of coerc common practice exercised by external forces (regulators, parents, teachers, friends) for the prevention of undesirable behaviors such as smoking, alcohol abuse, and (e.g., De Brouwere et al., 1998; Eiser & Vanderpligt, 1986). Studies have demonstrated that dissuasion can make a difference, at least in the case of alcohol abuse (Ba In the same vein, we believe that dissuasion is a common tactic employed by parents and social circles, which may be effective in reducing online game use and ultim addiction. Turel et al (Forthcoming) has shown that technology addicts have a distorted rationality, and dissuasion is one potential means for shaping and fixing one’s system. Therefore: H5a: Dissuasion reduces online game playing. H5b: Dissuasion reduces the level of addiction to online games. Rationalization/education refers to knowledge-focused or educational efforts aimed at one’s cognitions. As opposed to dissuasion which is an active effort agains cognitions, rationalization is aimed mostly at building good cognitive foundations, and can be self-managed. That is, it does not have to come from an external source parents and teachers. Rather, a person can educate herself regarding the risks of a certain program of behavior (e.g., by reading newspaper articles or watching TV new addiction stories). Some online game players may not naturally realize the potential negative consequences of online game-playing, and may have distorted beliefs and attitudes tow games (Yang & Tung, 2007). Exposure to appropriate education and guidance may encourage rational thinking so as to reduce the chance of excessive use and potent preventing addiction (Faggiano et al., 2008). This mechanism operates, like dissuasion, against the potential distorted rationality of online game players and addicts (T Forthcoming). Thus: H6a: Rationalization/education reduces online game playing. H6b: Rationalization/education reduces the level of addiction to online games. Parental influence is an important risk factor or protective factor of youth problem behaviors (Chen et al., 2008), and especially addictions (Loke & Wong, 2010 Wilson, 2008). Specifically, lack of parental monitoring is correlated with risky behavior of young children, leading to accidental injury, antisocial and delinquent beh substance use in adolescence (Dishion et al., 2003; Kiesner et al., 2009). Greater parental monitoring or parents’ knowledge of their children’s daily activities and wh are associated with less deviant behavior, risky behavior, and substance abuse among adolescents (Chuang et al., 2005). Monitoring in the case of online games is an effective strategy preventing users from engaging in seemingly unsupervised acts of excessive or inappropriate use Bruner, 2006; Young, 1998a; Young, 2010b). Even the location of one’s computer (how observable it is in one’s house) can affect online game playing and addiction
007). It is therefore reasonable to expect that stronger parental monitoring(through asking probing questions, placing the computer in an observable spot, tracking a whereabouts, showing interest in school performance etc. )will reduce ones playing time, and prevent higher levels of online gaming addiction. Hen H7a: Parental monitoring reduces online game playing H7b: Parental monitoring reduces the level of addiction to online games Individual's perceptions regarding the availability of resources(e. g, technical support)influence their usage of information systems(Taylor Todd, 1995). The applies to online games(Blakely et al., 2010). In this context resources, such as play time, funding, and equipment, can be constrained by parents/ guardians and teacl ase of adolescents, or by employers, life circumstances, and family members in the case of more mature individuals. Such constraints can reduce one's access to onli for example, by restricting playing time. Free time is associated with more frequent playing of online games(Wan& Chiou, 2006), and hence, by restricting it one co person's use time and levels of addiction. In many cases, other resources are also needed (e.g, money, network access, fast computers)(Jeong& Kim, 2007); and by ogic, if these are constrained, one s play time and level of addiction should be diminished. Hence: H8a: Resource restriction reduces online game playing. H8b: Resource restriction reduces the level of addiction to online games Prevention and harm reduction strategies focusing on increasing substance prices can diminish substance problems(Mosher, 1999; Ponicki et al, 2007). The san nould apply to potentially problematic use of information systems, and specifically online games. The perceived cost of using an IT artifact or service is an importan determinant of IS use because it influences its perceived value(Turel et al., 2007). If this cost is increased, the perceived benefits seem lower compared to the costs, a subsequently use is reduced (Turel et al., 2010). Thus, we expect that the perceived cost of online games should reduce one's willingness to play games, and his or a Note that in the Chinese online gaming market, gamers can incur any combination of four types of costs. One type of games is free of charge at lower levels, but dvance in the game they need point-cards which they can buy at stores. Another family of games is also free of charge, but gamers may need to purchase virtual prop o do better in these games. Regardless, a third type of cost is very common among adolescents in China. Many of them do not have computers at home, and even if ti lay want to socialize and/or avoid parental monitoring. Thus, the use of Intemet cafes for gaming purposes is very popular. The fees for Internet cafes are therefore a they may face. Lastly, even if a person has a computer, he or she may need to purchase special gamming gears and pay for high speed Internet connection. Thus, the ases there is some cost involved in online gaming, which is the basis for perceived cost assessments. H9a: Perceived cost reduces online game playing. H9b: Perceived cost reduces the level of addiction to online games The hypotheses translate into the nomological network presented in Figure I Figure 1 Research Model Methods A paper-based survey was used for data collection. The survey (see Appendix A[lD was developed through a simplified process of survey translation and adapta et al., 2000, Geisinger, 1994)which involved item generation, synthesis, forward and backward translation, and review. Whenever possible, valid existing scales were adjusting them to the context of online game playing, le scales were developed based on concepts described in the literature Content validity was established
2007). It is therefore reasonable to expect that stronger parental monitoring (through asking probing questions, placing the computer in an observable spot, tracking a whereabouts, showing interest in school performance etc.) will reduce one’s playing time, and prevent higher levels of online gaming addiction. Hence: H7a: Parental monitoring reduces online game playing. H7b: Parental monitoring reduces the level of addiction to online games. Individual's perceptions regarding the availability of resources (e.g., technical support) influence their usage of information systems (Taylor & Todd, 1995). The applies to online games (Blakely et al., 2010). In this context resources, such as play time, funding, and equipment, can be constrained by parents/ guardians and teach case of adolescents, or by employers, life circumstances, and family members in the case of more mature individuals. Such constraints can reduce one’s access to onli for example, by restricting playing time. Free time is associated with more frequent playing of online games (Wan & Chiou, 2006), and hence, by restricting it one co person’s use time and levels of addiction. In many cases, other resources are also needed (e.g., money, network access, fast computers) (Jeong & Kim, 2007); and by t logic, if these are constrained, one’s play time and level of addiction should be diminished. Hence: H8a: Resource restriction reduces online game playing. H8b: Resource restriction reduces the level of addiction to online games. Prevention and harm reduction strategies focusing on increasing substance prices can diminish substance problems (Mosher, 1999; Ponicki et al., 2007). The sam should apply to potentially problematic use of information systems, and specifically online games. The perceived cost of using an IT artifact or service is an importan determinant of IS use because it influences its perceived value (Turel et al., 2007). If this cost is increased, the perceived benefits seem lower compared to the costs, a subsequently use is reduced (Turel et al., 2010). Thus, we expect that the perceived cost of online games should reduce one’s willingness to play games, and his or ad levels. Note that in the Chinese online gaming market, gamers can incur any combination of four types of costs. One type of games is free of charge at lower levels, but advance in the game they need point-cards which they can buy at stores. Another family of games is also free of charge, but gamers may need to purchase virtual prop to do better in these games. Regardless, a third type of cost is very common among adolescents in China. Many of them do not have computers at home, and even if th may want to socialize and/or avoid parental monitoring. Thus, the use of Internet cafes for gaming purposes is very popular. The fees for Internet cafes are therefore a they may face. Lastly, even if a person has a computer, he or she may need to purchase special gamming gears and pay for high speed Internet connection. Thus, the cases there is some cost involved in online gaming, which is the basis for perceived cost assessments. H9a: Perceived cost reduces online game playing. H9b: Perceived cost reduces the level of addiction to online games. The hypotheses translate into the nomological network presented in Figure 1. Figure 1 Research Model Methods A paper-based survey was used for data collection. The survey (see Appendix A[1]) was developed through a simplified process of survey translation and adapta et al., 2000; Geisinger, 1994) which involved item generation, synthesis, forward and backward translation, and review. Whenever possible, valid existing scales were adjusting them to the context of online game playing, and some scales were developed based on concepts described in the literature. Content validity was established
literature review, interviews and panel discussions(described below). Published papers on game addiction, and intervention and prevention of various addictions w to prepare a broad understanding of the concepts, and a list of candidate items for new scale Several steps were then taken in order to make the survey suitable to adolescent game players. First, a semi-structured open-ended face to face interview was cor Two authors interviewed a convenience sample of three university students and two high school students who were highly engaged in online game playing. Each inte minutes. The interviews elicited gamers game playing history, patterns, motivations, and inhibitors, and opinions on and suggestions regarding our measure vo authors interviewed in a similar process a convenience sample of three high-school teachers and three parents, to better understand the prevention and harm reduc they have employed. Third, a focus group of six students( three university students and three high school students )was formed to validate the insights steps, and the measures that were formulated and refined throughout this process. This discussion was moderated by one of the authors and lasted about an hour. Last professors who are familiar with this line of research were invited to evaluate the questionnaire. Minor modifications were applied based on their feedback. The instrument based on all of these inputs was first drafted in English, and then translated into Chinese independently and cross checked by two of the authors v proficient in both languages. After receiving comments from game players for modification and clarification, the finally agreed Chinese version was then translated b English independently by these two authors to check for inaccuracies. Several adjustments were applied to the original version until the authors all agreed that the iter accurately reflect the intention of the measurement. All of the motivation constructs as well as Rationalization/ Education, Dissuasion and Cost prevention were opera reflective latent variables. The Attention Switching, Parental Monitoring and Resource Restriction constructs were operationalized as formative composite variable questionnaire also captured respondents gender and age. Below we provide details on the different measurement scales Online Game addiction: We follow Charlton and Danforth(2005)'s criteria, according to which technology addiction is captured by the magnitude of key symp havioral salience, conflict, withdrawal, and relapse/reinstatement. The measure has been reliable( Charlton, 2002: Charlton Danforth, 2007, Charlton danfort hence we use it. Four motivation factors based on the functional needs online game playing addresses were measured by a total 17 items with reference to prior research. These f Need for- Advancement, Mechanics, Relationship, and Escapism. Reflective scales that capture these concepts were adapted from Yee(2006) There were no well established measures for some of the prevention and harm reduction factors. We hence developed these scales utilizing extant frameworks fo development(Sweeney Soutar, 2001)as described above. Synthesizing inputs from academics, university and high school students with insight from the literature following measurement instruments were developed. All used a seven point Likert-type scale ranging from"completely disagree" to"completely agree Dissuasion: It was conceptualized as a reflective scale and measured with four items from Babor(1994), as reinforced by expert-matter interviewees and focus g Rationalization/ Education: We developed a four-item reflective scale based on the definition from Eisen et al. (2002; Eisen et al., 2003)which were then adju feedback from the expert-matter interviewees and focus groups Perceived Cost: We adopt the five- item scale from Wu and Wang(2005) Attention switching: This construct was conceptualized as a second-order factor consisting of two first order constructs: Inner Attention switching and External switching, each component factor is important, but not individually sufficient, for reflecting the latent construct. If addicted players participate in meaningful activitie higher priority over game-playing, their addiction-driven behavior will be restrained (Wan Chiou, 2006). These activities can come from internal sources (e.g,inte babies)or extemal forces(e.g, attending family events). Thus, the attention is shifted away from the addiction-driven behavior using two mechanisms: intemal and Expert-matter interviewees and focus groups assisted in adjusting this concept and items Parental monitoring: Based on the definition and our interviews with expert online gamers, parental monitoring can be active or passive. Passive monitoring ir getting some information, but not necessarily"spying" on one s children. In contrast, active monitoring includes more hands-on approaches to monitoring and ensurin boundaries set-up are not infringed. Following this conceptualization we operationalized parental monitoring as a second-order composite which includes two first or onstructs, PM Passive and PM Active. We adopt 6 scale items from Dishion and McMahon(1998)as modified based on comments from our expert game players. Resource Restriction: We developed four items that captured strictions users observe, based on key resource concems discussed in the literature: guidance quipment, and network connection availability(e.g, Jeong& Kim, 2007, Wan Chiou, 2006). The items were refined using inputs from the panels of experts. Thes focused on tangible resources, and not on time resources because it was assumed that time constraints are captured by internal and external attention switching activit time ts of this behavior, including the longest online playing time and the percentage online game playing occupies one Based on inputs from the panel these items best reflect the extent to which they are engaged in online game playing Pilot Test a pilot test was conducted to assess the scales. a total of 163 records were collected from adolescent online game players. They were recruited from a middle sc large city in China. Their ages ranged from 13 to 15. The data were used to run an array of reliability and factor analysis tests. A number of modifications were made nstrument based on feed back from respondents and reliability tests. The four subcomponents of motivation factors, advancement, mechanics, relationship, and escap obtained Cronbach's alphas of0.95, 0.71, 0.87 and 0.70 respectively. The addiction factor yielded a Cronbach's alpha of 0.83
literature review, interviews and panel discussions (described below). Published papers on game addiction, and intervention and prevention of various addictions were to prepare a broad understanding of the concepts, and a list of candidate items for new scales. Several steps were then taken in order to make the survey suitable to adolescent game players. First, a semi-structured open-ended face to face interview was con Two authors interviewed a convenience sample of three university students and two high school students who were highly engaged in online game playing. Each inte about 30 minutes. The interviews elicited gamers’ game playing history, patterns, motivations, and inhibitors, and opinions on and suggestions regarding our measure two authors interviewed in a similar process a convenience sample of three high-school teachers and three parents, to better understand the prevention and harm reduc they have employed. Third, a focus group of six students (three university students and three high school students) was formed to validate the insights gathered in the steps, and the measures that were formulated and refined throughout this process. This discussion was moderated by one of the authors and lasted about an hour. Last professors who are familiar with this line of research were invited to evaluate the questionnaire. Minor modifications were applied based on their feedback. The instrument based on all of these inputs was first drafted in English, and then translated into Chinese independently and cross checked by two of the authors w proficient in both languages. After receiving comments from game players for modification and clarification, the finally agreed Chinese version was then translated b English independently by these two authors to check for inaccuracies. Several adjustments were applied to the original version until the authors all agreed that the item accurately reflect the intention of the measurement. All of the motivation constructs as well as Rationalization/ Education, Dissuasion and Cost prevention were opera as reflective latent variables. The Attention Switching, Parental Monitoring and Resource Restriction constructs were operationalized as formative composite variable questionnaire also captured respondents’ gender and age. Below we provide details on the different measurement scales. Online Game addiction: We follow Charlton and Danforth (2005)’s criteria, according to which technology addiction is captured by the magnitude of key symp behavioral salience, conflict, withdrawal, and relapse/reinstatement. The measure has been reliable (Charlton, 2002 ; Charlton & Danforth, 2007; Charlton & Danfort hence we use it. Four motivation factors based on the functional needs online game playing addresses were measured by a total 17 items with reference to prior research. These f Need for - Advancement, Mechanics, Relationship, and Escapism. Reflective scales that capture these concepts were adapted from Yee (2006). There were no well established measures for some of the prevention and harm reduction factors. We hence developed these scales utilizing extant frameworks fo development (Sweeney & Soutar, 2001) as described above. Synthesizing inputs from academics, university and high school students with insight from the literature, following measurement instruments were developed. All used a seven point Likert-type scale ranging from “completely disagree” to “completely agree”. Dissuasion: It was conceptualized as a reflective scale and measured with four items from Babor (1994), as reinforced by expert-matter interviewees and focus g Rationalization/ Education: We developed a four-item reflective scale based on the definition from Eisen et al. (2002; Eisen et al., 2003) which were then adjus feedback from the expert-matter interviewees and focus groups. Perceived Cost: We adopt the five- item scale from Wu and Wang (2005). Attention switching: This construct was conceptualized as a second-order factor consisting of two first order constructs: Inner Attention switching and External switching; each component factor is important, but not individually sufficient, for reflecting the latent construct. If addicted players participate in meaningful activitie higher priority over game-playing, their addiction-driven behavior will be restrained (Wan & Chiou, 2006). These activities can come from internal sources (e.g., inte hobbies) or external forces (e.g., attending family events). Thus, the attention is shifted away from the addiction-driven behavior using two mechanisms: internal and Expert-matter interviewees and focus groups assisted in adjusting this concept and items. Parental monitoring: Based on the definition and our interviews with expert online gamers, parental monitoring can be active or passive. Passive monitoring in getting some information, but not necessarily “spying” on one’s children. In contrast, active monitoring includes more hands-on approaches to monitoring and ensurin boundaries set-up are not infringed. Following this conceptualization we operationalized parental monitoring as a second-order composite which includes two first or constructs, PM_Passive and PM_Active. We adopt 6 scale items from Dishion and McMahon (1998) as modified based on comments from our expert game players. Resource Restriction: We developed four items that captured the restrictions users observe, based on key resource concerns discussed in the literature: guidance equipment, and network connection availability (e.g., Jeong & Kim, 2007; Wan & Chiou, 2006). The items were refined using inputs from the panels of experts. Thes focused on tangible resources, and not on time resources because it was assumed that time constraints are captured by internal and external attention switching activit consume users’ time. Game playing: We focused on relevant aspects of this behavior, including the longest online playing time and the percentage online game playing occupies one’ Based on inputs from the panel these items best reflect the extent to which they are engaged in online game playing. Pilot Test A pilot test was conducted to assess the scales. A total of 163 records were collected from adolescent online game players. They were recruited from a middle sc large city in China. Their ages ranged from 13 to 15. The data were used to run an array of reliability and factor analysis tests. A number of modifications were made instrument based on feedback from respondents and reliability tests. The four subcomponents of motivation factors, advancement, mechanics, relationship, and escap obtained Cronbach’s alphas of 0.95, 0.71, 0.87 and 0.70 respectively. The addiction factor yielded a Cronbach’s alpha of 0.83
Factor analysis of the data partially supported the previous findings regarding the distinct motivations for playing online games. While some factors were not cor distinct within one construct(with somewhat high cross-loadings), we decided to refine and retain them. The logic was that first, our pilot test sample was somewhat cond, our scales were translated to Chinese and hence items may have slightly shifted from their original meaning, even though a back-and-forth translation procedr employed. As a result, the items adopted were preserved and the translation was rechecked. We modified some ambiguous expressions. The final English version be found in Appendix A Data Collection Participants were selected from middle schools in a large Chinese city. This was done for two reasons. First, from a convenience and practical stand point-it wa to encompass many online game players, which where potentially reachable by one of the researchers. Second, the penetration of Internet in this city is the second hig China( CNNiC, 2007), and presumably, adolescents in this city are susceptible and exposed to online games more than others in the nation. Two data collection approaches were taken: in school-and on street-collection surveys. First, 600 copies of paper-based que middle schools in this city over a period of two months. The surveys were administrated by a research assistant with the supervision and help of the teachers of the rel asses. Survey completion was voluntary, and was encouraged with small monetary incentives(less than S1). Additionally, 200 copies of the survey were handed out ocations on the streets. Potential adolescents were approached in person at locations such as McDonalds and Intemet Cafes. Potential participants were asked if they online games before given a copy of the survey. In total, 800 surveys were distributed and 682(85%)were returned, out of which 623(78%)were valid. A Multivari lied to the data showed that the source of data( school vs. street) had no significant omnibus effect(Pillais Trace =0. 16, p<0. 13), implyin were no significant differences between the datasets. Thus, subsequent analyses were performed on the whole dataset. Participants' ages ranged from 12 to 18 with an of about 15 years. The modal age was 14 with 36% of our sample representing this age group. The sample was slightly male dominant(56%). ANALYSIS AND RESULTS Since our research model contains both reflective and formative components, PLS (Parcial Least Square)was chosen for data analysis. PLS can easily support odels with no identification issues, as demonstrated in past MIS research( Chin Gopal, 1995; Turel et al., 2007). The hypothesis testing was conducted ma version 2.0(Ringle et al., 2005) following the two-step approach for model estimation(Anderson Gerbing, 1988). The measurement model We first examined factor loadings. Almost all were above 0.7, but the loading of Addiction item I was. 52. In addition, the average variance extracted (Ave)of ded threshold of 0.5. Hence, we deleted the problematic item. As a result, the Ave of Addiction with 6 it was acceptable(59). The same procedure was applied to the perceived cost construct. The loading of Cost5 was low(31), and the Ave wa able(57). Neve deleted the item. As a result, all loadings were over 0.7, and the AvE was 72. Consequently, reliability coefficients were above. 70 and all AVE scores were over .50 Appendix B). This indicated that the measurement scales were reliable and that the latent variables account for more than 50 percent of the variance in the items. As s Appendix B, the loadings are in an acceptable range and the t-values indicate that they are significant at least at the 01 level. The results in Appendix B further sugge discriminant validity, because the square root of the AVE is greater than all of the related inter-construct correlations( Chin, 1998). In order to further assess validity, a oadings table(Appendix C)was constructed. It can be seen that each item loading is much higher on its assigned construct than on the other constructs, supporting ac convergent and discriminant validity To further evaluate the formative composite variables(Attention Switching, Parental Monitoring and Resource Restrictions), we followed the guidelines provide Cenfetelli and Bassellier(2009). With the first guideline, we checked multicolinearity among the indicators with Variance Inflation Factor (VIF)scores. The highest V alculated was 1. 461 (Table 3)and was thus below the recommended upper border( Diamantopoulos Siguaw, 2006) Table 3. vif, factor weights, p-value and factor loadings for the formative measurement Factor Weights Attention switching 1.333 Parental Monitor PM Passive .246 73 RESI 043 RES2 0.05 131 108 15 <000
Factor analysis of the data partially supported the previous findings regarding the distinct motivations for playing online games. While some factors were not com distinct within one construct (with somewhat high cross-loadings), we decided to refine and retain them. The logic was that first, our pilot test sample was somewhat second, our scales were translated to Chinese and hence items may have slightly shifted from their original meaning, even though a back-and-forth translation procedu employed. As a result, the items adopted were preserved and the translation was rechecked. We modified some ambiguous expressions. The final English version of t can be found in Appendix A. Data Collection Participants were selected from middle schools in a large Chinese city. This was done for two reasons. First, from a convenience and practical stand point - it wa to encompass many online game players; which where potentially reachable by one of the researchers. Second, the penetration of Internet in this city is the second hig China (CNNIC, 2007), and presumably, adolescents in this city are susceptible and exposed to online games more than others in the nation. Two data collection approaches were taken: in school- and on street-collection surveys. First, 600 copies of paper-based questionnaires were randomly distribute middle schools in this city over a period of two months. The surveys were administrated by a research assistant with the supervision and help of the teachers of the rel classes. Survey completion was voluntary, and was encouraged with small monetary incentives (less than $1). Additionally, 200 copies of the survey were handed out locations on the streets. Potential adolescents were approached in person at locations such as McDonalds and Internet Cafes. Potential participants were asked if they online games before given a copy of the survey. In total, 800 surveys were distributed and 682 (85%) were returned, out of which 623 (78%) were valid. A Multivari of Variance procedure applied to the data showed that the source of data (school vs. street) had no significant omnibus effect (Pillai’s Trace = 0.16, p < 0.13), implyin were no significant differences between the datasets. Thus, subsequent analyses were performed on the whole dataset. Participants' ages ranged from 12 to 18 with an of about 15 years. The modal age was 14 with 36% of our sample representing this age group. The sample was slightly male dominant (56%). ANALYSIS AND RESULTS Since our research model contains both reflective and formative components, PLS (Parcial Least Square) was chosen for data analysis. PLS can easily support models with no identification issues, as demonstrated in past MIS research (Chin & Gopal, 1995; Turel et al., 2007). The hypothesis testing was conducted using Sma version 2.0 (Ringle et al., 2005) following the two-step approach for model estimation (Anderson & Gerbing, 1988). The Measurement Model We first examined factor loadings. Almost all were above 0.7, but the loading of Addiction item 1 was .52. In addition, the average variance extracted (AVE) of A with all items was .49 which is slightly below the recommended threshold of 0.5. Hence, we deleted the problematic item. As a result, the AVE of Addiction with 6 it was acceptable (.59). The same procedure was applied to the perceived cost construct. The loading of COST5 was low (.31), and the AVE was acceptable (.57). Neve deleted the item. As a result, all loadings were over 0.7, and the AVE was .72. Consequently, reliability coefficients were above .70 and all AVE scores were over .50 Appendix B). This indicated that the measurement scales were reliable and that the latent variables account for more than 50 percent of the variance in the items. As s Appendix B, the loadings are in an acceptable range and the t-values indicate that they are significant at least at the .01 level. The results in Appendix B further sugge discriminant validity, because the square root of the AVE is greater than all of the related inter-construct correlations (Chin, 1998). In order to further assess validity, a loadings table (Appendix C) was constructed. It can be seen that each item loading is much higher on its assigned construct than on the other constructs, supporting ad convergent and discriminant validity. To further evaluate the formative composite variables (Attention Switching, Parental Monitoring and Resource Restrictions), we followed the guidelines provide Cenfetelli and Bassellier (2009). With the first guideline, we checked multicolinearity among the indicators with Variance Inflation Factor (VIF) scores. The highest V calculated was 1.461 (Table 3) and was thus below the recommended upper border (Diamantopoulos & Siguaw, 2006). Table 3. VIF, factor weights, p-value and factor loadings for the formative measurement VIF Factor Weights p-value Attention switching InnerAS 1.333 .651 < 0.001 ExternalAS 1.333 .500 < 0.001 Parental Monitoring PM_Passive 1.246 .604 < 0.001 PM_Active 1.246 .573 < 0.001 Resource Restrictions RES1 1.253 .043 < 0.5 RES2 1.461 .104 < 0.05 RES3 1.343 .131 < 0.01 RES4 1.085 .153 < 0.001
The second guideline assumes that a large number of indicators will yield many non-significant weights. Due to the fact that our measurement model consists of formative indicators, this test may be irrelevant. Guideline three assumes the co-occurrence of negative and positive indicator weights, which could lead to a misinteny the results In our case, only positive weights were observed (Table 3), and the suppressor effect was thus not tested The fourth guideline discusses absolute versus relative indicator contribution. Indicators with a non-significant or low weight can still have an important absolute ontribution. All related indicators must be independently assessed from other indicators to prevent misinterpretation of formative indicator results. As Table 3 shows ght of RESI is not significant. Other formative construct indicators are significant at the 0.01 level. It suggests that they contribute significantly to the form Attention Switching and Parental Monitoring composites. Overall while the statistical evidence regarding the validity of the formative conceptualization is not always conclusive, when synthesized with the theoretical ba nd opinions of subject-matter experts, it points to potential plausibility. We hence proceed with treating these concepts as formative composites As with all self-reported data, there is a potential for common method biases(Podsakoff et al., 2003). We performed statistical analyses to assess the potential sev problem in our data. First, Harmons one-factor test was performed. In a Principal Component Analysis with no rotation 12 components emerged; and the first compo explained only 10.5%of the variance. Second, following Podsakoff et al. (2003), we included in the PLS model a latent common method factor whose indicators incl items. We then calculated the variance explained in our endogenous construct, online game addiction, with- and without- the latent common methods variance factor odel. The first was 0.418, and the second was 0.408. The difference is very small. Both tests point to the conclusion that the method is unlikely to be a major source varation Hypothesis Testing Table 4 presents the estimates obtained from PLS analysis( Bootsrapping with 200 re-samples)3]. An R- value of 408 indicates that the model explains a substa amount of variance in addiction. The results provide some support for the hypothesized partial - mediation role of game playing and the direct effects of functional nee otivating factors, and prevention and harm reduction factors on the formation of online game addiction. Among the motivation factors, need to master the mechanic was positively associated with game playing but not with game addiction. As expected, needs for relationship and escapism were positively associated with game addiction. Need for advancement had no significant influence on addiction Table 4. Test of Hypotheses t-value Need for Rela eed for Escapism → Game Playing ime Playing Rationalization Education- Game Playing 3 cost→ 9 Perceived Co→Am **P<.001;种P<01;P<0 The data suggest that attention switching has a significant negative impact on game playing and addiction. It implies that alternative activities could distract adol attention from online games and thus reduce their risk of high levels of addiction. Furthermore, rationalization/ education and cost had significant influences on game no direct effect on online game addiction. Thus, they can alleviate the level of addiction through the reduction of ones online play time Dissuasion was expected to reduce game playing and addiction. Nevertheless, contrary to the expectation, it was positively associated with game playing and on diction, It seems that dissuasion does not serve as a prevention factor but turns out to be more of a remedy which is plausibly exercised only after high levels of gan d online game addiction are observed. We also found that Resource Restrictions was positively associated with addiction and had no significant impact on game pl expected that when fewer resources are available, people will be less likely to play online games and develop online game addiction. But as the data demonstrates, ar
The second guideline assumes that a large number of indicators will yield many non-significant weights. Due to the fact that our measurement model consists of formative indicators, this test may be irrelevant. Guideline three assumes the co-occurrence of negative and positive indicator weights, which could lead to a misinterp the results. In our case, only positive weights were observed (Table 3), and the suppressor effect was thus not tested. The fourth guideline discusses absolute versus relative indicator contribution. Indicators with a non-significant or low weight can still have an important absolute contribution. All related indicators must be independently assessed from other indicators to prevent misinterpretation of formative indicator results. As Table 3 shows factor weight of RES1 is not significant. Other formative construct indicators are significant at the 0.01 level. It suggests that they contribute significantly to the form Attention Switching and Parental Monitoring composites. Overall while the statistical evidence regarding the validity of the formative conceptualization is not always conclusive, when synthesized with the theoretical ba and opinions of subject-matter experts, it points to potential plausibility. We hence proceed with treating these concepts as formative composites. Common Method Bias As with all self-reported data, there is a potential for common method biases (Podsakoff et al., 2003). We performed statistical analyses to assess the potential sev problem in our data. First, Harmon’s one-factor test was performed. In a Principal Component Analysis with no rotation 12 components emerged; and the first compo explained only 10.5% of the variance. Second, following Podsakoff et al. (2003), we included in the PLS model a latent common method factor whose indicators incl items. We then calculated the variance explained in our endogenous construct, online game addiction, with- and without- the latent common methods variance factor model. The first was 0.418, and the second was 0.408. The difference is very small. Both tests point to the conclusion that the method is unlikely to be a major source variation. Hypothesis Testing Table 4 presents the estimates obtained from PLS analysis (Bootsrapping with 200 re-samples)[3]. An R2 value of .408 indicates that the model explains a substa amount of variance in addiction. The results provide some support for the hypothesized partial -mediation role of game playing and the direct effects of functional nee motivating factors, and prevention and harm reduction factors on the formation of online game addiction. Among the motivation factors, need to master the mechanic was positively associated with game playing but not with game addiction. As expected, needs for relationship and escapism were positively associated with game play addiction. Need for advancement had no significant influence on addiction. Table 4. Test of Hypotheses Order Number Hypothesis Path coefficients t-value H1a Need for Relationship → Game Playing 0.11 2.76** H1b Need for Escapism → Game Playing 0.09 2.03* H1c Need for Mastering the Mechanics → Game Playing 0.21 3.53*** H1d Need for Advancement → Game Playing 0.01 0.25 H2a Need for Relationship → Addiction 0.09 2.28** H2b Need for Escapism → Addiction 0.14 3.55*** H2c Need for Mastering the Mechanics → Addiction 0.06 0.94 H2d Need for Advancement → Addiction 0.07 1.10 H3 Game Playing → Addiction 0.24 6.13*** H4a Attention Switching → Game Playing -0.11 2.41** H4b Attention Switching → Addiction -0.19 4.80 *** H5a Dissuasion→ Game Playing 0.08 2.31 * H5b Dissuasion → Addiction 0.22 6.34*** H6a Rationalization/ Education → Game Playing -0.17 4.34*** H6b Rationalization/ Education → Addiction -0.02 0.50 H7a Parental Monitoring → Game Playing -0.02 0.67 H7b Parental Monitoring → Addiction -0.10 2.66** H8a Resource Restriction → Game Playing 0.05 0.80 H8b Resource Restriction → Addiction 0.13 3.11** H9a Perceived Cost → Game Playing -0.13 3.53 *** H9b Perceived Cost → Addiction -0.02 0.24 *** p < .001; ** p < .01; * p < .05 The data suggest that attention switching has a significant negative impact on game playing and addiction. It implies that alternative activities could distract adol attention from online games and thus reduce their risk of high levels of addiction. Furthermore, rationalization/ education and cost had significant influences on game no direct effect on online game addiction. Thus, they can alleviate the level of addiction through the reduction of one’s online play time. Dissuasion was expected to reduce game playing and addiction. Nevertheless, contrary to the expectation, it was positively associated with game playing and on addiction. It seems that dissuasion does not serve as a prevention factor but turns out to be more of a remedy which is plausibly exercised only after high levels of gam and online game addiction are observed. We also found that Resource Restrictions was positively associated with addiction and had no significant impact on game pl expected that when fewer resources are available, people will be less likely to play online games and develop online game addiction. But as the data demonstrates, an
planation is in order. It is possible that if a person has high levels of online game addiction, he or she may need more resources and may thus feel stronger resource This proposition should be tested in future research. The data also demonstrate that parental monitoring has no significant effect on game playing while it does have a negative influence on online game addiction. It parental monitoring is an effective prevention method. Finally, as expected, excessive game playing helped in the formation of high levels of game addiction DISCUSSION This paper bridges a gap in the technology addiction literature by examining how motivating factors and prevention/ harm reduction factors shape adolescents'g behaviors and levels of online gaming addiction. While the majority of studies on game playing and addiction examined mostly the functional needs, and other motiv or drivers(such as demographics, personality, etc. ) Charlton Danforth, 2010, Hur, 2006; Yee, 2006)we included prevention and harm reduction factors to present omplete picture. As a result, we managed to explain over 40% of the variation in online game addiction In terms of motivating factors, the findings suggest that(1)needs for mastering game mechanics, relationship and escapism increase online game playing, (2)ne lationship and escapism increase online game addiction. In terms of prevention and harm reduction factors, the findings suggest that(D) education/rationalization reduce game playing, and (2)attention switching and parental monitoring can reduce online game addiction. In contrast to our expectations, nd recourse restrictions were positively associated with online game playing and addiction. It is therefore possible that such prevention/ harm reduction strategies are or pr arm reduction and functional needs/motivation factors on the formation of online game addiction. Based on these findings, we discuss below several important insig directions for future research Theoretical Contributions This study proposed a somewhat holistic game addiction model to explore the impact of motivation/ functional needs and prevention/harm reduction factors on a online game addiction. This broad perspective is a key contribution because it can potentially explain more variance in the phenomenon of interest, compared with st ocus only on one set of predictors( motivating or preventing). For example, focusing only on personality traits, 20% of the variation in addiction was captured(Chan Danforth, 2010), and focusing on a broad range of motivating functional need factors, as well as on demographics, a model explained 34% of the variation in online g addiction(Yee, 2006). We managed to explain over 40% of the variation in this concept, presumably because a broader set of predictors was taken into account, whic nore balanced view of the forces that operate on an individual and determine in part whether he or she develop high levels of online game addiction Given the added value of amalgamating prevention and harm reduction factors with the conventiona egarding motivating factors, our second contribution dentifying, conceptualizing, and measuring several important prevention and harm reduction factors. L e review, focus groups, interviews with expert online g lot study with a sample of 163 adolescents, and a full study with over 600 respondents have resulted in valid and reliable measures. These, and additional harm redu environmental factors, can be used in future research on technology addiction, to increase the contextual richness and explanatory power. It further shows that there is borrowing concepts from the vast literatures on addiction, substance abuse, and problematic behaviors, which has advanced and evolved over decades( Carter& Tiffa Conklin& Tiffany, 2002), to the study of technology-related addictions. It hence adds to previous research that points to similarities between substance and technolog (Ko et al., 2009b)and substance and behavioral addictions(Helmuth, 2001). This study also contributes to research by supporting and suplementing previous findings. First, the motivation side of the results generally agrees with Yee's but with some small differences. Both studies support the notion that motivating factors stemming from functional needs push people to play online games, and poten develop high levels of online game addiction. Second, this study also support past research that links technology addiction to loneliness, mood disorders or social defi Block, 2008; Ferraro et al., 2007: Hur, 2006; LaRose et al., 2003; Turel Serenko, 2010) in our study one's need for relationship and escapism were strong and sig predictors of game playing and addiction, and these are plausibly associated with some forms of social deficiency. Third, this study shows that at least some of the ac prevention and harm reduction strategies that are common in other contexts such as problem drinking, gambling, smoking and drug use may be also relevant in the co technology addictions(Dickson et al., 2002; Dishion& McMahon, 1998, Flay Petraitis, 1991; Hwang et al, 2004; Kiesner et al., 2009). Fourth, our study supplem body of research on the antecedents of technology addiction by focusing on proximal predictors. It goes beyond simplistic views that emphasize personality anteceden 2008)and looks at more proximal motivators and inhibitors, as well as behaviors( game playing) While the observed significant relationships are important, so are the observed non-significant effects. Thus, another contribution of our study is in poi propositions that should be studied in future research. The positive associations between addiction and two prevention factors: dissuasion and resource restriction, po possibility that these measures are taken by parents only after high levels of problematic online game playing and addiction are observed, i.e., as remedies or interven not as prevention factors. According to this view, the higher a childs online game playing time and level of addiction, the stronger the dissuasion and restrictions he c proposition by plausibly using designs that distinguish between prevention and intervention factors. Another possible explanatic psychological reactance may affect the effectiveness of dissuasion. Psychological reactance is a motivational force aroused to restore the loss of, or the threatened perceived behavioral freedoms, and ostensibly results in compensatory or corrective behaviors known as reactance effects (Johnson& Buboltz, 2000; Woller et al., 2( parents exercise too much dissuasion and resource restrictions on adolescent game players, psychological reactance will be evoked and online game players may perc
explanation is in order. It is possible that if a person has high levels of online game addiction, he or she may need more resources and may thus feel stronger resource This proposition should be tested in future research. The data also demonstrate that parental monitoring has no significant effect on game playing while it does have a negative influence on online game addiction. It parental monitoring is an effective prevention method. Finally, as expected, excessive game playing helped in the formation of high levels of game addiction. DISCUSSION This paper bridges a gap in the technology addiction literature by examining how motivating factors and prevention/ harm reduction factors shape adolescents’ g behaviors and levels of online gaming addiction. While the majority of studies on game playing and addiction examined mostly the functional needs, and other motiv or drivers (such as demographics, personality, etc.) ( Charlton & Danforth, 2010; Hur, 2006; Yee, 2006) we included prevention and harm reduction factors to present complete picture. As a result, we managed to explain over 40% of the variation in online game addiction. In terms of motivating factors, the findings suggest that (1) needs for mastering game mechanics, relationship and escapism increase online game playing, (2) ne relationship and escapism increase online game addiction. In terms of prevention and harm reduction factors, the findings suggest that (1) attention switching, perceiv education/rationalization reduce game playing, and (2) attention switching and parental monitoring can reduce online game addiction. In contrast to our expectations, and recourse restrictions were positively associated with online game playing and addiction. It is therefore possible that such prevention/ harm reduction strategies are employed only after high levels of game playing and addiction are observed. Finally, the findings suggest that online game playing partially mediates the effects of pr harm reduction and functional needs/motivation factors on the formation of online game addiction. Based on these findings, we discuss below several important insig directions for future research. Theoretical Contributions This study proposed a somewhat holistic game addiction model to explore the impact of motivation/ functional needs and prevention/harm reduction factors on a online game addiction. This broad perspective is a key contribution because it can potentially explain more variance in the phenomenon of interest, compared with stu focus only on one set of predictors (motivating or preventing). For example, focusing only on personality traits, 20% of the variation in addiction was captured (Char Danforth, 2010), and focusing on a broad range of motivating functional need factors, as well as on demographics, a model explained 34% of the variation in online g addiction (Yee, 2006). We managed to explain over 40% of the variation in this concept, presumably because a broader set of predictors was taken into account, whic more balanced view of the forces that operate on an individual and determine in part whether he or she develop high levels of online game addiction. Given the added value of amalgamating prevention and harm reduction factors with the conventional view regarding motivating factors, our second contribution identifying, conceptualizing, and measuring several important prevention and harm reduction factors. Literature review, focus groups, interviews with expert online g pilot study with a sample of 163 adolescents, and a full study with over 600 respondents have resulted in valid and reliable measures. These, and additional harm redu environmental factors, can be used in future research on technology addiction, to increase the contextual richness and explanatory power. It further shows that there is borrowing concepts from the vast literatures on addiction, substance abuse, and problematic behaviors, which has advanced and evolved over decades (Carter & Tiffa Conklin & Tiffany, 2002), to the study of technology-related addictions. It hence adds to previous research that points to similarities between substance and technolog (Ko et al., 2009b) and substance and behavioral addictions (Helmuth, 2001). This study also contributes to research by supporting and suplementing previous findings. First, the motivation side of the results generally agrees with Yee’s (20 but with some small differences. Both studies support the notion that motivating factors stemming from functional needs push people to play online games, and poten develop high levels of online game addiction. Second, this study also support past research that links technology addiction to loneliness, mood disorders or social defi (Block, 2008; Ferraro et al., 2007; Hur, 2006; LaRose et al., 2003; Turel & Serenko, 2010); in our study one’s need for relationship and escapism were strong and sign predictors of game playing and addiction, and these are plausibly associated with some forms of social deficiency. Third, this study shows that at least some of the add prevention and harm reduction strategies that are common in other contexts such as problem drinking, gambling, smoking and drug use may be also relevant in the co technology addictions (Dickson et al., 2002; Dishion & McMahon, 1998; Flay & Petraitis, 1991; Hwang et al., 2004; Kiesner et al., 2009). Fourth, our study supplem body of research on the antecedents of technology addiction by focusing on proximal predictors. It goes beyond simplistic views that emphasize personality anteceden 2008) and looks at more proximal motivators and inhibitors, as well as behaviors (game playing). While the observed significant relationships are important, so are the observed non-significant effects. Thus, another contribution of our study is in pointing to po propositions that should be studied in future research. The positive associations between addiction and two prevention factors: dissuasion and resource restriction, po possibility that these measures are taken by parents only after high levels of problematic online game playing and addiction are observed, i.e., as remedies or interven not as prevention factors. According to this view, the higher a child’s online game playing time and level of addiction, the stronger the dissuasion and restrictions he o face. Future research should test this proposition by plausibly using designs that distinguish between prevention and intervention factors. Another possible explanatio psychological reactance may affect the effectiveness of dissuasion. Psychological reactance is a motivational force aroused to restore the loss of, or the threatened los perceived behavioral freedoms, and ostensibly results in compensatory or corrective behaviors known as reactance effects (Johnson & Buboltz, 2000; Woller et al., 20 parents exercise too much dissuasion and resource restrictions on adolescent game players, psychological reactance will be evoked and online game players may perc