Ph.D. Thesis – Sepandar Sepehr; McMaster University – DeGroote School of Business. Understanding the Role of Competition in Video Gameplay Satisfaction Understanding the Role of Competition in Video Gameplay Satisfaction By Sepandar Sepehr, B.Sc., M.Sc. A Thesis 
Submitted to the School of Graduate Studies
 In Partial Fulfillment of the Requirements
for the Degree
 Doctorate of Philosophy, Business Administration McMaster University © Copyright by Sepandar Sepehr, December 2014 DOCTORATE OF PHILOSOPHY McMaster University Business Administration (Information Systems) - 2014 Hamilton, Ontario TITLE: Understanding the Role of Competition in Video Gameplay Satisfaction AUTHOR: Sepandar Sepehr, B.Sc., M.Sc. (McMaster University) SUPERVISOR: Prof. Milena Head PAGES: xiv, 179 ABSTRACT Considerable amount of time is spent playing video games in today’s society. There are various elements in video games that make them entertaining and satisfying, which can be leveraged to provide engaging and satisfying experiences in educational and workplace contexts. One of the key elements in many video games is competition. Based on Self-Determination Theory (SDT) and the Theory of Flow, this dissertation explores the process through which competition makes a video game satisfying. A structural model is proposed that examines the impacts of Situational Competitiveness (manipulated via different modes of competition) and Dispositional Competitiveness (as a personality trait) on gameplay experience. The proposed model is validated through an experimental design study with 104 university students. The results show that the perception of video game competitiveness has a strong direct and indirect (mediated through Challenge and Arousal) effect on Flow experience and Satisfaction. While an individual’s personality impacts the perception of a game’s competitiveness, this perception can also be influenced by the mode of competition (playing against a computer, stranger or friend). Additionally, Social Presence is found to play a role by mediating the relationship between the mode of competition and Situational Competitiveness. ACKNOWLEDGEMENTS I would like to thank several individuals and organizations for their generous support throughout the last few years. First, I would like to express my sincerest gratitude to my extraordinary supervisor, Dr. Milena Head. Without exaggeration, completion of this work would not be possible if it were not for her constant support and guidance. Since my first meeting with Dr. Head, before applying to the Ph.D. program, I noticed that I would be very fortunate to work under such a kindhearted person’s supervision. And I was not wrong. Receiving her continuous encouragement and knowing that I could always trust her guidance were the main reasons for me to continue through this challenging process. Dr. Head’s intellectual contributions and emotional support made this dissertation possible. Thanks also go to my supervisory committee members, Dr. Khaled Hassanein and Dr. Brian Detlor. They both made significant contributions to this dissertation by guiding me throughout various steps of the process, in particular on refining my proposed model, designing the experiments of this research, and preparing the final document. It was a great honour for me to have Dr. Hassanein and Dr. Detlor on my supervisory committee. I should hereby thank Dr. Norm Archer for his generous donations that enabled me to receive the Norm Archer Endowed Prize, and for his valuable feedback on my dissertation as the internal examiner and as professor in one of my Ph.D. courses. In addition, a thank you to my dear friends Amir Tavasoli and Farzad Nikfar, my siblings, in particular Babak and his family, my aunt Shohreh, and our new family member, Don Ferrito, with whom I never felt alone during the past few years. I would also like to thank the DeGroote Community at large, in particular Deb R. Baldry, Dr. Catherine Connelly, Iris Kehler, Sandra Stephens, and Carolyn Colwell for their tremendous support. I was very fortunate to receive the financial assistance of Social Sciences and Humanities Research Council of Canada and the Ontario Graduate Scholarship Program in the last two years, for which I am truly grateful. Last, but certainly not least, I cannot thank my wife (and PhD partner in crime) enough for her endless patience and belief in me, which motivated me to overcome the challenges I faced during the past few years and keep my sanity. Thank you. تقدیم به آنها که رفتند TABLE OF CONTENTS Chapter 1 – Introduction 1 1.1 Motivation and Research Objectives 2 1.2 Research Outline 4 Chapter 2 – Literature Review 6 2.1 Video Games 6 2.1.1 Serious Games 8 2.1.2 Beyond Serious Games: Gamification 15 2.2 Extant Literature Review on Video Game Experience 17 2.2.1 Studies with the Focus on Competition and Game Elements 17 Chapter 3 – Theoretical Framework and Development of Research Model 24 3.1 Self-Determination Theory 25 3.1.1 SDT and Competition 26 3.2 Flow Theory 34 3.2.1 Meta-analysis of Flow 37 3.3 Combining SDT and Flow 41 3.4 Proposed Research model and Hypotheses 43 Chapter 4 – Research Methodology 54 4.1.1 Data Collection Procedure 54 4.1.2 Pilot Study 55 4.1.3 Data Collection Procedure 56 4.1.4 Instrument and Model Validation 58 4.2 Manipulation Check 61 Chapter 5 – Data Analysis and Results 63 5.1.1 Pilot Study 63 5.1.2 Full Study 64 5.1.3 Participant Demographics and Control Variables 66 5.1.4 Research Model Validation 69 5.1.5 The Structural Model Evaluation 74 5.1.6 Effect Sizes 77 5.1.7 Post-hoc Analyses 78 Chapter 6 – Discussion and Conclusion 91 6.1 Research Findings 91 6.1.1 Research Objective 1: The Effect of Situational Competitiveness on Flow 91 6.1.2 Research Objective 2: The Role of Competition Mode on the Level of Situational Competitiveness and Social Presence 95 6.1.3 Research Objective 3: The Effect of Personality Traits on Situational Competitiveness 96 6.2 Contributions 98 6.2.1 Contributions to Theory 98 6.2.2 Contributions to Practice 100 6.3 Research Limitations 101 6.4 Future Research Agenda 103 6.5 Conclusion 106 References 108 Operationalization Mode of Flow/CA 126 Methodology 128 Sample 129 Analysis 131 Results 134 Moderator: Operationalization Method 134 Selection of Constructs Used in Meta-analysis 137 Unidimensional (Code 1) 137 Multidimensional (Code 2 & 3) 138 Miscellaneous (code 4) 140 Data and Calculations 141 Conclusion 147 References for the Meta-Analysis 147 Between Team Comparison 156 Experiment Guideline 162 Experiment document – Articles To read 1 166 Experiment document – Articles To read 2 169 LIST OF FIGURES, TABLES, AND FORMULAS Figures Figure 1 – Proportions of games within each primary educational content category (Ratan and Ritterfeld 2009) 13 Figure 2 - Situating Gamification (from Deterding et al. 2011) 16 Figure 3 - Finding the Gap in Literature on Video Gameplay 21 Figure 4 – Finding the Gap in Literature on Video Gameplay (continued) 22 Figure 5 – Overarching Theoretical Framework 25 Figure 6 - The self-determination continuum (source: Ryan & Deci, 2000) 32 Figure 7 - Flow Models: Original Flow Model on the left and Four-State Flow Model on the right 36 Figure 8 - Eight State Flow Model 37 Figure 9 – Proposed Research Model of the Study 44 Figure 10 - Screenshot of TypeRacer Game 57 Figure 11 – Result of PLS Analysis of the Proposed Structural Equation Model 74 Figure 12 – Research Model 128 Figure 13 – Variables with Increasing Means 155 Figure 14 – Variables with no Observable Mean Change 156 Figure 15. Variation of Team's Average Score for the Variables with the Largest Differences from T1 to T3 159 Figure 16. Average Values for Each Team at T1, T2, and T3 Based on Their Rankings 161 Tables Table 1 – Age Distribution of Participants 66 Table 2 – Frequencies of Demographic Variables 67 Table 3 – Participants' Previous Experience Statistics 68 Table 4 – Construct Reliability of the Constructs in the Model 70 Table 5 – Cross Loadings Matrix for al the First-Order Constructs (significant at 0.001) 71 Table 6 – Discriminant Validity Assessment Table using Construct Correlation Matrix and Square Root of AVE 72 Table 7 – Summary of Findings for Supporting the Proposed Hypotheses 75 Table 8 – Direct Relationships' Effect Sizes ( = 0.05) 77 Table 9 – Indirect Relationships' Effect Sizes ( = 0.05) 78 Table 10 – Unmeasured Latent Marker Construct Test Results for Assessing Common Method Bias 80 Table 11 – PLS Result for Non-hypothesized Paths in the Saturated Model 83 Table 12 – Impact of Control Variables on Model's Latent Constructs 84 Table 13 – Effect Sizes of Control Variables on Endogenous Constructs 85 Table 14 – Total number of studies for each code and each relationship 135 Table 15 – The results of the meta-analysis after controlling for the moderator (operationalization mode) 136 Table 16 – Studies Qualified for the Meta-analysis 141 Table 17 – The extracted data for the studies that were used in the final meta-analysis 143 Table 18 – Calculations for the studies that had correlation between flow and intention 145 Table 19 – Calculations for the studies that had correlation between flow and attitude 146 Table 20. Teams Ranking at each Round 156 Table 21. Largest Differences Between Teams 158 Formulas Equation 1 – Goodness of Fitness Formula (Wetzels et al., 2009) 76 Equation 2 – Formula (Cohen 1998) 77 LIST OF ACRONYMS AND SYMBOLS ANOVA Analysis of Variance AVE Average Variance Extracted CMV Common Method Variance EFA Exploratory Factor Analysis ERP Enterprise Resource Planning IS Information Systems IT Information Technology GoF Goodness of Fit MIS Management Information Systems MMO(RPG) Massively Multiplayer Online Role-playing Game PLS Partial Least Squares SDT Self-Determination Theory TAM Technology Acceptance Model VIF Variance Inflation Factor GLOSSARY OF TERMS Autonomy The propensity of people to have control over their actions and see themselves as the locus of causality Competence Tendency of individuals to feel capable performing a task (sense of self-efficacy). e-Learning The use of IT systems in education Edugame Educational Game, aka Game-Based Learning (GBL) Edutainment Educational Entertainment, entertaining material that has the purpose of educating users ERPsim Game like simulation environment that is used for teaching ERP concepts on SAP ERP Flow State of optimal holistic experience, capturing deep involvement in an activity Game-Based Simulation, Game-Based Simulator, Simulation Game Simulations that use video game environments for creating a more engaging environment, such as virtual universities Game-Based Model The games that use a simplified version of real-world models Game-Based Visualization, Game Visualization The category of systems that use visual technologies and techniques from games to create new forms of visualization and more accessible versions of visualization. These systems allow for higher levels of interactive visualization where user actions might affect resulting visualization Game-Based Interface, Game Like Interface, Game-Based UI A non-standard but highly symbiotic application interface design produced based on the design of a popular game Game-Based Production, Game-Based Authoring Use of tools or game/game engines created for videogaming to output some other piece of media. Game-Based Messaging/Advertising/Marketing, Advergames A type of Serious Games that are used to transmit a message, advertise a product/service, or market a product Game-Based Training Adding gameplay to enhance motivation to train, or effectiveness of content transfer, behaviour change, or specific goal of training Game-Based Education/Learning Using gameplay to enhance motivation to learn, engage education, or to enhance effectiveness of content transfer or other specific learning outcome Gamification Using game elements in a non-game system for achieving the engaging power of games SAP ERP The biggest ERP solution in the market, and the main solution of SAP AG. Serious Games Any game that is designed for a purpose that is not pure entertainment. Ph.D. Thesis – Sepandar Sepehr; McMaster University – DeGroote School of Business. xi Introduction Since the 1980s, video games have expanded rapidly and established a massive and growing industry (Cucuel, 2011; McGonigal, 2011). Some authors believe that the video games industry is one of the fastest growing in this era (Arnseth, 2006). Video games are pervasive in today’s society with reports showing that 67% of Americans play video games (Entertainment Software Association, 2010). This phenomenon is not restricted to children and teenagers. Statistics show that the average age of video gameplayers is above 30 years old and these adults are expected to play video games for the rest of their lives (Entertainment Software Association, 2010; Williams, Yee, & Caplan, 2008). As Millennials[footnoteRef:1] –the demographic cohort with birth years from the early 1980s to the early 2000s – enter the workforce, their mindsets and behaviours in regards to their working environment have become important areas of study. Dr. Jean M. Twenge names this generation, “Generation Me,” due to its individualistic attitude and lack of interest in social problems (Twenge, 2006). Millennials, when compared to their predecessors, appreciate extrinsic values more (e.g., money, image, fame) and intrinsic values less (e.g., self-acceptance, affiliation, community), particularly when compared to Generation X[footnoteRef:2] (Twenge, Campbell, & Freeman, 2012). It is essential for higher education institutions and employers to understand the differences and mindsets of young students/employees (Twenge, 2006). A significant characteristic of Millennials is the fact that they learn by doing and traditional learning/working environments are boring for them (Twenge, 2006). For this generation, video games are an inseparable communication and learning medium (Edery & Mollick, 2009). [1: Also known as Generation Y, Generation N, or the Net Generation] [2: Generation X is referred to the demographic cohort with birth years from the early 1960s to the early 1980s.] As Arnseth (2006) explains, “one striking feature of gameplay that seems to be particularly relevant for education is the fact that children and adolescents seem to invest a considerable amount of time and effort in accomplishing tasks that are often very difficult and time consuming.” Williams et al. (2008) found that in their sample of 7,000 massively multiplayer online (MMO) gameplayers, users spend on average of close to 30 hours a week playing video games, which is comparable to the amount of time a full-time employee spends at his or her job. Qualitative and quantitative research has shown that time loss among video gameplayers is independent of gender or age (Wood, Griffiths, & Parke, 2007). As a consequence of the recent surge of interest in gameplay, schools and workplaces face new challenges to adapt themselves to the gamer generation with its new behaviours and culture (Beck & Wade, 2004). From a learning perspective, instructors are challenged in finding ways to keep students engaged, attentive, involved and motivated during distinct learning processes at diverse educational levels (Feiertag & Berge, 2008). These challenges arise due to the fact that Millennials may find traditional school and work environments boring compared to the world they immerse in through playing video games (Collins & Halverson, 2009). Gee (2003) believes that “schools, workplaces, and families can use games and game technologies to enhance learning” (p. 1). Motivation and Research Objectives Some scholars have compared the learning performance of individuals in competitive versus cooperative situations. They tend to agree that cooperative tasks are more effective than competitive tasks, in particular for problem solving (Qin, Johnson, & Johnson, 1995). However, it should be noted that competition is not the opposite of cooperation (Udvari & Schneider, 2000), but it is an omnipresent phenomenon in human life, which can motivate students to engage deeply in the learning process. An ideal learning atmosphere should have an appropriate level of competition alongside a cooperative environment (Tauer & Harackiewicz, 2004). Competition, “the desire to challenge and compete with others” (Yee, 2006, p. 773), is an element that is shared among most existing games. Competition has been shown to be a major motivational factor for playing online video games (Yee, 2006). Reeves and Read (2009) have identified 10 main elements that every great game entails, and among them is “competition under rules that are explicit and enforced” (p. 80), meaning that for competition to be valued by gameplayers, it should be based on predefined rules that players could perceive and follow to see the result of their actions. Otherwise, if the results of a competitive game were random, players would not perceive the consequence of their actions and would not have any measure to improve their skills, which would discourage people from continuing to play. A game’s scoring system, which is a simple reward system, enables gamers and learners to compare their performance to their own previous scores and/or to compare it with their peers when the game is in a group, which creates competition (Kelle, Sigurðarson, Westera, & Specht, 2010). In other words, “good learning in games is a capitalist-driven Darwinian process of selection of the fittest” (Gee, 2003, p. 1). This can also be seen in the perspectives of frequent gamers who believe that “competition is the law of nature” (Beck & Wade, 2006, p. 81) more than non-frequent gamers. Recently, various disciplines—in particular operations management and information systems—have employed and studied video games for different purposes, including education (van der Zee, Holkenborg, & Robinson, 2012). For example, some scholars have studied the motivational factors behind playing video games (H. Chang & Wang, 2008; Wu, Wang, & Tsai, 2009), and some have used simulation games for learning (Lewis and Maylor 2007, Léger 2006). However, a review of extant literature shows that previous research studies have not rigorously examined the sense of competitiveness as a motivational factor. In particular, what engenders this sense of competitiveness in a gaming environment, and what is the outcome of this perception? Previous studies on video games have mainly focused on either the game design aspects or the psychosocial antecedents of Flow in video gameplay. Flow is a state of optimal experience where one is completely absorbed and immersed in an activity. Flow is an important phenomenon for studying and designing games due to its strength in explaining the state of deep engagement of gameplayers. Based on the literature review discussed in the next chapter, it can be concluded that there is a gap in existing research in understanding the relationship between competition and Flow of video gameplayers. In sum, this research seeks to examine the concept of competition in video games in order to understand how competition can be used to engage and satisfy students, the psychological state that is greatly desirable for learning[footnoteRef:3]. Specifically, the objectives of this research are: [3: Learning outcome per se is not the focus of this study. Instead, the focus was on measuring engagement, which can be seen as an intermediately state for enhanced learning.] To investigate how situational competitiveness of video games impacts antecedents to flow and satisfaction; To study how different modes of competition affect players’ perceptions of competitiveness and social presence in video games; and To explore the effect of the personality traits of gameplayers on their perception of competitiveness of video games. Research Outline The next chapters of this dissertation are organized as follow: Chapter 2 provides a more detailed literature review on games and their various modes. Chapter 3 includes the theoretical framework and research model development. The foundational theories, Flow Theory and Self-Determination Theory, which are used to support the proposed research model of Competitive Video Gameplay, are explained in detail. This leads to the development of a research model and support for its hypothesized relationships Chapter 4 outlines the research methodology that is utilized for testing the proposed research model of Competitive Video Gameplay. Chapter 5 describes the results of the analysis for both studies. The proposed model of Competitive Video Gameplay is validated and post-hoc analyses are presented to provide further insights into the results. Lastly, the discussion, conclusion, and limitations of this research are provided in Chapter 6. Contributions of this research to theory and practice as well as suggestions for future research are also presented. Literature Review This chapter first examines various types of video games and their impacts on today’s society. Next, an overarching review of extant research on video game experiences is provided in order to illustrate the gap in literature, which this research aims to address. Video Games Having outlined the significance of video games in the lifestyle of Millennials and its potential impact for educators and employers, it is necessary to frame the general categories of video games that are related to the workplace and educational environments. In this section, an overview of the main categories of video games that have educational and motivational roles for schools and work environments will be presented. These types of games are referred to as Serious Games, Educational Games, or Gamification. As mentioned in the previous chapter, some individuals spend a significant amount of time every week playing video games. This might raise the concern of negative effects of video games on students and employees performance. Thus, before starting the discussion on the use of video games to gain positive outcomes, it is important to ask: Does the highly engaging aspect of video games make them addictive and result in problematic behaviour? The fact that people spend a considerable amount of time playing video games does not indicate that it will have a negative effect on their lives. In fact, time spent playing video games does not have a direct correlation with addiction to video games or poor scholastic performance (Skoric, Teo, & Neo, 2009). In contrast, time loss associated with playing video games can have positive outcomes such as relaxation and escaping from reality (Wood et al., 2007). Some scholars advocate the use of video games in educational and work-related environments to take advantage of their highly engaging characteristics (McGonigal, 2011; Sánchez & Olivares, 2011; Sepehr & Head, 2011). These Serious Games have received attention from both academics and video game practitioners alike (Crookall, 2011). Consequently, video games can be used for diverse positive outcomes, such as for educational and work-related benefits (Moreno-Ger, Burgos, Martínez-Ortiz, Sierra, & Fernández-Manjón, 2008) Many scholars have analyzed the deep involvement of computer users in video games and during Internet use from the addiction standpoint (Acier & Kern, 2011; Fisher, 1994; Grüsser, Thalemann, & Griffiths, 2007; Turel, Serenko, & Giles, 2011). However, we need to be very cautious using terms like ‘addiction’ when referring to high engagement states. One should be cognizant of the difference between addiction and high engagement in a certain task. Charlton and Danforth (2007) have shown that people who experience high engagement in online video games do not share the same behaviour as addicted online gameplayers. High engagement and addiction share some criteria, but addiction goes beyond high engagement and includes elements of conflict and withdrawal symptoms. The addiction criteria (Charlton & Danforth, 2007) are as follow: · Behavioural Salience: the extent to which the activity dominates one’s behaviour. · Relapse and reinstatement: despite trying to quit, continuing the activity the same as before. · Withdrawal symptoms: having negative emotions and physical effects while not doing the activity. · Conflict: the extent to which the activity results in conflict with the person’s normal life including social relationships and his/her other activities. Engagement, on the other hand, entails the following criteria (Charlton & Danforth, 2007): · Cognitive Salience: the extent to which the activity dominates one’s mental state in life (the person keeps thinking about the activity). · Euphoria: feeling a ‘buzz’ or a ‘high’ from doing the activity. · Tolerance: having to engage in the activity progressively longer to gain the same ‘buzz’. There is evidence in literature that shows the role of dispositional and situational factors that can push a highly engaging task to become addictive, in particular in relation with video games (Sepehr & Head, 2012b). Moreover, prevalence of video game addiction can be affiliated with less than 10% of the gameplayers (Sepehr & Head, 2013b), making it much less common than pure engagement experiences among gameplayers. Empirical evidence shows that “for the vast majority of individuals, online gaming is an enjoyable and harmless activity” (Griffiths, 2010, p. 39). Overall, it should be clarified that engagement and addiction are separate concepts in video gameplaying. This study aims to investigate competition as an element of games that enables high engagement rather than addiction. Thus, the focus here will not be on the addictive nature of video games. The engagement that is experienced through playing video games can be leveraged to increase the effectiveness of Information Technology (IT) systems and other non-IT based systems in various contexts, including that of education and the workplace. Serious Games Since the early 1980s, which experienced a boom in computer games, there has been an interest in studying computer games to inform Human-Computer Interaction (HCI) interface design. For example, in his seminal article, Malone (1981) analyzed different aspects of computer games in order to understand why computer games create intense engagement and how this engagement can be used for creating enjoyable learning experiences. Information Systems (IS) researchers have also explored such enjoyable experiences during IT use in the workplace and introduced a construct named “playfulness” (Webster & Martocchio, 1992). The HCI community began taking video games as a domain of research more seriously in the 2000s. Sweetser and Wyeth’s (2005) article is one of many studies that put forward a model for evaluating the enjoyment in playing video games. While research on video games in the HCI field only dates back one or two decades, games have been used for various purposes for several decades. Particularly, the military has used games significantly for training officers and soldiers (Prensky, 2001). In fact, the term “Serious Games” was first proposed in the 1980s prior to the pervasiveness of video games and the booming video game industry (Abt, 1987). However, as video game audiences and spectrum have expanded broadly since the end of the 20th century, other industries began following the lead of the military to deploy video games to advance their agendas. Today, video games have been applied to various applications in many sectors, such as government, education, corporations, healthcare, and as mentioned above, the military (Susi, Johannesson, & Backlund, 2007). As Sawyer and Smith (2008) have remarked, serious games go beyond games that are used only for learning or training. Even though the term “Serious Games” seems to be an oxymoron since games are, by their nature, developed for fun and not serious issues (Newman, 2004), serious games is a term that refers to games (mainly digital) that are “used for purposes other than mere entertainment” (Susi et al., 2007, p. 1). More precisely, serious games can be defined as: “Any form of interactive computer-based game software for one or multiple players to be used on any platform and that has been developed with the intention to be more than entertainment” (Ritterfeld, Cody, & Vorderer, 2009, p. 6). Based on Sawyer and Smith’s (2008) taxonomy, Serious Games can be divided into, but not limited to, the following categories: · Game-Based Simulations: Also known as Game-Based Simulators or Simulation Games are simulations that use video game environments for creating a more engaging environment, such as virtual universities. · Game-Based Models: Games that use a simplified version of real-world models, such as building bridges as in Bridge Build™ or real-world/life simulations as in The Sims. · Game-Based Visualizations: Also known as Game Visualizations is the category of systems that use visual technologies and techniques from games to create new forms of visualization and more accessible versions of visualization. These systems allow for higher levels of interactive visualization where user actions might affect resulting visualization, such as in the game of Holt Forestry™. · Game-Based Interfaces: Also referred to as Game-Like Interfaces or Game-Based UI is a non-standard but highly symbiotic application interface design produced based on the design of a popular game such as Doom™ in Doom Linux™. · Game-Based Productions: Use of tools or game/game engines created for videogaming to output some other piece of media. For example, Electroplankton™ is a video game in which players use animated planktons to create music. This category of Serious Games could also be referred to as Game-Based Authoring. · Game-Based Messaging/Advertising/Marketing: Also known as Advergames are a type of Serious Game that are used to transmit a message, advertise a product/service, or market a product. BMW M3 Challenge£, Johnson & Johnson®’s Tooth Protectors™, and GMC® truck’s Hit the Pros™ are a few examples of this type of games. · Game-Based Training: Adding gameplay to enhance motivation to train, or effectiveness of content transfer, behaviour change, or another specific goal of training, such as Binary Game™. · Game-Based Education/Learning (also known as Edugames): Using gameplay to enhance motivation to learn, engage education, or to enhance effectiveness of content transfer or other specific learning outcome. ERPsim™, Historia™, Math Chimp™, and PhyFun™ are a few examples of this group of serious games. One of the key categories of serious games, Game-Based Education/Learning, is the focus of the current investigation and will be discussed in more detail below. Game-Based Learning (Educational Games) The main branch of serious games has always been the application of games in teaching and training, which is referred to as Game-Based Learning (GBL) (Ratan & Ritterfeld, 2009; Sawyer & Smith, 2008). The premise of GBL is to “enhance motivation to learn, engage education, or to enhance effectiveness of content transfer or other specific learning outcome” (Sawyer & Smith, 2008, p. 11). GBL is also known as Game-Based Education, Educational Games, or Learning Games. GBL is closely related to other concepts like e-Learning and edutainment. E-Learning refers to a more general concept, entailing “computer- enhanced learning, computer-based learning, interactive technology, and commonly, distance learning” (Susi et al., 2007, p. 2). Edutainment, on the other hand, translates as any entertainment content that is developed with the goal of education rather than pure “fun.” Based on these definitions, one can say that GBL is a subset of both e-Learning and edutainment. Even though some indicate that the US military is “the world’s largest spender “ on Digital Game-Based Learning (Prensky, 2001, p. 2)[footnoteRef:4], academia (having curriculum-based and extracurricular content) has the biggest consumer base of GBL, representing 63% of 612 serious games analyzed by Ratan and Ritterfeld (2009). Figure 1 shows the proportions of the analyzed educational games based on their primary content. As depicted in Figure 1, the second most common usage of GBL is for “social change”, representing 14% of the games analyzed. Social change games are related to political issues or social issues such as solving poverty. Next, the most common usage is related to occupational and health-related contents, each with 9% and 8% of all the studied games, respectively. Occupational games aim to teach new skills and knowledge related to people’s occupation. Finally, health-related games are used to improve one’s health or encouraging coping in stressful situations. [4: The money that was spent in 2005 on military war games and simulation equipment was estimated to be $4 billion (Michael & Chen, 2005).] Serious games have also been used in business school curricula for more than two decades. One of the most famous games that has been widely used in managerial education is the “role-playing simulation” serious game called the “Beer Game” (Sterman, 1992). The Beer Game allows students to participate in managing a distribution system with the goal of minimizing total costs by controlling the inventories under uncertain situations. Computer-based versions of this game have also been used for many years. By using computers for this game, educators have achieved lower setup and training time, while increasing users’ concentration on specific tasks, reducing the number of errors made by players, and enhancing individual students’ analysis and evaluation of their gameplay results (Coakley, Drexler, Larson, & Kircher, 1998). Figure 1 – Proportions of games within each primary educational content category (Ratan and Ritterfeld 2009) Instructors in the field of IS have increasingly employed video games for teaching students and training employees/managers. Perhaps the most common game used in the IS curricula is the HEC Montreal’s ERP (Enterprise Resource Planning) simulation game (also known as ERPsim, Léger, 2006). ERPsim enables instructors to create a hands-on experience similar to the real-world environment working with the SAP™ ERP system, which is the leading ERP solution available in the market. During this gameplay, students, working in groups, collaborate with group members and compete against other teams, to produce and distribute a series of products in order to achieve the highest income in a virtual market. ERPsim encourages students to take responsibility of different supply chain stages of a simple product production; through this exercise students learn the various functionalities of the SAP ERP system. Due to its gaming structure, ERPsim has been successful in teaching basics of an ERP system despite limited knowledge of participants in regards to supply chain and ERP systems in general (Foster & Hopkins, 2011). Effectiveness of GBL Scholars have found some support for the effectiveness of GBL (Van Eck, 2006). Sitzmann (2011) has recently conducted a meta-analysis of simulation-based GBL pool of 65 articles and a sample size of 6,476 participants, which have also showed the positive effect of the GBL approach. The results show that “declarative knowledge was 11% higher for trainees taught with simulation games than a comparison group; procedural knowledge was 14% higher; retention was 9% higher; and self-efficacy was 20% higher” (Sitzmann, 2011, p. 520). However, due to complicated attributed inside video games, it is not practical to design a single GBL experimental design to capture all the goals of video games. In fact, the desired outcome of a game will vary based on its intention to support a specific pedagogy (Young et al., 2012). Generally, the argument for GBL is that the time spent on video games should not be seen to necessarily be in contradiction with school or work time. In fact, gameplay is “an important component of attention, involvement, and productivity, and it’s capable of energizing behaviour of all sorts” (Reeves & Read, 2009, p. 173). Thus, games are not only about delivering content to the users (in which they are not efficient), but they can also successfully be employed as a mechanism for motivating and engaging individuals in educational settings (Arnseth, 2006; Corti, 2006; Reeves & Read, 2009). Researchers have shown that when learners are engaged and involved, there is a positive effect on learning outcome (Bandura, 1977; Oncu & Cakir, 2011; Shin, 2006). As mentioned in the previous chapter, Millennials have a different mentality, which affects the way they learn. As Feiertag and Berge (2008) put it: “Generation N’s[footnoteRef:5] learning style is hands-on and not necessarily linear in fashion. Forget instruction manuals, tech tips and lecture-based lessons; this is a generation that plays to learn. Many of today’s video games are based upon trial and error and Gen N sees it as a metaphor for learning” (p. 458). [5: Millennials] Furthermore, the focus on “doing” instead of “knowing”, when it comes to teaching Millennials, has been advocated due to the higher value of “actions” compared to “accumulation of facts” in the current era (Oblinger, 2003). It is often recommended that teachers focus more on creating interactive and experiential learning environments (Oblinger & Oblinger, 2005). The significance of engagement in the learning process has been emphasized in the work of the famous educational theorist, Kolb (1984), known as the idea of experiential learning. Other distinguished scholars have also advocated the positive effect of active involvement of students during learning processes (Papert, 1980; Piaget & Roberts, 1976; Vygotsky, 1978). Therefore, GBL is important as video games have the potential to encompass experiential learning principles (Gee, 2003). Beyond Serious Games: Gamification “Gamification”[footnoteRef:6] has become a popular term to describe the use of games in non-conventional contexts. Deterding, Dixon, Khaled, & Nacke (2011) define Gamification as “the use of game design elements in non-game contexts.” Foursquare[footnoteRef:7] is perhaps the most successful example of Gamification. Foursquare designers have used game design elements such as points, badges, leaderboard, levels, as well as competition to motivate users to return to continuously use their service, which has increased their user activity significantly. The main difference between serious games and Gamification is in the level of analysis. Serious games tend to use a complete game in contexts that are beyond mere enjoyment, while in Gamification one aims to use design elements of games in other contexts (e.g. marketing, social networks, sport, and such) without implementing a complete gaming environment. This comparison can be visualized in Figure 2. [6: Based on Gartner’s report in 2011, by 2015 more than 50% of organizations will gamify their innovative processes (web link: http://www.gartner.com/it/page.jsp?id=1629214)] [7: A location-based social networking website that is widely used by smartphone users: https://foursquare.com/] Figure 2 - Situating Gamification (from Deterding et al. 2011) The dimension that separates games from toys and “playful design”, according to Deterding et al. (2011), is the difference between the concepts of play and game. Play is a broader category than games due to its free form and lack of structure (Caillois & Barash, 2001). Games, unlike play that is also referred to as playfulness, are formal systems that follow a set of explicit rules (Barr, Noble, & Biddle, 2007). Extant Literature Review on Video Game Experience Through a detailed examination of extant literature, existing gaps in the literature on video game engagement were identified. A graphical summary of this literature review is presented in Figure 3 by visualizing the relationships that other scholars have previously studied. This overarching model also distinguishes the empirical versus non-empirical results by using different relationship lines as validated or proposed relationships. As shown in Figure 3, research on video games and Flow (high engagement) in video games can be divided into two main groups: first, studies that focus on different elements of video gaming systems, in particular competition (left hand side of the figure); and second, studies that evaluate engagement or Flow during video gameplay. The first group of studies are labelled with letters ‘a’ to ‘o’, and the second group of studies are labelled with numbers 1 to 18. The first group of these studies is discussed below. The second group of the studies that focus on the construct of Flow, its antecedents and consequences will be discussed in the next chapter (section 3.2) where details of Flow theory and its various dimensions are presented. Studies with the Focus on Competition and Game Elements Wu et al. (2009) (paper ‘a’) studied online games from a Uses and Gratifications (U&G) perspective, which discusses the usage of media to gratify various users’ needs and wants. Based on Uses and Gratification Theory, the authors explore the antecedents (i.e. gratifications and presence) of continuance intention in video games use. Based on 343 completed responses from online gamers in Taiwan it was shown that Gratification (Achievement, Enjoyment, and Social Interaction) is the main predictor of intention to continue playing games in U&G. Yee (2006) (paper ‘b’) also investigated the factors that motivate people to play video games through an empirical study. Through principle component analysis technique of 3,000 MMORPG players 10 factors were extracted (shown in Figure 3). These 10 factors were organized under three main components of Social, Immersion, and Achievement. Yee (2006) included competition (i.e. Dispositional Competitiveness) as a factor of Achievement. Some studies explored the effect of competition in more detail. Competition has been hypothesized to have a direct effect on enjoyment of individuals in entertaining media, resulting in higher learning (Vorderer, Klimmt, & Ritterfeld, 2004) (study ‘k’). Reeve and Deci (1996) (paper ‘c’) studied the role of different elements of a competitive situation (i.e. competitive set, competitive outcome, and interpersonal context) on creating intrinsic motivation among participants who played a puzzle game. The results of this study showed that competitive outcome (i.e. winning vs. losing) and context (i.e. pressured vs. nonpressured) influence intrinsic motivation. The results of their path analysis also showed that winning (compared to losing) increased intrinsic motivation and a pressured (versus nonpressured) context decreased intrinsic motivation. As Udvari and Schneider (2000) (study ‘d’) showed, the effects of competition vary in different people in different situations. In this study, the authors investigated how (intellectually) gifted children react to competition. The results of the research supported the claim that gifted children would work harder under competition and achieve higher performance compared to the situations where they do not compete with their classmates. However, these findings are not limited to gifted children. Through a set of studies, Maciejovsky and Budescu (2007) (study ‘e’) investigated the role of competitive auctions in groups. Their studies showed that competitive situation resulted in higher learning effects, higher knowledge sharing, and higher knowledge transfer to new reasoning problems. Other studies has also shown that gameplayers’ motivation to compete has a significant effect on the extent to which they perceive the game to be competitive (Vorderer, Hartmann, & Klimmt, 2003a) (study ‘k’). Various situational factors are expected to define competition and reactions of players to competition. Garcia and Tor (2009) (study ‘m’) introduced the N-Effect in competition, which explains that the number of competitors (N) can have a defining role in competitive motivation of players. That is, as the number of competitors increases, it is more likely that the competitive motivation of players will decrease. The results, however, were more significant among individuals who are high in Social Comparison Orientation (SCO). But, SCO became less important as the number of competitors increased. Garcia and Tor (2009) also discussed that the closeness of relationship between competitors affects the social comparison and consequently competitive behaviour of players. The cultural differences among individuals (e.g., individualistic vs. collectivist) have also been shown to have a defining role in how cooperative or competitive individuals react to a group task (Cox, Lobel, & McLeod, 1991) (study ‘n’). Video games may also have negative outcomes. For example, as Skoric et al. (2009) (study ‘f’) showed, addictive tendencies of children towards video gaming (ages 8 to 12 years) negatively correlates with scholastic performance. Anderson and Brown (1984) (study ‘g’) further studied the role of psychological feelings in creation of problematic behaviours such as gambling. Their findings showed that “arousal and excitement are major mediators of reinforcement and internal cues for gambling behaviour” (p. 406). This study showed that students who participated in the experiment would have higher heart rates when their gambling behaviour increased. Nonetheless, arousal does not only predict problematic behaviour. As Koo and Lee’s (2011) study of 406 online consumers (study ‘h’) showed, arousal increases pleasure and results in higher attention to the activity. This is in line with earlier research on arousal that supported the existence of a relationship between arousal and attention and performance in memorizing information (Eysenck, 1976) (study ‘o’). Some studies have examined the effect of competition in creating Flow among gameplayers. In particular, Weibel, Wissmath, Habegger, Steiner, and Groner (2008) (study ‘i’) conducted a multivariate experiment study that compared playing against a computer-controlled versus playing against a human-controlled opponent. Their findings showed that participants who played against a human-controlled opponent reported higher levels of Flow experience. It is important to note the research depicted in Figure 3 is not meant to be an exhaustive representation of all extant literature in this domain. However, it does represent the essence of the research from the related fields, through which research gaps can be identified. As Figure 3 shows, there is a gap in extant literature in understanding the relationship between competition and Flow/engagement. Earlier studies have not investigated the process through which competition can influence the level of engagement in video gameplaying. Figure 3 - Finding the Gap in Literature on Video Gameplay Figure 4 – Finding the Gap in Literature on Video Gameplay (continued) In sum, regardless of the category of games (Serious Games or Gamification), competition is one of the design elements that are shared among most of the games that should be further researched regarding its outcomes on gameplayers. Based on the importance of the competition element in video games, this study further investigates the effect of various competition modes and perception of competitiveness during gameplaying. As such, the next chapter will propose a new theoretical model to help answer this study’s research questions based on two theories of motivation. Theoretical Framework and Development of Research Model Self-Determination Theory (SDT) (Deci & Ryan, 1985a) and Flow Theory (Flow) (Csikszentmihalyi, 1975) were chosen as the foundational theories for this study. While these two theories originated in the field of psychology, they have been studied and tested extensively in various disciplines and have been applied to different contexts. Both SDT and Flow are rooted in the investigation of intrinsic motivation among people in their daily activities. As explained below in more detail, both of these theories have a long history of being used to explain the motivational factors in engaging people while they play video games. As such, they are well suited for the current investigation. The strength of SDT is in its richness and rigour in explaining the psychological aspects of humans in seeking intrinsic motivation. As shown in Figure 5, SDT is able to explain the process through which motivation is distilled for a person, in particular for a video gameplayer. On the other hand, researchers can use Flow for evaluating individuals’ state of complete absorption during an activity. Flow Theory was first introduced by Mihaly Csikszentmihalyi in 1975 (Csikszentmihalyi, 1975) and evolved to its most seminal works in 1990s (Csikszentmihalyi, 1991, 1997). Flow theory explains the state of deep engagement, which is the state that often video gameplayers experience during gameplay. SDT was a theory developed to understand intrinsic motivation based on psychological needs in 1980s (Deci & Ryan, 1985a) while acknowledging Flow. Since their inception, SDT and Flow have evolved together. Figure 5 presents an overarching theoretical framework, employing and interfusing SDT and Flow theories. As the figure shows, SDT clarifies the procedure through which competition can create motivation, while Flow captures the state of engagement that results from this motivation. The final outcome of engagement would be to increase performance/adoption/etc. To establish the connection between SDT and Flow, the two theories are explained in further detail below. The conjunction of these two of theories is also discussed. Figure 5 – Overarching Theoretical Framework Self-Determination Theory One of the main theories scholars deploy for explaining the formation of individual motivation in various fields is Self-Determination Theory (SDT; Deci & Ryan, 1985a, 1991). SDT is not a single theory, but is an “organismic meta-theory” that frames five mini-theories on intrinsic and extrinsic motivation and related personality and behaviour (Ryan & Deci, 2000a). This theory aims to understand the basic psychological needs that are the roots of intrinsic motivation, and consequently, what conditions support or prevent people from satisfying these needs – and as a result intrinsic motivation. These basic innate needs are competence, autonomy, and relatedness (Deci & Ryan, 1980, 1991, 2000). The need for competence refers to the tendency of individuals to feel capable of performing a task (sense of self-efficacy). Autonomy, on the other hand is the propensity of people to have control over their actions and see themselves as the locus of causality. Autonomy – the feeling of self-initiation of the task – is also referred to as perceived self-determination (Deci & Ryan, 1985b). The last need, relatedness, suggests that people innately demand social connectedness. SDT aims to understand social conditions and contexts as well as individual differences that enable motivation among humans as “active organisms” (Ryan & Deci, 2000a). This theory, therefore, explains why and how various forms and levels of motivation are created in people. SDT directed research explain the effects of environment and personality on motivation (Ryan & Deci, 2000a). The studies that are based on SDT also approach motivation research with the goal of understanding the implications of different forms of motivation. Since SDT’s introduction in 1985, it has been employed in research of various fields and domains such as education, healthcare, organizational behaviour, psychotherapy and counselling, sport and physical education, and video games. As such, SDT provides a very strong addition to the theoretical framework of this study for understanding competition as a defining factor of motivation and engagement. SDT and Competition Various scholars have studied competition based on SDT. Since competition can be seen in sports, games, education, jobs, and basically everywhere, it has been examined by researchers in diverse fields. Researchers have attempted to understand if competition is a source of intrinsic or extrinsic motivation and how it affects individuals to compete (Deci, Betley, Kahle, Abrams, & Porac, 1981; Vallerand, Gauvin, & Halliwell, 1986b). Extrinsic motivation is often distinguished from intrinsic motivation based on the source or the reward that provokes the motivation. Specifically, “the reward for extrinsically motivated behaviour is something that is separate from and follows the behaviour” (Deci et al., 1981, p. 71). Deci et al. (1981) have employed a “free-choice time” technique to measure the intrinsic motivation of participants after playing with a puzzle game and concluded that competition, regardless of the impact on performance, changes the nature of motivation from intrinsic to extrinsic. Free-choice time has been used by many other scholars to measure intrinsic motivation (e.g. Tauer & Harackiewicz, 1999; Vallerand et al., 1986), where after the conclusion of experimental tasks, participants are left in the experiment room for a few minutes while being observed to see if they spontaneously continue playing the particular game of the experiment. In competitive situations, when the focus is on the outcome of a task, the emotion of individuals is defined by the result of competition, namely winning and losing (Weinberg & Jackson, 1979). As Vallerand, Gauvin, and Halliwell (1986) have shown, participants who lose in competition, perceive their intrinsic motivation lower than their opponents who won. In fact, after losing a competition, people evaluate their performance lower than their expectation for themselves, resulting in a decrease in their perceived competence (Tauer & Harackiewicz, 1999) that is a requisite for being intrinsically motivated. In other words, if competition is understood as a tool to provide extrinsic reward (such as monetary rewards, deadlines, and prizes), similar to other types of extrinsic rewards it can have a negative effect on intrinsic motivation (Deci, Koestner, & Ryan, 1999). Deci and Ryan (1985a), based on Ross and den Haag's (1957) work, present two different forms of competition: direct and indirect. In direct competition, individuals compete against one another, while in indirect competition the focus is on impersonal standards such as performance. The results of previous studies show that direct competition decreases intrinsic motivation (Deci & Ryan, 1985a). Indirect competition, on the other hand, can enhance competence and consequently intrinsic motivation if the attention is diverged from winning to improving performance. When the focus is on increasing performance, people who are successful (success as a subjective measure in contrast to objective measure of winning/losing) in a competitive situation also show higher intrinsic motivation and enjoyment (McAuley & Tammen, 1989). Despite the negative effect of losing in competition on intrinsic motivation, the competition context in itself is a rich tool to create engagement through feedback (Tauer & Harackiewicz, 1999). Much of the research on the motivational aspects of competition has focused on the consequences of losing versus winning a competitive task. Based on the three mini-theories, SDT explains the role of competition in creating motivation to participate in an activity through three principles: (1) The focus on competition’s motivational forces should be shifted from the outcome results to the processes of competition regardless of outcomes; (2) Competition should not be seen as a pure extrinsic element; rather, it is best seen as a midpoint of motivation that is valuable in situations where intrinsic motivation does not exist by nature; and (3) Different people have different personality traits; thus, reactions of different people to competition may differ. These arguments will be depicted and the rationale for them will also be provided based on three of SDT mini-theories. These three mini-theories are Cognitive Evaluation Theory, Organismic Integration Theory, and Causality Orientation Theory. Each of these mini-theories have addressed intrinsic/extrinsic motivation, including within competition contexts, forms the basis of this research. Cognitive Evaluation Theory (CET) The first and most important mini-theory of SDT is Cognitive Evaluation Theory (CET). Deci and Ryan (1985a) presented CET to explain how social context can influence intrinsic motivation (positively or negatively). Based on CET, intrinsic motivation is the result of interpersonal events and structures (such as feedback and rewards) that increase the feeling of competence, accompanied by satisfaction of the need for autonomy during an action to create intrinsic motivation (Ryan & Deci, 2000b). As mentioned before, competition can be direct or indirect. Indirect competition entails two aspects for providing feedback to the competitors: “Informational” and “Controlling” (Deci & Ryan, 1985a). The Controlling aspect is dominant when competitors put all their effort into winning or feel pressure to beat a standard. This Controlling aspect is similar to other extrinsic reward systems, which decrease intrinsic motivation (Deci et al., 1981). On the other hand, the Informational aspect helps a person to increase his/her competence in the task he/she is competing in through mechanisms such as feedback. Through competition, one is able to assess their performance and build competence. As Vallerand, Gauvin, et al. (1986) explain, “competition constitutes a social event that can provide the individual with competence/incompetence information because social comparison processes are very prominent” (p. 467). Reeve and Deci (1996) further clarify how external events affect motivation: “External events influence perceived competence via their informational aspect and influence perceived self-determination via their controlling aspect” (p. 24). An “Informational aspect” can have positive or negative influence, having positive effect increases the competence while negative affect decreases the competence of individuals. Therefore, in a competitive environment that supports autonomy of competitors by prohibiting controlling situations, informational feedback can impact intrinsic motivation positively. Moreover, social context of competition is claimed to be motivating by having influence on perception of autonomy and competence of players (Vallerand & Losier, 1999). In video games, non-controlling instructions and giving more options in a competitive situation is one approach to enhance informational feedback “and, in turn, intrinsic motivation” (Ryan, Rigby, & Przybylski, 2006). Traditionally, the video gaming industry has been very successful at providing a medium for gameplayers in which they can satisfy their basic psychological needs. Clear goal structure and scoring systems have always been the main elements of video games, enabling gamers to evaluate and improve their competence. Recently, video games have become more flexible in their goal structures and have implemented more open and less structured playing environments (Przybylski, Rigby, & Ryan, 2010). This change has created a gaming environment that supports autonomy of the users more than the classic styles of video games. In more recent years, video games have also improved their support of relatedness need among the gamers by expanding their territory to online virtual worlds (such as in games like World Of Warcraft). However, one can still observe the main element that stays dominant and maintains the attractiveness of video games is their simple scoring and goal structure, being a perfect example of an approach of competence enhancement. Competition has always been one of the main reasons that people play video games, which permits the players to compare their performance and evaluate their competence in gameplay (Sherry, Lucas, Greenberg, & Lachlan, 2006). In sum, based on CET, competitive environments can be a factor for increasing motivation among players. Given that a video game avoids controlling elements and justifies autonomy of gameplayers, it can use informational feedback to increase competence. Organismic Integration Theory (OIT): The second mini-theory introduced in SDT is Organismic Integration Theory (OIT), which elaborates the concept of extrinsic motivation (Deci & Ryan, 1985a). Based on OIT, extrinsic motivation takes distinct forms based on the locus of causality of tasks and how much people internalize and integrate the motivation of a task. These extrinsic motivation types shape a continuum, ranging from pure extrinsic to pure intrinsic motivations as shown in Figure 6. On the left side of the continuum is the lack of motivation, which results in Amotivation of a person who does not care about the task. On the right side of this continuum, the traditional form of intrinsic motivation is depicted, in which behaviour is merely due to complete innate enjoyment of the self. Externally Regulated motivation can be seen in tasks that are fuelled by external rewards and controlled by an outside factor. This state relates to the classic extrinsic motivation that has been previously discussed. Rather than having a binary view of extrinsic and intrinsic motivation, OIT differentiates between various forms of extrinsic motivation based on the approach to internalization and integration of the extrinsic motivation. This phenomenon attributes to the fact that often for an individual the source of motivation is not entirely extrinsic. Instead, people tend to receive a value or regulation and transform it into their own in a way that the source of value or regulation is no longer external, but rather internal to some extent. This process is referred to as internalization and integration. The second form of extrinsic motivation, Introjected Regulation, is the first form of extrinsic motivation that addresses the internalization of an external factor. In this internalization process, people expect the motivation element to be initiated by themselves, but not completely. As a result of this controlled situation, one participates in the act “to avoid guilt or anxiety or to attain ego enhancements such as pride” (Ryan & Deci, 2000b, p. 62). The next form of extrinsic motivation, Identified Regulation is another form of motivation where the source is more internalized for the actor. In this state, people personally value the factor of motivation and feel autonomous in the task. At one end of the spectrum of extrinsic motivations, Integrated Regulation represents the most consciously self-regulated/self-determined type of motivation that is very similar to pure intrinsic motivation, but has an external motivating element. Figure 6 - The self-determination continuum (source: Ryan & Deci, 2000) Intrinsic motivation in the purest form is more desirable compared to any of the extrinsic motivation types. However, in the middle of the continuum, there are other types of extrinsic motivation that can be appropriate in situations of low intrinsic interest. There are situations that an individual lacks intrinsic motivation in an activity – e.g. when goal is to have all the students in the class to pay attention to a subject. In these cases, creating other forms of extrinsic motivation that are closer to intrinsic motivation on the spectrum of extrinsic motivation (i.e. Identified Regulation, Integration Regulation, or Introjected Regulation) could be beneficial for engaging people in the task. Video games are occasionally successful in creating various forms of extrinsic motivation based on the expectations of gamers (King & Delfabbro, 2009). Competition in video games is one of those tools that can help create a moderate form of extrinsic motivation. During competition, people might become ego-involved –aiming to prove their values– as a result of trying to achieve a certain outcome (Vansteenkiste & Deci, 2003). On the extrinsic motivation continuum, this competitive condition can be characterized as Introjected Regulation form of motivation. As the internalization of the motivation to participate in the task increases, the person increases his/her perception of self-regulation and, as a result, experiences a more intrinsic feeling of motivation. This change of autonomy would be dependent on focusing more on the context of competition rather than the outcomes of competition (Tauer & Harackiewicz, 1999). Increase of autonomy, as mentioned before, is dependent on lack of a controlling and presence of an informative environment. Altogether, a competitive situation can be a desirable tool –in the absence of intrinsic motivation– to create an environment of modest extrinsic motivation, which can eventually enhance the intrinsic motivation among the players. Causality Orientation Theory (COT): The last SDT mini-theory that is used here for background of this study’s framework is Causality Orientation Theory (COT), which addresses the differences among people’s behaviour regarding their dominant preference for motivation based on the level of causality in interaction with their environment (Deci & Ryan, 1985a, 1985b). This categorisation of “causality orientation” includes: 1) autonomy orientation, 2) control orientation, and 3) impersonal orientation. In Autonomy Orientation, a person’s behaviour sees the locus of causality to be the self (person initiating the behaviour). This group entails behaviours that are rooted in “choices based on an awareness of one’s needs, feelings, and integrated goals” (Hodgins, Liebeskind, & Schwartz, 1996). Control Orientation, on the other hand, is a characteristic of a person who has the tendency to value external factors such as rewards, approval, or achievements for shaping one’s behaviour. Lastly, in Impersonal Orientation, behaviour is mainly shaped by feelings of helplessness concerning competence, resulting in a lack of intention to perform the related task. This last group is also referred to as Amotivation Orientation (Deci & Ryan, 1985b). Respectively, it is expected that during a competitive task in video games, people with different tendencies would react to competition differently based on the value they give to the environmental cues. Thus, we would expect the personality of people to be an important factor in the process of engaging in competitive tasks. Previous research has also shown that people with higher orientation towards achievement perceive competition to be more challenging and are more intrinsically motivated (Tauer & Harackiewicz, 1999). Similar distinction can be noticed between males versus females participating in the same competitive task in such a way that females prefer competition that leads to success while males prefer to achieve domination (Przybylski et al., 2010), and females who are high in achievement orientation enjoy competitive tasks more than males who are high in achievement orientation (Tauer & Harackiewicz, 1999). These studies have not directly examined the individual dissimilarities in competitive evaluation in the context of video games. However, there is no evidence to suggest that compared to non-video games, video games have different influence on the role of personal characteristics regarding orientation towards competition. In contrast, Ryan et al. (2006) have shown that “there is considerable variance between individuals in their overall experience of and motivation for computer games” (p. 354). In sum, based on COT it is expected that individual differences such as personality and gender will influence the process of motivation caused by competition. Flow Theory In the past decade there has been increased focus on hedonic systems and how people interact with them in the field of Information Systems (IS) (Lin & Bhattacherjee, 2010; Van der Heijden, 2004). With this focus on hedonic systems, researchers have developed constructs to measure concepts such as ‘enjoyment’ and ‘playfulness’ (Holsapple & Wu, 2007; Moon, 2001) of hedonic systems use. However, there is a need for the advancement of theories in the field of IS to better explain the use of hedonic systems. As Lin and Bhattacherjee (2010) illustrate: “prior models of utilitarian system usage provide a limited understanding of one’s usage of hedonic systems, given the motivational differences between using these two types of systems” (p. 163). Aside from the concepts that have been well established in this field, there are some theories that have been regularly used for explaining user interaction with hedonic systems. The theory of ‘Flow’ is one of those theories (Csikszentmihalyi, 1991), which aim to explain the “optimal holistic experience” (L. Deng, Turner, Gehling, & Prince, 2010) while performing a task. Flow was initially studied among artists who feel complete involvement in their work (Csikszentmihalyi, 1991). This theory was further expanded to other leisure activities in order to study the engagement of people in what they do (Csikszentmihalyi, 1991). Flow aims to capture the state in which people are highly immersed in their act and feel intensely involved, which is a positive and desirable state for performing any kind of activity (Csikszentmihalyi & LeFevre, 1989). According to Flow theory, the balance between challenge and skill in a task results in a deeply engaging experience (Csikszentmihalyi, 1997; Ellis, Voelkl, & Morris, 1994; Massimini & Carli, 1988). The original model of Flow was a simple explanation of optimal experience as an equal balance between challenge and skill (Csikszentmihalyi, 1975) that is shown in Figure 7 (Left diagram). According to the original model, people experience the state of Flow while performing a task that has the same level of challenge as the level of skill the actors require to carry out that task. Csikszentmihalyi and Csikszentmihalyi (1988) later modified this model by proposing a four-state model, which “provides us a description of the experiential component of an activity as people's skill develops through repeated exposure to the activity” (Ellis et al., 1994, p. 338). Based on this model, Flow is experienced only when the level of challenge and skill of an activity is higher than normal day-to-day life experiences. Thus, low levels of challenge and skills that result in lower extremes of Flow experience would create the feeling of apathy. Deep engagement, arousal, enjoyment, or focused immersion that are characteristics of the state of Flow would not be experienced in the stage of apathy. As it can be seen from both of the models, higher than average level of skills with low levels of challenge in a task causes a person to feel bored or in other words, to be in ‘boredom’ phase. On the other hand, when people are highly challenged and the level of challenge does not match their potential skills, this would make one anxious or to be in the state that Flow Theory classifies as “anxiety.” Figure 7 - Flow Models: Original Flow Model on the left and Four-State Flow Model on the right Since the advent of the original Flow model, there have been some other variations introduced, based on the level of skill and challenge. The most well-known model is the eight-state model (Csikszentmihalyi, 1997; Csikszentmihalyi & LeFevre, 1989; Massimini & Carli, 1988; Takatalo, Nyman, & Laaksonen, 2008) shown in Figure 8, which extends the previous models by adding four new states of Arousal, Control, Worry, and Relaxation that can be seen in the diagram in the respective regions of each state. The eight-state model is a more comprehensive model than the earlier versions and still has more parsimony compared to more complicated models (such as the 16-state model). This model also includes the state of Arousal. Figure 8 - Eight State Flow Model Flow Theory has been adapted to the field of IS by Agarwal & Karahanna (2000), where it is referred to as “Cognitive Absorption” (CA). CA is defined as a multidimensional construct, which reflects the satisfaction of users with IT systems, and consequently, their continuing usage intension (Agarwal & Karahanna, 2000, p. 665). By incorporating the dimensions of constructs that were previously developed based on Flow, CA encapsulates five dimensions of temporal dissociation, focused immersion, heightened enjoyment, control, and curiosity, overarching Flow and engagement dimensions (Choi et al., 2007; Chou & Ting, 2003; Korzaan, 2003; Novak, Hoffman, & Yung, 2000; Shin, 2006; Skadberg & Kimmel, 2004). Similarly, based on SDT (explained below), competitiveness of video games is hypothesized to be a tool to create the state of Flow (CA). Meta-analysis of Flow Different scholars have studied the positive effects of being in the state of Flow on various outcomes. These outcomes include positive attitudes and behavioural intentions (Agarwal & Karahanna, 2000; Chin-Lung Hsu & Lu, 2004; Korzaan, 2003; Koufaris, 2002), learning performance (Choi, Kim, & Kim, 2007; Xia Skadberg & Kimmel, 2004), and exploratory behaviour (Chou & Ting, 2003; Hoffman & Novak, 1996). However, despite the general agreement on the basic conceptualization of Flow by Csikszentmihalyi (1997), there is no consensus on the instruments that are developed to operationalize Flow. Researchers have argued that this construct is “too broad and ill-defined due to the numerous ways it has been operationalized, tested, and applied” (Choi et al., 2007). Given the importance of Flow in the current investigation, a meta-analysis was conducted to provide insight into the following Flow-related questions: (1) Among the empirical studies that have examined the positive impacts of Flow for particular hedonic systems, is there a consensus in their findings? (2) Are there any significant differences in the impact of findings between the measurements that are developed for the construct of Flow? To answer these questions, a meta-analysis was conducted in order to consolidate the findings of previous studies that have used the theory of Flow to investigate hedonic IT use. Meta-analysis is an appropriate research method for answering the research questions mentioned above due to the richness and diversity of previous research in this domain. Details of the meta-analysis study can be found in Appendix 1. Previous studies have shown that the construct of Flow has a moderate to large effect size on predicting relevant constructs of the Technology Acceptance Model (TAM) in IT usage, namely attitude towards use and intention to use. The number of published studies on the influence of Flow on attitude was not sufficient to draw a certain conclusion about its influence on attitude, despite the proper confidence interval. On the other hand, many empirical studies have been conducted that could be used for performing meta-analysis on the relation of Flow/CA on intention to use. These studies support the claim that Flow is a suitable construct to predict the intention to use of IT. Additionally, it was shown that the operationalization approach of Flow plays an important role on the effect size of the relation between this construct and intention to use. This moderation effect indicates that the unidimensional instrument has medium prediction power compared to the high prediction power of multidimensional instruments (CA, engagement, and their variations) on intention to use hedonic systems. The Q-test[footnoteRef:8] also showed that the studies used for this meta-analysis were homogeneous in their effect size and the results are stable for this relationship, for both operationalization approaches. It was shown that multidimensional instruments for Flow/CA, to some extent, have similar effect size based on the low Q level in the meta-analysis (high homogeneity, showing high reliability of the general findings). [8: Please refer to Appendix 1 for the explanation of the analyses conducted, including the definition of Q-test.] There are both advantages and disadvantages in using any of the measurements with unidimensional or multidimensional questionnaires. The disadvantage of the multidimensional approach is that it increases the complexity of study, resulting in more complicated analysis and extending the length of questionnaire. However, unidimensional measures may not have the predictability power of multidimensional ones (like CA) and may not reflect the conceptual idea of Flow as well, despite their ease of administration and simplicity. In fact, unidimensional measures fail to include different dimensions of the main concept. In addition, using structural equation modeling (SEM) techniques, evaluation of models with multidimensional constructs would be easier[footnoteRef:9]. [9: For more information on multidimensional constructs and the guidelines for modeling them, one may refer to a recent article by Polites, Roberts, & Thatcher (2012). ] Hoffman and Novak (2009) recommend that whenever possible, both multidimensional and unidimensional measures should be deployed to strengthen the power of the instrument in a study. In other words, using multiple measurements, “the researcher would be in a position to analyze the data in multiple ways, observe consistencies or inconsistencies, and most importantly, to compare their results with researchers who used different approaches to measuring Flow” (Hoffman & Novak, 2009, p. 29). Nevertheless, it is not clear how using both measurements can augment the calculations other than a simple comparison of two measurements (while creating a longer questionnaire). One use of gathering both measurements would be to follow the modeling of Guo and Ro (2008), in which one can use unidimensional measure’s correlation with different dimensions in order to find the dimensions of the concept of Flow that are relevant to the context; thus, eliminate the dimensions that are not correlating with the unidimensional measure. Yet, using techniques such as PLS outer model loadings it is possible to identify the dimensions that would be proper for measuring Flow (similar to Agarwal and Karahanna, 2000). This approach can be based on a qualitative and/or pilot study and/or literature review to detect the relevant dimensions of Flow in a specific context (e.g. in interaction with recommendation agents or avatars). Hence, it is expected that using multidimensional measurements – whenever possible – to be more comprehensive and create less ambiguity for participants, therefore, to reduce measurement error. It is also possible for researchers to select a subset of the multidimensional construct, based on the relevance to the context and/or outcome of interest in their studies grounded in sound and reasonable theoretical justification (Polites, Roberts, & Thatcher, 2012). Siekpe (2005) and Lin et al. (2008) are two examples of this approach (selecting a subset of Flow dimensions). As the meta-analysis results showed, Flow has positive outcomes on attitude and intention; these outcomes are best captured when Flow is conceptualized in a multidimensional way. Therefore, multidimensional representation of Flow is the recommendation for researchers and what is being followed in the current investigation. For this research, regardless of the ease of instrument administration, it is critical to find the measurement that best represents the concept of Flow to ensure valid and reliable results. As such, in this study, three of the dimensions that were relevant to the context of video game-playing, namely Temporal Dissociation, Focused Immersion, and Heightened Enjoyment, were chosen to measure the concept of Flow. These dimensions will be used in this research and will be further discussed in the methodology section. Combining SDT and Flow Many of the studies on SDT and competition have explained the negative effect of competition outcomes on intrinsic motivation, in particular for losers. As a result, it should be mentioned that this study’s framework is to evaluate motivation during competition prior to receiving the outcome that can decrease people’s competence due to losing the game. Motivation during the game is independent of the outcome and is even more important than after the gameplay (Tauer & Harackiewicz, 1999). This will allow for understanding the process that creates engagement in gamers. In other words, “effort during competition is not necessarily aligned with post-competition emotional responses” (Liu, Li, & Santhanam, 2013). Based on SDT, people have tendency to satisfy their basic needs (competence, autonomy, and relatedness). This tendency is stronger specifically prior to receiving the final results (winning or losing). During competition, regardless of the outcome, people engage in the activity in order to achieve competence and autonomy, causing them to be motivated (Tauer & Harackiewicz, 1999). This motivation is not captured in earlier studies through the free-choice period[footnoteRef:10] approach, as this measurement is gathered after the game is over (e.g. in Vallerand et al., 1986b). In fact, rather than evaluating the motivation after the competition is finished and putting emphasis on outcomes, focus should be on the process through which the competitive situation motivates people to pay attention to the task they participate in. [10: Free-choice period the time at the end of an experiment that participants are left alone. Participants are monitored during this period in order to observer whether they continue performing the activity that they had completed during the experiment or not.] As mentioned earlier, in order to provide a theoretical background for the effect that competition can have in engagement of video games, Flow Theory and SDT are used in conjunction. SDT explains the process in which competition in video games can motivate gamers by focusing on the context of the game and addressing individual differences. However, the goal of this research is not to evaluate pure intrinsic motivation of gamers, as video games can deploy extrinsic elements that highly engage players. One can expect that the competence factor of SDT is captured in the balance of challenge and skill in Flow Theory. Flow Theory also describes that very high competent players who are not challenged can experience boredom. Some authors have mentioned that Flow does not capture all the motivating factors in video games, which can be explained using SDT (Rigby & Przybylski, 2009). That said, as described above, the focus of this study will not be on capturing all the dimensions of video games, but to understand the process through which competition creates engagement in gamers. Thus, studying competition as a fundamental element of video games, the foundational theories of this research explain that in absence of controlling elements (autonomy diminishing factors), competence becomes the major psychological need to consider (Jang, Reeve, Ryan, & Kim, 2009). Przybylski et al. (2010) have shown that each of the dimensions of SDT have an independent effect on gaming behaviour and perception, which shows the validity of investigating the effect of only one aspect independent of the other. This research’s theoretical framework expresses that SDT predicts motivation of people under the condition that autonomy is preserved (by avoiding the use of controlling feedback in the gaming context, conveying control to players). This motivation will lead to the experience of Flow (balance between challenge and skill), caused by competence evaluation due to informational feedback (explain in § 3.1.1). Deci & Ryan (1985) have also expressed that Flow state is in “agreement” (p. 332) with their SDT’s concept of intrinsic motivation. In other words, while performing an activity, “when highly intrinsically motivated, organisms will be extremely interested in what they are doing and experience a sense of Flow” (Deci & Ryan, 1985, p. 29). Flow theory allows us to properly capture the state of high engagement that can be the outcome of competitive feature motivators of video games. Proposed Research model and Hypotheses In this section the proposed research model is shown in Figure 9 and an associated ten hypotheses are presented. Hypotheses are explained from right to left (from H1 to H10), starting with the endogenous variable and its antecedent relationships. H1 to H6 aim to address the first research objective in regards to the effect of Situational Competitiveness on the antecedents of Flow and Satisfaction. H7, H8, and H9 are hypothesized in order to answer the questions related to the second research objective of this dissertation in regards to the effect of Competition Mode on Situational Competitiveness. Lastly, H10 addresses the third research objective of this research in regards to the effect of Dispositional Competitiveness on Situational Competitiveness. Figure 9 – Proposed Research Model of the Study Satisfaction has been defined as “a sense of contentment that arises from an actual experience in relation to an expected experience” (Hernon & Whitman, 2001, p. 32). Satisfaction with an IT system is an important factor due to its effect on attitude, intention, and continued usage of a technology or service (L. Deng et al., 2010; Oliver, 1977). In the current investigation, the game that was chosen as the basis of the empirical study was not one that participants would be likely to use in the future. As such, satisfaction with the game was deemed to be a more appropriate indicator of success with this technology experience and was chosen as the ultimate endogenous construct of the model. Satisfaction can be attributed to a person's feelings or attitudes toward a variety of factors affecting a particular situation (Wixom & Todd, 2005). Based on the theory of reasoned action (TRA), object-based attitude, recognized commonly as satisfaction, plays the role of an external variable for behavioural beliefs and attitude (i.e. perceived ease of use and perceived usefulness), which in return affect behavioural attitude and intention (Ajzen & Fishbein, 1980). Users’ satisfaction with a product is an important predictor of their attitude and intention toward that product (Oliver, 1977, 1980). DeLone and McLean’s (1992, 2002, 2003) IS success model reasons that for measuring the success of an IS product, the satisfaction of users after using the IS product can predict their intention to use, and therefore future usage of that IS product. The positive impact of satisfaction on attitude and intention to use have been shown in various contexts including continued use of online websites (Chia-Lin Hsu, Yu, & Wu, 2014; Kang & Lee, 2010), continued usage of Application Service Providers[footnoteRef:11] (Kim, Hong, Min, & Lee, 2011), customer loyalty (Deng, Lu, Wei, & Zhang, 2010), and online games (C.-C. Chang, 2013). [11: Application Service Provider (ASP) delivers application features to multiple customers from a data center across a wide area network (WAN) (Kim et al., 2011).] Based on the antecedents of satisfaction that DeLone and McLean (1992) introduced, i.e. information and system quality, Wixom and Todd (2005) divided satisfaction into information and system satisfaction. However, information quality is not relevant to the context and research questions of the current investigation. User’s system satisfaction or satisfaction from the game, which is the main endogenous construct in this study, is referred to as satisfaction for simplicity. According to Expectation Confirmation Theory (ECM/; Oliver, 1977, 1980), expectation reflects anticipated behaviour, and when a product or service outperforms our expectations, or creates positive disconfirmation for us, we would have satisfaction from using that product or service. The Flow experience during the video gameplay is the result of disconfirmation. This positive disconfirmation, according to ECM, results in satisfaction of the players from playing the video game. Therefore, in alignment with earlier research (C.-C. Chang, 2013; Chin-Lung Hsu, 2010; Shin, 2006) it is expected to see a direct relationship between Flow and reported Satisfaction of video gameplayers. Thus, it is hypothesized that: Hypothesis 1: Higher levels of Flow experience of video gameplayers will increase their satisfaction. As explained earlier, Flow theory indicates that in order to reach the optimal state of flow, an activity should require skills from the performers that are in balance with the challenge that the activity creates. Shin (2006) defines challenge as “the degree to which individuals find it difficult to cope with specific task involved” (p. 706). If the level of Perceived Challenge compared to necessary skills in a computer task is too low, the users will lose interest in performing that task and the task becomes boring (Ghani & Deshpande, 1994). Therefore, in order to reach the state of Flow, a video gameplayer should perform a task that requires a balance of challenge and skills (Csikszentmihalyi, 1991, 1997). In video games, where players learn the skills required for playing the game, increasing the level of challenge can create the balance between challenge and skill in playing the game, which can result in experiencing Flow. Preliminary research shows that direct measurement of balance using the construct of Balance of Challenge and Skill, such as Guo and Poole’s (2009) two item construct[footnoteRef:12] of Balance, tends to measure only the Skill antecedent of Flow rather than the actual balance between skills and challenges of participants. In fact, Guo and Poole’s (2009) results showed that there is an abnormally high correlation between Balance and Skill constructs (0.82), which may be an indication of multicollinearity. The alternative to measuring balance of Challenge and Skill would be to measure both of these constructs separately and develop a new calculated variable that would represent the construct of Balance. An example of this approach is proposed by Shin (2006) to create a new variable called flow condition (fc) by calculating the difference between the (standardized) level of challenge and skill of the participants. However, using the standardized difference between two variables ignores the potential polynomial relationship between the three variables (i.e. Challenge, Skill, and Balance). Therefore, the only valid solution to measure the relationship between Challenge and Skill’s balance and Flow would be to use the polynomial regression technique. However, structural equation modelling does not allow the integration of polynomial regressions. As such, this relationship needs to be analyzed in separate analyses. [12: The items of Balance construct are: 1) My abilities matched the high challenge of the situation; and 2) I felt I was competent enough to meet the high demands of the situation.] Generally, “along with individual skills, the challenges presented by an activity are the most important predictors of Flow” (Koufaris, 2002, p. 212). Consistent with Csikszentmihalyi’s (1975) discussion on the role of challenge, other scholars have also found Perceived Challenge to have the highest effect on engagement in a computer related activity (Ghani & Deshpande, 1994; Webster & Ho, 1997). Thus, in line with extant literature on Flow (Ghani & Deshpande, 1994; Shin, 2006; Pearce et al., 2005; Hoffman and Novak, 1996; Novak et al., 2000), the effect of Challenge on Flow will be measured independent of Skills in this investigation. This is based on the fact that the setting of the experiment of this research does not include a longitudinal component that would increase the level of Skill of the participants. Given that Skill does not considerably change throughought the experiment, it is expected that only Challenge will be manipulated based on the antecedents of the model. With a constant level of Skill, by increasing Challenge from low levels, it is hypothesized that Challenge will be matching the Skill level of video gameplayers and Flow would increase. As a result, consistent with previous research, we expect to see the same relationship between the perception of challenge in a video game and experiencing Flow by gameplayers. In summary, it is hypothesized that: Hypothesis 2: Higher levels of Perceived Challenge reported by the video gameplayers, will lead to a higher levels of Flow. In online gaming, feelings similar to arousal have been identified as one main category in the criteria for engagement (Charlton & Danforth, 2007), which can be associated with the experience of Flow. Neuroscientists have shown that emotional arousal enhances declarative memory, which can be the result of attention due to the arousal (Cahill & McGaugh, 1998; Coull, 1998). Extant literature supports the effect of arousal on attention (an important dimension of Flow) for various tasks and contexts (Matthews & Davies, 2001; Robbins, 1997). In the context of gambling, Brown (1986) explains that the increase of arousal in individuals narrows their attention. The theory of Optimum Stimulation Level (Raju, 1980) indicates that people prefer and function better in an optimum level of stimulation (arousal). Similarly, in video games, this sensation seeking behaviour can be seen as the root of the motivational factors for gamers, which influences their intense attention and enjoyment (Brown & Cairns, 2004) that are other aspects of Flow experience. Pace's (2004) grounded theory of Flow of web users shows the effect of challenge in information seeking tasks on the focused attention on that activity. In the video game context, it is expected to find the mediating effect of arousal on the relationship between challenge and attention. Moreover, Léger et al. (2010) have shown that arousal is highly correlated with heightened enjoyment. Tauer & Harackiewicz (1999) found that positive affective responses –including arousal– mediate the relationship between competition and task enjoyment. In summary, video gameplayers’ arousal during the gameplay affects various dimensions of Flow. Thus, it is hypothesized that: Hypothesis 3: Higher arousal levels of video gameplayers will increase their Flow experience. Competition can be conceptualized as an individual personality trait (outlined below in hypothesis 9) or as a characteristic of the environment that an individual perceives. The latter form of competitiveness is known as “Situational Competitiveness”, which explains the behaviour of people in a competitive context (Graziano, Hair, & Finch, 1997). Individuals can react differently to various contexts – being cooperative in one context and being competitive in another. Arousal, being a “reported subjective feeling”, is the opposite of sleepiness (Russell, Weiss, & Mendelsohn, 1989). When playing a video game, gamers face various challenges (Jennett et al., 2008), which result in emotional involvement potentially causing physiological alteration (Eysenck, 1976; Graziano, Feldesman, & Rahe, 1985). According to the theory of Flow (Csikszentmihalyi, 1997), when challenge and skill are well balanced, individuals experience arousal. In fact, challenge is the most important determinant of Flow (ibid). However, higher levels of challenge that are not balanced with the skill level of the person might cause that person to become anxious and frustrated, which create emotional arousal in a person. Thus, it is hypothesized that: Hypothesis 4: Higher levels of Perceived video game Challenge will increase feelings of Arousal. Little is known about the mechanism through which competitiveness of a video game impacts one’s motivation to play, and potentially to be addicted. Scholars studying addiction have indicated that gamblers experience feelings similar to being “hyped up”, which they refer to as arousal (Titz, Andrus, Miller, & others, 2001). Arousal has been conceptualized by Holsapple & Wu (2007) as “the state of emotional and mental activation or alertness,” (p. 87) which is an emotional response during the use of a hedonic IT product. Similar to gambling (Titz et al., 2001), it is expected to observe excitement among players of competitive video games due to having expectation in receiving rewards (often intrinsic) upon winning the game. Ravaja et al. (2006) support this claim by expressing that “social-competitive situation” increases arousal. Thus, it is hypothesized that: Hypothesis 5: Higher Situational Competitiveness of a video game will increase Arousal of the players. Challenge has been closely connected to competition (Vorderer, Hartmann, & Klimmt, 2003b), where creating competitive situations is easily manifested through the creation of challenge (Csikszentmihalyi, 1991). Reeve and Deci’s (1996) research on the effect of competition on intrinsic motivation showed that when competition provides proper forms of feedback, the task can become more challenging than not only when the task does not involve competition, but it can also be more challenging than when competition involves no feedback. Previous research has also shown that some people tend to search for competitive situations in order to create competition for themselves (Udvari & Schneider, 2000). Webster & Ho (1997) have used comparison techniques that motivate competitiveness in order to distill the level of challenge among students. In the context of video games, competition can be used to create challenge. Previous studies have shown that “competitive elements” used in video games provide interactivity and clear and immediate feedback, which enables “active engagement” and a sense of challenge (Vorderer et al., 2003a). Thus, it is hypothesized that: Hypothesis 6: Higher Situational Competitiveness of a video game will increase Perceived Challenge of the players. Social presence (the degree to which an individual is aware of another person in a communication interaction) has been studied in various contexts, in particular in website use, where scholars have explained the positive effects of different human-centric features such as ‘human images’ (Cyr, Head, & Ivanov, 2009; Hassanein & Head, 2007). Social Facilitation Theory (SFT) (Zajonc, 1965) posits that the presence of others triggers the motivation to compete, and consequently, how people evaluate competitiveness of an environment. Moreover, previous research has shown the presence of others to directly affect people’s competitive behaviour (Garcia & Tor, 2009), indicating a salient effect of social presence on the perception of competitiveness. In other words, “social comparison processes fuel the motivation to compete” (ibid. p. 5). Thus, Hypothesis 7: Higher perceptions of Social Presence will increase Situational Competitiveness of video game experience. In video gameplay, players can have various modes of competitive playing. Some games, for example sandbox or open world games, provide immense freedom for players to explore and build new things. In these types of games, players do not have a means for comparison with other individuals, providing no medium for competition. In other types of games, which provide competitive elements, competition can be seen in three forms. In some video games, the game includes intelligent agents that can play sophisticatedly against a human player. Thus, they create a mode of competition against the computer. In other situations where games can be played with multiple users, players can compete against one another. In this case, the mode of competitio