The Investigation of Role of Game Experience on Satisfaction and Replay Intention
Feelings, Flow, Immersion, Social Experience, Satisfaction, and Intention to Replay:
An Empirical Investigation of Online Game Experience
PhD Candidate: Bader Albatati
Student No: 21028048
This thesis is presented for the degree of Doctor of Philosophy of The University of Western Australia Business School
2017
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THESIS DECLARATION
I, Bader Albatati, certify that:
This thesis has been substantially accomplished during enrolment in the degree.
This thesis does not contain material which has been accepted for the award of any other degree or diploma in my name, in any university or other tertiary institution.
No part of this work will, in the future, be used in a submission in my name, for any other degree or diploma in any university or other tertiary institution without the prior approval of The University of Western Australia and where applicable, any partner institution responsible for the joint-award of this degree.
This thesis does not contain any material previously published or written by another person, except where due reference has been made in the text.
The work(s) are not in any way a violation or infringement of any copyright, trademark, patent, or other rights whatsoever of any person.
Third party editorial assistance was provided in preparation of the thesis by ICS & ProofReading.Com.
The work described in this thesis was funded by the Saudi Cultural Mission.
This thesis does not contain work that I have published, nor work under review for publication.
Signature: Bader Albatati
Date: 28 October 2017
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ABSTRACT
Massively Multiplayer Online Role Play Games (MMORPG) are a genre of games that has enjoyed phenomenal market success due to the business model of in-game purchases and push advertising. MMORPGs are an exciting trend in entertainment consumption and provide the opportunity for both industry and academia to examine player consumption and habitual patterns.
Two studies were conducted using samples of MMORPGs players from India and the U.S. with a structured questionnaire. Both samples were collected from online panels from Amazon
Mechanical Turk (Mturk). Study 1 adopts an Indian sample of 319 respondents with a research aim to examine the effects that both positive and negative feelings have on flow experience, immersion experience, and social experience. Results show that positive feelings have positive effects on flow, immersion and social experience. However, negative feelings do not appear to affect flow, immersion or social experience.
Study 2 collects a sample of 425 US respondents for the purpose of examining the effects positive feelings have on flow experience, immersion experience, social experience, game satisfaction and intention to replay. This study also investigates the effect of flow, immersion, and social experience on game satisfaction and intention to replay. The results show that positive feelings have positive effects on flow, immersion and social experience. Furthermore, positive feelings have positive effects on players’ satisfaction and intention to replay. Flow experience appears to have a positive effect on both game outcomes (satisfaction and intention to replay), whereas immersion had no significant effect on either of the game outcomes. Social experience demonstrates a positive effect on players’ satisfaction but an insignificant effect on intention to replay. The study also detects a positive relationship between game satisfaction and replay intention.
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Bader Albatati PhD Thesis October 2017
The findings of the two studies offer important insights on game experiences and their influences on game satisfaction and reply intention. A few theoretical and managerial implications are uncovered and discussed.
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TABLE OF CONTENTS
THESIS DECLARATION ...... i ABSTRACT ...... 1 ACKNOWLEDGEMENTS ...... 10 CHAPTER 1 RESEARCH OVERVIEW ...... 12 1.1 Introduction ...... 12 1.2 Background to the Study ...... 12 1.3 Massively Multiplayer Online Games ...... 14 1.4 Previous Research on Digital Games ...... 18 1.5 Research Rationale ...... 22 1.6 Research Questions and Objectives ...... 28 1.7 Research Contributions ...... 30 1.8 Research Methods...... 31 1.9 Thesis Structure ...... 31 CHAPTER 2 LITERATURE REVIEW ...... 32 2.0 Introduction ...... 32 2.1 An Overview of Experience ...... 32 2.1.1 Experience in Education ...... 32 2.1.2 Experience in Tourism ...... 33 2.1.3 Experience in Marketing...... 35 2.1.4 Dimensions of Experience ...... 41 2.2 Massively Multiplayer Online Role-Playing Games (MMORPGs) ...... 43 2.2.1 Genre and Game Choice ...... 45 2.2.2 Graphics and Virtual Environment ...... 46 2.2.3 Player Motivation ...... 48 2.3 Game Experience ...... 50 2.3.1 Definition of Game Experience ...... 51 2.3.2 Feelings ...... 52 Positive Feelings ...... 55 Negative Feelings ...... 59 2.3.3 Flow Experience ...... 62 2.3.4 Immersion Experience ...... 66 2.3.5 Social Experience ...... 69
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2.4 Outcomes of Game Experience ...... 72 2.4.1 Consumer Satisfaction ...... 72 2.4.2 Intention to Replay ...... 75 2.5 Hypothesis Development ...... 78 CHAPTER 3 METHODOLOGY ...... 80 3.0 Introduction ...... 80 3.1 Hypotheses ...... 80 3.2 Research Design and Method ...... 82 3.2.1 Research Rationale ...... 82 3.2.2 Quantitative Research Instruments – Survey and Questionnaire ...... 83 3.3 Measurement ...... 85 3.4 Data Collection Methods ...... 87 3.4.1 Reliability of AMT ...... 90 3.5 The Data Sample ...... 93 3.6 Structural Equation Modelling ...... 94 3.7 Measurement of Constructs and Items ...... 94 3.7.1 Feelings ...... 95 3.7.2 Flow Experience ...... 98 3.7.3 Immersion Experience ...... 99 3.7.4 Social Experience ...... 100 3.7.5 Satisfaction ...... 101 3.7.6 Intention to Replay ...... 102 Chapter 4 STUDY ONE ...... 103 4.0 Introduction ...... 103 4.1 Objective of the Study ...... 103 4.2 Data Collection Method ...... 105 4.3 Data Cleaning and Missing Data ...... 105 4.4 Data Analysis ...... 106 4.4.1 Factor Analysis and Multicollinearity ...... 107 4.4.2 Reliability Check ...... 109 4.5 Assessment of Statistical Fit ...... 109 4.5.1 Measurement Fit ...... 113 Flow Experience ...... 121 Immersion Experience ...... 122
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Social Experience ...... 123 4.5.2 Final Model and Hypothesis Testing ...... 124 The First Full model ...... 124 4.6 Summary of Results of Study One ...... 130 4.6.1 Findings of Study One Hypotheses Testing: ...... 131 CHAPTER 5 STUDY TWO ...... 132 5.0 Introduction ...... 132 5.1 Objective of the Study and Hypotheses ...... 132 5.2 Data Collection Method ...... 134 5.3 Data Cleaning and Missing Data ...... 134 5.4 Players’ Demographics ...... 135 5.5 Factor Analysis and Reliability Test...... 136 5.6 Measurement Fit ...... 140 5.7 Final Model and Hypothesis Testing ...... 150 5.7.1 Respecified Model 1 ...... 152 5.7.2 Respecified Model 2 ...... 153 5.7.3 Hypothesis Testing ...... 154 5.8 Summary of the Results in Study Two ...... 158 5.9 Hypotheses Testing in Study Two ...... 159 CHAPTER 6 DISCUSSION AND CONCLUSION...... 161 6.0 Introduction ...... 161 6.1 Discussion ...... 161 6.1.1 Objective One ...... 162 Positive feelings and flow experience ...... 162 Positive feelings and immersion experience ...... 163 Positive feelings and social experience ...... 164 Summary ...... 165 6.1.2 Objective Two ...... 166 Negative feelings and flow, immersion and social experiences...... 166 6.1.3 Objective Three ...... 169 The effect of positive feelings on satisfaction and intention to replay ...... 169 6.1.4 Objective Four ...... 171 The effect of flow experience on satisfaction and intention to replay ...... 171 The effect of immersion experience on satisfaction and intention to replay ...... 173
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The effect of social experience on satisfaction and intention to replay ...... 175 6.1.5 Objective Five ...... 176 Satisfaction and intention to replay ...... 176 6.2 Summary and Theoretical Contributions ...... 178 6.3 Managerial implications ...... 180 6.3.1 Game design ...... 180 6.3.2 Emotional appeal ...... 181 6.3.3 Advertising ...... 181 6.3.4 Player retention ...... 182 6.3.5 Balance and Flow ...... 182 6.3.6 Maximisation ...... 183 6.3.7 Positivity ...... 183 6.3.8 Product placement ...... 184 6.3.9 Cultural appeal ...... 185 6.3.10 Market segmentation ...... 185 6.4 Limitations and future studies ...... 186 6.4.1 Genre specificity ...... 186 6.4.2 Online data collection and control ...... 186 6.4.3 Light/heavy player distinction ...... 187 6.4.4 Gender differences ...... 187 6.4.5 Product performance measure ...... 187 6.4.6 Brand engagement ...... 188 6.4.7 Cultural differences ...... 188 6.4.8 Values ...... 188 6.5 Conclusion ...... 189 REFERENCES ...... 192 APPENDICES ...... 212 Appendix A: Information Letter, Consent Form, and Online Questionnaire ...... 212 Appendix B: Factor Analyses Results of Study 1 (Indian players) ...... 224 Appendix C: The Reliability (Cronbach's Alphas) for Study 1 (Indian players)...... 227 Appendix D: Covariance’s Modification for Negative Feelings (Indian Players) ...... 232 Appendix E: Factor Analysis related to Study 2 (US Players) ...... 233 Appendix F: The Reliability (Cronbach's Alphas) for Study 2 (US players) ...... 236 Appendix G: Covariances and Correlations for Study 2 (US players) ...... 240
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LIST OF FIGURES
Figure 1-1. Screen Play of TinyMUD ...... 15 Figure 1-2. Classification of Digital Games ...... 16 Figure 3-1. Hypothesis Mapping on Concept Map ...... 82 Figure 4-1. Study One investigations and hypotheses...... 104 Figure 4-2. Fit Measurement Statistics for Powerful Feeling ...... 114 Figure 4-3. Fit Measurements Statistics for Fun Feeling ...... 115 Figure 4-4. Measurement Fit Statistics for Imaginative Factor ...... 116 Figure 4-5. Measurement Fit Statistics for Imaginative Factor ...... 117 Figure 4-6. Measurement Fit Statistics for Sensory Feeling ...... 118 Figure 4-7. Measurement Fit Statistics for Escapism Feeling ...... 118 Figure 4-8. Measurement Fit Statistics for Negative Feeling...... 120 Figure 4-9. Measurement Fit Statistics for Negative Feeling...... 121 Figure 4-10. Measurement Fit Statistics for Flow Experience ...... 122 Figure 4-11. Measurement Fit Statistics for Immersion Experience ...... 123 Figure 4-12. Measurement Fit Statistics for Social Experience ...... 124 Figure 4-13. Measurement Fit Statistics for Full Model ...... 126 Figure 4-14. Measurement Fit Statistics for the 2nd Model ...... 128 Figure 5-1. Study Two Investigations and Hypotheses ...... 133 Figure 5-2. CFA model fit statistics for Powerful Feeling ...... 141 Figure 5-3. CFA Model Fit Statistics - Modified Measurement Model of Powerful Feeling ...... 142 Figure 5-4. CFA model fit statistics for Fun Feeling ...... 143 Figure 5-5. CFA model fit statistics for Imaginative Feeling ...... 144 Figure 5-6. CFA model fit statistics for Sensory Feeling...... 145 Figure 5-7. CFA model fit statistics for Escapism Feeling ...... 146 Figure 5-8. CFA model fit statistics for Flow Experience ...... 146 Figure 5-9. CFA model fit statistics for Immersion Experience ...... 147 Figure 5-10. CFA model fit statistics - Modified Measurement Model for Immersion Experience ..... 148 Figure 5-11. CFA model fit statistics for Social Experience...... 148 Figure 5-12. CFA model fit statistics for Satisfaction ...... 149 Figure 5-13. Re-specification of CFA model fit statistics for Satisfaction ...... 150 Figure 5-14. CFA fit statistics for Intention to Replay ...... 150 Figure 5-15. Measurement Fit Statistics for the Final Model with all Interactions ...... 151 Figure 5-16. Measurement Fit Statistics for the Final Model with all Interactions ...... 153 Figure 5-17. Re-specified model fit statistics for the model with item correlations ...... 154
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LIST OF TABLES
Table 3-1. List of Hypotheses and Related Studies ...... 81 Table 4-1. List of Hypotheses and Relationships to be tested ...... 105 Table 4-2. Measurement Fit Indices for all factors ...... 124 Table 4-3. CFA Final Model Results for Flow, Immersion and Social ...... 126 Table 4-4. SEM Results for Flow, Immersion and Social ...... 127 Table 4-5. CFA results for Flow, Immersion and Social in the 2nd Model ...... 128 Table 4-5. Relationships of Positive Feelings to Flow, Immersion and Social ...... 130 Table 4-7. Relationship between Positive Feelings on Fun, Powerful, Imaginative, Sensory and Escapism ...... 131 Table 4-8. Findings of the Hypotheses Testing ...... 131 Table 5-1. List of Hypotheses and Relationships to be tested ...... 133 Table 5-2. List of Constructs and Associated Items ...... 136 Table 5-3. Measurement Fit Indices for all Factors ...... 140 Table 5-4. Measurement Fit Statistics for the Final Model with all Constructs ...... 152 Table 5-5. SEM results for all Factors ...... 155 Table 5-6. List of all Hypotheses Results ...... 159
Table B- 1. Factor Analysis Results for Powerful feeling ...... 224 Table B- 2. Factor Analysis Results for Fun ...... 224 Table B- 3. Factor Analysis Results for Imaginative Construct ...... 224 Table B- 4. Factor Analysis Results for Sensory Construct ...... 225 Table B- 5. Factor Analysis Results for Escapism Construct ...... 225 Table B- 6. Factor Analysis Results for Negative Feeling ...... 225 Table B- 7. Factor Analysis Results for Flow ...... 225 Table B- 8. Factor Analysis Results for Immersion ...... 226 Table B- 9. Factor Analysis Results for Social ...... 226 Table B- 10. Factor Analysis Results for Satisfaction ...... 226 Table B- 11. Factor Analysis Results for Intention to replay ...... 226
Table C- 1. Item-Total Statistics for Powerful ...... 227 Table C- 2. Item-Total Statistics for Powerful after removal of an item ...... 227 Table C- 3. Item-Total Statistics for Fun ...... 227 Table C- 4. Item-Total Statistics for Imaginative ...... 228 Table C- 5. Item-Total Statistics for Sensory ...... 228 Table C- 6. Item-Total Statistics for Escapism...... 228 Table C- 7. Item-Total Statistics for Negative Feeling...... 229 Table C- 8. Item-Total Statistics for Immersion ...... 229 Table C- 9. Item-Total Statistics for Flow ...... 229 Table C- 10. Item-Total Statistics for Flow after removal of an item ...... 230 Table C- 11. Item-Total Statistics for Social ...... 230 Table C- 12. Item-Total Statistics for Social after removal of an item ...... 230
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Table D- 1. Covariance’s Modification Indices of the First Specified Model of Negative Feeling for India Players ...... 232
Table E- 1. Factor Analysis for Powerful Feeling ...... 233 Table E- 2. Factor Analysis for Fun ...... 233 Table E- 3. Factor Analysis for Imaginative ...... 233 Table E- 4. Factor Analysis for Sensory ...... 233 Table E- 5. Factor Analysis for Escapism ...... 234 Table E- 6. Factor Analysis for Negative Feeling ...... 234 Table E- 7. Factor Analysis for Immersion ...... 234 Table E- 8. Factor Analysis for Flow ...... 235 Table E- 9. Factor Analysis for Social ...... 235 Table E- 10. Factor Analysis for Satisfaction ...... 235 Table E- 11. Factor Analysis for Intention to Replay...... 235
Table F- 1. Item-Total Statistics for Powerful...... 236 Table F- 2. Item-Total Statistics for Fun ...... 236 Table F- 3. Item-Total Statistics for Imaginative ...... 236 Table F- 4. Item-Total Statistics for Sensory ...... 237 Table F- 5. Item-Total Statistics for Escapism ...... 237 Table F- 6. Item-Total Statistics for Immersion ...... 237 Table F- 7. Item-Total Statistics for Flow ...... 238 Table F- 8. Item-Total Statistics for Social ...... 238 Table F- 9. Item-Total Statistics for Satisfaction ...... 239 Table F- 10. Item-Total Statistics for Intention to Replay ...... 239
Table G- 1. Correlation between items For Model One In Chapter 5 ...... 240 Table G- 2. Correlations between positive feelings constructs ...... 240
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ACKNOWLEDGEMENTS
I would like to express my deepest gratitude to both of my supervisors Dr. Fang Liu and,
Professor Dick Mizerski for their continuous support as well as their patience, motivation, and immense knowledge throughout this academic journey. Their guidance and mentoring were invaluable in progressing this research to completion. Moreover, I express my gratitude to them for their support and assistance in helping through my difficult personal issues over the course of my study.
I extend special thanks to Professor Dick Mizerski who encouraged me in selecting such an interesting research topic that I am sure will retain academic and industry interest for many years into the future. It was Professor Dick Mizerski who suggested that the topic would be absorbing and have the propensity for further development. His encouragement was the reason for my selection of video games and in particular MMORPGs for the PhD research.
I offer special thanks to Associate Professor Fang Liu for always being there for me and helping me bring this thesis to completion following Professor Dick Mizerski’s retirement. I would like to also thank her for providing me with the opportunity to join her teaching team as a tutor for the advertisement and promotion unit. This experience was an incentive to broaden my knowledge with different perspectives and help me in building my future career in marketing.
My sincere thanks also go to Professor Geoff Soutar for his insightful comments and encouragement. My thanks go to all faculty members within the marketing discipline for all their help and support.
I would like to express my appreciation for the support received from the Saudi Cultural Mission for all the help I received during my PhD. I thank the University of Western Australia administration staff and the graduate research school for all the help and support that I received during my PhD journey.
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I thank my fellow friends from the University of Western Australia for all the stimulating discussions, and for all the fun we had over the years. Special thanks go to my dear friend Dr.
Yunous Vagh for all his support and help in reading and editing most of my writings. His insights of his own academic journey, life experience and anecdotes sustained me through some difficult times and helped me in my personal development and self-improvement. I thank all of my personal friends who helped me directly or indirectly in completing this thesis.
Last but not the least, I would like to thank my family; my parents and my brothers and sister for supporting me spiritually throughout writing this thesis and my life in general. Special and heartfelt thanks are reserved for my mother Fareedh Bazeed, and father Khaled Albatati for all their support, encouragement, and prayers for my thesis to be completed. As much as I would like to express my total gratitude to my family, I know that no amount of written or spoken words will ever be enough.
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CHAPTER 1 RESEARCH OVERVIEW
1.1 Introduction
This chapter provides an overview of the thesis wherein the specific aims and objectives of the research are outlined. It commences with a commercial and historical background to the gaming industry. Research rational and gaps will then be discussed. Finally, it concludes with objectives, scope and contributions.
1.2 Background to the Study
The Electronic Entertainment Expo, commonly known as E3, is an annual event presented by the Entertainment Software Association and is usually held yearly around June (Harvey-Gurr,
2011). The expo allows computer companies to showcase their market and pre-market equipment and game titles. In 2011, the Global President of Nintendo Wii, Satoru Iwata, announced that the most important question that needs to be addressed by the gaming industry is the question of who, why and how individuals play games (Wolverton, 2011). Mr. Iwata called for the production of new and improved games and devices to provide richer experiences and greater appeal for gamers. Moreover, the President of Nintendo America, Mr. Reggie Fils-
Aime, also asserted that new developments in the gaming industry enrich game experience through enhanced in-game personal and social experiences, and increased choices and methods of game play (GameSpot, 2011).
The digital gaming industry is a large consumer market that is classified into two streams of games, online and offline. Gaming in the computer industry began in 1976 and since then its growth has rapidly expanded. The whole market has gone through a number of phases, starting with offerings by “Fairchild” and “Atari” and ending with full online console gaming from industry leaders such as PlayStation (PS3), Nintendo (Wii), and Microsoft (Xbox) (Gallagher
& Park, 2002). Recently, game devices and game play have become revolutionised. Companies,
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Bader Albatati PhD Thesis October 2017 such as Sony and Microsoft, have introduced their new consoles PlayStation 4 and Xbox One.
These consoles have improved the game quality (graphics, sounds, etc.) and online features
(Humphries, 2017). Moreover, players can play the games on other devices, such as PCs and smartphones, each of which provides players with a unique experience (Kokil, Luis, & Sánchez, n.d.).
Since the advent of the Internet and its current ubiquitous nature, entertainment has become easier to access and enjoy, and is available to a world-wide market. People are just a mouse-click (PC) or finger touch (tablet/phone) away from their favourite movies, games, or to socialize with their friends. Online games are part of the entertainment industry which offers products that feature fun, involvement, storytelling, and a place where friends congregate in a virtual world environment. The popularity and development of the Internet has opened new opportunities for the gaming industry. Game vendors can reach a broader consumer market and make timely installments to the game to retain market share. The online features have made the video game market more appealing as players from different parts of the world are gathered in the same virtual space at the same time. Thus, the experience and the enjoyment of players is enriched (Weibel, Wissmath, Habegger, Steiner, & Groner, 2008).
In the US alone, game sales increased from 2.2 billion US dollars in 1996, to almost 15.9 billion US dollars in 2010, representing a 623% increase in sales (Association, 2008). ComScore
Inc., one of the leading online companies in measuring digital consumption, reported that the number of online game players in the U.S. alone reached 86 million in 2008 (Lipsman, 2009).
According to DFC Intelligence, the video game market generated $52 billion in revenue worldwide for 2011 alone. Revenue for games played on PCs, TV-based consoles and mobile devices is expected to increase beyond $118 billion in 2019 (Newzoo, 2016). In addition, DFC
Intelligence also predicts that by 2017, two thirds of gaming software will be delivered through the electronic web. Most of these games will be played online, and will offer less expensive play
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Bader Albatati PhD Thesis October 2017 than console based games (DFC Intelligence, 2012). The diverse and growing online game market has triggered the interest of academics who are eager to understand this market phenomenon.
1.3 Massively Multiplayer Online Games
Massively Multiplayer Online Role-Playing Games (MMORPGs) is an important format of online games that has undergone a number of changes over the last thirty years. Since its inception, many new titles and genres became available for players, and currently, different gaming manufacturers are seriously competing for market-share.
In earlier days, MMORPGs were restricted to text-based games called “Multi User
Dungeons” (MUD). Multi User Dungeons provided players in the early eighties the option to explore an online world, to play with other players and to battle computer-based creatures or each other. The earliest MUD game was developed in 1979 by university student Roy Trubshaw.
In 1989, another student from Carnegie Mellon University developed a new form of the MUD game called TinyMUD, depicted in Figure 1-1, that was accessible to any player on the Internet using IP addresses and port numbers for individual gaming sessions. The relative ease of use of
TinyMUD was responsible for the popularity of MUD games in the late eighties to early nineties until the development of MMOs (Massive Multiplayer Online Games) (Meredith, Hussain &
Griffiths, 2009; Petitte, 2012).
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Figure 1-1. Screen Play of TinyMUD
It was not until 1993 that the first massively multiplayer online game (MMO) entitled
“Doom” appeared on the commercial market. Within two years of its release, ten million people were playing the game. In 2012, it was estimated that around 400 million people were playing
MMOs globally (Petitte, 2012). This growing market popularity is mostly attributed to the variety, interactive delivery and lower player cost of MMO games ( Meredith et al., 2009).
Since the development of “Doom”, MMO production has increased manifold. Currently, there are different categories with a correspondingly increased number of MMOs per category.
This increase in MMO games is related to improvements in design, graphics and device capability. Furthermore, increasingly faster internet connections and global availability has seen more people becoming gamers.
Notwithstanding the exponential rise in the number of online games and customers, relatively few empirical studies investigated the causes for playing the games. However, more focus was directed by researchers on demographics and the time players spend on online game playing. For example, a study done by Schiano, Nardi, Debeauvais, Ducheneaut & Yee (2011) on players of World of Warcraft’s (WoW), a famous MMORPG with more than 12 million
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Bader Albatati PhD Thesis October 2017 subscribers, found that patterns of play are different between countries and regions. However, the study lacked a theoretical rationale from consumer perspectives.
Figure 1-2. Classification of Digital Games
Digital Games
Off Line Online Games Games
Puzzles, Action, Action, RPGs, Puzzles, Cards, and Social Games sports, RPGs, MMO Strategy Boards, Cards Boards etc.
First Person Sports MMORPGs Shooter
Online games (see Figure 1-2) are defined as products that include the physical features of graphics, design, internet connection, and game controllers. Most of the sites and writers who discuss online games agree that the term “online game” is a form of entertainment where players can download or play the game on the World Wide Web, by using different platforms like consoles, PCs and phones using the internet (DFC Intelligence, 2005; Kleeberger & Hummel,
2002). Downloadable games are usually downloaded by the player to devices, like phones or
PCs, and are played as standalones. Many Web games are played online in a virtual place, but they also provide interaction with other real players. Web games are usually referred to as online games.
Online games have many genres from which players can choose. The type of online games varies from puzzles to social games. Massive Multiplayer Online Games can be further divided into different categories like fantasy, horror, and sports. Hamlen (2011) identified 14 types of games and Figure 1-2 depicts the various genres of online games available. Multiplayer
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Bader Albatati PhD Thesis October 2017 online role-playing-games (MMORPGs) is a form of digital gaming that is predominantly played online. According to Barnett & Coulson (2010), the word “massively” means that a huge number of people play online games simultaneously. Players immerse themselves in an online fantasy world and compete against each other or cooperate to achieve certain goals within the game in real-time (Barr, Noble, & Biddle, 2007). This emphasises the fact that people need an internet connection to play MMORPGs in the online world simultaneously, unlike other forms of video games where only an individual copy is sufficient to play the game.
The role-playing aspect refers to players who interact with the gaming world and other players by using customisable avatars to launch in the fantasy world. These avatars can be of different races and classes and are chosen by players to propel their actual personality into a make-believe virtual one (Barnett & Coulson, 2010). Depending on the avatar’s race and class, each player has unique skills and abilities that help with game progression. All members of a particular race or class are designed to share certain qualities of appearance and strength which make them distinctive (Monson, 2012). Once players design their avatars they can log into the fantasy world that is changing consistently.
A study by Castronova (2001) on players of EverQuest (famous online game developed by Sony Entertainment) identified three features characteristic of the virtual world. Firstly, he identified “interactivity” as the most important feature. Interactivity occurs when a number of people from the globe enter the virtual world simultaneously, wherein their decisions and actions affect the whole community. The second feature is “physicality”, which is described as the reaction of players to the game environment. This environment is ruled by cyber game laws in conjunction with the scarcity of resources that make the game challenging. The third feature is “persistence”, where MMORPG world continues to change regardless of whether it is used or not. This aspect means that the game will remember the location of people and things to facilitate continuance of game-play despite intervals of non-use. Moreover, people in the virtual
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Bader Albatati PhD Thesis October 2017 gaming world have their own economy with corresponding trade, auction and game currency.
They can even sell their individual avatars after accumulation of experience points on the real market. MMORPGs have become a live environment where the product combines the graphics of a 3D game (e.g., Fallout, a famous offline open world role playing game developed by
Interplay Entertainment) with a chat-based social interaction system originally used in MUDs
(Castronova, 2001).
1.4 Previous Research on Digital Games
Most of the studies on video and computer games were in the field of education, psychology, medicine, and computer science research (Griffiths, Davies, & Chappell, 2004b). Online games depend on internet technology that has become progressively very advanced and easy to access by millions around the world. MMOs are becoming a trend as revealed by the rising number of people joining these game communities and the huge numbers of games introduced to the market. It has also become a significant area for research because of controversial issues relating to potential medico-social pathology that games can have on the well-being of players, such as game addiction or withdrawal symptoms.
The main concern identified within the body of digital game literature was the player intention for playing games (Koo, 2009). For example, computer science research is focused on the context, negative effects gaming leaves on players (i.e. aggression) and why they play
(game-play motivations). Theories, such as Theory of Planned behavior (TPB), is also applied to digital game playing (Lee & Tsai, 2010). These social studies were more directed to understand how attitudes of gamers may explain intention to play games.
Researchers in the medical field tend to focus their attention on two negative aspects of video gaming. These are the effect on children and adolescents who play games excessively, and/or if playing games make children and teenagers violent (Wood, Griffiths, Chappell, &
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Davies, 2004). A study by Silvern & Williamson (1987) found that children aged between four to six who play violent video games and watch violent cartoons, are likely to demonstrate increased aggressive behaviour patterns. Another study by Anderson & Dill (2000) tested the effect of playing violent online games such as Doom for a long period of time and its consequent increase in aggression in both males and females (Anderson & Dill, 2000). Anderson & Dill's
(2000) study also claimed that even short periods of online game play led to an increase in aggressive behaviour in both genders. However, a number of studies argued whether electronic game play have any negative consequences on players (Durkin & Barber, 2002; Wood et al.,
2004).
Conversely, recent studies showed that playing video games may have positive effects, such as enhancing skills in problem solving, communication and team building. For example,
Durkin & Barber (2002),through their study of game play of high schoolers, suggest that gamers can be more productive in school due to their various adjustment and risk taking abilities. Durkin
& Barber (2002) also found that game players scored higher than non-players on “family closeness”, “activity involvement”, “positive school engagement”, “positive mental health”,
“substance use”, self-concept”, “friendship network”, and “disobedience to parents” (p. 373).
Regardless of which side of the social argument on the effect of playing digital games one leans; the gaming market is rapidly increasing.
Knowledge of purchase trends and in-game behaviour is of paramount importance to the gaming industry. For example, how players purchase a certain type of MMORPG is crucial to marketers. Svenson (1979) stated that human decision making cannot be fully understood through the study of the final decision alone. In other words, consumer decisions need to be studied as the process of engaging with the product. The decision-making process may be focused on the cognitive, emotional or habitual aspect.
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When consumers make a purchase decision, they often go through one or more types of processes, which may or may not result in a commitment to buy (Punj, 1987). Depending on the complexity of the decision, consumers may go through a cognitive-focused scenario, such as in the case of buying an automobile (Nayeem, 2012; Quester et al., 2007). Alternatively, it can be less complex, where consumers rely more on emotions and experience, such as in the case of buying hedonic products such as music (Holbrook & Hirschman, 1982) .
According to East, Wright & Vanhuele (2008), consumers engaged in a cognitively- focused process, will carefully analyse the benefits and the costs of the product prior to purchase.
Consumer’s attitudes play an imperative role in making this type of purchase decision. This has been referred to as the “extended problem solving model” in numerous studies (Holbrook,
Chestnut, Oliva, & Greenleaf, 1984). Similarly, when consumers are buying something for the first time they will take time to gather information about the product and evaluate its utilitarian or functional aspects (Bosnjak, Osti, & Bosnjak, 2010). For example, if a gamer is playing a video game for the first time, the quality of the game or other cognitive aspects will be important in the decision-making. This notion was applied extensively throughout the gaming literature by explaining player intentions through motivation and attitudes (Koo, Lee, & Chang, 2007;
Przybylski, Rigby, & Ryan, 2010; Yee, 2006a).
Motives directly affect consumer choices and are often categorized into two broad types known as extrinsic and intrinsic motivation. According to Ryan & Deci (2000), behaviours pursued to access enjoyable end states or avoid aversive ones are extrinsic motives, whereas those pursued for their own sake or inherent satisfaction are called intrinsic motives. Some researchers who believe in extrinsic motives as the major influencer of game preference explain gameplay from the ease of use, performance and game characteristics (e.g., rich visuals, high speed) (Hsu & Lu, 2007; Kong, Kwok, & Fang, 2012). Other researchers who believed in the intrinsic motives argue that game experiences are most important in predicting gameplay
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Bader Albatati PhD Thesis October 2017 intentions. They believe that gamers choose games that appeal to their emotional and social experiences (Dickey, 2007; Jeng & Teng, 2008; Ryan, Rigby, & Przybylski, 2006).
Another body of game literature examined the effect of players’ attitudes towards online gameplay. As an outcome of problem solving, consumers usually begin a cognitive learning process about the pros and cons of the product of purchase interest. The learning process can influence the decision to purchase the product. Consumers will commit to purchase the product if their perspective of the benefits exceed the cost of the product. Following the purchase, the consumer will begin to evaluate the expected performance of the product to the actual performance, which may influence any future re-use or repeat purchase of the product.
As a result of this evaluation, consumers develop a set of beliefs that are formed as attitudes towards the product (Ajzen, 1985; Ajzen & Fishbein, 1980). This cognitive process can affect player behaviour to replay a MMORPG in terms of quality and performance of the game (Davis & Lang, 2011).
Studies such as Hsu & Lu (2004) adopted the Technology Acceptance Model (TAM) and merged it with the model of the Theory of Reasoned Action (TRA) to predict players’ attitudes and intention to play. The study collected data from 233 visitors of game related websites, and found that attitudes towards playing have a strong effect on intention to play online games. Hsu & Lu (2004) could not find any direct relation between the social norms and intention to play online games. However, they did find that flow experience (e.g., how well the game progresses) was an important predictor for intention to play an online game. In their study,
Hsu & Lu (2004) covered aspects of flow experience such as “control”, “concentration” and
“intrinsic interest” (p. 858).
Similarly, Wu & Liu (2007) adopted the Theory of Reasoned Action (TRA) to predict intention to play an online game. They also found that attitude towards the game was crucial in
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Bader Albatati PhD Thesis October 2017 predicting the play intention. They found that feelings (such as enjoyment) in playing a game has a major influence in forming positive attitudes towards a specific genre of game and the associated intention to play.
In summary, many previous studies in the gaming literature and social computer science adopted the Theory of Reasoned Action to predict intentional behaviour. However, these models often neglected other factors (such as experience and satisfaction) and their influences on game play intentions.
Despite the importance, studies on game experiences and satisfaction were very limited, and more research was necessary (Davis & Lang, 2011). Intention to play games may be better understood with theories that focus on game experiences (Holbrook et al., 1984; Schmitt, 1999).
If a player continually plays the same game, experience may have an even greater effect on replay intentions (Jolley, Mizerski, & Olaru, 2006; Lacher & Mizerski, 1994). The experience- focused approach to player behaviour can provide a wider understanding of why players continue to play games including MMORPGs. However, there is inadequate research that explores and explains the behaviour of game players from an experiential perspective.
1.5 Research Rationale
Experience marketing theory is a major school of thought in marketing which investigates how consumers think, feel and relate to other people who use or buy a product (Schmitt, 1999). The concept of experiential marketing has shifted from the view of the rationality of consumers who rely on the utilitarian aspects of products, to greater emphasis on feelings to cater for consumers that seek enjoyment and self-esteem from consumption. This shift is evident in products such as entertainment, education, and personal challenge products such as games.
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Experiential marketing occurs when the customers physically interact with products.
Pine & Gilmore (1998) defined experience as a memorable event that engages each consumer individually with products in a personal way. This experience can stimulate the senses and provide consumers with sufficient information to make a decision. The major difference between experience products (i.e., entertainment products) and other goods and services is that experiences are memorable, sensational and personal.
According to Verhoef et al. (2009), customer experience is a holistic construct including customer’s cognitive, affective, emotional, social and physical responses to a product. Holbrook et al. (1984) suggested in their paper entitled “Play as a Consumption Experience”, that consumption of a product may have experiential benefits which may be enjoyable. Experience is important in decision making and it can be applied to digital gaming because of its entertainment value (Holbrook, 2000). Moreover, experiential aspects of consumption are influenced by a consumer’s ability to explore, feel, and be involved with the product to observe its desirable benefits (Klein, 1998). The experience concept can be applied in various channels of the Internet including blogs, virtual communities, and multiplayer game play MMOs (Chen et al., 2008).
Massively multiplayer online role-playing games (MMORPGs) are hedonic products where players interact with the product in order to entertain themselves. This interaction results in experiencing the product along different avenues such as having fun, immersing themselves in a fantasy world, and socialising with friends. These facets are viewed as part of a holistic view for customer experience where the consumption process can be assisted by customers’ cognitive, affective, emotional, social, and physical responses to the brand (Verhoef et al.,
2009).
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Purchasing decisions that are based on product experience rely more on the intangible aspects of a product. A study conducted by Lacher & Mizerski (1994) on the purchase of rock music found that consumers based their purchase decisions on feelings (or emotions) that can create a sense of fantasy, immersion, fun, and other psychological emotional states. These feelings help customers develop an optimal experience. The same concept may be applied to online gaming, as both music and gaming share similar entertainment value to consumers. This indicates that games are re-played based on the outcome of the in-game experience.
In addition, feelings are regarded as a means to generate consumer satisfaction and intention to repurchase and replay, especially with hedonic products such as music and games
(Holbrook et al., 1984; Lacher & Mizerski, 1994). Furthermore, online games need to be physically played and experienced in order to prompt feelings of excitement, fun, and enjoyment
(Dönmez, 2011). Players’ experiences with MMORPGs are mostly identical to other types of product experiences. One difference may be that MMORPGs require a large number of people playing together while other products may involve just one consumer without any interaction with any other consumer (Kleeberger & Hummel, 2002).
Gameplay (or game) experience is often defined as a collective procedure that involves player’s sensations, thoughts, feelings, actions, social interaction and meaning-making in a gameplay setting (Ermi & Mayra, 2005). This experience is developed from gamers direct interaction with the game (Hunicke, LeBlanc, & Zubek, 2004). This direct interaction of players with the game, facilitates the construction of game experiences that can be either pleasant or unpleasant.
According to Holbrook et al. (1984), video game playing is considered as a strong hedonic consumption experience that is strongly related to feelings of excitement, fun and enjoyment. These positive feelings can lead to positive game experiences that can affect game
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Bader Albatati PhD Thesis October 2017 satisfaction and replay intention. This claim is consistent with findings obtained from studies on other hedonic products (such as music consumption and shopping online) that positive feelings affect consumers’ overall experience with the product and their repurchase intentions
(Chen, Ching, Luo, & Liu, 2008; Lacher & Mizerski, 1994).
Besides positive feelings, negative feelings may also affect online game play is negative feelings. For example, when a player loses a challenge in an online game, it sometimes prompts feelings of anger and frustration with the game. According to Verhagen & van Dolen (2011), negative feelings (such as boredom) may have a negative influence on exploration behaviour in online shopping. In the video game context, there has been no study that examined negative feelings and its effect on game experience and intention behaviour. Games that challenge player skills (e.g., through frequent loss and difficult to achieve goals) may make players want to play more in order to overcome that obstacle (Rad, 2016).
Thus, game experiences may be influenced by positive and negative feelings which in turn may determine future gameplay intention. Feeling good about certain game sessions can result in players replaying the same game frequently. In contrast, feeling bad may discourage future play and diminish player satisfaction.
There are several characteristics presented within the culture of multiplayer online games (MMOs) that constitute game experience. According to Rollings & Adams (2003), the gameplay experience concept is the outcome of a large number of contributing elements. Players may not express all the different characteristics of MMOs as it largely depends on the game design and how it suits certain game styles. Most, MMORPGs revolve around definite themes that construct gameplay. These themes can be identified by the nature of significant game elements such as the fantasy environment, competitiveness of the game, and play with or against
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Bader Albatati PhD Thesis October 2017 others. As a result, three key aspects of game experiences have been discussed prominently in game literature; they are flow experience, immersive experience and social experience.
Flow was first introduced by Csikszentmihalyi (1988) as an ultimate experience that people obtain from game activities. This experience occurs when people act with total engagement with products, events, and other activities such as role play. In order for a person to experience flow, a certain amount of skill is required to meet the challenges raised from being involved in an activity (Hamlen, 2011; Pace, 2004). Most games are built around achieving goals that may pose certain challenges to players. To overcome these challenges, players need certain specific skills. When the balance between the skill of players and the challenges of the game is achieved, a state of flow occurs (Wu & Liu, 2007).
A few studies, such as Hsu & Lu (2004), examined the effect of flow experience on intention to play and found that the state of flow affects player intention to play online games.
However, Hsu & Lu’s (2004) study was based on players who play online games in general, and not on MMORPGs specifically. Furthermore, they did not examine the intention to replay
MMORPGs. Nevertheless, their findings suggest that flow may have an impact on player intention.
Another aspect of game experience is immersion. Immersion has been defined as
“involvement and emotional engagement” (Cuny, Fornerino, & Helme-Guizon, 2015, p. 1026;
Schultze, 2010). It can be a form of a psychological state bound by the perception of an individual to be “wrapped by, included in, and interacting with an environment that can provide a continuous stream of stimuli and experience” (Cuny et al., 2015, p. 1026). In-game environments, usually set the stage for immersive experience of players. Game immersion occurs through the interfaces which mimic physical experience in the real world.
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Immersion has been examined by different scholars, such as Murray (1997), Ryan
(1999) and Ryan (2001). However, the concept of game immersion has not been carefully examined from a marketing point of view. Ermi & Mayra’s (2005) study is an exception which found that game immersion is influenced by a player’s sensory and imaginative faculties, and the concept of game challenge. In their study, they found that immersion was important for players to have a positive experience. However, the study did not examine the relationship between immersion and player behaviour such as purchase and re-play intention.
Finally, social experience is often examined as an aspect of game experience. According to Koo et al. (2007), digital gaming can provide a sense of belonging and social inclusion where gamers meet, team up or chat with each other. Yee (2006a) explained that social activities for online gaming can motivate players to re-play. He also argued that gamers are interested to make friends, build relationships and collaborate with other players to overcome game challenges.
A study conducted by Schiano et al. (2011) on World of Warcraft (WoW), a MMORPG game, found that gamers from four regions (Europe, Hong Kong, Taiwan and the US) preferred the company of other players in the game. Moreover, Schiano et al. (2011) found that the existence of a successful social platform such as the one in WoW, led to players making new friends. The study concentrated on social relationships developed within the game and their influences on real life relationships. However, social experience was not examined in relation to repeat game play.
In general, together with feelings (or emotions), three other aspects of game experience, such as flow, immersion, and social experiences have been found to be able to satisfy players’ needs and aspirations and engaging them with the game. This engagement with the game may be influenced by feelings and emotions of players. For example, positive or negative emotions during the game can impact the length of game play.
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In summary, this study will attempt to study how each of the other three aspects of game experience (flow, immersion, social) is influenced by both positive and negative feelings, and how these aspects affect players’ satisfaction and intention to replay.
1.6 Research Questions and Objectives
The increasing number of online games has changed player preferences in games. As players become more individualised in behaviour, it is harder to predict preference, based only on demographics and attitudes (Choi & Kim, 2004). As mentioned previously, consumer decision- making in gaming should be analysed from an experiential marketing point of view rather than the cognitive based (the extended problem solving) aspect only. This can be achieved by viewing game experience in a holistic fashion influenced by factors such as feelings (or emotions), flow, immersion and social experience. This study will focus specifically on these areas to explain intention to replay a MMORPG from a commercial and industry perspective.
As discussed in earlier sections, flow relates to the balance of the challenge a player faces and his or her skills, which provides players with a focus on game advancement.
Immersion is where players are deeply engrossed in the game. Social experience is where players interact with or against each other. These three aspects are essential to holistic game experiences, which will form the scope and focus of this study.
Various studies have examined flow, immersion and social experience in a separate manner (Chen, Duh, Phuah, & Lam, 2006). None of them researched game experience as a combination of all key factors together with their impact on game satisfaction and player retention. Consequently, this study will investigate the relationship between feelings, flow, immersion and social experience. Furthermore, the relationship between experience, satisfaction, and intention to replay will also be explored.
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The aim of the study will be to answer the following research question, “What makes players continue to play an MMORPG?” The rationale is that flow, immersion and social experiences will influence the intention to play or re-play a game. In order to answer the main research question, the following key-questions and objectives will be addressed in the study:
1. How does positive feelings influence flow, immersion and social experiences?
To answer this question, the objectives are to examine the effect of:
a. Positive feelings on flow experience.
b. Positive feelings on immersion experience.
c. Positive feelings on social experience.
2. How do negative feelings influence the flow, immersion, and social experiences of
MMORPG players?
To answer this question, the objectives are to examine the effect of:
a. Negative feelings on flow experience.
b. Negative feelings on immersion experience.
c. Negative feelings on social experience.
3. How does positive feelings influence game outcomes (satisfaction and intention to replay)?
To answer this question, the objectives are to examine the effect of:
a. Positive feelings on satisfaction.
b. Positive feelings on intention to replay.
4. How does flow experience, immersion experience, and social experience influence game
outcomes (satisfaction and intention to replay?
To answer this question, the objectives are to examine the effect of:
a. Flow experience on satisfaction.
b. Immersion experience on satisfaction.
c. Social experience on satisfaction.
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d. Flow experience on intention to replay.
e. Immersion experience on intention to replay.
f. Social experience on intention to replay.
5. How does satisfaction influence intention to replay?
To answer this question, the objective is to examine the effect of:
a. Satisfaction on intention to replay.
1.7 Research Contributions
This empirical project examines the relationships among feelings, flow, immersion, social experience, satisfaction and replay intention, from a consumer behaviour perspective. The findings of the study will provide important industry implications, for example, helping game designers and marketers develop a better understanding of MMORPG players, and subsequently enable them to design effective strategies that can ensure satisfaction and player retention.
Findings from the project will also add to the growing body of literature on gaming experience from a marketing perspective. One major theoretical contribution is the examination of four major aspects related to game experience, namely feelings, flow, immersion and social experience. In other words, this project will be one of the first attempts to empirical examine game experiences and their influence in the context of MMORPGs and from a holistic point of view. A further contribution of the study is that it is also one of the few studies which examine both positive and negative feelings as determinants of flow, immersion and social experiences as well as satisfaction and intention to replay in a gaming context. It is expected that the findings of this study will help fill a several gaps in the gaming and consumer literature.
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1.8 Research Methods
This research project was based on self-reported online surveys of MMORPG gamers. It will rely on players’ responses to questions related to their feelings, flow experience, immersion experience, social experience, game satisfaction, replay intention, and past game behaviour.
This thesis includes two separate but related studies. The first study was designed to examine players’ feelings and how feelings affect flow, immersion, and social experience. The second study was designed to investigate how players’ feelings affects not only flow, immersion, and social experience, but also satisfaction and intention to replay a MMORPG. In addition, Study
Two also investigates the relationships among flow, immersion, social experience, satisfaction and intention to replay. The questionnaire for data collection was designed in Qualtrics software and collected from Amazon Turk (online survey panels).
1.9 Thesis Structure
The thesis chapters are organised in the following manner as depicted in the layout schematic.
Chapter 1 • Introduction
Chapter 2 • Literature Review
Chapter 3 • Methodology
Chapter 4 • Study One
Chapter 5 • Study Two
Chapter 6 • Discussion and Conclusion
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CHAPTER 2 LITERATURE REVIEW
2.0 Introduction
This section is a thorough review of relevant academic literature in the field of online games, with perspectives from game designers, players, marketers and game researchers. The chapter commences with a discussion on experience in general, followed by game experience in particular. Player motivation, positive and negative feelings, consumer satisfaction, and various other issues related to game experience are also discussed.
2.1 An Overview of Experience
Experience, as a concept, has been discussed in many different fields and contexts, such as education, marketing, tourism and sociology.
2.1.1 Experience in Education
Experience and education are closely related, as interacting with the environment can add to the knowledge of a person (Dewey, 2007; Kolb & Kolb, 2005). Individuals progressively gain more experience in their field with time as a process of learning from past experiences. A recent and emergent theory of experience describes learning as a process of creating knowledge by transformation through experience. Experiential learning therefore, has been given a central role in theories of human learning and development by prominent 20th century scholars such as
John Dewey, Kurt Lewin, Jean Piaget, William James, Carl Jung, Paulo Freire, Carl Rogers and others.
Previous educational studies focused on different aspects of experiential learning. One stream focused on experiential learning as a concept whereby tools and techniques are used by learners to gain new learning experiences. Another stream described learning as a mindless record of past experiences. Although experiential learning in education is founded on the theory
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Bader Albatati PhD Thesis October 2017 of experience philosophically, traditional education focuses less on theory and more on practice
(Kolb & Kolb, 2005) .
The Experiential Learning Theory (ELT) views learning as a dynamic learning cycle driven by action/reflection and experience/abstraction resolutions (Kolb & Kolb, 2005). From a holistic viewpoint, learning is defined as the adaptation of a person to an environment wholly, not only in formal classroom settings but in all areas of life. Learning from experience occurs at every time and place and is present in all human activity. As a holistic process, it occurs at all levels in individuals, groups, organizations, and to society as a whole. Worldwide research on
ELT has supported the cross-cultural applicability of the model.
According to ELT, knowledge occurs when experience is both grasped and transforming
(Kolb & Kolb, 2005). Two related modes of grasping experience are portrayed by ELT namely,
Concrete Experience (CE) and Abstract Conceptualization (AC), whereas the related modes of transforming experience are depicted as Reflective Observation (RO) and Active
Experimentation (AE). Experiential learning is the process of the construction of knowledge involving a creative tension between four learning modes in response to demands of a specific context. The process is an ideal of a learning cycle or spiral where learners experience, reflect, think, and act recursively in response to the learning situation and content. Immediate or concrete experiences are foundations for observations and reflections of a learner. The reflections are absorbed and abstracted into concepts upon which new implications for actions are made (Kolb & Kolb, 2012).
2.1.2 Experience in Tourism
Tourism has been acknowledged as an experience of socio-psychological dimensions (Iso-
Ahola, 1983; Mannell & Iso-Ahola, 1987). Some sociological factors (e.g., income and socio- economic status) may affect tourist behaviour, but they may not determine the quality of
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Bader Albatati PhD Thesis October 2017 experience (Mannell & Iso-Ahola, 1987; Van Raaij & Francken, 1984). Instead, tourist experience is considered to be highly subjective, and may strongly be influenced by feelings
(Ross & Iso-Ahola, 1991). Some researchers view tourist experiences as a function of different encounters and interactions (Chan, Kim, & Baum, 2007). In fact, tourist experience is a complex phenomenon, as a combination of factors make up the feelings and attitudes of tourists during a visit (Page & Dowling, 2002).
The attractions of a tour site may be seen as a product with experiential dimensions, such as feelings (or emotions) (Arnould & Price, 1993). It has also been argued that it is essential to create a symbiotic relationship between the visitor and resources (McArthur & Hall, 1996).
Furthermore, tourism experience is also viewed as part of life experience. Therefore, visitor experiences should regard needs fulfilment or other emotional (or hedonic) aspects as core elements (Chen & Chen, 2010).
Recognition of the importance of tourist experiences has grown in a variety of tourism sectors, and empirical research of these types of experiences were conducted in various settings, including museums (Rowley, 1999), river rafting (Arnould & Price, 1993; Fluker & Turner,
2000), skydiving (Lipscombe, 1999), heritage parks (Prentice, Witt, & Hamer, 1998) and heritage sites (Masberg & Silverman, 1996; McIntosh, 1999). Researchers in leisure and tourism also tend to focus on satisfaction (Crompton, 1979; Neal, Sirgy, & Uysal, 1999; Otto & Ritchie,
1996), as experience is believed to have an important effect on tourist satisfaction and revisit intentions.
Tourists usually travel for pleasure and other benefits that extend beyond the boundaries of their normal life-space. They believe experiences available externally are not found within their usual lives locally, in order to make travel a worthwhile endeavour. Thus, a person may
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Bader Albatati PhD Thesis October 2017 find relief from life’s tensions when attractions outside local boundaries are desired, which cannot be easily fulfilled locally (Cohen, 1979).
Practitioners within the tourism and hospitality sector seek to create unique and memorable experiences. Billions of dollars are spent on souvenirs or “tokens of memories” that are often sold well above market value, due to the memories to which they refer. Researchers in experience often argue that positive feelings about the visit create memorable experiences
(Williams, 2006).
Last but not least, experience is viewed as multi-dimensional in tourism. For example, six expressive dimensions were identified by Chan, Kim, & Baum (2007) to describe positive experiences in ecotourism. These dimensions include hedonic, interactive, novelty, comfort, stimulation and personal safety, and they resonate the dimensions of service experience as identified by Otto & Ritchie (1996).
2.1.3 Experience in Marketing
Economists consider experiences as an important offering for marketing products. Providing experiences is distinct from providing services and products (Pine & Gilmore, 1998). Economic experience is identifiable as an offering simply because consumers want experiences, and an increasing number of businesses are responding to these needs through design and promotions
(Pine & Gilmore, 1998). Experiences are the progression of economic value, similar to the marketing of services as a commodity after the purchase of goods, and the sale of long distance telephone services based on price alone (Pine & Gilmore, 1999). A number of companies wrap experiences around goods to boost sales. The full benefit of staged experiences, are realised when businesses design engaging experiences that attract a fee (Holbrook, 1994). The transition from the sale of product to the sale of experiences is not easy for established businesses. Thus, a number of scholars have tried to understand and explain marketing experience.
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Gentile, Spiller & Noci (2007) traced the origins of experience marketing to the mid-
1980s when Carbone and Haeckel initiated the “experience movement” in the late 1980s.
Around the same time, Holbrook & Hirschman’s (1982) proposed “consumption experience”.
Hirschman & Holbrook defined consumption and marketing experience as “those facets of consumer behaviour that relate to the multi-sensory, fantasy, and emotive aspects of product use” (Hirschman & Holbrook, 1982, p. 99). Their experience-based approach to consumer behaviour was in contrast to the traditional information-processing approach (Pham, 1998).
They treated the experiential aspects of the consumption experience subjectively, and succinctly developed the “three Fs” which includes fantasies, feelings and fun (Holbrook & Hirschman,
1982). They later developed a consumer framework of 4Es, including experience, entertainment, exhibitionism and evangelising (Holbrook, 2000; Holbrook et al., 1984).
One of the major contributions of Hirschman & Holbrook (1982) was that they interpreted the entire consumption experience from start (pre-purchase) to end (disposal/ outcomes). Secondly, they emphasized the crucial role of emotion (or feelings) in consumption experience, an idea which subsequently gained momentous interest (Frazer Winsted, 2000;
Tynan & McKechnie, 2009; Bagozzi, Gopinath, & Nyer, 1999)
Richins (1997) demonstrated that emotions were context-specific, in that the situation influences the experiences consumers obtain from consumption. Holbrook & Hirschman (1982) supported this claim and further explained that experiences are not only received in a multisensory mode, they also produce various types of immediate responses and reactions, depending on the context. Lastly, experience contains a “nostalgic” element (evoking memory of the past) and “imaginative” element (summoned the future by way of imagination) (Holbrook
& Hirschman, 1982).
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The role of feelings has often been discussed along with the development of experience marketing. For example, Poulsson & Kale (2004) claimed that a successful experience will include strong emotions which are generated from feelings. Feelings such as “love, hate, fear, joy, boredom, anxiety, pride, anger, disgust, sadness, sympathy, lust, ecstasy, greed, guilt, elation, shame, and awe” can enhance the value of consumption experience (Holbrook &
Hirschman, 1982, p. 137). Emotions and feelings can be used interchangeably (Holbrook &
Hirschman).
According to Tynan & McKechnie (2009), experience marketing clearly has a long history within several industries, such as retailing (Poulsson & Kale, 2004; Pine & Gilmore,
1998; Gilmore & Pine, 2002a; Kozinets et al., 2002; Puccinelli et al., 2009; Verhoef et al., 2009), tourism (Leighton, 2007), arts and entertainment (Holbrook et al., 1984; Pine & Gilmore, 1998;
Petkus, 2004), and hospitality (Gilmore & Pine, 2002b). Within these different industries of marketing, experience marketing includes service delivery taking place as a dramatic performance from a scripted interaction between customer and marketer at the consumption point within a particular industry (Grove & Fisk, 1997).
Most managers believed that sustainable competitive advantage could no longer be maintained by differentiation on the traditional elements of product, price and quality alone
(Shaw & Ivens, 2005). One avenue of survival for companies is the creation of lasting competitive advantage with a stronger focus on customers, so as to provide them with a holistic customer experience (Craig & Douglas, 2000; Kotler & Keller, 2006; Peppers & Rogers, 2000;
Gentile et al., 2007).
Experiences are shaped by encounters at various points of contact which result in various experiences terms, such as product experiences, shopping and service experiences, customer experiences, and entertainment experiences (Maclaran & Brown, 2005; Meyer & Schwager,
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2007; Poulsson & Kale, 2004; Tynan & McKechnie, 2009). The following section will explore some specific marketing experience concepts with reference to customer, product, service, consumption and brand experiences.
Customer/Consumer Experience: The concept of “customer experience” or “consumer experience” was first introduced in the early 1980s when most of the literature on consumer behaviour considered customers as rational decision makers (Hirschman & Holbrook, 1982). At the time, the new approach to experience marketing presented an alternate view to consumer behaviour (Gentile et al., 2007) where customer/consumer experience was examined as a different notion to the traditional marketing concepts based on rationality alone. Customer experience is defined as the interaction and the relationship that customers have with a product or/and organisation during the life of the relationship (Peppers & Rogers, 2016; Verhoef et al.,
2009).
In the customer experience approach, the experiential aspects of consumption are not only rational, but also emotional, for example, including customers’ fantasies, feelings, and fun
(Holbrook et al., 1984). Contrary to the traditional marketing view of consumption with an emphasis on product attributes and utilitarian functions, Holbrook & Hirschman’s (1982) experiential view has shifted the focus to symbolic meaning, emotion processes, and nonverbal cues resulting from consumption. Gentile et al. (2007) later claimed that “customer experience is strictly personal and implies customer involvement at different levels (rational, emotional, sensorial, physical, and spiritual)” (p. 397).
Product Experience: Product experience refers to the interaction between a consumer and a specific product when the consumer acts to search, examine and evaluate products (Hoch, 2002).
The experience may be direct or indirect depending on whether, as well as to what extent, there is a physical (i.e. using the product) or virtual contact (i.e. through viewing an advertisement)
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Bader Albatati PhD Thesis October 2017 with the product (Hoch & Ha, 1986; Kempf & Smith, 1998). Direct and/or indirect product experiences can affect consumer attitudes, preferences, buying intentions, recall and judgements of consumers, to name just a few (Hoch & Deighton, 1989; Hoch & Ha, 1986; Huffman &
Houston, 1993).
Service Experience: Service experience research is largely focused on consumer-product interactions with a physical store, its personnel, and its policies and practices (Hui & Bateson,
1991; Kerin, Jain, & Howard, 1992). Numerous studies found that the atmospheric variables and salespeople had important influences on service experience and/or satisfaction (Arnold,
Reynolds, Ponder, & Lueg, 2005; Boulding, Kalra, Staelin, & Zeithaml, 1993; Grace & O'Cass,
2004; Jones, 1999; Ofir & Simonson, 2007).
Consumption Experience: Consumption experience research is focused on the experience related to the consumption process, which is believed to be multi-dimensional (Holbrook &
Hirschman, 1982). Some researchers found that goals or expectations associated with consumption experience influenced the actual experience in contexts such as museum visits, white-water rafting, baseball and skydiving (Arnould & Price, 1993; Joy & Sherry Jr, 2003)
Brand Experience: Following the shift from products to brands in marketing, brand experience has received increasing research attention. Brand experience occurs when consumers have an interest or personal connection with a brand (Brakus, Schmitt, & Zarantonello, 2009).
Brand experience may be related to brand involvement, brand attachment and brand engagement. However, Ha & Perks (2005) and Schmitt (2009) argued that it is not necessary for consumers to be highly involved with a brand for the strongest experiences to be evoked.
Brand experiences may differ even when strong emotional bonds exist between a consumer and a brand (Park, MacInnis, & Priester, 2006; Whan Park, MacInnis, & Priester,
2010). In contrast to strong emotions evoked by brand attachment, brand experience is not
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Bader Albatati PhD Thesis October 2017 necessarily an emotional only relationship concept (Dolbec & Chebat, 2013; Malär, Krohmer,
Hoyer, & Nyffenegger, 2011). Most brand experiences are accompanied by ordinary sensations, feelings, cognitions, and behavioural responses when brand stimuli are used.
Brand experiences are also different from brand perceptions/associations and image
(Keller, 2003a). Consumers make brand associations with benefits, products, people, places, and other objects in a broad associative network (Keller, 2003a, 2003b). For example, a brand may be associated with human traits and characteristics (such as “warm or “competent”), or personality evaluation, such as sincerity, excitement, competence, sophistication or ruggedness
(Aaker, 1997; Aaker, Benet-Martinez, & Garolera, 2001). Consumers make brand inferences when they make such associations (Johar, Sengupta, & Aaker, 2005). The feelings of sincerity or excitement are not about the brand itself but mere traits that are projected onto the brand.
Although a brand may be seen to contribute to consumer knowledge and meaning, an actual brand experience may or may not be created (Berry, 2000).
Brand experiences are not simply associations but dynamic sensations, cognitions, and behavioural responses (Van Noort, Voorveld, & Van Reijmersdal, 2012). However, similar to brand associations, brand experiences are stored in consumer memory after the immediate experience. The experiences are most likely stored both semantically and episodically, so a trace of the sensations and emotions that made up the association with the brand is preserved
(Barsalou, 1999; Philippot & Schaefer, 2001).
Finally, brand experience has been viewed as a dimensional concept that can affect direct and indirect consumer satisfaction. Several papers presented useful concepts of experience
(Chattopadhyay & Laborie, 2005; Pine & Gilmore, 1999; Schmitt, 1999, 2011; Schmitt, 2010;
Smith & Wheeler, 2002a; Smith & Wheeler, 2002b). Meyer & Schwager (2007) distinguished the dimensionality of brand experience and constructed a four-dimensional scale to include
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Bader Albatati PhD Thesis October 2017 sensory, affective, intellectual and behavioural aspects. Brakus et el. (2009) claim “that the dimensions and its scales are reliable, valid and distinct from other brand dimensions and scales including brand evaluations, brand involvement, brand attachment, customer delight and brand personality” (p.2). These claims by Brakus et el. (2009) highlight the fact that experience can be multi-dimensional. These dimensions may make different contributions to the overall satisfaction of consumers which may in turn affect customer retention.
2.1.4 Dimensions of Experience
A number of studies on experience considered experience as multi-dimensional with at least three basic dimensions, such as sensation, emotions, and cognition (Brakus, 2001; Gentile et al.,
2007; Schmitt, 1999; Sheng & Teo, 2012). The sensory dimension refers to senses of sight, hearing, touch, taste and smell, where consumers can observe the experience and experience it through emotional reactions. The emotional dimension stimulates the affective system through the creation of moods and feelings in a way that fosters an affective relationship with a company, its brands and products (Sheng & Teo, 2012). Brands such as Barilla and Kinder Surprise exploit this emotional link to attract customers. The cognitive component is used by companies to connect with customers using the thinking or conscious mental faculty to engage in situations that require creativity and problem solving (Schmitt, 1999).
Pine & Gilmore (1998) suggested that the experience dimension can be viewed from the perspective of two aspects, where one depends on consumer participation and the other depends on the environmental relationship. Consumer participation refers to whether the customer is passive or active. According to Pine & Gilmore (1998), passive consumers do not affect the performance such as when listening to music whereas active consumers can affect the event such as when playing games. The enviromental relationship consists of two parts, one of which lies in absorption and the other in immersion. Pine & Gilmore (1998) describe customers who
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Bader Albatati PhD Thesis October 2017 are viewing the event taking place in front of them as engaged in absorbtion, while those affected by the surroundings such as sights, smells and sounds are immersed.
Furthermore, Pine & Gilmore (1998) argued that experiences can be further differentiated into four different dimentions. Each dimention is identifed by its place on the spectrum. These categories are entertainment, education, escapist, and aesthetic. They identify entertinment as activites where customers act more passively (e.g. going to the movies) while eductional consumers are more active (e.g. taking ski lessons). Furthemore, Pine & Gilmore
(1998) described escapist experiences as those that involve more customer immersion, such as acting in a play. However, customers become less active in the event when it becomes an aesthetic experience, such as a tourist engaged in sight viewing. Therefore, each experience is unique to the individual depending on the level of interaction and mentality of the consumer
(Pine & Gilmore, 1998; Pizam, 2010). Pine & Gilmore’s (1998) study became a catalyst for marketing scholars to investigate the dimensionality of customer experience.
Schmitt (1999) developed five dimensions for customer experience, the first of which is
“sense”, where marketing stimuli appeal to the senses such as sight and smell. The second dimension is “feel”, where the product experience appeals to consumers and affects their inner emotions. The third is the “think” component, where analytical and divergent/imaginative thinking and problem solving is part of the experience. The fourth is the “act” component, where actions and behavioural aspects constituted the “act” experience. Finally, the fifth component is the “relate”, where relatedness refers to the social dimension of a consumption, such as connection to a reference group.
Dubé & Le Bel (2003) introduced several hedonic-based dimensions, such as intellectual, emotional, social, and physical pleasures. Another study conducted by Gentile et al. (2007) proposed six dimensions as part of customer experience. They are in the form of
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Bader Albatati PhD Thesis October 2017 sensory, emotions, cognitive (experience associating with thinking and creativity), pragmatic
(acting, i.e. experience drawing from product consumption), lifestyle (customer drawing experience from their system of values and beliefs), and relational component (social component). Furthermore, Gentile et al. (2007) suggest that each component of customer experience will be perceived as single feeling by customers. Gentile et al. (2007) framework for customer experience was adopted by Verhoef et al. (2009). Verhoef et al. (2009) viewed customer experience as a holistic construct within the retail environment where companies need to take into account the cognitive, affective, emotional, social and physical responses of the customer to the brand.
In summary, experience is believed to be a multi-dimensional concept that is largely decided by the situation or context (Brakus, 2001; Gentile et al., 2007; Pine & Gilmore, 1998;
Schmitt, 1999; Brakus et al., 2009; Holbrook et al., 1984).
2.2 Massively Multiplayer Online Role-Playing Games (MMORPGs)
Popular words and phrases with gamers in describing their favorite games are immersive, challenging and interacting with friends (Gilleade, Dix, & Allanson, 2005; Cox, Cairns, Shah,
& Carroll, 2012). However, player opinions on the experience of playing MMORPGs can be diverse. Generally, the answers and opinions depend on how much they focus on the environment, on overcoming game challenges or their relationship to other gamers. These focuses make MMORPGs different from other games.
MMORPGs are a form of digital gaming that is played online. According to Barnett &
Coulson (2010), the word massively states that a huge number of people play online games simultaneously. Players immerse themselves in an online fantasy world and compete against each other or cooperate to achieve certain mutual goals in the game (Barr et al., 2007). This emphasises the fact that people need an internet connection to play MMORPGs within the same
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Bader Albatati PhD Thesis October 2017 online world, unlike other forms of video games where only an individual copy is sufficient to play the game.
Within these environments, customers use a customisable representative of themselves
(avatars) to launch in the fantasy world and to interact virtually for various purposes (Sawyer et al., 2011; Zhou, Fang, Vogel, Jin, & Zhang, 2012). These avatars can be of different races and classes (Barnett & Coulson, 2010). Depending on the race (e.g. elf, orc or human) and the class, each player gains unique skills and abilities that help in game progress. All members of a particular race or class are designed to share certain qualities of appearance and strength which distinguishes them within the game (Monson, 2012).
After choosing avatars, players may choose to join a faction within the game which provides a sense of belonging to a larger group or guild with more success in defeating superior enemies (Ducheneaut, Yee, Nickell, & Moore, 2006a; Ducheneaut, Yee, Nickell, & Moore,
2006b; Kong et al., 2012). The selection features such as avatars and guilds occur at the start of the game and are a necessary step for every player, as potentially it may have a major impact on the eventual outcome and whole game experience.
Another major feature of online games, such as MMORPGs, is that players remain mutually anonymous through use of pseudo names and aliases which provide the opportunity for players to experiment with different online identities (Paik & Shi, 2013). There are numerous tangible and intangible features which attract players to MMORPGs. These include genre, choice of character (avatar), morality (good or evil character), attachment to character, in-game social experience and communication, type of play (individual or group play), virtual communities, visuals and video clips and regular instalments of extensions and new content
(Dickey, 2007; Yee, 2006a). Apart from the intangible aspects of feelings, flow, immersion, and social experience, much of the prior research on gaming has focused on external game
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Bader Albatati PhD Thesis October 2017 characteristics (e.g. avatar design), whilst little attention has been paid to the experiential aspects of playing such as feelings, flow, immersion and social experience.
2.2.1 Genre and Game Choice
Online games have many genres (types of games) from which players can choose and the type of game strongly influences online experience. For example, playing a simple card game online is different to playing an MMORPG. The former can involve players in simple thinking and playing against a small number of people, whereas the latter is more complex in that players are compelled to engage more with the game. Therefore, game types can be categorised by complexity. Hamlen (2011) identified 14 types of games ranging from simple card games to more complicated games where players need to subscribe to the game, design their avatar and launch it in a virtual world.
Most MMORPGs update the in-game monsters on a regular basis. Fighting monsters may be a novel and rewarding activity for players, as their defeat allows players to obtain new feature items that may be used in a variety of activities, such as crafting weapons, building, research and technology along with many more usages. Occasionally, players may decide to sell these items for virtual money used to buy other game artefacts with different uses. Furthermore, players may engage in boss hunting (a higher-level monster that can drop rare items for players or give them other rewards (Meredith et al., 2009; Ondrejka, 2004). These bosses can be difficult to destroy and may raise a serious challenge which can be alluring for gamers. According to
Sweetser & Wyeth (2005), video games need to provide different levels of challenges to get players into a state of flow which is vital to the success of online games.
One of the major attributes of MMORPGs is the in-game provision of choices for players
(Yee, 2002, 2006b). These choices grant players avenues to develop the relative experience of players. At the outset, players commence design of their own avatars and selection of an action
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Bader Albatati PhD Thesis October 2017 class which will affect their role and game play. Each class of player, ranging from a “melee fighter” to a “spell caster” in a game such as World of Warcraft, has different abilities that help in defeating enemies.
The layout of MMORGs is also different from other types of online games. MMORPGs have particular features such as the freedom of choice and control of actions within the games that make the experience unique (Yee, 2006b). These features are designed by game producers in order to get players immersed, to feel the flow, and to play interactively with friends.
Moreover, Companion & Sambrook (2008) found that class choices depend on the gender of the player. In this regard, women show preferences for classes that are specialised in medics, whereas men prefer the combat classes (Companion & Sambrook, 2008). Upon finalisation of avatar creation, game-play commences and players begin to immerse themselves in the game environment. Designing avatars is a fun activity which simultaneously has an impact on the level of immersion and player satisfaction (Teng, 2010).
2.2.2 Graphics and Virtual Environment
Game environments, referred to as the experience world for gamers, are an important avenue for staging player experiences. MMORPG worlds are specific environments with complex three-dimensional (3D) features. Visitors to these virtual worlds are able to tour them actively and create new experiences in a playful way. The reason for production of these worlds is less to do with entertainment and more to do with providing customers an integrated and whole sensory experience (McCarthy, Wright, Wallace, & Dearden, 2006). Thus, customers are engaged through all five senses. Multiple stimuli (e.g., sound, text, pictures, etc.) allows for a message to be sent in diverse ways to make the experience more rewarding (Kroeber-Riel, 1979;
Weiner, 1990). Zhou et al. (2012) argued that virtual worlds have a number of attracting aspects
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Bader Albatati PhD Thesis October 2017 such as design features, immersive presence interfaces and collaborative platforms that attract users.
Within these environments, customers use a representative of themselves (avatars) to interact virtually for various purposes (Sawyer et al., 2011; Zhou et al., 2012). Zhou et al. (2012) argue that virtual worlds have a number of attracting aspects such as design features, immersive presence interfaces, and collaborative platforms that attract users. This new graphic-rich virtual online world is an opportunity and an incentive for players to explore the game (Griffiths,
Davies, & Chappell, 2004a). Virtual world users are engrossed in their pursuit of exploration amongst other feelings. Games that have environments that are described by attractiveness and high-quality graphics will have an impact upon a user’s perceptions. Virtual worlds that are appealing can generate a traffic of users who will participate repeatedly. Thus, a virtual world and the associated traffic provides the opportunity for marketers to gain profit through retaining customers.
Players are more likely to be absorbed when the game has a strong and interesting narrative, plot, or story. In the story writing context, good narratives have long been used in best seller books which typically use storytelling as a tool to create immersion. According to Bracken
& Skalski (2006), this is equally true in video and online games. Games that are well designed
(with good graphics and storylines) allow players to enter into a fantasy world where they can escape real life (Wood et al., (2004). A well-designed MMORPG creates a state of spatial immersion when the user starts to feel that they are physically present in the game world. In this sense, the experience of immersion by players means that choices that make sense exclusively in the context of the virtual world are only considered (Bracken & Skalski, 2006).
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Bader Albatati PhD Thesis October 2017
2.2.3 Player Motivation
There has been limited research that address the matter or question of what exactly motivates gamers to play. Most of the studies such as (Choi & Kim, 2004; Yee, 2006a) conducted thus far in the gaming industry failed to explain exactly and completely why gamers replay a MMORPG. This shortcoming is apparent as most of the research done was from the point of view of game designers, rather than from the player or consumer point of view.
Other researchers such as (Linderoth, 2012), indicated a concept termed “involvement” which is a desire of a user to perform in a fantasy world, to draw parallels between personal reality and fantasy, and to revel at which personal design changes are possible within the environment. This acknowledges that some people like to role-play, which impacts on the level of their immersion (Dauriat et al., 2011). Others prefer games of challenge and desire to progress in the game similarly to being in a state of flow (Jin, 2012). Some wish to play with different people and enjoy the social experiences that the game offers (Lenhart et al., 2008). To understand these different motivations, the following section will highlight the reasons that compel gamers to play and which contribute to their experience.
Kim & Ross (2006) found that players who played online sports games were motivated by knowledge of the game, application, identification with the relevant sport, fantasy, competition, entertainment, social interaction and diversion. They argued that player motivation is more related to the psychological and cognitive abilities of gamers than to specific game design. Although Kim & Ross’s study was exclusively on sports games, their findings may be of interest for insight as to why people play online video games in general.
Early studies by Bartle (1996) and others on multi-users virtual environment games
(MUDs) found that gamers can be differentiated into four groups according to the benefit they desired from a MUD game. Bartle labelled those who like digging for secrets in the virtual world as “explorers”, those who prefer communicating and helping other players as “socialisers”,
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Bader Albatati PhD Thesis October 2017 those obsessed with power and completing tasks as “achievers”, and those who enjoy destroying other players as “killers” (p. 7-8). However, Bartle`s study was questioned because his conclusions were based primarily on a qualitative study that relied on participant observation of a MUD community (Alix, 2005). Furthermore, MUD games are text-based with no in-game graphics and scenario video clips. These limitations indicate that the four groups identified by
Bartle may not reflect a comprehensive view of why gamers play or are grouped under as they were. Bartle also admitted that the study was not an academic study based on empirical research.
Thus, the findings may not be generalized to other gamer populations (Alix, 2005).
Furthermore, Bartle (1996) separated the different types of players based on his own experience as a designer of these textual worlds (MUD). His proposal did not create a motivational framework for MMORPGs players; rather, his suggestion included a more exploratory framework for Multi-Users Dungeons (MUD). Regardless of type of framework,
Bartle's (1996) study inspired other researchers to examine MMO player motivation and the nature of the attraction to play.
In contrast to Bartle, Yee’s (2006c) study was based more on visual games that use graphics, sounds and avatars to play. Yee`s research was conducted online by surveying a sample cohort playing a MMORPG. The sample included 3,000 players from popular online games of Ever Quest, Dark Age of Camelot, Ultima Online, and Star Wars Galaxies (Yee,
2006c). According to Yee, player motivation may be influenced by the three main game outcomes of achievement, social experience, and immersion in the game.
Each outcome includes sub-outcomes, which further explain why gamers play. For example, players who are achievers are more motivated by advancement or flow in the game, while socialisers prefer to build relationships and help others. Yee (2006c) also reported that some players may play games for different benefits. For example, he found that females play
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Bader Albatati PhD Thesis October 2017 because they want to socialise with others (socialisation), while males play because they value the ability to achieve goals in games (achievement).
Wood et al. (2004), in their study on game characteristics important to gamers, found that gamers regard good quality (e.g. graphics and sound), competition, socialising and communication, long duration of games (take months to years to complete), control options of the game, game dynamics (e.g. quests and exploring), and playing with others as important features of good video games. Their findings show that gamer motivation can be due to a multitude of reasons of similar influence, rather than a single explanation. Moreover, the sample included people who played games in general, which may be inherently different to playing a
MMORPG with a social component.
2.3 Game Experience
There is an upsurge in the number of MMORPGs in the market competing for gamers’ subscriptions. Playing MMORPGs these days is like playing a single player game (games on consoles) because of the fast Internet connection. Moreover, MMORPGs appeal to a broader international market due to connectivity facilitation. Players from around the globe are interested in joining some of these games due to the promise of a unique experience that
MMORPGs designers offer. Game producers promise game experiences that are fun, engaging, and personalised to individual player needs (Federoff, 2002; Yee, 2006b). However, only few games succeed in providing a unique game experience that make players actually return to play.
Only when designers produce enjoyable games that deliver on promises, will they have the ability to capture the imagination and attention of players worldwide (Shapiro, Peña-
Herborn, & Hancock, 2006). It is unquestionable that games that produce enjoyable experiences will have a competitive advantage against other games in the industry (Von Ahn & Dabbish,
2008). Game designers are thus understandably concerned from a marketing point of view, with
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Bader Albatati PhD Thesis October 2017 the elements that constitute a substantial game experience for players such as player motivation, consumer satisfaction and intention to play.
2.3.1 Definition of Game Experience
Games are identified as products that include the physical features of graphics, design, internet connection, and game controllers. According to game designers, the game’s physical features can affect the satisfaction of players and their tendency to replay the game (Davis & Lang,
2011). Online games are products that need to be played and experienced in order to prompt feelings like excitement, fun, and enjoyment (Dönmez, 2011). Moreover, some games require a large number of people playing together to induce experiential feelings (Kleeberger &
Hummel, 2002).
Feelings affect the consumption of digital games and consumers may develop increased positive or negative feelings depending on their experiences (Havlena & Holbrook, 1986).
Feelings are usually tied to specific cues or stimuli that can result in a short spate of arousal
(Gardner, 1985). For example, a player may feel angry due to the loss in a game. However, this feeling can change to joy as a result of overcoming that loss.
Experiences were always the core of the entertainment industry (including gaming),
(Pine & Gilmore, 1998). Multiple genres of experiences have arisen out of new technologies, such as interactive games, chat rooms and multiplayer games, motion-based simulators, and virtual reality. The ever growing phone/computer processing power not only creates more immersive experiences, but fuels demand for goods and services which can provide hedonic experiences (Pine & Gilmore, 1998).
Experiential marketing relies on player-game interaction where consumers get to explore, feel, and be involved with the product in order to derive desirable benefits from it
(Klein, 1998). The game experience can provoke different kind of feelings that impact the game
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Bader Albatati PhD Thesis October 2017 session of players. It challenges the skills of gamers and absorbs players in fascinating worlds, or it can be the main source of enjoying time with friends.
Other factors may also help create game experience. These nuances in experience factors differ in operation and interaction, and are dependent on the emotional state of players.
Successful online game experience “encourages consumer participation, cultivates obsession, and thrives on hyper dynamic updates” (Stacey & Herron, 2002, p. 2). In game experience, players’ feelings play an important role in forming an enjoyable experience and a strengthening of engagement with the game (Holbrook et al., 1984). Players with strong feelings seek to immerse themselves in the game, enjoy the challenge of beating the game, and take pleasure in forming social relationships with other players. These three aspects are referred to in the literature as flow, immersion and social experience. The following sections explores the relationships among feelings and the other three major experience dimensions, namely flow, immersion, and social experience.
2.3.2 Feelings
Several studies surmise that consumers may experience different kinds of feelings when consuming hedonic (type of a pleasure experience) products. These feelings may range from joy, love, and happiness (positive feelings), to rage, sadness, and anger (negative feelings)
(Gatewood, 1927; Hargreaves, 1982; Mizerski, Pucely, Perrewe, & Baldwin, 1988; Schoen &
Gatewood, 1927; Yingling, 1962). Feelings are usually tied to specific cues or stimuli that can result in a short state of arousal (e.g., incidental anger due to an in-game loss or incidental joy due to an in-game success) (Gardner, 1985).
Feelings or emotional experience has long been considered an experiential aspect of a consumption (Addis & Holbrook, 2001). Consumers get to explore, feel, and be involved with a product to observe its desirable benefits (Klein, 1998). Moreover, the emotional experience
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Bader Albatati PhD Thesis October 2017 can be presumed even before consumption in the form of imagining and day dreaming about the product (Carù & Cova, 2003; Arnould, 2012). Therefore, feelings are believed to have a major impact on customer experience (Holbrook & O'Shaughnessy, 1984). Previous research has found that feelings may determine other types of experiences, such as flow, immersion and social experience (Harvey, 1998; Koh, Kim & Kim, 2003; Mathwick, & Rigdon, 2004).
Research in consumer experience has also acknowledged the role of feelings in consumer behaviour or behavioural intention (Addis & Holbrook, 2001). This emotional aspect of customer experience is gaining more attention in recent years, and more applications are being introduced with offerings that can help create feelings, in particular, positive feelings
(Gentile et al., 2007; Holbrook, 2000, 2006).
As mentioned earlier, feelings are important in predicting consumer behaviour
(Hirschman & Holbrook, 1982). Consumers nowadays want more than functionality from products, such as hedonic needs (Gentile et al., 2007). According to Gentile et al, (2007), feelings play an important role in shaping customer preferences which subsequently influence their purchase decisions. Classical consumer behaviour theories often position consumers as logical thinkers whose purchasing decisions are rationally based whereas. However, more recent studies propose experiential marketing (Holbrook, 2006; Holbrook & Hirschman, 1982). This new stream of experiential marketing provides scholars with a new perspective into consumer behaviour that surpasses the utilitarian features of a product, and incorporates intangible elements that are linked to feelings, and the emotional values perceived by customers (Gentile et al., 2007; Holbrook, O’Shaughnessy, & Bell, 1990).
Specifically, feelings are related to the multi-sensory relationship between the consumer and the product during consumption (Holbrook et al., 1984). This is why feelings determine other experiential aspects. The terms multisensory, fantasy and emotion evoke a variety of
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Bader Albatati PhD Thesis October 2017 meanings. Multi-sensory means the receipt of experience in multiple sensory modalities including taste, sound, scent, tactile impression and visual imagery.
Feelings can also be produced as a result of stimuli consumption and experience of a product through the different sensory faculties of the body. These feelings are translated to impulses that can be an important form of consumer response to products (Lacher & Mizerski,
1994). For example, consumers may respond to an external stimulus (such as playing a game) by generating feelings such as fun and excitement within themselves (Holbrook, 2000). Playing an MMORPG may cause the consumer to not only react physically with the game, but to also generate internal imagery, sights, fantasies, and an exciting feeling, all of which are experienced by the player.
Thus, to create a customer experience, especially with hedonic products (such as online games), consumers must get to experience a set of emotions before, during, and after consumption. According to Zajonc & Markus (1982), feelings can be important to consumption as it can define the type of experience for consumers. For example, having positive feelings towards a product can result in an overall compelling experience as a consequence of positive feelings. These emotional responses can be viewed as the determinants for absorbing game experiences (Kleiber, Larson, & Csikszentmihalyi, 2014).
Feelings (or emotions) are found to influence other aspects of a consumption experience and are important components of the consumer decision-making process (Holbrook &
Hirschman, 1982; Holbrook et al., 1990). Several studies suggest that feelings have a more important role in predicting consumer behaviour than rationality (Allen, Machleit, & Kleine,
1992).
In general, feelings include both positive and negative feelings. According to Holbrook
& Hirschman (1982), consumer experience covers both negative and positive emotions which
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Bader Albatati PhD Thesis October 2017 may sometimes be intertwined. Moreover, Shaver, Schwartz, Kirson & O'connor (1987) stated that people feel love and happiness the same way they encounter sadness and anger. A study conducted by Jones (2003) of video games, found that players’ perceptions from game-play may vary in polarity from excitement and fun to boring and stressful.
Positive Feelings Positive feelings relate to fun and enjoyment whilst playing a game. During play, gamers get influenced by positive or negative emotions that can impact the length of game play. Holbrook et al. (1984) explained that positive feelings can be a result of hedonic consumption. This type of consumption is usually associated with multi-sensory, fantasy and emotional aspects of product usage (O'curry & Strahilevitz, 2001; Titz, Andrus, & Miller, 2012). According to
Holbrook et al. (1984), video game playing is considered a hedonic consumption experience that generates feelings of excitement, fun and enjoyment. These positive feelings lead to positive desires of game experiences that can ultimately affect re-play intention. In a number of studies on hedonic products, such as music consumption and shopping online, researchers found that positive feelings can affect the overall quality of the consumption experience with the product and the intention of coming back for more (Chen et al., 2008; Lacher & Mizerski, 1994).
Some researchers argued that thinking (cognitive) aspects are more salient than feeling
(affective) in the gaming context. For example, Wu & Liu (2007) adopted the Theory of
Reasoned Action (TRA) to explain the intention to play an online game. They argued that game players’ attitudes are crucial in predicting the playing intention of gamers. Some previous studies in the e-commerce area also preferred this model. However, Wu & Liu (2007) also found that enjoyment in playing a game has a major influence in forming positive attitudes towards a genre of games, and the subsequent intention to play.
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Bader Albatati PhD Thesis October 2017
Nevertheless, the TRA has been criticised on many bases. The model assumes that each time a customer is engaged in decision-making they spend time in problem solving and thinking about the product. This may only be the case for engagement with the game for the first time in the decision to do so, not subsequently. However, other studies show that when consumers are familiar with the product, other factors, such as feelings, satisfaction and habit, may influence the decision (East et al., 2008).
In relation to online games such as MMORPGs, positive feelings refer to feelings that are pleasurable to players (hedonic) and may correspond to game experiences. For example, players may feel a sense of accomplishment after completion of difficult levels in the game or they may feel a sense of belonging as they play side by side with friends. As stated by Holbrook
& Hirschman (1982), the act of playing games usually produces feelings that could either be positive or negative. Features of MMORPGs such as sounds and visual graphics, are designed for and likely to produce positive feelings.
Yee (2006a, 2006c) identified “powerful” feelings to be of importance for MMORPGs players. He argued that players drive their positive game experience by building up skills, reaching goals, and gaining power, recognition and status with other players. In addition, some players go to the extent of paying real money to purchase equipment and skills that make them feel powerful. Players influenced by the powerful feeling tend to be more excited and enthusiastic about the game (Yee, 2006a; Chang et al., (2008). Moreover, powerful feelings are manifested by players wanting to have strength, exercise control, be heroic, and seek thrills and excitement (Asmus, 1985; Holbrook et al., 1984; Lacher, & Mizerski, 1994; Voss, Spangenberg,
& Grohmann, (2003).
The most intriguing aspect of MMORPGs that makes the game fun and enjoyable, is the ability of a player to become part of a virtual community and engage in an enjoyable social
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Bader Albatati PhD Thesis October 2017 experience (Blanchard & Horan, 1998; Castronova, 2001; Chen et al., 2006). Fun and enjoyment are considered another two important aspect of feelings commonly associated with playing games (Holbrook et al., 1984). MMORPGs have characteristic ability to stimulate fun in gamers. Thus, the idea of playing MMORPGs, being in a fantasy world and interacting with thousands of players generates positive feelings for gamers (Barnett & Coulson, 2010; Yee &
Bailenson, 2007).
Video games belong to a broad class of products that are under the “entertainment” family (Holbrook, 2000). Entertainment is also a key feeling derived when products are absorbed through the senses, such as when viewing a performance, listening to music, or reading for pleasure (Lacher & Mizerski, 1994). This indicates that entertainment products such as video games need to be experienced for the benefits to be realised by users. The benefits of such products were related to emotional responses that are generated during and after the consumption process (Lacher & Mizerski, 1994).
Entertainment influences flow and social experience (Hamari, Shernoff, Rowe, Coller,
Asbell-Clarke, & Edwards, 2016). A state of flow is where players can sustain pleasurable experience, as a direct result of playing games and not feeling bored or anxious (Hoffman &
Novak, 2009), whereas interacting and socialising with friends during online gameplay can provide entertainment value and stimulation to players (Cole & Griffiths, 2007). In this sense, entertainment contributes to pleasurable experiences that lead to positive feelings.
Two other important positive feelings associated with game play are imagination and fantasy. According to Hirschman & Holbrook (1982), internal multisensory images can be of two types. Firstly, imagery involves recalling a previous incident that actually occurred. These imageries, memories or situations may be evoked by the product during consumption. For example, listening to a piece of music by an individual, triggers images that may stimulate
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Bader Albatati PhD Thesis October 2017 memories of a first love or prom night (Lacher & Mizerski, 1994). According to Dowling &
Harwood (1986), when music is experienced, indexical associations may occur. These associations are the result of combining a certain memorable time or a previous feeling with the product. In a similar fashion, playing MMORPGs may remind a player about the good times spent with friends.
Fantasy occurs when the consumer responds by producing an image that is not drawn directly from prior experience (Singer, 1966). Instead of remembering a historic sequence, the consumer builds an imaginary one (Martin, 2004; Singer, 1966). These images come together as a result of viewing different objects with diverse designs and colours.
Consequently, the fantasy feeling can influence game experiences. A study on the Sims2 game, showed that male gamers immerse themselves in a fantasy environment when they play.
The immersion happens because it provides opportunities for gamers to fantasise and to put some of these fantasies about the game and their projections into practice (Griebel, 2006). The importance of fantasy among male players suggests that they enjoyed the Sims2 game because it provided them with the tools to create their own virtual world, through the fantasy of owning a huge home, being rich, or starting a family (Griebel, 2006).
Thus, hedonic consumption can be a result of various themes of fantasies that affect the absorbing experience created by the product (Triantafillidou & Siomkos, 2014). Moreover, feelings, such as fantasies, leads consumers to dip into their own experiences of products
(dipping). The concept of dipping into an activity is identified as the immersion experience
(McMahan, 2003).
Another type of positive feeling is escapism, which has been identified as a key motivational factor for players to play video games. This is related to the nature of games as a source of escape (Calleja, 2007a, 2007b). Messerly (2004) described the escapism feeling as a
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Bader Albatati PhD Thesis October 2017 primary appeal for players for engagement with a game. Moreover, players desire to escape real life because of its inherent difficulties and complexity, and instead choose to experience a vivid and compelling game environment in a fantasy world (Calleja, 2010). A body of research in game literature attributes escapism to be one of the main reasons why people play. According to Yee (2006a), games provide players with the feeling of escaping in order to avoid real life.
Wood et al. (2004) stated that players experience a feeling of escapism when they enter the fantasy world of a video game environment.
Escapism has also been referred to as a means to relieve feelings of boredom and loneliness. A study conducted by (Bloch, Ridgway, & Dawson, 1994) on shopping mall visitors, found that shoppers are motivated by the feeling of escapism to eliminate boredom. Finally, escapism has been associated with aspects such as getting away from problems, pressure, and experiencing a loss of time (Hirschman, 1983; Titz et al., 2012).
Feelings influence the overall experiences (Bryce & Rutter, 2003; Choi & Kim, 2004).
In addition, Schmitt (1999) suggested that positive emotions in consumption however originated, can influence consumer satisfaction and purchase. In other contextual studies, emotions were found to be influential in consumer satisfaction and purchase behaviour of virtual store customers (Li, 2001). Mizerski, Pucely, Perrewe & Baldwin (1988) found that emotions have a strong positive relationship with purchase intention of music. This lead to the expectation that positive feelings have a positive impact on player satisfaction and intention to replay.
Negative Feelings The second set of emotions that is generated by playing online games is negative feelings.
Negative feelings are identified as the extent to which a person feels distress, irritation, and troubled emotion (Verhagen & van Dolen, 2011). For example, when a player for whatever reason loses a challenge in an online game, feelings of anger and frustration with the game may
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Bader Albatati PhD Thesis October 2017 be evoked. According to Verhagen & van Dolen (2011), negative feelings such as boredom can have a negative influence on exploration behaviour in online shopping. In other words, it can be assumed from the aforementioned studies, that negative feelings disrupt the flow of play in relation to online games. However, negative feelings are much less studied in the game literature.
Individuals may sometimes experience negative and positive feelings simultaneously.
Several experiments by Andrade & Cohen (2007) demonstrated that individuals can activate both negative and positive effects simultaneously. Individuals go into a “protective frame”, detached from the apparent danger and armed with a confidence of being able to handle it in the understanding that it poses no real danger. Although the research was limited to horror movies, they argued that their findings were relevant and could be extended to any experience that encompassed fear, such as extreme sports and dangerous occupations.
In a similar vein but with lesser intensity, feeling both good and bad experienced simultaneously is quite common in personal indulgences such as impulse purchases of luxury goods, consumption of sumptuous but unhealthy desserts and idle time-wasting. The initial feeling is good but is followed by feelings of stress, guilt, and regret. Ramanathan & Williams
(2007) tested subjects with an indulgent cookie and found their emotional experiences were complex and were both “hedonic” (i.e., spontaneous) and “self-conscious” (i.e., higher-order).
While both impulsive and prudent people experience ambivalent hedonic emotions (both positive, e.g., pleasure and delight, and negative, e.g., stress, emotions at the same time), they were for different reasons. Impulsive people were ambivalent for conflicting hedonic emotions, whereas prudent people were ambivalent because of hedonic and negative self-conscious emotions (e.g., regret and guilt). Impulsive people tend to repeat the experience with a different indulgence in that they may replace the cookie with potato chips. Conversely, prudent people choose to wipe away their negative emotions by choosing a notebook rather than potato chips.
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One of the aspects that remains unknown in game literature is the relationship between negative feelings and immersive game experience. Negative feelings were usually associated as something that can damage immersive experience with the product (Verhagen & van Dolen,
2011). When consumers encounter a bad consumption experience, it impacts on their perception negatively and may result in discontinuation of play and/or purchase. Play of MMORPGS can sometimes produce negative feelings that may impact the game experience. For example, losing in the game or having the character killed by other players can produce feelings of anger and distress. In contrast to other products, experience of negative feelings by players may lead them to want to play more to overcome these emotions. Furthermore, players can view negative feelings as a challenge which they need to overcome or compete against.
The negative feelings associated with being killed by other players especially for some gamers may be discouraging in terms of game continuation. This has been ameliorated in games that allow battles between guilds. In order to reduce unfortunate encounters singly, players may join other players to form a group with common goals to maintain their enjoyment of the game
(Ducheneaut et al., 2006a). However, there are some players that take the game more seriously than others. They are referred to as “griefers” who by nature typically encounter a “source of deep mental anguish, annoyance or frustration” (Foo & Koivisto, 2004, p.2).
Surprisingly, negative feelings can influence “griefers” to play MMORPGs more in order to overcome their feelings. Moreover, “griefers” derive their enjoyment by performing actions that impact other players’ game experiences. According to Foo & Koivisto (2004),
“griefers”, much like internet trolls, enjoy acts that may have negative impacts on other players’ game experiences and social interactions. This in turn can be viewed as a cycle where revenge as a negative emotion, becomes a motivation to play more of the game. Reasons that create the resolve for revenge may be ill-feeling, boredom or frustration with other players. However, negative feelings may or may not negatively impact on immersion (Barnett & Coulson, 2010).
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Some studies on technology argue that negative feelings of consumers can negatively impact their experience (e.g. flow and social experience) in online stores and virtual environments (Ermi & Mäyrä, 2005; Hoffman & Novak, 2009; Verhagen & van Dolen, 2011).
For example, players who are bored with the game cannot experience a state of flow
(Csikszentmihalyi, 1997). Therefore, it can be argued that negative feelings have the potential to impact game experience such as flow, and social experience negatively, as MMORPGs are considered an activity that possibly provoke negative emotions.
2.3.3 Flow Experience
In 1978, Csikszentmihalyi developed a theory that emphasised the flow of an experience
(Siekpe, 2005). At times, the concept of flow and how it is applicable has been less than clear
(Drengner, Gaus, & Jahn, 2008; Siekpe, 2005). Flow has been defined in different ways, but it is often referred to as the experience of meeting the challenges and activities (Nakamura &
Csikszentmihalyi, 2002). Flow can be experienced when an individual is engaged with an enjoyable activity (Csikszentmihalyi, 1997).
Csikszentmihalyi (1990) proposed eight elements of flow, which are balance between challenges of activity and skills required to meet those challenges, clear goals and feedback, concentration on the task on hand, a sense of control, merging of action and awareness, a loss of self-consciousness, a distorted sense of time, and the autotelic experience (Berta, Bellotti, De
Gloria, Pranantha, & Schatten, 2013). It is apparent that only the first six elements actually relate to flow generation as they are prerequisites for flow to occur. On the other hand, the last two are better categorised as outcomes as they occur as a consequence of flow.
Feelings is strongly related to flow. Several studies (Novak, Hoffman, & Yung, 2000) suggest that feelings (sensation) influence online users flow experience. Csikszentmihalyi
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(1990) also claimed that flow is influenced by feelings such as boredom, relaxation, apathy, worry, arousal and anxiety.
The state of the optimal flow experience is achieved by balancing a person’s skills to meet the challenges of the activity (Hoffman & Novak, 1996). A person is directed by clear goals, concentration on the activity on hand, clear feedback from encountering the task, an amalgamation of action and awareness, balancing the skills with the challenges faced from the task, personal control, and intrinsic reward from engaging in the activity (Novak, Hoffman, &
Duhachek, 2003). This suggests that a person becomes totally engaged with the task through directing maximum attention to the activity (Hsu & Lu, 2004).
A task is an important element of flow, as a state of flow can only be achieved during performance of a task. In other words, one has to be engaged in a task to experience flow.
Furthermore, task concentration connotes that the task needs to be automatic and at the same time demand cognitive effort from the performer to have a sense of control over actions, and that those actions are impactful (Pavlas, 2010). Put simply, for a person to experience flow, a certain amount of skill is required to meet the challenges arising from being involved in an activity (Hamlen, 2011; Pace, 2004). Flow is related to the skill-challenge pairing, for if an individual’s skill does not meet the challenge, anxiety results and flow is not achieved.
Each fresh wave of new technology means that users need to devote considerable time and energy to learn and adjust. The motivation to do so has been periodically examined by researchers who subsequently proposed the theory of optimal flow as a framework to use in the study of user experiences (Ghani, Supnick, & Rooney, 1991). In the past two decades, researchers found the notion of an optimal state of experience to be of use in theory.
Consequently, they employed the concept in their studies in a variety of activity settings such as rock-climbing, ocean cruising, meditation and even daily work activities (Csikszentmihalyi,
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1975, 1988). The theory places emphasis on context instead of individual differences to explain motivation (Weiner, 1990). Furthermore, the theory justifies behaviour with reference to contextual variables in terms of what a particular situation means to an individual.
Although definitions of flow may vary, flow is often believed to possess two common characteristics. One is total concentration on a task or the prerequisite, and the other is feelings of delight derived from its performance or the outcome (Ghani & Deshpande, 1994). Abundant research exists which describe the state of (flow) and (the effect of enjoyment) enjoyment experienced during computer usage (Ghani et al., 1991; Malone & Lepper, 1987; Webster,
Trevino, & Ryan, 1994). An important determinant of the quality of experience derived by an individual engaged in an activity, is the perception of the challenge in the mind of the person, as noted in research on flow, play and its intrinsic motivation (Csikszentmihalyi, 1975; Deci &
Ryan, 1985). Csikszentmihalyi (1990) argued that "the best moments usually occur when a person's body or mind is stretched to its limits in a voluntary effort to accomplish something difficult or worthwhile” (p. 3).
Studies in human-computer interaction reveal that the often-most quoted reason for people’s captivation by computer games, is the belief of control that is derived by players during game-play (Lepper & Malone, 1987). Challenge and skill are related to each other. An inordinately high challenge will cause a lack of control over the environment for the player and may result in anxiety and frustration. In contrast, when the activity is less than challenging, it will cause a loss of interest (Csikszentmihalyi, 2014). Thus, a sense of control over the environment is the second factor which affects flow. Csikszentmihalyi (1990) describes the determinants of flow by noting that a sense of exhilaration and deep enjoyment is felt when “we feel control of our actions, instead of being buffeted by anonymous forces” (p. 3).
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In his examination of the consequences of flow, Turkle (2005) found that computer users feel the flow in the form of increased experimentation, browsing, and exploratory activities
(Carroll & Rosson, 1987; Katz, 1988). Ghani et al. (1991) found flow to be significantly tied to exploration. Another related consequence of the state of flow is that users lose track of time, a phenomenon of which they are not unaware (Csikszentmihalyi, 1990). At its extremity, the flow state could end up in what is called the pathology of perfectionism (Katz, 1988), where a user tends to rework material continuously, beyond all reason and with no cognisance of the passing of time (Ghani & Deshpande, 1994). Thus, flow may be proportional to time expended by computer users in that the more time spent in an activity, the greater is the state of flow.
In the marketing context, consumers need to experience flow by engaging with the brand. This notion was applied to some games studies that investigated flow and its effects on playing games (Hsu & Lu, 2004). In Hsu & Lu's (2004) study on the user-acceptance of online games, it was found that flow can have an impact on the intention to replay games.
Flow experience has also been claimed as a reason for addictive behaviour (Chou &
Ting, 2003). Players are described as being in a state of flow when they are engaged in an activity, that they become unaware of time, or the amount spent playing. However, flow experience is more associated with positive factors such as learning, and other enjoyable activities. Moreover, Csikszentmihalyi (1988) explained flow as a move to a common mode of experience when engaged in an activity. This suggests that flow is more affected by emotions that produce enjoyment for players during game activities (Chou & Ting, 2003;
Csikszentmihalyi & Hunter, 2003).
In summary, flow experience is an important dimension of a subjective personal experience related to game playing (Webster et al., 1994). Flow can lead players to engage in
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Bader Albatati PhD Thesis October 2017 a cycle of further exploratory behaviour which makes them want to play the game even more
(Chou & Ting, 2003).
Relatively few studies examined the effect of flow on consumer satisfaction and intention to repurchase in an online context (Hausman & Siekpe, 2009). Although the concept of flow has been used in technology literature, it has not been well explained (Smith &
Sivakumar, 2004). However, some of the conceptual links are based on evidence of the relationship between flow and enjoyment in other contexts such as sports and the work industry
(Hoffman & Novak, 1996).
Flow experienced during internet browsing may lead to a number of positive outcomes such as consumer exploratory behaviour, satisfaction and return intention of consumers to the website (Hoffman & Novak, 1996). Smith & Sivakumar (2004) stated that internet shoppers enjoy being in a state of flow such that they become satisfied and wish to revisit the website as frequently as possible. Other studies also suggest that online users usually engage with online shopping activities as a result of the flow experience (Hausman & Siekpe, 2009). A study of consumers who visit an internet store for the first time found that flow influences intention to return (Koufaris, 2002). Koufaris (2002) also found that flow experience is an important concept in consumer satisfaction and intention to return to the site to shop again. In addition, Xin Ding,
Hu, Verma & Wardell (2010) found that flow can affect satisfaction and return behaviour of online users.
2.3.4 Immersion Experience
Immersion is referred to as the pleasurable experience of being transported to an elaborately simulated place (Douglas & Hargadon, 2001; Murray, 1997). Immersion occurs when people are surrounded by a completely different world that presents for them another reality unlike the real world. For example, a reader of a fantasy novel may be drawn by a narrative which
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Bader Albatati PhD Thesis October 2017 transports him/her to that particular world (McMahan, 2003). The presence in a parallel world may require the full attention and perceptual abilities of the individual to that world.
Game designers wish to engage players with the world created in the game by using game aspects such as game design to enhance the feeling of presence. As a result, players tend to focus all their attention onto the game and become completely immersed in it (Ryan, 1999).
This requires the person to be physically at the concert, with a full attention on the event.
Similarly, when a player is completely immersed in a virtual world of the game environment, a feeling of presence results (Riva, Davide, & IJsselsteijn, 2003).
According to McMahan (2003), players engross fully with a game when they are in state of deep play in which they actually feel that they are present inside, and within the game’s realistic environment. The more players absorb and feel immersed, the more they feel present
(being there) in the game. Moreover, players that are immersed will engage with the game environment and other players more (McMahan, 2003). This will create a cycle of positive immersion experience for players.
Thus, the central theme of the immersion (or immersive) experience in a gaming environment is largely attributable to the shift of attention of players from the real world to the fictional world of video games wherein they feel presence. However, the immersion experience is influenced by more than just pretty graphics and good storylines. McMahan (2003) argued that even though photo and audio realism can be important to create a virtual reality environment to produce a sense of immersion, individuals get immersed as a result of three factors, the first of which is that player expectations must be meet. Secondly, players’ actions need to have a significant impact on the game environment and thirdly, the game world needs to have some aspects that match reality (i.e., the environmental natural features). McMahan (2003) also
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Bader Albatati PhD Thesis October 2017 suggests that in addition to narrative, players can experience immersion as a result of winning, achieving goals, and showing off their skills to other players during the game and afterward.
Player avatars also have an influence on the immersion experience. According to
Ducheneaut et al. (2006a), player avatars shape the demographics of the fantasy world and help in creating a parallel environment where players enjoy and immerse themselves. The immersion in the game environment together with the attachment to a character is almost immediate. In fact, Banks & Bowman (2013) found that players may become attached to their characters emotionally, quickly after the game. The feelings of a game player influence his or her immersion level. For example, players may feel sadness if their character is killed, or joy and pride if their character defeats an enemy
When players become immersed in a game they become absorbed in their activities which may lead to a positive overall game experience (Ermi & Mäyrä, 2005). The higher the level of immersion of players with the game the more they are satisfied (Kao, Huang, & Wu,
2008). Thus, immersion may also influence customer satisfaction and repurchase behaviour.
Studies conducted by Kao et al. (2008) concluded that immersion is important in achieving customer satisfaction and for revisitation of a theme park. The same outcome of immersion can be applied to online games such as MMORPGs (Ermi & Mäyrä, 2005).
Immersion has also been suggested in previous literature to be a key component of consumer experience in several contexts and situations such as tourism, book readers, online usage, and brand engagement (Douglas & Hargadon, 2001; Hollebeek, 2011a; Mossberg, 2008;
Simon, 2010). For example, customer experience in tourism industry was described as the ability of a consumer to be absorbed by the activity (Hollebeek, 2011a; Mossberg, 2008).
Furthermore, being immersed in an activity has been related to interaction and engagement with the product at the customer level (Hollebeek, 2011a).
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2.3.5 Social Experience
Social experience is another major type of experience that players gain from online games such as MMORPG. Social experience has been identified according to the virtual community literature as members being a part of community where they interact with each other (Blanchard
& Horan, 1998; Castronova, 2001). Moreover, the definition was extended to online gaming were players interact with other players by playing with or against each other, and forming communities in the online game environment (Chen et al., 2006; Yee, 2006b). According to
Griffiths et al. (2004b), over 400,000 people play Ever Quest (a famous MMORPG produced by Sony Entertainment) on different servers worldwide with around 2000 players on any given server at a time. Most players join groups, and even lone or sole gamers inevitably interact with others depending on the situation, such as when buying or selling items, engaging in guilds, battles and construction. The choice of group or sole play may also depend on the support from the game, as some games make certain goals difficult to be achieved by a single player. Group communications are provided by the game where for example, players can use in-game screen text sessions to communicate with other players.
Social experience can offer social benefits perceived by people as the value of belonging to a community of individuals who share similar interests and goals (Algesheimer, Dholakia, &
Herrmann, 2005; Dholakia, Bagozzi, & Pearo, 2004; Wang & Fesenmaier, 2004). Interacting with other consumers can affect the intent to purchase or adopt a certain product. Engagement with others can generate a beneficial relationship where consumers can perceive the social value positively to enhance their use of certain brands (McWilliam, 2000; Suh, 2009). Furthermore, social interaction between buyers may also impact the intention of buying and repurchasing products (Suh, 2009).
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Digital gaming can provide a sense of belonging and social inclusion where gamers can meet, team up or chat with each other (Koo et al., 2007). According to Yee (2006c), social activities for online gaming can motivate players to play or replay. He argued that gamers are interested in making friends, building relationships with players and collaborating with others to overcome challenges. According to his factor analysis of reasons for play, Yee found that the social experience together with achievement and immersion, accounted for 55% of the variance in reasons to play.
Another study by Choi & Kim (2004) reported that players’ social experience with the game, affects customer loyalty. The study has limitations as they collected 1993 responses with only 433 of them being females. This can be problematic as it did not cover an adequate female representation. Female players may be more motivated by social reasons than men (Yee, 2006a).
Therefore, the small group of women in the sample may tend to underestimate the social interaction motives of the population studied.
Game designers build games rotating around a social world that imitates real life especially in terms of support and economy (Ducheneaut et al., 2006b). In order to survive in such an environment, players need to cooperate with others. Players start to make friends and build trust amongst themselves to direct their efforts in achieving goals within the game that benefit the whole group (Ducheneaut et al., 2006a). Players may form different groups and these groups are referred to as “guilds”. All players share the same values and goals of the guild that they are part of, and their particular guild’s status among other groups (Riegle & Matejka, 2006).
Players from different parts of the real world unite as a single community to form uniquely named guilds (or associations) where members share certain common cooperative values such as aim, skill, ability, rank and prestige (Zhong, 2011).All members are bound together by a set of guild membership criteria determined by the founder or by consensus, which
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Bader Albatati PhD Thesis October 2017 are directed towards improving their rank among other guilds. In general, association with a guild, has been identified with generating positive feelings in terms of social interaction (Hsiao
& Chiou, 2012).
Some of these guilds exist for many years and across different MMORPGs (Seay,
Jerome, Lee, & Kraut, 2004). An interesting example of this is “The Syndicate”, which was established in 1996 and has been running ever since ("The Syndicate," 2017). Moreover, they have a presence across several games such as World of Warcraft and Ultima Online. This is one of the best social groups which has become so famous, that other game developers now use them to test new MMORPGs before market release.
Social experience is also influenced by communication. Communication can be done privately (just two individuals) or as broadcast messages within a group of players (Griffiths et al., 2004a). Recently, online games such as MMORPGs support a more advanced method of communication that use headphones and microphones. This advanced method of communication has made the games more enjoyable and the connection with friends easier.
Furthermore, communicating directly with advanced headphones can improve the social experience as players talk to each other instantly and without delay, especially when the situation is critical (such as a fight scenario) where immediate communication can be the difference between winning (life) and losing (death).
Game experience is also impacted upon by the desire of players to form and maintain interpersonal relationships, a characteristic which renders online games to be more enjoyable and engaging due to the social experience. Thus, social experience is a fundamental part of online games (such as MMORPGs) as it allows gamers to form friendships, build communities, and engage with each other (Chen et al., 2006). Overall, social experience has been stated as the most important feature that increased the popularity of MMORPGs (Friedl, 2002).
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Designers of MMOs strive in promoting the social aspects of the game due to the social and interactive nature of the games. According to Ducheneaut et al. (2006a), social experience in online games is essential to the success of the game. The social factor of MMOs is the most distinctive activity in the game, even though some players can be motivated to play because of other reasons such as immersion (Yee, 2006c). Nevertheless, shared experience between players, collaborative nature of the game, and the reward attained from socialising with other players is gaining repute with gamers within the community as the most distinguished feature of MMORPGs. The social aspect of MMORPGs can indeed contribute to the long-life and survival of the game in the market.
Furthermore, a number of studies identified social interaction between players as one of the major motivational factors for players (Griffiths et al., 2004a; Yee, 2006c). Social experience plays an important role in the overall customer experience (Schmitt, 1999). In products such as social network and virtual reality, social experience has been linked to consumer satisfaction
(Xu, Ryan, Prybutok, & Wen, 2012). Xu et al. (2012) found that the social experience has an impact on internet users for intention to revisit social network sites. Therefore, social experience may have an impact on gamers’ satisfaction and intention to replay.
2.4 Outcomes of Game Experience
There are several outcomes to game experience of which the two under consideration in this research are consumer satisfaction and intention to replay.
2.4.1 Consumer Satisfaction
A number of studies relate satisfaction to cognitive based decision-making. The attribution involves a complex process where the focus is on the tendency of the consumer to give more attention to the benefits of the product. According to East et al. (2008), consumers who are engaged in this type of process will evaluate the benefits and the costs of the products before
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Bader Albatati PhD Thesis October 2017 purchase. This has been referred to in consumer behaviour literature as the extended problem solving model (Pritchard & Howard, 1997).
In a gaming context, a gamer will begin to evaluate expected performance of the game to the actual performance they experience. This cognitive and affective comparison leads to the gamer’s satisfaction or dissatisfaction with the performance of the game, and the future decision to replay.
Most online games (such as MMORPG) designers realize the importance of satisfying the needs and wants of gamers and their perceived game performance to keep revenue flowing to the owners of the games. A large body of research on gaming tries to explain behaviour through measuring satisfaction. Satisfaction is not a new concept in explaining intention to re- purchase and customer loyalty (Oliver, 1999). In fact, Oliver (1999) claimed that companies in the United States and Europe who pursue customer satisfaction, notice the importance of this construct in successful marketing of both goods and services.
Consumers’ satisfaction tends to rely on their consumption experience. The evaluation process depends on customer expectation compared to the perception of actual performance of the product (Wirtz & Lee, 2003). The outcome of the experience with the product needs to be satisfactory in order to result in long term customer retention and in the case of games, play history (Oliver, 1999). However, if consumers believe that their consumption process was unsatisfactory compared to their expectation, they become dissatisfied and will tend to discontinue using the product (GameSpot) or brand (Dark Age of Camelot). This kind of cognitive comparison is covered in Disconfirmation Theory (Oliver, Rust, & Varki, 1997).
Satisfaction and repurchasing intention have also been adopted in online consumer behaviour and online games in particular. The performance of a game may be perceived by players to have extrinsic values which can affect the overall satisfaction of users (Koo et al.,
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2007). These evaluations are based on qualities such as graphics, ease of use, and internet connection. In a study done by Kwak, McDaniel & Kim (2009) of customer satisfaction with a sports video game, gamer satisfaction was measured as the evaluation of the performance of the game based on functions and graphics. In that study, no effect of gamer satisfaction with regard to staying with the same game was found.
Other researchers, such as Davis & Lang (2011), suggested that gamers purchase behaviour may be influenced by ease of use of the game (Davis & Lang, 2011). They found that the easier the game is to learn, the more likely are players to replay it. However, Davis & Lang
(2011) did not relate perceived game performance and players experience with player satisfaction.
Some empirical studies show that there is a link between satisfaction and intention to re- purchase a product (Bitner, Booms, & Tetreault, 1990; Mittal & Kamakura, 2001). These studies report that satisfied customers are more likely to have a strong behaviour intention to return to the same brand. However, there has been only one study that linked satisfaction of playing a game with intention to replay a game, where satisfaction was used to predict player intention to replay (Said, 2006). However, Said (2006) actually found no significant link between satisfaction and intention to replay an online game.
The viewpoint of mere satisfaction or dissatisfaction with a game can be narrow, in that it may not give a full understanding of why players continue to play a game. Analysts need to recognise that games are an interactive form of entertainment that requires it to be experienced in terms of the full range of subjective feelings and emotions (Dönmez, 2011). As a result, a more comprehensive and inclusively subjective view needs to be proposed in order to understand why players continue to play a MMORPG.
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2.4.2 Intention to Replay
Purchase intention is held as the judgment of an individual’s intention to purchase a specific brand. This intention is affected by variables such as thought and anticipation to buy a specific brand. It is challenging to measure behaviour intention as it relies on consumer expectation to buy as well as to explain the antecedents that define consumer intent (Laroche, Kim, & Zhou,
1996; Teng, Laroche, & Zhu, 2007). The decision-making process for purchase intention requires making a clear overall evaluation of all brands in that particular product category. This may seem a complex process where the consumer is involved in extended problem solving in choosing a brand. Marketers traditionally depend on cognitive aspects to explain purchase behaviour.
According to Ajzen & Fishbein (1980) the most important predictor of a behaviour is the individual’s intention to perform it. They stated that behavioural intention is formed through attitudes to behaviour. This has been illustrated in Ajzen & Fishbein’s (1980) famous theory, the Theory of Reasond Action (TRA). The TRA assumes that consumer attitudes are measured by the like or dislike of a product by the consumer, and is a function of a consumer’s intention to buy. Consumers believe that a certain behaviour will lead them to certain outcomes, and their evalution of these outcomes leads them to conclude if the intention is favourable or not. If the outcomes regarding a certain action is percieved to be beneficial by the buyer, the intent to perform the behaviour is strong and more likely (Sheppard, Hartwick, & Warshaw, 1988). In contrast, if a person developed a negative attitude towards an object, the person will have a weak intention to perform the behaviour.
The Theary of Reasond Action (TRA) later has been developed into the Theory of
Planned Behaviour (TPB), by adding a component of percevied behavioural control which refers to the control the person believes he/she has over their own behaviour. TRA together
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Bader Albatati PhD Thesis October 2017 with TPB are considered to be the most important models in explaning consumer purchasing intentions. However, they also have limitations, for example, consumer behaviour and decision making may be affected by more than what they think or believe (Ogden, 2003) but also what they experience(Hoegg & Alba, 2011).
The perceived value of a product is also based on the way consumers understand the various product benefits and on which they depend when making purchase decisions. It is this overall assessment of product outcomes which influences consumer intention to buy, either negatively or positively. The latter will have a favourable outcome when consumers buy or continue to buy a product, whereas negative influences can have a devastating effect on consumer intention to buy (Turel, Serenko, & Bontis, 2010). For example, in a travel scenario a customer is involved in booking a flight with a certain airline and traveling with it to the destination. As a result of consumer experience outcomes with that particular airline (perceived positively or negatively), customers may continue or discontinue to fly with the same airline.
Consumers’ purchase decisions are affected by different aspects of perceived value from the product. These values can explain why consumers choose to buy or not to buy a particular product. In addition, as a result of consumer perception of product benefits, regardless of whether they are tangible or intangible, consumers may continue buying the same brand.
According to this perceived value, buyers may continue purchasing the same product over a period of time (or at least have the intention to re-buy). These benefits can be identified as functional, emotional and social (Sheth, Newman, & Gross, 1991).
Some consumer behaviour researchers demonstrated that consumers have the ability to quantify things and evaluate products from its utilitarian benefits. The reasoning behind this school of thought is that a consumer is rational and seeks high cognitive involvement with the
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Bader Albatati PhD Thesis October 2017 decision process. Consumers thus purchase products for functional and utilitarian reasons such as design and price (Kivetz & Simonson, 2002).
Other consumer purchase decisions may be driven by non-cognitive benefits such as emotional responses or hedonic benefits. (Babin, Darden, & Griffin, 1994). There are a number of products such as entertainment offerings that possess the ability to generate emotional responses. These responses may satisfy needs for fantasies, feelings and fun (Babin et al., 1994).
In order to increase consumer purchase intention by increasing the favourability towards the functional or emotional aspect, marketers will engage consumers with the product. This engagement can be on different levels of the consumer decision making process e.g. at the initial interest stage or the price comparison stage. For example, video game producers will influence playing intention through making free demos of the game available for players.
Some experience aspects may have stronger influences on intentions than other aspects.
For example, flow has been identified to have an impact on consumer satisfaction and purchase intention (Calvo-Porral, Faíña-Medín, & Nieto-Mengotti, 2017; Chang, 2013; Hausman, 2011;
O'Cass & Carlson, 2010). This is because flow is associated with the pleasurable activities experienced by users (Alba & Williams, 2013; Peterson, Park, & Seligman, 2005). In online shopping, flow has been shown to have a significant impact on behaviour intention when consumers visit and purchase products from websites (Hoffman & Novak, 2009; Luna,
Peracchio, & Juan, 2003; Siekpe, 2005). Similarly, when players of MMORPGs experience a state of flow, it makes the whole experience enjoyable, which ultimately influences their replay intentions (Chou & Ting, 2003; Trevino, 1992).
Immersion is another experience that can influence satisfaction and purchase intention
(Deng, Turner, Gehling, & Prince, 2010; Kao et al., 2008; Kao, Huang, & Yang, 2006). Players falling into deep play, experience the feeling of being present inside the game. According to
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Riva et al. (2003), immersion is “the feeling of being there” in a virtual realty environment.
Immersion may impact on user behaviour. For example, having a feeling of being submerged in the MMORPG environment will affect overall game experience and result in playing again in the future (Yee, 2006c).
According to Sashi (2012), social interaction or experience will result in satisfaction and repeat behaviour between social media users. Users who continue interacting with other users and sellers, will progress to satisfaction and repeat visit of a website. Tikkanen, Hietanen,
Henttonen & Rokka (2009) identified social experience as an important feature for virtual worlds in order to increase the chances of revisiting behaviour for users. As discussed earlier, social experience is one of the most important features that distinguish MMORPGs from other online-games. When players chat and engage with each other, they become more involved with the game, as it creates a sense of belonging, affiliation, and support. Choi & Kim (2004) reported that the social experience of gamers significantly affects customer loyalty and repeat play/purchase behaviour.
2.5 Hypothesis Development
Feelings or emotions are strongly related to other aspects of game experience such as flow, immersion and social experience. The emotional state of customers during consumption can influence attitudes, arouse excitement and extend the duration of the product-user relationship.
This research will specifically examine how both positive and negative are related to flow, immersion and social experience. As a result, the following hypotheses are proposed:
• H1: Positive feelings are positively associated with flow experience in gameplay. • H2: Positive feelings are positively associated with immersion experience in gameplay. • H3: Positive feelings are positively associated with social experience in gameplay.
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• H4: Negative feelings are negatively associated with flow experience in gameplay. • H5: Negative feelings are negatively associated with immersion experience in gameplay. • H6: Negative feelings are negatively associated social experience in gameplay.
As discussed earlier, LaSalle & Britton (2003) and Shaw & Ivens (2005) found that customer experience results in customer satisfaction. Other researchers affirmed that experiences affect repeat purchases as well (Prahalad & Ramaswamy, 2004; Verhoef et al.,
2009). As a result, the following hypotheses are proposed:
• H7: Positive feelings are positively associated with players’ satisfaction.
• H8: Positive feelings are positively associated with players’ intention to replay the
game.
• H9: Flow experience is positively associated with players’ satisfaction.
• H10: Immersion experience is positively associated with players’ satisfaction.
• H11: Social experience is positively associated with players’ satisfaction.
• H12: Flow experience is positively associated with players’ intentions to replay.
• H13: Immersion experience is positively associated with players’ intentions to replay.
• H14: Social experience is positively associated with players’ intentions to replay.
• H15: Players’ satisfaction is positively associated with players’ intentions to replay.
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CHAPTER 3 METHODOLOGY
3.0 Introduction
This chapter outlines the two main investigations of the research project designated as Study
One and Study Two. This is followed by the rationale for the research design and an explanation of the questionnaire design, along with the measures of the questionnaire. The choice, popularity, validity and reliability of Amazon Mechanical Turk as a survey method is also explained. Details of the data sampling are provided along with the justification for the use of structural equation modelling for the analyses of the hypotheses’ variables. Finally, a detailed explanation of each construct and its relation to a specified hypothesis is discussed.
3.1 Hypotheses
Study One examines the relationships among feelings, and flow, immersion, and social experience, while Study Two examines the effects of feelings, flow, immersion, and social experience on satisfaction and intention to replay. Both investigations use responses from online self-reported surveys of gamers.
The first study will be based on 359 respondents from India who play MMORPGs. This study will help to evaluate the parameters used in measuring the feelings and the effects of feelings on the players’ flow, immersion, and social experiences. Hypotheses 1 to 6, as listed in
Table 3-1, has been developed for this part of the study and these will be tested for validity in the investigation.
Study Two will be based on 491 respondents from the United States who play
MMORPGs. It is expected that the second investigation will provide an overview of how feelings influence flow, immersion, and social experience as well as how game experience affects players’ satisfaction and intention to replay. Hypotheses 7 to 15, as listed in Table 3-1, were specifically developed for Study Two.
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Table 3-1. List of Hypotheses and Related Studies
Number Hypothesis Study H1 Positive feelings are positively associated with flow experience in One & Two game play.
H2 Positive feelings are positively associated with immersion One & Two experience in game play.
H3 Positive feelings are positively associated with social experience in One & Two game play.
H4 Negative feelings are negatively associated with flow experience in One game play.
H5 Negative feelings are negatively associated with immersion One experience in gameplay.
H6 Negative feelings are negatively associated with social experience in One gameplay.
H7 Positive feelings are positively associated with players’ satisfaction. Two
H8 Positive feelings are positively associated with players’ intention to Two replay the game.
H9 Flow experience is positively associated with players’ satisfaction. Two
H10 Immersion experience is positively associated with players’ Two satisfaction. H11 Social experience is positively associated with players’ satisfaction. Two
H12 Flow experience is positively associated with players’ intention to Two replay.
H13 Immersion experience is positively associated with players’ Two intentions to replay.
H14 Social experience is positively associated with players’ intentions to Two replay.
H15 Players’ satisfaction is positively associated with players’ intentions Two to replay.
Figure 3-1 shows each of the constructs on the concept map with the matching hypothesis for each interaction.
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Figure 3-1. Hypothesis Mapping on Concept Map
3.2 Research Design and Method
Inspiration for the research design was drawn from a number of studies that investigated consumer decision-making (Holbrook et al., 1984; Lacher & Mizerski, 1994). As discussed in chapter 2, two major approaches emerged to explain consumer behaviour. The first approach views consumers as extended problem-solvers. This school of thought regards the utilitarian aspects of products as the most important reason for consumption. The second approach focuses on consumers’ experience including emotions in the consumption process (Holbrook &
Hirschman, 1982). A number of studies that follow the second approach have used quantitative research design to reach their conclusions (Holbrook et al., 1984; Lacher & Mizerski, 1994).
This study will apply the quantitative approach in order to understand players’ behaviour.
3.2.1 Research Rationale
There are two main types of analysis in consumer behaviour research: quantitative and qualitative. Schiffman & Kanuk (2007) claimed that quantitative research, based on positivism, is an effective method to predict consumer behaviour. It is common for theories or hypotheses that are generalisable across settings to be tested using quantitative research (Amaratunga,
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Baldry, Sarshar, & Newton, 2002). According to Onwuegbuzie & Leech (2005), quantitative researchers predict behaviour by using statistical techniques and subjective inferences to make decisions about what their data mean in the context of an a priori theoretical or conceptual framework. The data used for quantitative research must be ‘hard’ and ‘generalisable’ survey responses for multivariate techniques to be applied to predict behaviour (Onwuegbuzie &
Leech, 2005; Sieber, 1973).
Conversely, qualitative research, based on interpretivism, is to gain a general idea and understanding of consumer behaviour (Schiffman & Kanuk, 2007). The main focus of qualitative research is to construct a hypothesis or theory (Amaratunga et al., 2002). Qualitative research relies on ‘deep, rich observational data’ instead of ‘hard generalisable survey data’.
Sieber (1973) explained that qualitative study is a phenomenon, where procedures and views of reality are used to extract meaning (Onwuegbuzie & Leech, 2005). Consequently, generalisations to larger populations cannot be inferred, due to the type of information required for qualitative research (Schiffman & Kanuk, 2007).
Both qualitative and quantitative studies are commonly used for online gaming research
(Meredith et al., 2009; Chen & Duh, 2007). The focus of the current research is on the decision- making with regard to playing MMORPGs; therefore, the quantitative research method is appropriate in this instance.
3.2.2 Quantitative Research Instruments – Survey and Questionnaire
Surveys or questionnaires are tools used to gather quantitative data and information about the purchase preferences, experiences, purchase and information-related behaviour of the purchaser. The particular method used in this research is online surveys. Online surveys provide ease of use in terms of computation and translation of the necessary data and fields for analysis (Malhotra, 2010). Online surveys allow researchers to access large numbers of
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Bader Albatati PhD Thesis October 2017 respondents in a cost-effective manner and at fast response rates. Initially, these were serious constraints on the research. Online surveys may be anonymous, which effectively eliminates social-desirability bias and renders the answers more reliable.
Despite these advantages, online surveys are not without drawbacks, as no interviewer is present to get clarifications on questions from participants (Evans & Mathur, 2005). Online surveys may also have the effect of reducing the age demographic, as they limit access to the older generation or people with limited income who may not have internet access (Evans &
Mathur, 2005). As the older-age demographic is not the typical segment for this study, this limitation is expected to have a minimum impact on the results.
Typically, online surveys may have a low response rate due to the possibility of the invitations being perceived as spam email. The global reach of the internet, however, tends to negate the reduced response rate (Evans & Mathur, 2005). Nevertheless, the convenience of online surveys and the ubiquitous nature of email means that a reasonable sample size for the research is possible, enough to overcome the negative considerations associated with the method (Malhotra, 2010). As a result, an online survey was chosen as the method of data collection.
Questionnaires are another tool for surveys within a target population and are used for focus on correlation analyses of the various factors being hypothesised (Malhotra, 2010).
Generally, questionnaires may be used to gather facts about individuals, such as their demographic segment and habits, as well as psychological factors, such as feelings and attitudes.
Items on a questionnaire may be differentiated by the type of questions; either factual (objective) or nonfactual (subjective). Factual items have an answer that is verifiable if true. The question about behaviour in the form of ‘Have you played online games in the last month?’ is an example of a factual question.
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Conversely, a non-factual item requires a subjective response that is more complicated to verify, as it relates to aspects of mind. An example of this type of question is the question regarding satisfaction levels. However, when the response stability of a non-factual item is assessed, it may be assumed to have an approximate ‘true’ value (Schoon, 1998). A suitable way to assess response stability is to use more than one question to examine a factor or to employ multi-item scales. Multi-item scales are more advantageous than their single-item counterparts.
Single-item scales are usually associated with simple constructs (Conant, Mokwa, &
Varadarajan, 1990) and others where measurement error is disregarded (Boyd & Reuning-
Elliott, 1998). Multi-item scales tend to be more reliable than single-item scales as they have more than one indicator that serves to enhance the validity of the related construct (Kline, 1998).
3.3 Measurement
The survey questionnaire included a cover letter that explained the purpose of the study. Prior to administering it, the questionnaire was submitted to the Human Ethics Committee at the
University of Western Australia for ethical approval of this research.
In order for questions to be pertinent to all participants, questionnaire items must have
‘stimulus equivalence’ (Schoon, 1998). This means that each item should have a specific meaning that is understood by participants. Accordingly, prior to the question in this study
‘Have you played online games in the last month?’, the respondents were provided with a brief definition of what is meant by ‘online games’ to ensure that non-online gamers excluded themselves by a negative answer by default because they did not have full understanding of the term. Only respondents with a positive answer to this screening question were invited to complete the questionnaire.
The questionnaire consisted of two parts and included 76 items as multiple indicators of constructs. Three items related to intention to replay, nine items were for game satisfaction,
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38 items were for feeling responses, eight items were for immersion experience, nine items were for flow experience and nine items were for social experience. The exceptions were the demographics questions, which were a single-item selection where the respondents only ticked the specific option that fit or reported their answer, such as age, by writing it instead.
For multi-item measures, the questionnaire was in the form of the widely adopted and easily administered five-point Likert Scale (Åhlström & Westbrook, 1999; Kotey, 1999).
Rating-scale items typically ask participants to rate responses for importance, frequency or intensity on a graded scale (Schoon, 1998). The five-point frequency Likert Scale was the main rating scale used in this study. Likert Scales are a popular choice for researchers, as they are easy to conduct and analyse, while at the same time simple enough for consumers to answer
(Ross, James, & Vargas, 2006). Unlike semantic differential scales, where consumers guess the meaning of each number in the scale, each number in a Likert Scale is annotated to provide information to consumers so that they know exactly what their selections mean (Schiffman &
Kanuk, 2007).
As a self-reported survey instrument, the questionnaire will collect responses to questions on how gamers evaluate game experiences such as emotional and social experience, and past behaviour, to ascertain their importance in the decision to replay. The online survey panel from Amazon (US), known as MTurk, will be used for this purpose. The study was designed to measure the effects of feeling on flow, immersion and social experiences, and also various game experiences on satisfaction and intention to replay for multiplayer online role- playing-games (MMORPG) players. Data collected from Amazon Mechanical Turk (AMT) will be analysed using SPSS/IBM statistical software and AMOS for structural equation modelling
(SEM).
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3.4 Data Collection Method
The data collection instrument used in both Study One and Study Two was AMT. AMT is an online service where surveys are completed by respondents called ‘workers’, who are recruited by different ‘requesters’ to complete studies. AMT, correspondingly has two separate account types viz. a worker account and a requestor account.
In the worker account, a worker logs in to the system and selects a suitable Human
Intelligence Task (HIT) for which there is a monetary reward offered. These HIT tasks may be too complex to be computerised yet simple enough not to require any specialisation by workers
(Amazon.com, 2013; Rouse, 2015). Human Intelligence Tasks (HITs), which may be self- contained jobs as short as one-minute long. HITs include work identifying objects in pictures, transcribing unclear copies of receipts, or visiting website to comment on the clarity of the site information. Typical HITs are short and the pay is as little as a few cents.
In the requestor account, requesters provide tasks (HITs) for worker completion. This is the account a researcher uses, in which access to all built-in survey tools as well as the entire worker population is granted to researchers. Requesters have the option to lodge multiple HITs, select worker numbers and collect data simultaneously (Amazon.com, 2013; Rouse, 2015). The
HIT (task) payment set by the requester is multiplied by the number of workers the requestor has selected for the HIT in order to give the requester the exact total cost (which includes a 10% commission to Amazon Ads) for their service of payment distribution to the selected workers
(Amazon.com, 2013). This facility streamlines the process of data collection and thus allows the researcher to concentrate on the design and analysis of survey data. Many researchers use similar online data-collection resources and AMT is a popular choice for researchers in psychology and other fields due to its promise of data collection (Reimers & Stewart, 2007).
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AMT rewards were originally in the form of cash for workers with a U.S. bank account and Amazon.com gift cards for others. This policy resulted in fewer workers from other countries and 70% to 80% of workers from the U.S., who were predominantly representative of the population of American Internet users (Paolacci, Chandler, & Ipeirotis 2010; Ghose,
Ipeirotis & Sundararajan 2009). The population dynamics of AMT recently shifted significantly due to a change in the bank account policy, resulting in proportionally more Indian participants
(Eriksson & Simpson, 2010).
As a survey platform, AMT has a number of unique benefits for running online experiments. Firstly, in terms of subject pool, AMT provides researchers with subjects they would not otherwise have had, in the case of researchers from smaller colleges and universities with limited subject pools (Smith & Leigh, 1997), or non-academic researchers who are limited to online ads, such as study lists, e-mail lists, social media and publicly posted flyers. Although this characteristic is similar to that of other online recruitment methods, AMT is unique in that it offers researchers a relatively stable and existing pool of subjects over an extended period of time. Apart from occasional daily and weekly variations, subject availability through AMT is reasonably constant and subject-pool fluctuations result mostly from variability in the job market (Paolacci et al., 2010).
Secondly, another advantage of AMT is that the workers tend to be from very diverse backgrounds, spanning a wide range of age, ethnicity, socio-economic status, language and country of origin. The diversity on AMT (or Mechanical Turk), facilitates cross-cultural and international research at a very low cost and can broaden the validity of studies beyond the undergraduate population of the research institution (Eriksson & Simpson, 2010).
Thirdly, low cost and built-in payment mechanisms is another distinct advantage of
AMT over the other online panels. AMT offers a low cost at which studies can be conducted, a
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Bader Albatati PhD Thesis October 2017 situation that clearly compares favourably with paid laboratory subjects and comparably to other online recruitment methods. For example, Paolacci et al. (2010) replicated classic studies from the judgment and decision-making literature at a cost of approximately $1.71 per hour per subject and obtained results that paralleled the same studies conducted with undergraduates in a laboratory setting. Göritz, Wolff & Goldstein (2008) showed that the hassle of using a third- party payment mechanism, such as PayPal, can lower initial response rates in online experiments. Mechanical Turk avoids this issue by offering a built-in mechanism to pay workers
(both a flat rate and bonuses) that greatly reduces the difficulties of compensating respondents.
All respondents were payed an amount of 2 US dollars for each complete and successful task.
Moreover, the payment mechanism shortens the period of data extraction and shortens the experiment cycle. One implicit goal in research is to maximise the efficiency with which one can go from generating hypotheses to testing them, analysing the results and updating the theory. Ideally, the limiting factor in this process would be the time needed to do careful science, but all too often, research is delayed because of the time needed to recruit subjects and recover from errors in the methodology (Paolacci et al., 2010). With access to a large pool of subjects online, recruitment is vastly simplified. Moreover, experiments and surveys can be built and put on AMT (Mechanical Turk) easily and rapidly, which further reduces the time to iterate the cycle of theory development and experimental execution (Paolacci et al., 2010).
Fourthly, Amazon policy ensures that both recruiters and workers maintain anonymity and confidentiality. Respondents are required to read a brief description of the study followed by a screening question about their game play in the last week prior to commencement. A linked
ID issued by Amazon serves to guarantee that the study can only be taken once (Paolacci et al.,
2010). These measures are ‘qualifications’ and a pre-screening method that ensures that all respondents are recent gamers. In addition, requesters have the ability to reward good and complete responses and punish poor-quality responses by denying them the reward. This AMT
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Bader Albatati PhD Thesis October 2017 measure ensures that the study includes complete responses with more control over data quality.
AMT attracts respondents of all genders and different nationalities thereby ensuring a representative sample of the population (Paolacci et al., 2010).
Fifthly, AMT is also user-friendly, as it allows a researcher to include any type of question, such as Likert, categorical (for example male or female), and/or questions and open- ended questions (written answers). This study uses both a Likert Scale and categorical responses as supported by the AMT platform (Amazon.com, 2013). The study has a group of open-ended questions to ensure that answers given by respondents are consistent with responses to the Likert
Scale questions. Moreover, it allows surveys that are built on different platforms, such as
Qualtrics, to be imported to AMT very easily (Amazon.com, 2013).
Sixthly, AMT provides a fast recruiting process and quick access to a large pool of respondents (Paolacci et al., 2010). A previous use of this service collected 1232 surveys of sales promotion in three days, with only 4% of these respondents rejected. This study can benefit from the high respondent rates that AMT enjoys to get the desired sample size in a two-week period for the questionnaire, and it is the most appropriate method to reach the target group
(Jansz & Tanis, 2007).
3.4.1 Reliability of AMT
Researchers have long been open-minded about adopting new technologies to aid in the process of conducting research while staying committed to protecting the integrity of that process
(Rouse, 2015). This openness was evident in the use of computer banks requiring punch-card data entry in the 1950s (Elliott & Stevens, 1951), the adoption of ‘mini-computers’ to run experiments in the 1970s (Castellan, 1975), the administration of personality tests via computers in the 1980s (Butcher, 1987), and the use of online surveys for data-collection purposes at the start of the 21st century (Birnbaum, 2000).
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Paolacci et al. (2010) investigated 1000 AMT workers. Around of 50 percent of them were residents of the U.S., and another large proportion was from India. Two-thirds of the workers were women. Even though the average level of education of workers from the U.S. was higher than that of the general U.S. population, their reported salaries were lower, and the majority of workers cited the reason for HIT completion as entertainment or an activity to pass the time.
Paolacci et al. (2010) replicated a series of classic decision-making research tasks to test the validity of AMT as a research instrument. Their results revealed that the judgment error rate for AMT workers differed only slightly in comparison to traditional sampling methods. Paolacci et al., (2010) drew conclusions from their comparative studies that the use of AMT as a data sampling tool was similar to other samples collected.
Buhrmester, Kwang & Gosling (2011) compared the demographics of AMT samples to internet and other traditional data collection samples. They found that AMT workers were more diverse than either traditional college-student or Internet samples, as workers originated from more than 50 countries (Buhrmester et al., 2011).
As AMT workers are paid and requesters preferred inexpensive data collection, the quality and speed of data collection could be affected accordingly. In order to test the effects of these two factors, Buhrmester et al. (2011) manipulated the task length and payment amount.
Their results showed that, although shorter tasks and higher-payment tasks attracted respondents more quickly, data quality was not adversely affected (through reliability alpha measurements), even in relation to tasks attracting the lowest payments (Buhrmester et al., 2011).
Buhrmester et al. (2011) tested the Big Five Inventory (BFI) through both AMT and non-AMT samples and found no significant differences between them. All reliability alphas used for the comparison were found to be within the acceptable range (Buhrmester et al., 2011).
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Furthermore, they found that the reliabilities of the five factor data collected through AMT surpassed the reliability for the BFI from previous literature such as Gosling, Rentfrow, &
Swann (2003). Psychometrics of this calibre led Buhrmester et al. (2011) to conclude that the
AMT method of data collection was promising and reliable.
Following the publication of the primary study by Buhrmester et al. (2011) using AMT as a research instrument, its use as a data-collection method has grown exponentially, from 353 citations on Google Scholar as of February 27th, 2013 to 8230 citations as of October 1st, 2016.
Furthermore, the body of literature on AMT in social-science applications has grown steadily
(Amir & Rand, 2012; Horton, Rand, & Zeckhauser, 2011; Mason & Suri, 2012). The results of the present study are additional testimony to the efficacy of data collection on emotions and culture through the use of AMT.
Other studies such as Holden, Dennie & Hicks (2013) tested the M5-120 questionnaire using AMT. The M5-120 is a personality test developed by Johnson in 2001 (Hurt, Grist,
Malesky, & McCord, 2013). Holden et al. (2013) recruited workers to do a pre-test and pro-test for the M5-120. Analysis of the paired-sample t-tests led Holden et al. (2013) to conclude that participants’ pre- and post-test responses to the M5-120 did not differ significantly. Moreover, the r correlations of Pearson on the M5-120 for all personality types indicated good reliability for test–retest correspondences. In fact, Buhrmester et al. (2011) found similar correlations using the BFI (r values between 0.86 and 0.94).
In summary, AMT is relatively innovative method of data collection. It provides researchers with access to relatively large subject pools with low cost and fast collection
(Paolacci et al., 2010). Moreover, a number of studies have demonstrated that AMT is a reliable source to gather data (Buhrmester et al., 2011; Holden et al. 2013).
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3.5 The Data Sample
The data sample required a typical cohort of participants who play video online games. Several studies found that playing video games results in different consumption patterns between different age groups and genders (Williams, Consalvo, Caplan & Yee, 2009; Schott, & Horrell,
2000; Beasley, 2002). For example, video game players between 18 to 49 years old from both genders typically accounted for 53% of the total U.S. players (Association, 2008). Therefore, this project included both men and women in the sample to account for the effect of gender.
The U.S. is one of the largest game markets. In the US alone, game sales increased from
2.2 billion US dollars in 1996, to almost 15.9 billion US dollars in 2010, representing a 623% increase in sales (Association, 2008). Online game players in the U.S. alone reached 86 million in 2008 (Lipsman, 2009). In 2016, 27% of the U.S. game players played MMORPGs with expectations of more increases in the future (Statistica, 2018). The MMORPGs is believed to reach 26 billion US dollars in 2019 (Osborne, 2016).
Another growing market for online games is India where the online gaming industry is expected to add around 190 million gamers with a total market value of one billion US dollars by 2021 (KPMG, 2017). The Indian market is dominated by males under the age of 24. More than 27% of the digital game players in India play MMORPGs with expectations that the market will continue to grow as more localised games are introduced to the region (Kuveke, 2017;
KMPG, 2017). Due to their market sizes and potentials, this project selects the U.S. and India as the two markets for examination.
The project comprises of two studies (Study One and Study Two) wherein all respondents were current players of MMORPG. Respondents who had not met the criteria for both studies were excluded from the final analysis (e.g. not played MMORPG in the previous month and not from India or the US). Study One included 359 Indian players (see Chapter 4 for
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Bader Albatati PhD Thesis October 2017 sample details). Study Two included 491 players from the U.S (see Chapter 5 for sample details). Both of these respondent sample sizes were considered sufficient for Structural
Equation Modelling (SEM) (Malhotra, 2010).
3.6 Structural Equation Modelling
SEM is statistical tool that allows a researcher to investigate relationships between one or several Independent Variables (IVs) and Dependent Variables (DVs). SEM is based on a general approach to the theoretical model through which the structural relationships among constructs are statistically tested (Holmes-Smith, 2001). SEM is also known as Analysis of Covariance
Structures, Latent Variable Analysis, or Causal Modelling. The two models that make up SEM are the measurement model and the structural equation model (Chin, 1998; Randall & Richard,
1996).
According to Bollen & Long (1993), SEM has several advantages over conventional statistical methods, as it (1) does simultaneous equations with many endogenous variables; (2) takes measurement errors into consideration; (3) performs factor-analysis procedures and tests multiple indicators of a construct; and (4) enables researchers to specify structural relationships among the constructs and thus tolerates more general measurement models.
3.7 Measurement of Constructs and Items
This section discusses measures that were adopted from previous studies to measure game experience for players from Study One and Study Two. The constructs that were measured are positive feelings (powerful, fun, sensory, imaginative, fun and escapism), negative feelings, flow experience, immersion experience, and social experience. Finally, the section also discusses measures of game outcomes (e.g., satisfaction and intention to replay) that were used in both studies.
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3.7.1 Feelings
As discussed in the literature review section, positive feelings represent a gamers’ perceptions of those aspects of their experience that involved powerful, fun, imaginative (i.e. fantasies), sensorial (body movements), and escapism feelings. All these measurements have been adopted form previous studies on hedonic consumption. All items used in the study were adapted from
Lacher & Mizerski (1994) except for escapism.
Respondents were asked to rate their level of argument on Likert Scales (1 = strongly disagree and 5 = strongly agree). Powerful feeling was measured by 8 items, such as “I feel strong when playing online games”. Fun feeling was measured by eight items such as, ‘Playing online games is a fun activity’. Imaginative feeling was measured by six items, such as ‘After playing, I still fantasise about the game’. Sensory feeling was measured by five items such as
‘While playing, I keep moving part of my body’. Finally, escapism feeling which was adopted from Mathwick & Rigdon (2004). Escapism used three items, such as ‘Playing online games makes me feel like I am in another world’. The items used to measure powerful feelings are depicted in Table 3-2. The other positive feeling items are shown from Table 3-3 to Table 3-6.
Table 3-2. Items used to measure powerful feelings
Construct Item Item Number
1. When I advance in online games, I feel powerful.
2. When I complete quests in online games, I feel I gain status among other players. Powerful 3. Developing my online character and increasing their power makes Feeling me feel good about myself.
4. I feel strong when playing online games.
5. I am energetic when playing online games.
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6. I am enthusiastic about playing online games.
7. I am excited when playing online games.
8. Playing online games is an exciting experience.
The specific items used to measure the feeling of fun are depicted in Table 3-3.
Table 3-3. Items used to measure fun
Construct Item Item Number
9. Playing online games is a fun activity.
10. I enjoy exploring new areas within an online game.
11. I enjoy designing my own avatar in the game.
12. Developing my character and making it powerful is an Fun enjoyable process. 13. I enjoy the story line within the game.
14. I feel entertained when playing online games.
15. I feel amused when I play a good game.
16. I feel relaxed when playing online games.
The specific items used to measure the feeling of imagination are depicted in Table 3-4.
Table 3-4. Items used to measure the imaginative construct
Construct Item Item Number
17. Online games create a picture in my mind.
18. I enjoy doing impossible things in a virtual world.
Imaginative 19. I enjoy taking risks in online games.
20. After playing, I still think about the game.
21. After playing, the game prompts images in my mind.
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22. After playing a good online game, I still fantasise about it.
The specific items used to measure sensory feelings are depicted in Table 3-5.
Table 3-5. Items used to measure the sensory construct
Construct Item Item Number
23. While playing, I keep moving part of my body (e.g. head or foot).
24. When playing good online games, my heartbeat becomes faster.
Sensory 25. While playing, I yell from excitement.
26. During playing a good online game, I swear.
27. My facial expressions change while playing an online game.
The specific items used to measure the feeling of escapism are depicted in Table 3-6.
Table 3-6. Items used to measure escapism
Construct Item Item Number
28. Playing games online gets me away from all the problems that I have. Escapism 29. Playing online games makes me feel like I am in another world.
30. I get so involved when I play that I forget everything else.
In contrast to positive feelings, negative feelings have mostly been measured as a single dimension construct in the online gaming context (Verhagen & van Dolen, 2011). Negative feelings were measured by five items that were adopted from previous studies (Lacher &
Mizerski, 1994; Mizerski et al., 1988). All items were measured on a 5-point Likert Scale (1 = strongly disagree to 5 = strongly agree). Respondents were required to state their level of disagreement or agreement with statements such as ‘I hate losing in online game’ and ‘I feel
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Bader Albatati PhD Thesis October 2017 mad when I don’t win’. The specific items used to measure negative feelings are depicted in
Table 3-7.
Table 3-7. Items used to measure negative feelings
Construct Item Item Number
31. When I lose in online games, I become upset.
32. I hate losing in the online game.
33. I feel mad when I don’t win.
Negative Feeling 34. I become angry when I don’t win.
35. I am annoyed when I cannot finish the game.
36. I feel frustrated when I cannot complete the quest in the game.
3.7.2 Flow Experience
Notwithstanding non-clarity of the definition of flow, available analysis of its measurements revealed that it was frequently understood to be multidimensional in construct (Agarwal &
Karahanna, 2000), meaning that it had more than one dimension, all of which were grouped under the same latent construct (Law & Wong, 1999). However, other studies such as Martin &
Jackson (2008) and Hsu & Lu (2004,) measured flow as one dimension. In this study flow was measured as a single dimension as well.
Furthermore, flow experience was measured using the flow short scale developed by previous studies (Kubey, & Csikszentmihalyi, 1990; Jackson, & Csikszentmihalyi, 1999; Martin
& Jackson, 2008). All items were measured on a 5-point Likert Scale. The flow scale includes four items, such as ‘I feel I am competent enough to meet the high demands of the game’ and ‘I have a good idea while I am playing about how well I am doing’. The specific items used to measure flow are depicted in Table 3-8.
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Table 3-8. Items used to measure the feeling of flow
Construct Item Item Number
37. I feel I am competent enough to meet the high demands of the game.
38. I have a strong sense of what I want to do in the game. Flow 39. I have a good idea while I am playing about how well I am doing.
40. When playing online games, I become completely focused on the task at hand.
3.7.3 Immersion Experience
In this study immersion experience was adopted from Lacher & Mizerski’s (1994) music study.
Each item was anchored by strongly agree/strongly disagree options. Immersion was measured by 6 items that were structured by statements such as” I felt ‘carried off’ by the online game that I play” and” I ‘got into’ the online game that I play”. The specific items used to measure the feeling of immersion are depicted in Table 3-9.
Table 3-9. Items used to measure immersion
Construct Item Item Number
41. I felt ‘carried off’ by the online game that I play.
42. I felt as if I were part of the online game.
43. I felt deeply about the online game that I play.
Immersion 44. I will feel the experience of the online game that I play for a while.
45. I ‘got into’ the online game that I play.
46. I lose myself in the online game, when I am experiencing it.
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3.7.4 Social Experience
In general, social experience has been measured as one-dimension factor in game studies (Yee,
2006c; Koo, 2009). In this study social experience is also measured as one dimension by using nine items that was adopted from Lee & Robbins (1995). All items were anchored with a 5- point Likert Scale. The items of the social experience were to reflect players’ companionship, affiliation and connectedness. Specifically, players were asked to rate statements such as ‘I feel related to the people who play the same game as me’ and ‘Playing online games with others is more enjoyable than playing alone’. The specific items used to measure this social construct are depicted in Table 3-10.
Table 3-10. Items used to measure social feelings
Construct Item Item Number
47. I feel related to the people who play the same game as me.
48. I enjoy being a part of a community.
49. An online game virtual world is the best place to make new friends.
50. I always prefer to play online games with other people rather than playing against the computer (non-person characters).
51. Playing online games with others is more enjoyable than playing Social alone.
52. I enjoy helping other players in an online game.
53. I am more at ease playing online games together with other people.
54. I enjoy sharing my experience of the online game with other players.
55. Playing side by side with other players is more comfortable than playing alone.
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3.7.5 Satisfaction
According to Oliver & Winer (1987), satisfaction is an indication of how well a product meets consumer expectations. Thus, the same terminology applies to online games, where player satisfaction is a measure of the quality of the game to player expectations. Questions used in this study to measure satisfaction were adapted from previous studies (Bolton, 1998; Jolley
(2002). Players were asked seven questions about their degree of satisfaction with the graphics quality, login and logout, their game performance and their level of satisfaction with online games in general. Each item for degree and level was measured on a five-point scale (1=very dissatisfied to 5=very satisfied). The specific items used to measure satisfaction are depicted in
Table 3-11.
Table 3-11. Items used to measure satisfaction.
Construct Item Item Number
56. Overall, how satisfied are you with the game server?
57. Overall, how satisfied are you with the online support in the game?
58. Overall, how satisfied are you with the quality of the game
(e.g. graphics, controllers, and challenge)?
59. Overall, how satisfied are you with login and logout Satisfaction procedures for the game? 60. Overall, how satisfied are you with the time that is required to download the game?
61. Overall, how satisfied are you with the communication tool used to communicate with other players?
62. Overall, how satisfied are you with the internet connection?
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3.7.6 Intention to Replay
In this study, ‘intention to replay an online game’ was defined as a gamer’s intention to play an online game again in the future, given current conditions. The scale for this study was measured by three items adopted from Chiu, Chang, Cheng & Fang (2009). The three questionnaire items consist of a five-point Likert Scale where 1 = unlikely and 5 = likely. The specific items used to measure intention to replay are depicted in Table 3-12.
Table 3-12. Items used to measure intention to replay
Construct Item Item Number
63. How likely are you to play the same online-role playing game again, if the opportunity arises?
64. I will play the same online-role playing game again with Intention to replay my friends.
65. How likely are you to play the same online-role playing game over the next week?
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Chapter 4 STUDY ONE
4.0 Introduction
This chapter consists of five sections and provides a summary of the findings from the analyses of the Indian data collected through AMT. The first section describes the objective of the study.
The second section explains the data collection process. The third section covers data cleaning, factor analysis and scale reliability. In the fourth section, the technique used in the measurement models and main model fit are discussed. The final section reports the findings through the use of the Structural Equation Modelling method of analysis.
4.1 Objective of the Study
As previously discussed in the literature, feelings are likely to influence flow, immersion, and social experiences in game play. However, no previous empirical research has examined their relationships. The current study, argued that players with more positive feelings are likely to be in a positive state that cause them to gain a more positive flow, immersion, and social experience. In contrast, negative feelings of players are more likely to have a negative impact on flow, immersion, and social experience.
This study evaluates the relationships between feelings and the associated level of flow, immersion and social experience through the use of Structural Equation Modelling (SEM). SEM was used to test the hypothesised relationships amongst the research variables. The main research question addressed in this study is “How can feelings influence flow, immersion, and social experience?”. This research question can be answered through the following two sub- questions and associated investigations as follows:
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1. How will positive feelings (powerful, fun, imaginative, sensory, and escapism)
influence flow experience, immersion experience, and social experience? This
investigation is achieved through examination of the effect of:
a. positive feelings on flow experience.
b. positive feelings on immersion experience.
c. positive feelings on social experience.
2. How will negative feelings influence flow experience, immersion experience, and
social experience of MMORPG players? This second investigation is achieved
through examination of the effect of:
d. negative feelings on flow experience.
e. negative feelings on immersion experience.
f. negative feelings on social experience.
Each of the aforementioned investigations together with the associated hypotheses are shown in Figure 4-1.
Figure 4-1. Study One investigations and hypotheses
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In line with the aforementioned objectives and investigations the following relationships will be tested as shown in Table 4-1.
Table 4-1. List of Hypotheses and Relationships to be tested
Number Hypothesis
H1 Positive feelings are positively associated with flow experience in game play. H2 Positive feelings are positively associated with immersion experience in game play. H3 Positive feelings are positively associated with social experience in game play. H4 Negative feelings are negatively associated with flow experience in game play. H5 Negative feelings are negatively associated with immersion experience in gameplay. H6 Negative feelings are negatively associated with social experience in gameplay.
4.2 Data Collection Method
As suggested by Creswell (1994), for a hypothesis to be tested using SEM, a self-reported survey of Indian gamers (quantitative) as a means of collecting primary data is sufficient. The advantages of using such a method relies in its simplicity, flexibility and relatively low cost
(Schoon, 1998). The survey collected 359 Indian gamer respondents who played MMORPGs in the past month. All respondents were required to answer two parts of the questionnaire. The first part was related to feelings, flow, immersion, and social experience of the player. These factors were measured by multiple different items using a 1-5 range Likert Scale. The second section gathered demographic information of players. All the data was collected through AMT.
4.3 Data Cleaning and Missing Data
Due to the nature of the questionnaire, which required respondents to have played MMORPGs games in the month prior to the study, sections of the survey included missing data. Players who did not play in the previous month were automatically eliminated from the survey resulting in
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Bader Albatati PhD Thesis October 2017 several incomplete sections. Some respondents terminated the survey before completion causing a suite of incomplete surveys. These were therefore excluded from the total data set. Moreover, univariate outliers were identified by examining the standardised scores (Hair, Black, & Babin,
2010). A further test of missing and excluded datasets was done in order to identify the nature of the missing data or reason for exclusion.
After the elimination of non-complete surveys, the remaining sample for Indian players was 319. The expectation-maximisation (EM) technique in SPSS was used to examine if there was any missing data with the sample. The process of the analysis was to identify data frequently classified according to completely missing at random (McArthur & Hall, 1996) or missing at random (MAR) categories. The EM analysis generates a little MCAR table with x2 statistic. The result showed that there was no missing data with the Indian sample and therefore all data were maintained for further analysis. As a result, the technique of Maximum Likelihood Estimation
(MLE) in Amos was adopted to proceed with the analysis (Jöreskog & Sörbom 1996). Bollen-
Stine bootstrap was also adopted as a fitness indicator for the study (Anderson & Gerbing 1991;
Kline 1998).
4.4 Data Analysis
Of the 319 Indian players, 201 were males and 118 were females. The average age of Indian players was around 30 years, with 29 years as the average age for males and 31 years for females.
Moreover, the study revealed that the average weekly playtime for both male and female Indian players of a MMORPG was about eight hours. Interestingly, the maximum weekly hours played for both genders were the same at 48 hours. The average length of game play for Indian gamers was about four years. In general, females reported an average of four years continual play of
MMORPGs whereas males played for around three years. This suggested that Indian female gamers stay in the games longer than males.
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Moreover, a Pearson product-moment correlation coefficient was computed to assess the relationship between weekly playtime of MMORPGs and the player’s age for males and females separately. The data showed no significant relationship between weekly play hours and player age in males (r = -0.122, p = 0.146, p>0.05). In addition, the data revealed no significant correlation between weekly hours played and player age for females (r = 0.015, p = 0.894, p>0.05). This may suggest that the player’s age does not have any influence on the playtime of
MMORPGs, irrespective of gender.
In addition, a Pearson product-moment correlation coefficient was conducted to assess the relationship between weekly playtime of MMORPGs and the number of years played for both males and females. The data showed no significant relationship between weekly play hours and number of years played for males (r = 0.103, p = 0.294, p>0.05). Furthermore, the data revealed no significant correlation between weekly hours played and number of years played for females (r = 0.177, p = 0.122, p>0.05). This may suggest that the player’s number of years played does not influence playtime of MMORPGs for both genders.
Furthermore, an independent t-test was conducted to compare weekly hours played by both males and females. There was no significant difference between the male (M= 7.99, SD=
6.99) and female players (M= 8.10, SD= 9.87; t (229) = -0.099, p = 0.921, p>0.05). These results suggested that there was no gender difference with regards to playtime of MMORPGs.
4.4.1 Factor Analysis and Multicollinearity
The second step of the analysis was to run an exploratory factor analysis (EFA) on each construct. The correlation matrix from the EFA between all items for each respective factor
(e.g., Powerful Feeling, Fun Feeling, Imaginative Feeling, Sensory Feeling, Escapism Feeling,
Negative Feeling, Flow Experience, Immersion Experience, and Social Experience) was examined. All of the correlations were between 0.300 and 0.600 which were deemed acceptable
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(Hair, 2009). The overall KMO for all factors was above 0.70 except for the constructs of escapism and intention to replay. This was not remarkable as these two constructs had three items for measurement only. Nevertheless, escapism and intention to replay were still acceptable with a KMO above 0.60 (Hair, 2009). The EFA results indicated that factor analysis was appropriate for all components.
A principle component method of factoring with a Varimax rotation procedure
(orthogonal rotation technique) was adopted for each factor to minimize the number of variables that do not factor well on its respected factor. The EFA analysis found that some items did not load on the factors as expected. For example, the item “I enjoy taking risks in online games” was not loaded on the “imaginative feeling” construct. This item was subsequently removed from the second factor analysis and the result improved in terms of variance explained by the factor (see Appendix B). The item “During playing a good online game, I swear” connected with the “sensory feeling” factor, had a loading below 0.5 which affected the variances explained by the factor. This could be because this item was implicitly negative which might have caused some confusion and was therefore subsequently removed.
The underlying assumption that some items did not load well on any factor might also be related to the wording of items. Some items were perceived as the same, such as item “I am excited when playing online games” and item “Playing online games is an exciting experience”.
Another issue is the ambiguity of some questions such as the item, “I like to be in the presence of other players when playing online games”, where it is not obvious if the presence meant is virtual or real. Every time the analysis showed an item that did not load well on its respective factor, it was subsequently removed. Finally, all constructs showed a total variance explained above 0.5 which is considered acceptable (Malhotra, 2010). The final items included in the confirmatory factor analysis are shown in Appendix B.
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4.4.2 Reliability Check
All factors obtained from the EFA were further investigated through the application of a reliability check using Cronbach Alpha in SPSS. The purpose of the analysis was to ensure that the items attained from the factor analysis were internally consistent. Thus, Cronbach’s alpha helped to measure reliability of the constructs prior to confirmatory factor analysis and the application of SEM (Kirriemuir & McFarlane, 2004). Values of 0.6 to 0.7 for Cronbach’s alpha were taken to be acceptable within the lower limits (Hair et al., 1998).
Most constructs had Cronbach Alphas of 0.7 or above, which indicate a good level of reliability for all factors (see Appendix B) (Hair, 2009). However, for the sensory and escapism factors, the Cronbach Alphas were 0.694 and 0.689 respectively. These results are still considered within the acceptable range (Hair, 2009). Furthermore, the items loading from the factor analysis were deemed acceptable for both constructs.
The Cronbach Alpha values obtained for each construct were better than the Cronbach's
Alpha values when any items were deleted. In summary, all the results suggest each factor had a good reliability level (Kirriemuir & McFarlane, 2004).
4.5 Assessment of Statistical Fit
SEM is a statistical technique which applies a confirmatory approach (i.e. hypothesis testing) to multivariate analysis of structural theory (Byrne, 1998). A primary goal in SEM application is to test model fit to actual data. The test for statistical significance was achieved through examination of the significance of estimated coefficients in conjunction with standard errors for each co-efficient (Hair, 2009).
In order to solve equations simultaneously in SEM, sufficient information is required in sample data for the estimation of all parameters specified by the hypothesised model. The
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Bader Albatati PhD Thesis October 2017 process to determine the existence of unique values for the parameters of the specified model is referred to as “identification”. Three possible scenarios exist within model identification. A model is said to be ‘under-identified’ when it has more parameters than observations, ‘over- identified’ when it has less parameters than observations, and ‘just-identified’ or ‘saturated’ when the number of parameters and observations are equal (Schumacker & Lomax, 1996).
Researchers such as Baumgartner & Homburg (1996), Kline (1998) and Schumacker & Lomax
(1996), noted that two essential requirements for identification exist; observations must equal free parameters and; each construct must have a scale. For a model to be optimally identified, selection of estimation techniques in SEM based on the distributional properties of the variables under scrutiny must be continuous.
In order to achieve a good SEM, the data was further analysed via AMOS by using a number of statistical procedures. The first step involved fit of the measurement model and its individual parameters were assessed for validity and the reliability by using confirmatory factor analyses (CFA). Some of measurement models had issues in terms of poor fit to the data. This may be due to an output from linear dependency or collinearity between two variables. In order to solve this output problem, Wothke (1993) suggested that such variables be discarded from the analysis. Hair, Anderson, Tatham & William (1998) recommended to allow the measurement errors to be correlated to address any collinearity between variables. The second step involved assisting the fitness of the models through assessing the goodness of fit measurements (e.g., chi-square, probability p, Bollen-Stine bootstrap p, RMSEA, GFI, NFI, CFI and AGFI).
Measurement models based on each construct were independently tested with confirmatory factor analysis (CFA) in AMOS (Teng et al., 2007). CFA in Amos was used to test the singular dimensionality of latent constructs as well as for confirmation of support for the theoretical factor structure (Holmes-Smith, 2011). Moreover, confirmatory analyses
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Bader Albatati PhD Thesis October 2017 applications are used to test the results from the exploratory factor analysis (Kline, 1998). The complementary use of CFA together with EFA was for the purposes of data reduction, the aim of which was to reduce the large number of measured covariances to a small number of underlying related factors (Holmes-Smith, 2011). Congruent to the work of Holmes-Smith &
Rowe (1994), confirmatory factor analyses of the measurement models was initially performed for this study. Within this stage, tests for the reliability and validity of each construct were done.
Both convergent and discriminant validity were used for the validation of latent constructs in the SEM models (Hair et al., 1998). The assurance that factors converge or share a high proportion of variance is termed convergent validity and it is assessed through factor loadings, average variance extracted (AVE) and construct reliability (CR) (equation below).
Hair and colleagues (1998), suggest an AVE greater than 0.5 and CR greater than 0.6 as acceptable levels. According to Anderson & Gerbing (1991), factor loadings in excess of 0.5 suggest good single dimensionality.
Li is the standardised factor loading I is the number of factor items n is the total number of factor items ei is the number of I factor item’s error variance term
The Fornell & Larcker (1981) method was used in this study to test discriminant validity, or the extent to which a latent variable is discriminate from other latent variables. Discriminant validity is achieved when a latent variable accounts for more variance in the observed variables linked to it than measurement error, similar external unmeasured influences, or other constructs within the conceptual framework (Farrell & Rudd, 2009). Validity of the individual indicators and the construct becomes questionable if discriminant validity is unachieved (Farrell & Rudd,
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2009; Fornell & Larcker, 1981). Measured AVE scores greater than the squares of the correlations between the items, is an indication that the items compared are different to each other and consequently measure theoretically different concepts.
Chi-square was used to test the null hypothesis after the distributional assumptions for the data of the specified model have been satisfied. This is done to check for any significant difference between both the covariance matrix of the observed variables and the population. The p value for the population is provided with the chi-square in order to determine the differences between the covariant matrix (Malhotra, 2010). The greater the p value (p>0.05), the greater is the chance that the covariance matrices are equal (Malhotra, 2010). In addition, another measure for the fit of the model is to divide the chi-square by the degree of freedom (χ2/df). According to Holmes-Smith, Coote & Cunningham (2006), the most desirable result for the χ2/df should be under three. Others such as Hair et al. (2009) and Hair et al. (2010) suggested that values of
χ2/df above three can be accepted given that other measurements are indicating a good fit.
Moreover, the chi-square measure can be affected by sample size. In order to overcome this issue, parameter estimation can be achieved when a model is assessed for fit through the Bollen-
Stine bootstrap p for non-normal data (Enders, 2005; Bollen & Stine, 1992) and the p value of the normal chi-square (χ2) test. In this study, both methods were used for model assessment. In addition to chi-square (χ2), p value (probability), and Bollen-Stine bootstrap (p value), a number of other goodness-of-fit indices were used for the examination of the hypothesised constructs and their relationships. In this study, the suite of fit statistics reported included RMSEA, GFI,
NFI, CFI and AGFI. These measurements were used to provide more adequate support for the model.
Root mean square error of approximation (RMSEA) and goodness-of-fit index (GFI) were the two absolute fit indices used in this study. RMSEA is used to estimate the goodness-
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Bader Albatati PhD Thesis October 2017 of-fit if the model was estimated in the whole population, instead of the sample drawn for estimation only, where values from 0.05 to 0.08 are considered acceptable (Hair, 2009; Chen,
Curran, Bollen, Kirby, & Paxton, 2008). Contrastingly, GFI is a non-statistical metric ranging in value from 0 (poor fit) to 1.0 (perfect fit) and represents the overall degree of fit, without any adjustment for the degrees of freedom. The aforementioned model fit indices have particular applications in practice. GIF for example, is sample size sensitive, whereas RMSEA is meritorious for detailing the precision of the fit estimate (Rigdon, 1996). As a result, a variety of methods were employed to compare the fit of the data to the model.
The evaluation of the fit between the proposed model and the sample data was done using three incremental fit indices. These were normed fit index (NFI), comparative fit index
(CFI), and adjusted goodness of fit index (AGFI) which measure the proposed model against a baseline model, or a null model where all observed variables have no correlation (Hu & Bentler,
1998). Specifically, the normed fit index (NFI) uses χ2 to compare the lack of fit of the proposed model to that of an independent model. In this instance, the estimate of the degree of improvement per degree of freedom of the proposed model is given by the value of the index, where a value of 0.90 or greater is recommended (Hair, 2009). The comparative fit index (CFI) is appropriate for testing smaller sample sizes and compares the fit between the proposed model and a null or independent model. A reasonable fit is assumed for values in excess of 0.95 within a range of 0 to 1 (Hair, 2009; Hair et al., 1998). The adjusted goodness of fit index (AGFI) is used as a GFI index extension to adjust the degrees of freedom of the proposed model to the null model (Hair, 2009). Levels greater than or equal to 0.90 are recommended to be acceptable.
4.5.1 Measurement Fit
Powerful feeling: The powerful feeling construct was measured by a range of five different items deemed appropriate by the literature. All items showed an acceptable loading on the
“powerful feeling” construct which was above 0.5, as per Figure 4-2 (Brown, 2014). Although,
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Bader Albatati PhD Thesis October 2017 the average variance was considered relatively lower than 50% (AVE=0.454), as shown in Table
4-2 (page:124), the construct reliability (CR) of 0.803 was above 0.50. Any AVE above 0.4 is still acceptable given that the construct has an adequate composite reliability (CR) (Huang,
Wang, Wu, & Wang, 2013). The argument of using composite reliability (CR) with Cronbach’s alpha is that many constructs in social science are considered as latent variables which are not directly observed, as in the case of “powerful feeling”. In this case, the factor is measured using multiple indicators which are then summed to develop composite scales (Holmes-Smith, Coote
& Cunningham, 2005). The importance of CR is to assist in overcoming the SEM’s requirement for a large sample (Holmes-Smith & Rowe 1994). Composite reliability will also be used for all other factors. For the powerful feeling factor, the construct reliability was 0.803 which was above 0.50 and therefore acceptable. Other indicators for the model also showed an acceptable fit to the data (GFI=0.998, NFI=0.997, CFI=1.000, AGFI=0.995 and RMSEA=0.000) (see
Table 4-2, page: 124). Moreover, the χ2/df was less than three with an acceptable p value (χ2
=1.407, df=5, p=.924, p>0.05, Bollen-Stine bootstrap p = 0.975). The measurement fit statistics for the powerful feeling are shown in Figure 4-2.
Figure 4-2. Fit Measurement Statistics for Powerful Feeling
Fun feeling: The average variance explained from the confirmatory factor analysis for the fun feeling was slightly below 0.5 (AVE=0.435). However, the CR was acceptable at almost 0.8.
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According to Fornell & Larcker (1981), if the AVE is less than 0.5, but the composite reliability is higher than 0.6, the convergent validity of the construct is acceptable. The χ2/df was equal to
2.328 which was less than three with a Bollen-Stine bootstrap p value greater than 0.05, which is acceptable (χ2=11.643, df=5, p=0.04, Bollen-Stine bootstrap p = 0.100, p>0.05) (see Table 4-
2, page: 124). In general, the overall model fit indicators were good with acceptable levels
(GFI=0.986, NFI=0.972, CFI=0.984, AGFI=0.958, and RMSEA=0.065). In addition, all the factor loadings were above 0.5 with a good indication of convergent validity where items were loading highly on the fun feeling as per Figure 4-3. A good fit for the measurement model was indicated.
Figure 4-3. Fit Measurements Statistics for Fun Feeling
Imaginative feeling: Imaginative feeling had five items, the factor loadings for all of which were satisfactory which were all above 0.5, as per Figure 4-4. However, the χ2/df was equal to
7.63 which was above three and the chi-square returned a significant p value which might indicate that the measurement model was not satisfactory (χ2 =38.173, df=5, p <0.001).
Although the average variance explained was low (AVE=0.413), the model was still acceptable with a CR that was above 0.6 (CR=0.778), and considered acceptable for the model. The other model fit indicators (GFI=0.955, NFI=0.907, and CFI=0.917) were within an acceptable range
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Bader Albatati PhD Thesis October 2017 of above 0.90. Overall the results suggested that the factor should be further analysed (Holmes-
Smith, 2002, 2011).
Figure 4-4. Measurement Fit Statistics for Imaginative Factor
Further examination of the factor found that when removing the item “I enjoy doing impossible things in a virtual world”, the whole model improved significantly with all loadings above 0.5, as per Figure 4-5. The other items were more about the images that the game prompted in players, where enjoying doing impossible things is more related to the situation of play (Lacher & Mizerski, 1994) According to Holbrook & Grayson (1986), the images that are prompted by hedonic products can be tied to words and samples. As a result, the item “I enjoy doing impossible things in a virtual world” has been removed from analysis.
The overall indicators of the model were all within acceptable ranges (GFI=0.989,
NFI=0.973, CFI=0.980, AGFI=0.943, and RMSEA=0.094). The AVE and the CR were also acceptable (AVE=0.429 and CR=0.749). The χ2/df was equal to 3.7 which was slightly above three. The chi square p value was deemed as significant (χ2 =7.579, df=2, p=0.023, p<0.05).
This may be due to the fact that chi-square statistics are usually sensitive to sample sizes (Hair et al. 1989). However, the Bollen-Stine bootstrap shows a p value above 0.05 (p=0.154, p>0.05),
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Bader Albatati PhD Thesis October 2017 which deems it insignificant. As a result, this technique corrects the chi-square result. Other measurements for the model suggested that imaginative factor was a good fit (see Table 4-2: page:124).
Figure 4-5. Measurement Fit Statistics for Imaginative Factor
Sensory feeling: The “sensory feeling” construct originally contained five items. After the exploratory factor analysis study of the initial model with five items, the “swear” item (During playing a good online game, I swear) loaded below five (0.47) on the sensory factor. As a result, this item was removed from any further analyses.
The CFA for the remaining four items showed the AVE value below 0.5 to be under the acceptable level (AVE=0.368) (Table 4-2, page:124). Alternately, the CR value was within the acceptable range for the model (CR=0.697). The convergent validity of the final model was sound with all items loading above the threshold value of 0.5, as per Figure 4-6 (Holmes-Smith,
2011). The χ2/df was below three with a p value above 0.05 (χ2 =0.544, df=2, p =0.762, p>0.05)
(see Table 4-2, page:124). The other fit indicators showed that the model was acceptable
(GFI=0.999, NFI=0.997, CFI=1.000, AGFI=0.996, and RMSEA=0.000) as seen in Table 4-2.
At this stage, removal of any items from the construct will be questionable as it can cause model overfit. Consequently, all four items were retained.
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Figure 4-6. Measurement Fit Statistics for Sensory Feeling
Escapism Feeling: Upon examination of the convergent validity from the CFA, the analysis was able to distinguish that all three items for escapism were above 0.5, as per Figure 4-7. The average variance explained was at an acceptable level of above 0.40, with the construct reliability near to 0.7 (AVE=0.429 and CR=0.692) (see Table 4-2, page:124). However, the model fit showed an over fit to the data as chi-square was close to zero, and GFI, NFI, and CFI were equal to one. This was understandable as the model only had three parameters which were under the recommended level of four, for SEM analysis in AMOS. Nevertheless, a decision of maintaining the construct as part of positive feelings was made, as it is a strong factor in playing games (Calleja, 2010; Koo et al., 2007).
Figure 4-7. Measurement Fit Statistics for Escapism Feeling
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Negative Feeling: Negative feeling was chosen to represent negative emotional responses that can arise from playing MMORPGs. All five items (upset, hate, mad, annoyed, and frustrated) from the EFA demonstrated a strong relationship with the latent construct “negative feelings”.
All independent variables showed a factor loading above 0.7. Likewise, the total variance explained was above 50% with a good reliability (Cronbach’s alpha = 0.790) (Appendix B). The five items were retained to be further analysed through CFA.
In CFA, the items demonstrated a range of loadings from 0.58 to 0.78, as per Figure 4-
8. Altogether the indicators specify a good measurement for the latent variable. Moreover, the
AVE value was 0.432 and CR value was 0.790, which were both acceptable. However, there were some issues with the overall model fit. The χ2/df measure was equal to 11.2 which was above three. The chi square p value was less than 0.05 (χ2 =56.032, df=5, p<0.001). Other fit measures also indicated a poor fit baseline model to the data (NFI=0.879, CFI=0.887,
AGFI=0.798, and RMSEA=0.179). Thus, the measurement model of “negative feelings” went through additional specification to achieve the best fit model.
From the modification indices table (Appendix D), there is a high multicollinearity among variables “feeling annoyed “and “feeling frustrated”. Based on this, modification was made by allowing the correlation between the errors of the two previous mentioned indicators, and the model was respecified (Saris, Satorra, & Sörbom, 1987).
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Figure 4-8. Measurement Fit Statistics for Negative Feeling
The respecified model contained all five indicator variables that are specified previously for the negative factor. By allowing the correlation between “feeling annoyed” and “feeling frustrated” the whole measurement fit improved significantly. All items loading on the negative factor were above 0.5, as per Figure 4-9, which were in the acceptable range. The fit indices showed the model having a better fit than the previous one with the data (χ2 = 3.985, df=4, p
=0.408, p>0.05; GFI = 0.995, NFI =0.991, CFI=0.999, AGFI = 0.981) (see Table 4-2, page:124).
The root means square error (RMSEA) was below 0.08 which renders the data as a good fit to the model. Finally, the AVE and CR values decreased slightly from the first measurement model, but were both still within the acceptable ranges (AVE=0.412 and the CR=0.772). At this stage, all five items for the negative feelings were retained for the final model.
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Figure 4-9. Measurement Fit Statistics for Negative Feeling
Flow Experience
Flow experience: Four items to measure flow experience were adopted from Martin & Jackson
(2008). The CFA for “flow experience” showed that the four items were strongly related to the factor with values ranging from 0.61 to 0.74, as per Figure 4-10. However, the chi square for the model was not satisfactory and the χ2/df was equal to 4.53 which was slightly above three
(χ2 = 9.075, df=2, p=0.011, p <0.05), but the Bollen-Stine bootstrap showed an acceptable p value (p=0.08, p>0.05). Finally, the fit indexes together with both AVE and CR showed a good level of fit for the model (GFI=0.985, NFI=0.968, CFI=0.974, AGFI=0.926, RMSEA=0.105,
AVE=0.432, CR=0.751) (Table 4-2, page:124).
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Figure 4-10. Measurement Fit Statistics for Flow Experience
Immersion Experience
A five-item immersion construct that was adopted from Lacher & Mizerski (1994), was retained for confirmatory factor analysis. From CFA, the average variance explained (AVE) was 0.418 and the construct reliability (CR) was 0.782, which were both acceptable (Huang et al.,2013).
Moreover, the χ2/df was equal to 1.614 which was below three (χ2 =8.070, df=5, p=.0.152, p>0.05), and the Bollen-Stine bootstrap p equal to 0.343 which is above 0.05. The RMSEA indicator was 0.044 and demonstrates that the model was a good fit. Finally, the other model indicators (GFI=0.990, CFI=0.992, NFI=0.979, and AGFI=0.971) were all greater than 0.9 (see
Table 4-2, page:124). Overall, the measurement model of “immersion” established an adequate fit for the data with all loadings above 0.5, as per Figure 4-11.
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Figure 4-11. Measurement Fit Statistics for Immersion Experience
Social Experience
The construct of social experience originally contained nine items which were adopted from
Lee, & Robbins (1995). Only five items were retained from the exploratory factor analysis.
Furthermore, the exploratory factor analysis for the remaining five items of the “social experience” construct yielded a good factor solution with all item loadings and a Cronbach’s alpha within acceptable ranges (Appendix B). The CFA for the five indicators illustrated good loadings of higher than 0.5, as per Figure 4-12. The fit indices describe the model as having a good fit with the data. The χ2/df value was equal to 2.15 which was less than three (χ2 = 10.799, df=5, p=0.056, p>0.05). Furthermore, the Bollen-Stine bootstrap p was above 0.05 (p=0.139, p>0.05). Moreover, the other incidences showed a good fit for the model (GFI = 0.986; NFI
=0.973; CFI=0.985; AGFI = 0.958; RMSEA=0.060). As a final point, the AVE and the CR were at acceptable levels (AVE=0.424 and CR=0.786). All five items for the “social experience” item seemed to reflected the construct properly.
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Figure 4-12. Measurement Fit Statistics for Social Experience
The following table summarises the final measurement fit for each model discussed
previously.
Table 4-2. Measurement Fit Indices for all factors
Construct χ2 d/f p RMS GFI NFI CFI AGFI AVE CR BS EA Bootstrap p
Powerful 1.407 5 0.924 0.000 0.998 0.997 1.000 0.995 0.454 0.803 0.975 Fun 11.643 5 0.040 0.065 0.986 0.972 0.984 0.958 0.435 0.793 0.100 Imaginative 7.579 2 0.023 0.094 0.989 0.973 0.980 0.943 0.429 0.749 0.154 Sensory 0.544 2 0.762 0.000 0.999 0.997 1.000 0.996 0.368 0.697 0.786 Escapism 0.000 0 NA 0.404 1.000 1.000 1.000 NA 0.429 0.692 NA Negative 3.985 4 0.408 0.000 0.995 0.991 1.000 0.980 0.412 0.772 0.582 Flow 9.075 2 0.011 0.105 0.985 0.968 0.974 0.926 0.432 0.751 0.080 Immersion 8.070 5 0.152 0.044 0.990 0.979 0.992 0.971 0.418 0.782 0.343 Social 10.799 5 0.056 0.060 0.986 0.973 0.985 0.958 0.424 0.786 0.139
4.5.2 Final Model and Hypothesis Testing
The First Full model
After the process of confirmation of each single-factor model measurement, complete
congeneric model tests were done to validate the hypotheses for feelings (positive and negative),
flow experience, immersion experience, and social experience. Structural Equation Modelling
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Bader Albatati PhD Thesis October 2017 using the maximum-likelihood estimation method was used to test the hypothesis (Ullman &
Bentler, 2003). A single-congeneric measurement model was used in the analyses of the measurement model as it is the simplest to illustrate the regression of a set of indicators to all constructs. As a result, the model was specified and analysed as a single indicator latent variable model (Speirs & Martin, 1999; Coffman & MacCallum, 2005). This approach was done to increase the ratio of the number of cases to the number of parameters estimated (Kline, 2015).
Moreover, these types of models demonstrate that the regression coefficient obtained from it are identical to those obtained from modelling latent variables at the item level (Sass & Smith,
2006).
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Figure 4-13. Measurement Fit Statistics for Full Model
Composite scores were used to represent the measures for each construct (positive feelings, negative feelings, flow, immersion, and social experience), as per Figure 4-13. The model fit indices show that the model had some issues, thus making it a poor fit for the data
(Table 4-3). The χ2/df for the model was equal to 5.67 which was greater than three and the p value for the chi square was under 0.05 (χ2 = 141.818, df=25, p <0.001). Moreover, the AGFI and the RMSEA showed that the model was possibly a poor fit to the data which made the model questionable (AGFI=0.848, and RMSEA=0.121).
Table 4-3. CFA Final Model Results for Flow, Immersion and Social
χ2 Degrees of freedom p RMSEA GFI NFI CFI AGFI BS Bootstrap p 141.818 25 0.000 0.121 0.915 0.935 0.946 0.848 0.005
The effects of all independent variables for game experience are presented in Table 4-4.
Therefore, hypothesis one through three are all supported with a significant relationship
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(p<0.001). The results showed that positive feelings had a significant effect in all three experiences (flow, immersion, and social). In contrast, hypothesis four through six which are related to the effect of negative feelings on flow, immersion, and social experience were not supported. Negative feeling does not have a significant effect on flow, immersion, and social experience with all p values were greater than 0.05 (p>0.05). However, the model was examined further by removing the negative feeling.
Table 4-4. SEM Results for Flow, Immersion and Social
Standardized Regression Weights Estimate S.E. C.R. P Flow <--- Positive feeling 0.414 0.058 18.720 0.000 Immersion <--- Positive feeling 0.345 0.051 17.306 0.000 Social <--- Positive feeling 0.302 0.042 18.181 0.000 Flow <--- Negative feeling 0.003 0.010 0.327 0.744 Immersion <--- Negative feeling 0.010 0.010 1.047 0.295 Social <--- Negative feeling 0.012 0.008 1.697 0.090
The Second Full Model All relationships of negative feeling with flow, immersion, and social experience were insignificant from the first model (see Table 4-5). This may result in the model being a poor fit.
Removal of the negative feeling construct from the final model resulted in an overall improvement, as shown in Figure 4-14. The fit indices for the second model showed a better fit with the data. The χ2/df was equal to 1.85 which was below three. The Bollen-Stine p value was greater than 0.05 which is a correction for the original p value (χ2 = 37.130, df=20, p=0.011, p<0.05, Bollen-Stine p=0.234, p>0.05) (See Table 4-5). Moreover, the other incidences showed a good fit for the model (GFI = 0.971; NFI =0.982; CFI=0.992; AGFI = 0.947, RMSEA=0.052).
Overall, the second model demonstrated a better fit to the data than the first model that included the negative feeling.
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Figure 4-14. Measurement Fit Statistics for the 2nd Model
Table 4-5. CFA results for Flow, Immersion and Social in the 2nd Model
χ2 Degrees of freedom p RMSEA GFI NFI CFI AGFI BS Bootstrap p 37.130 20 0.011 0.052 0.971 0.982 0.992 0.947 0.234
The relationships between positive feeling and flow, immersion and social experiences was investigated.
The effect of positive feelings on flow experience: Positive feelings showed a significant and positive effect on flow experience (β=0.416, p<0.001) (see Table 4-6). This indicates that positive feelings influence players flow experience. Therefore, Hypothesis One (H1) was fully supported.
The effect of positive feelings on immersion experience: Positive feelings demonstrated a strong positive effect on immersion. A significant relationship between positive feelings and immersion was established (β=0.351, p<0.001) (see Table 4-6). As a result, Hypothesis Two
(H2) was fully supported.
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The effect of positive feelings on social experience: Positive feelings displayed a significant and positive effect with on social experience (β=0.310, p<0.001) (see Table 4-6).
This result indicates a strong influence of positive feelings on social experience. Therefore,
Hypothesis Three (H3) was fully supported.
Generally, the results indicate that when Indian players play MMORPGs, they exhibit positive feelings that contribute to their game play. This indicates that the more the players feel good about their game session the more they will be in a state of flow, immersion and be more likely to socialise with other players.
Findings from the first model as depicted in Table 4-4, showed that in contrast to positive feelings, negative feelings showed no significant relationship with flow experience, immersion experience, and social experience.
The effect of negative feelings on flow experience: Negative feelings did not have an effect on game flow (β=0.003, p=0.744, p>0.05). This means that negative feelings of Indian players do not influence their flow experience. Consequently, Hypothesis Four (H4) was not supported.
The effect of negative feelings on immersion experience: Negative feelings had no effect on game immersion (β=0.010, p=0.295, p>0.05). Thus, negative feelings for Indian players have been deemed as uninfluential on immersion experience in game play. As a result, Hypothesis
Five (H5) was not supported.
The effect of negative feelings on social experience: Negative feelings had showed no effect on social experience (β=0.012, p=0.090, p>0.05). In other words, negative feelings did not influence the social experience of Indians players. Therefore, Hypothesis Six (H6) was not supported.
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Table 4-6. Relationships of Positive Feelings to Flow, Immersion and Social
Standardized Regression Weights Estimate S.E. C.R. P Flow <--- Positive feeling 0.416 0.058 18.789 0.000 Immersion <--- Positive feeling 0.351 0.051 17.491 0.000 Social <--- Positive feeling 0.310 0.043 18.417 0.000
4.6 Summary of Results of Study One
The objective of study one was to test the influences feelings (positive and negative) have on flow experience, immersion experience, and social experience. The final conceptual model of
“game experience” contains four valid and reliable concepts including positive feeling, flow, immersion and social experience. In addition, all five congeneric measurements (powerful, fun, imaginative, sensory and escapism) illustrated a high relationship with positive feelings. All the five indicators of positive feelings showed loadings above 0.6 as per Figure 4-14 in the second model. Moreover, the five indicators demonstrated a strong relationship to the positive feeling latent variable (see Table 4-7). The relationship among players’ positive feelings, flow experience, immersion experience and social experience were all significant as depicted in Table
4-6. From the analysis, positive feelings had positive influences on Indian players’ flow experience, immersion experience, and social experience. These findings support the claims of
Holbrook et al. (1984) as well as those of Schmitt (1999), in that positive feelings play an important role in the context of hedonic consumption such as online games. This can be related to the fun nature of multiplayer online role-playing games (Yee, 2006a).
In contrast, negative feelings showed an insignificant path to flow experience, immersion experience and social experience; in other words, negative feelings did not have any influences on other major game experience dimensions examined in the study. This may be attributed to the fact that players play MMORPGs mainly for hedonic reasons (such as fun)
(Yee, 2006a). Another reason for the absence of effect of negative feelings is that negative feelings may be elevated to positive feelings in the game, and as a result, the effect is minimised
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(Andrade & Cohen, 2007). The final hypotheses findings for all six relationships are summarised in Table 4-8.
Table 4-7. Relationship between Positive Feelings on Fun, Powerful, Imaginative, Sensory and Escapism
Standardized Regression Weights Estimate P Fun <--- Positive feeling 0.863 0.000 Powerful <--- Positive feeling 0.795 0.000 Imaginative <--- Positive feeling 0.803 0.000 Sensory <--- Positive feeling 0.709 0.000 Escapism <--- Positive feeling 0.831 0.000
4.6.1 Findings of Study One Hypotheses Testing:
The complete findings from Study One for the testing of all Hypotheses is summarised in Table
4-8. In summary, Hypothesis One, Two and Three were supported, whereas Hypothesis Four,
Five and Six were not.
Table 4-8. Findings of the Hypotheses Testing
Number Hypothesis Result H1 Positive feelings are positively associated with flow experience Supported in game play. H2 Positive feelings are positively associated with immersion Supported experience in game play. H3 Positive feelings are positively associated with social Supported experience in game play. H4 Negative feelings are negatively associated with flow Not experience in game play. supported H5 Negative feelings are negatively associated with immersion Not experience in gameplay. supported H6 Negative feelings are negatively associated with social Not experience in gameplay. supported
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CHAPTER 5 STUDY TWO
5.0 Introduction
This chapter consists of three sections and provides a summary of the findings from Study Two based on the US sample collected through AMT. The first section describes the objective of the study. The second section explores factor analysis and scale reliability related to the study. The final section tests the hypotheses and reports the findings through the use of Structural Equation
Modelling.
5.1 Objective of the Study and Hypotheses
One important aim of Study Two was to examine the relationships between positive feelings, flow, immersion, and social experiences within the US sample. In Study Two, positive feelings were hypothesised to have an influence on flow experience, immersion experience and social experience. The findings will uncover whether there are any cultural differences in game experience as the hypotheses examined in Study One with the Indian sample.
As previously outlined in the literature review (Chapter Two), satisfaction and intention to replay the same game are both salient outcomes of positive feelings and other types of game experience. As a result, positive feelings were hypothesised to have a positive effect on game outcomes (satisfaction and intention to replay). Another major aim of Study Two is to investigate the effect of flow experience, immersion experience and social experience on games outcomes (satisfaction and intention to replay); therefore, in Study Two, it is hypothesized that players with more favourable flow, immersion, and social experiences will have a high level of game satisfaction and intention to replay. Figure 5-1 and Table 5-1 outlines hypotheses investigated in Study Two.
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Figure 5-1. Study Two Investigations and Hypotheses
In line with the aforementioned objectives the following hypothetical relationships will be tested as shown in Table 5-1.
Table 5-1. List of Hypotheses and Relationships to be tested
Number Hypothesis H1 (same as Positive feelings are positively associated with flow experience in game Study One) play. H2 (same as Positive feelings are positively associated with immersion experience in Study One) game play. H3(same as Positive feelings are positively associated with social experience in game Study One) play. H7 Positive feelings are positively associated with players’ satisfaction. H8 Positive feelings are positively associated with players’ intention to replay the game. H9 Flow experience is positively associated with players’ satisfaction. H10 Immersion experience is positively associated with players’ satisfaction. H11 Social experience is positively associated with players’ satisfaction. H12 Flow experience is positively associated with players’ intentions to replay. H13 Immersion experience is positively associated with players’ intentions to replay.
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H14 Social experience is positively associated with players’ intentions to replay. H15 Players’ satisfaction is positively associated with players’ intentions to replay.
5.2 Data Collection Method
Similar to Study One, Study Two, adopted a self-reported survey among a group of US players.
The survey included questions related to positive feelings, flow experience, immersion experience, social experience, satisfaction, and intention to replay. Respondents were all anonymous, and only segregated data was used. The data was collected online from a US panel who play MMORPGs. All data was collected through the medium of AMT. As discussed in
Chapter Three, this method was deemed sufficient as a means of collecting primary data. The advantages of using such a method relies in its simplicity, flexibility and relatively low cost
(Schoon, 1998). The survey collected a total of 491 US game players respondents who played
MMORPGs in the month prior to the survey date.
All respondents were required to answer three parts of the questionnaire. The first part was related to players’ feelings, flow experience, immersion experience, and social experience.
These factors were measured by different items using a 1-5 range Likert Scale. The second part was related to player satisfaction and intention to replay. Satisfaction was measured by using different items on Likert Scale from 1 to 5 (where 1 was very dissatisfied and 5 was very satisfied). Intention to replay was also measured on a Likert Scale with three different items where 1 was very unlikely and 5 was very likely. The last section of the survey gathered data on player demographics.
5.3 Data Cleaning and Missing Data
Similar to Study One all respondents who did not play MMORPGs prior to the survey were automatically eliminated via the screening question. Moreover, all incomplete surveys were
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After the elimination of non-completed surveys, the remaining sample for US players was 425. The expectation-maximisation (EM) technique in SPSS was used to examine if there was any missing data (McArthur & Hall, 1996). The result showed that there was no missing data with the US sample and as a result, all data were maintained for further analysis.
5.4 Players’ Demographics
Study Two included 271 male players and 154 female players. This gender ratio fits the general players’ profiles of MMORPGs worldwide (Griffiths et al., 2004b; Park & Lee, 2011).
Moreover, the study revealed that the average weekly play time for both genders of US players of MMORPGs was 12 hours. Time played by males was around 12 hours with 11.5 hours for females. Interestingly, the maximum weekly hours played was 66 hours by a female player. The average age for MMORPG players in the US was around 32 years, with male players with an average age of 31. Female players showed a slightly older average age of 33. The average number of years played by males was around eight years, whereas for females, it was seven years.
In addition, a Pearson product-moment correlation coefficient was computed to assess the relationship between time played during the week for MMORPGs and the age of players.
This analysis was done separately for males and females. For males, the data showed no significant relationship between the number of hours played during a week and the player’s age
(r = -0.040, p = 0.523, p>0.05). Similarly, there was no significant correlation between the number of hours played by females and their age (r = -0.028, p = 0.735, p>0.05). This suggests that the age of a player seemed to have no impact on the playing duration.
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Another Pearson product-moment correlation coefficient was conducted to assess the relationship between weekly playtime of MMORPGs and the number of years played for both genders. The data showed a significant positive relationship between weekly play hours and number of years played for males (r = 0.255, p<0.001). In contrast, the data revealed no significant correlation between weekly hours played and number of years played for females (r
= 0.143, p = 0.078, p>0.05). This may suggest that the relationship between the number of years played and weekly playtime is different between male and female players.
Furthermore, an independent t-test was conducted to compare weekly hours played by both males and females during the week. There was no significant difference in the scores for male (M=12.29, SD=9.45) and female players (M=11.76, SD=10.19, t (416) = 0.530, p = 0.596, p>0.05). These results suggested no difference between male and female players in their weekly playing hours.
5.5 Factor Analysis and Reliability Test
Exploratory factor analysis was conducted on all constructs of feelings, flow experience, immersion experience, social experience, and satisfaction and intention to replay. Measures of positive feelings includes constructs such as powerful feeling, fun feeling, imaginative feeling, sensory feeling, and escapism feeling, as well as for flow, immersion, and social experiences, and were the same as for Study One. The satisfaction scale was adopted from Bolton (1998) and
Jolley (2002). Intention to replay was adopted from Chiu et al. (2009). The items and factors are summarised in the Table 5-2.
Table 5-2. List of Constructs and Associated Items
Construct Item Item Number 66. When I advance in online games I feel powerful. Powerful 67. I feel strong when playing online games. Feeling 68. I am energetic when playing online games.
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69. I am enthusiastic about playing the online game. 70. I am excited when playing online games. 71. Playing online games is a fun activity. 72. I enjoy exploring new areas within an online game. Fun 73. I enjoy designing my own avatar in the game. Feeling 74. Developing my character and making it powerful is an enjoyable process. 75. I feel entertained when playing online games. 76. Online games create a picture in my mind. Imaginative 77. After playing, I still think about the game. Feeling 78. After playing, the game prompts images in my mind. 79. After playing a good online game, I still fantasise about it. 80. While playing, I keep moving part of my body (e.g. head or foot). Sensory 81. When Playing good online games, my heartbeat becomes faster. Feeling 82. While playing, I yell from excitement. 83. My facial expressions changes while playing an online game. 84. Playing games online gets me away from all the problems that I have. Escapism 85. Playing online games makes me feel like I am in another world. Feeling 86. I get so involved when I play that I forget everything else. 87. I feel I am competent enough to meet the high demands of the game. 88. I have a strong sense of what I want to do in the game. Flow 89. I have a good idea while I am playing, about how well I am doing. Experience 90. When playing online games, I become completely focused on the task at hand. 91. I feel I am competent enough to meet the high demands of the game. 92. I felt as if I were part of the online game. 93. I felt deeply about the online game that I play. Immersion 94. I will feel the experience of the online game that I play for a while. Experience 95. I “got into” the online game that I play. 96. I lose myself in the online game, when I am experiencing it. 97. I always prefer to play online games with other people than playing against the computer (non-person characters). 98. Playing online games with others is more enjoyable than playing Social alone. Experience 99. I am more at ease playing online games together with other people. 100. I enjoy sharing my experience of the online game with other players. 101. Playing side by side with other players is more comfortable than playing alone. 102. Overal l how satisfied are you with the game server? 103. Overall how satisfied are you with the online support in the Satisfaction game? 104. Overall how satisfied are you with the quality of the game (e.g. graphics, controllers, and challenge.)?
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105. Overall how satisfied are you with log in and log out procedures for the game? 106. Overall how satisfied are you with the time that is required to download the game? 107. Overall how satisfied are you with the communication tool which is used to communicate with other players? 108. Overa ll how satisfied are you with the internet connection? 109. How likely are you to play the same online-role playing game again, if the opportunity arises? Intention to 110. I will play the same online-role playing game again with my Replay friends. 111. How likely are you to play the same online-role playing game over the next week?
Multicollinearity
The correlation matrices from the factor analysis between all items for each respective outcome
(Satisfaction, and Intention to Replay) were examined for validity. All of the correlations were between 0.2 and 0.6, which were acceptable except for two items for the “intention to replay” construct (Hair, 2009). For example, the items “How likely are you to play the same online-role playing game over the next week?” and “How likely are you to play the same online-role playing game again, if the opportunity arises?”, which were significantly correlated with a p value of
0.01. The correlation coefficient between the two items was 0.724. However, as the intention to replay construct contained only three items which were adopted from previous studies, three items were maintained (Churchill Jr, 1979). Finally, these variations in correlations among variables within their respective constructs suggest that factor analysis was appropriate (Teng et al., 2007). As a result, all items were maintained for further investigation using exploratory factor analysis (EFA).
The overall Kaiser-Meyer-Olkin measure of sampling adequacy (KMO) statistic for all factors were above 0.7, except for the construct of escapism which was 0.661. Nevertheless, the results for escapism were still within acceptable limits with a KMO above 0.6 (Hair, 2009).
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Consequently, the results indicated that factor analysis was appropriate for all components. As a result, factor analysis was conducted for all items.
Similar to Study One, each of the identified measurements was examined separately to check for underlying structure within the data and for item interrelations. Principle component method of factoring, with a Varimax rotation procedure (orthogonal rotation technique), was applied to each factor to minimize any variables that loaded on two factors. Again, the technique was used to distinguish any items that did not persistently factor well with other individual constructs.
The exploratory factor analysis showed all items loaded well on their perceived components. For instance, all item loadings presented from the factor analysis of each construct were above 0.60. In fact, the item with the lowest loading of 0.618 was from satisfaction
(“Overall how satisfied are you with the internet connection?”). Moreover, all factors explained a resemble variance with all constructs above 50% (see Appendix E). These results suggested that all constructs were acceptable as unidimensional and could be maintained as measurements for Study Two.
Similar to Study One, all factors obtained from the exploratory factor analysis were investigated further through a reliability scale check in SPSS. The purpose of the analysis was to ensure that items attained from the factor analysis were internally consistent and joined together in the measurement of each respective construct.
All constructs showed a Cronbach Alpha between 0.7 and 0.9, which suggested good internal consistency of the constructs (Hair et al., 2010) (see Appendix E). Finally, all items suggested good loading for their consecutive construct from factor analysis and good reliability
(Cronbach Alphas). As a result, a decision was made to retain the items for confirmatory factor analysis (CFA).
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5.6 Measurement Fit
Similar to Study One, the measurement model fit for all constructs were evaluated using multiple fitness indices. They include chi-square (χ2), goodness-of-fit index (GFI), adjusted goodness of-fit index (AGFI), normed fit index (NFI), comparative fit index (CFI), and root of mean square error of approximation (RMSEA). Table 5-3 depicts the measurement fit incidences for all ten constructs used in Study Two.
Table 5-3. Measurement Fit Indices for all Factors
Construct χ2 d p RMS GFI NFI CFI AGFI AVE CR BS /f EA Boots trap p Powerful 86.325 5 0.000 0.196 0.923 0.907 0.912 0.769 0.536 0.850 0.005 Fun 10.872 5 0.054 0.053 0.989 0.987 0.993 0.968 0.539 0.852 0.244 Imaginative 12.935 2 0.002 0.114 0.986 0.980 0.983 0.928 0.543 0.820 0.010 Sensory 2.905 2 0.234 0.033 0.997 0.992 0.997 0.983 0.418 0.738 0.403 Escapism 0.000 0 NA 0.462 1.000 1.000 1.000 NA 0.481 0.732 NA Flow 3.694 2 0.158 0.045 0.996 0.992 0.996 0.978 0.479 0.785 0.308 Immersion 31.579 5 0.000 0.112 0.972 0.955 0.962 0.915 0.478 0.864 0.005 Social 11.330 5 0.045 0.055 0.990 0.986 0.992 0.969 0.531 0.849 0.154 Satisfaction 13.571 5 0.019 0.064 0.987 0.971 0.981 0.961 0.400 0.765 0.095 Intention to 0.000 0 NA 0.685 1.000 1.000 1.000 NA 0.590 0.876 NA replay
A CFA was performed to assess the psychometric properties of the measures. The CFA results showed that all constructs had acceptable construct reliability above 0.7. For the average variance extracted (AVE), most constructs exceeded 0.5 while some fell slightly below 0.5. The constructs with AVE below 0.5 were sensory, escapism, immersion, social, and satisfaction.
However, all of the constructs were still within the acceptable range of above 0.4 (Fornell &
Larcker, 1981). Fun, sensory, flow, social, and satisfaction demonstrated a good measurement fit with all χ2/df values below three and a p value greater than 0.05 (see Table 5-3, page 141).
One exception was the social experience p value which was below 0.05. However, after a Bollen-Stine bootstrap was conducted for the social experience construct, a p value above 0.05
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Bader Albatati PhD Thesis October 2017 was returned, which suggested an acceptable p value. Furthermore, all other model fit indices for all of the five constructs (GFI, NFI, CFI and AGFI) were above 0.9. For the constructs of escapism and intention to replay, the results indicate an overfit, but this was understandable as both constructs had only three items for measurement. The remaining factors (Powerful,
Imaginative, and Immersion) showed a significant chi-square with a p value slightly below 0.05
(see Table 5-3, page:141). All of the other measurement fit indexes for the three constructs in question illustrate a good fit for the measurement models. The next section explained each construct in detail.
Powerful feeling: All items from the Figure 5-2 show acceptable loadings on the powerful construct as all items had loadings of above 0.5. However as some of the measurement indicators, such as the chi-square and AGFI, showed undesirable results, it meant that the model could be further improved in order to reflect a better fit (see Table 5-3, page:141).
Figure 5-2. CFA model fit statistics for Powerful Feeling
Measurement indices between the two items “feeling powerful” and “feeling strong” showed a high multicollinearity among them. After allowing for correlation between “feeling powerful” and “feeling strong”, the items still showed good loadings. Even though the loading
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Bader Albatati PhD Thesis October 2017 of the two correlated items dropped, it was still within an acceptable range of above 0.5, as shown in Figure 5-3. The average variance explained from the confirmatory factor analysis for the powerful feeling construct was above 0.5 and the CR was also acceptable at 0.838 (Fornell
& Larcker, 1981). This indicated a good convergent validity of the construct. In general, the overall model fit indicators were good with values of χ2/df equals to 2.139 which is less than three and a p value above 0.05 (χ2 =8.556, df =4, p =0.073, p>0.05). The Bollen-Stine bootstrap p value was 0.159 which was also satisfactory. All the other fit indexes were acceptable
(GFI=0.992, NFI=0.991, CFI=0.995, AGFI=0.970, and RMSEA=0.052). As a result, a good fit for the measurement model was achieved. In conclusion, all items shown in Figure 5-3 were maintained for the powerful feeling construct.
Figure 5-3. CFA Model Fit Statistics - Modified Measurement Model of Powerful Feeling
Fun feeling: The CFA results in Figure 5-4, showed that the loadings of all five items were above 0.5. The lowest loading item was “enjoy designing my own avatar in the game”, which had a loading slightly above 0.5. All items illustrated a good relationship with the fun feeling construct. The average variance explained from the confirmatory factor analysis for the fun component was 0.539 (see Table 5-3, page:141). The construct reliability (CR) was acceptable at 0.8. Both the AVE and CR indicated a good reliability for the fun feeling factor (Fornell &
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Larcker, 1981). In general, the overall model fit indicators were good. The χ2/df value was equal to 2.174 which is below three (χ2 =10.872, df = 5, p =.054, p>0.05). The Bollen-Stine bootstrap p value was also greater than 0.05 which was acceptable (p = 0.244). The other measurement fit indicators also showed that the measurement model of the fun feeling was a good fit to the data
(GFI=0.989, NFI=0.987, CFI=0.993, AGFI=0.968, and RMSEA=0.054). Therefore, a good fit for the measurement model was achieved.
Figure 5-4. CFA model fit statistics for Fun Feeling
Imaginative feeling: The imaginative construct was measured by four items. All items had loadings within an acceptable range, as depicted in Figure 5-5. Although the item “creating a picture in my mind” had a loading (0.50), which was lower than the others on the imaginative construct, it was still acceptable (Hair, 2009). In contrast, the value for the χ2/df was equal to
6.468, which was above three. This may suggest that the indicator was not a very good fit for the model (χ2=12.935, df = 2, p =0.002, p<0.05). Even though the χ2/df was above three, it must be recognised that chi-square statistics are usually sensitive to sample sizes (Hair et al. 1989).
The AVE value measurement was 0.543 and the CR was 0.82, which both indicated a good construct model (Fornell & Larcker, 1981). The overall indicators of the model were all above
0.9 (GFI=0.986, NFI=0.980, CFI=0.983, and AGFI=0.928). According to Hair (2009), at least
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Bader Albatati PhD Thesis October 2017 three of the indicators within an acceptable range can suggest that the model has a good fit.
Moreover, removal of one of the items in this stage will reduce the construct to only three items and result in model overfit. Consequently, all four items shown in Figure 5-5 for the imaginative construct were maintained.
Figure 5-5. CFA model fit statistics for Imaginative Feeling
Sensory feeling: The diagram in Figure 5-6 depicts the four items for the sensory feeling scale, as well as the loadings for each item. Three items (“heartbeat become faster”, “yell from excitement”, and “changing facial expresions”) had loadings of above 0.6. The fourth item “ moving part of my body” had a loading of 0.5, which was acceptable (Hair, 2009). The AVE was 0.418, a considerably low score. Although the average variance was lower than 50%, the construct showed an acceptable reliability of 0.738 (see Table 5-3, page:141). According to
Huang et al. (2013), any AVE above 0.4 is still acceptable given that the construct has an adequate composite reliability (CR). In this case, the AVE was above 0.4 and the CR was 0.738 both of which meant that the scale was reliable. Other indicators also suggested a relablie measurment model (see Table 5-3, page:141). The χ2/df was equal to 1.45 which was below three, with a p value greater than 0.05 (χ2= 2.905, df = 2, p =0.234, p>0.05). Other measurement fit values showed that the model was acceptable with solid figures (GFI=0.997, NFI=0.992,
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CFI=0.997, AGFI=0.983, and RMSEA=0.033). At this stage, removal of any items from the construct would bring issues to the model. Therefore, all of the four items were retained for the measurment model.
Figure 5-6. CFA model fit statistics for Sensory Feeling
Escapism Feeling: The original escapism scale adopted from Mathwick & Rigdon (2004) is a three- item scale. Upon examination of the items scale in CFA, the analysis was able to distinguish that all three items for escapism were loading above 0.5 as shown in Figure 5-7. The average variance explained was at an acceptable level of above 0.40 (AVE = 0.481), with the construct reliability of 0.732 (see Table 5-3, page:141). However, the model showed an overfit to the data with GFI, NFI and CFI equal to one. This is understandable as the model only had three parameters which is under the recommended level of four for SEM analysis in AMOS
(Hair, 2009). Nevertheless, no changes were done to the scale and all three items were maintained.
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Figure 5-7. CFA model fit statistics for Escapism Feeling
Flow Experience: The CFA for “flow experience” showed that the four items of the construct were loaded on the measurement with values ranging from 0.61 to 0.74, as per Figure 5-8, which suggested an acceptable level. The χ2/df for the model yielded a good fit for the model with a value of 1.847 that was under three (χ2 = 3.694, df=2, p=158, p >0.05). Both the AVE (0.479) and the CR (0.785) explained a good fit of the model. The other incidences also explained that the scale was reliable (GFI=0.996, NFI=0.992, CFI=0.996, AGFI=0.978, and RMSEA=0.045)
(see Table 5-3, page:141). As a result, the flow experience construct was considered reliable.
Figure 5-8. CFA model fit statistics for Flow Experience
Immersion Experience: The immersion experience construct consisted of five items, as depicted in Figure 5-9. The average variance explained was 0.478 and the construct reliability was 0.864, which were considered acceptable (Huang et al. 2013). However, the χ2/df showed a score of 6.315 that was above three (χ2 =31.579, df=5, p<0.001). The RMSEA indicator was at 0.112 and demonstrated that the model was not a very good fit. A decision was made to remove the lowest scoring item, “I lose my self”, in order to improve the model.
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Figure 5-9. CFA model fit statistics for Immersion Experience
The CFA results after the removal of “I lose my self”, was shown in Figure 5-10. The average variance explained was 0.521 which showed a significant increase from the value of
0.47 with the five-item model. The construct reliability remained closed to 0.811. Thus, the revised model provided a strong reliability measure. The chi-square measurement also improved
(χ2 =9.164, df=2, p=0.01, p<0.05), with a χ2/df close to three. The Bollen-Stine bootstrap p value was 0.02. The RMSEA indicator improved from 0.112 to 0.09, which was considered acceptable. Finally, the other model indicators were all greater than 0.9 (GFI=0.989, CFI=0.987,
NFI=0.983, and AGFI=0.946). Overall, the revised measurement model of immersion experience demonstrated an adequate fit.
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Figure 5-10. CFA model fit statistics - Modified Measurement Model for Immersion Experience
Social Experience: The social experience construct was measured by five items, as shown in
Figure 5-11. All items loaded strongly on the scale with loadings being all above 0.5 (Hair,
2009). The CFA for the five items yielded a reasonable AVE of 0.531. The construct reliability was 0.849, which indicated a good level of reliability. The χ2/df was equal to 2.266 which was less than three (χ2 = 11.330, df=5, p =0.045, p<0.05). The Bollen-Stine bootstrap p value was
0.154 which was greater than 0.05 (see Table 5-3, page:141). The other indices were all satisfactory (GFI =0 .990, NFI =0.986, CFI=0.992, AGFI = 0.969, and RMSEA=0.055). All five items of the social experience construct appeared to reflect its measurement properly.
Figure 5-11. CFA model fit statistics for Social Experience
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Satisfaction: As illustrated in Figure 5-12, satisfaction was measured by five items. All items yielded loadings of 0.5 or above, which were considered acceptable. The chi-square with a p value less than 0.05, which was considered problematic (χ2= 13.571, df=5, p=0.019, p<0.05).
Moreover, the AVE of the model were very close to 0.40, which was not verry good. As this could result in issues with the overall model fit, it was recommended that the model measurement be re-specified (Malhotra, 2010).
Figure 5-12. CFA model fit statistics for Satisfaction
As per the Figure 5-13 the model was respecified by dropping the item “satisfied with the internet conection”, as it had the lowest loading (0.49) and demonstrated a weaker relationship with satisfaction than other items as shown in Figure 5-12. The revised measurement model achieved a very good level of fit as seen in Figure 5-13. The chi-square was insignificant with a p value grater than 0.05 (χ2= 1.415, df=2, p=0.493, p>0.05). Moreover the χ2/df value was below three. The AVE value was improved to 0.432 with a CR value equal to 0.751. Other measurments also demonstrated a good fit (GFI=0.998, NFI=0.996, CFI=0.999,
AGFI=0.992). The RMSEA was below 0.08, which was acceptable. In summary, the revised satisfaction measurment contained four valid and reliable indicators as shown in the Figure 5-
13.
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Figure 5-13. Re-specification of CFA model fit statistics for Satisfaction
Intention to replay: The CFA analysis found that all three items of intention to replay loaded strongly on the factor with values of above 0.7, as shown in Figure 5-14. The AVE was at an acceptable level of above 0.5 (AVE=0.590), with a good construct reliability of 0.876 (see Table
5-3, page:141). However, as there were only three items, the model fit showed a slight over fit to the data. Nevertheless, all items were retained for the construct.
Figure 5-14. CFA fit statistics for Intention to Replay
5.7 Final Model and Hypothesis Testing
Similar to Study One, all hypotheses of Study Two were analysed using SEM in Amos. As there were no concerns for any individual missing data that could affect the analysis, the Maximum
Likelihood Estimation (MLE) technique was adopted. Moreover, the Bollen-Stine p value was used as a fitness indicator (Anderson & Gerbing 1988; Kline 1998). A congeneric model was used in order to develop the final model (Coffman & MacCallum, 2005).
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Composite scores were used on the latent variables of feelings in order to develop a
“positive feelings” construct, as shown in Figure 5-15. The model fit indices showed that the model was not a good fitting model (see Table 5-4). The χ2/df for the model produced was 10.5 which was above three (χ2 = 294.939, df=28, p <0.001). Moreover, all the indices for model fitness were below 0.9 (GFI, NFI, CFI, and AGFI). The RMSEA was equal to 0.150 which was considered above the threshold value of 0.08. This suggested that the model was questionable and that it might not explain the data well.
Figure 5-15. Measurement Fit Statistics for the Final Model with all Interactions
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The measurement fit indices of the overall model with all constructs is shown in Table 5-4.
Table 5-4. Measurement Fit Statistics for the Final Model with all Constructs
χ2 Degrees of freedom p RMSEA GFI NFI CFI AGFI BS Bootstrap p 294.939 28 0.000 0.150 0.864 0.871 0.881 0.733 0.005
The congeneric score of the positive feeling construct demonstrated reasonable loadings of the composite measurements. The measurements were loading from 0.49 (sensory) to 0.85
(powerful), as shown in Figure 5-15. However, the modification index table estimates showed that there were four possible correlations between the measurement errors for the composite measurements of the positive feeling construct. Values from the modification indices table showed there was a high multicollinearity among variables “sensory” and “fun”, “sensory” and
“escapism”, “imaginative” and “escapism”, and “imaginative” and “sensory”. Moreover, a simple Pearson correlation between the items has showed that all items of positive feeling were significantly correlated (Appendix G). Based on this modification, the model was redefined into two suggested models. The first was a more ambitious model made by combining all of the items of “positive feelings” into one composite. The second was more conservative model, which was based on correlating the variance errors of the indicators in the original full model.
5.7.1 Respecified Model 1
The model fit indices showed that the model had some issues that might suggest a saturated model. The χ2/df of the model produced was below three with a p value greater than 0.05 (χ2 =
1.385, df=2, p=0.500, p >0.05), which suggested that the chi-square was acceptable (Malhotra,
2010). The GFI, NFI, CFI and AGFI were almost equal to one (value of 0.999). The AGFI was equal to 0.988 which is in the acceptable range. The RMSEA was below 0.08. These findings indicate that the model could be good. However, combining all of the items of “positive feeling”, as shown in Figure 5-16 would be viewed as an ambitious approach and might, therefore not
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Bader Albatati PhD Thesis October 2017 explain the relationship of the variables adequately (Hair et al., 2010). Based on the results, a second re-specification of the model was proposed.
Figure 5-16. Measurement Fit Statistics for the Final Model with all Interactions
5.7.2 Respecified Model 2
By allowing the variance errors to correlate for the second refined model, an overall improvement was achieved. Three of the fit indices for the second model showed a better fit with the data (GFI=912, NFI=0.910, and CFI=0.919). According to some researchers, if at least three of the model fit indicators show that the model is reasonable, the model can adequately explain the data (Marsh, Balla, & Hau, 1996). The χ2/df was 8.5 which was above three and might not be a good indicator of the model (χ2 = 206.188, df=24, p<0.001). This can be explained by the sensitivity of the chi-square measurement to sample size. Several studies explained that chi-square cannot be used with sample sizes greater than 400 as it would be
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Bader Albatati PhD Thesis October 2017 always significant (Malhotra, 2010; Tanaka, 1987). Therefore, it was advisable in this case to report other fit incidences as the sample size exceeded 400. The AGFI was equal to 0.8 which was acceptable with caution (Hair, 2009; Malhotra, 2010). Also, the RMSEA was acceptable with caution (RMSEA=0.134). Therefore, the model shown in Figure 5-17 was identified as satisfactory and will be used for hypothesis testing.
Figure 5-17. Re-specified model fit statistics for the model with item correlations
5.7.3 Hypothesis Testing
After examining the data with Structural Equation Modelling and maximum-likelihood method, the following results were achieved, as shown in Table 5-5.
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Table 5-5. SEM results for all Factors
Hypothesis Standardized Regression Estimate S.E. C.R. P Weights H1 Flow Positive 0.350 0.097 11.835 **** H2 Immersion Positive 0.395 0.110 12.062 **** H3 Social Positive 0.400 0.136 9.868 **** H7 Satisfaction Positive 0.288 0.106 8.842 **** H8 Intention to Replay Positive 0.355 0.130 9.097 **** H9 Satisfaction Flow 0.022 0.007 3.077 0.002 H10 Satisfaction Immersion 0.000 0.007 0.002 0.998 H11 Satisfaction Social 0.032 0.013 2.381 0.017 H12 Intention to Replay Flow 0.029 0.008 3.527 **** H13 Intention to Replay Immersion -0.012 0.008 -1.484 0.138 H14 Intention to Replay Social 0.020 0.015 1.346 0.178 H15 Intention to Replay Satisfaction 0.043 0.012 3.657 ****
All the relationships between positive feelings and flow, immersion and social experience as well as between positive feelings and satisfaction and intention to replay were tested. In addition, the relationship between flow, immersion, and social experiences and satisfaction and intention to replay were also tested. The results were discussed in the following:
Testing the effect of positive feelings on flow: Positive feelings had a significant and positive relationship with the flow experience (β=0.350, p<0.05). This suggested that the more positive the US players felt, the better the flow experience they had. Thus, Hypothesis One (H1) was fully supported.
Testing the effect of positive feelings on immersion experience: Positive feelings showed a significant and positive relationship with the immersion experience (β=0.395, p<0.05). Thus, the more positive the US players felt about the game, the greater their immersion experience.
Therefore, Hypothesis Two (H2) was fully supported.
Testing the effect of positive feelings on social experience: Positive feelings showed a strong and significant relationship with social experience (β=0.400 and p<0.05). This indicated
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Bader Albatati PhD Thesis October 2017 that the stronger the positive feelings of US players the stronger the social experience they would have. As a result, Hypothesis Three (H3) was fully supported. The results of H1, H2, and H3 suggested that positive feelings were positively related to the flow experience, immersion experience, and social experience. This also meant that players who experienced positive feelings are more likely to have a stronger flow, immersion, and social experience. Moreover,
H1 through H3 were all significant for the US sample (Study Two) as was H1 through H3 for the Indian sample (Study One). This may suggest that when it comes to game experience there are no cultural differences between the two cohorts.
Testing the effect of positive feelings on satisfaction: A significant and positive relationship was found between positive feelings and satisfaction (β=0.288, p<0.05). In other words, the more positive the players feel in their gameplay, the more satisfied they were with the game. Therefore, Hypothesis Seven (H7) was fully supported.
Testing the effect of positive feelings on intention to replay: Positive feelings had a significant and positive effect on intention to replay the game (β=0.355, p<0.05). Thus, the more positively players felt in the gameplay, the more likely they were to play again. Therefore,
Hypothesis Eight (H8) was fully supported. The results of H1, H2, H3, H7, and H8 suggested that positive feelings were important in determining players’ satisfaction and replay intention.
Testing the effect of flow experience on satisfaction: Flow experience had a significant and positive effect on game satisfaction (β=0.022, p<0.05). Thus, the stronger the flow experience, the stronger the satisfaction. Therefore, Hypothesis Nine (H9) was also supported.
Testing the effect of immersion experience on satisfaction: Immersion experience had an insignificant effect on satisfaction (β= 0.000, p=0.998, p>0.05). In other words, immersion experience did not influence players' satisfaction. Therefore, Hypothesis Ten (H10) was not supported.
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Testing the effect of social experience on satisfaction: Social experience demonstrated a significant effect on satisfaction (β=0.032, p=0.017, p<0.05). This suggested that social experience affected players’ satisfaction. Thus, Hypothesis Eleven (H11) was supported. The results of H1, H2, and H3 indicated that flow and social experience influenced players’ satisfaction, but immersion experience did not.
Testing the effect of flow experience on intention to replay: Flow experience had a significant and positive effect on intention to replay the game (β=0.029, p<0.001). The stronger the flow experience, the higher the intention to replay the game. Therefore, Hypothesis Twelve
(H12) was supported.
Testing the effect of immersion experience on intention to replay: Immersion experience had an insignificant effect on intention to replay (β= -0.012, p=0.138, p>0.05). In other words, immersion experience did not influence players’ intentions to replay. Therefore, Hypothesis
Thirteen (H13) was not supported.
Testing the effect of social experience on intention to replay: Similar to immersion experience, social experience did not have any significant effect on intention to replay (β=0.020, p=0.178, p>0.05). This suggested that social experience did not appear to influence players’ intention to replay. Thus, Hypothesis Fourteen (H14) was not supported.
Testing the effect of satisfaction on players intention to replay: Satisfaction had a significant and positive effect on intention to replay (β=0.043, p<0.001) Thus, players’ satisfaction influenced players’ intentions to replay. Therefore, Hypothesis Fifteen (H15) was supported.
In addition, results suggested that players’ satisfaction may play a mediating role between positive feelings and intention to replay, as well as between flow experience and
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Bader Albatati PhD Thesis October 2017 intention to replay. This also indicated that satisfaction mediated only some game experiences and intention to replay. These findings explained that satisfaction alone may not be able to explain players’ intention to replay MMORPGs.
5.8 Summary of the Results in Study Two
The full model demonstrated that positive feelings had a significant and positive relationship with all other factors tested. As a result, all hypotheses related to positive feelings were supported. These findings supported the notion that positive feelings affect customer experience
(Holbrook & Hirschman, 1982; Schmitt, 1999). Finally, Study Two confirms the findings in
Study One with regards to the relationship between positive feelings and flow, immersion, and social experiences. These findings suggested that there were no cultural differences in game experience between US and Indian players
Positive feelings also showed a significant and positive effect on game outcomes
(Satisfaction and Intention to Replay). These findings supported the those by Holbrook &
Hirschman (1982) and Woodruff & Gardial (1996) that positive feelings help develop other aspects of the consumption experience and create consumer value.
Flow and social experiences had a significant and a positive effect on players’ satisfaction. This finding may suggest that these two game experiences will contribute to the overall game satisfaction. These findings are aligned with that of other researchers who found that flow and social experiences positively affect consumer behaviour (Hausman & Siekpe,
2009; Xu et al., 2012). In contrast, immersion experience was found not to have any significant effect on players’ satisfaction. This finding was inconsistent with the findings by Kao et al.,
(2008) and Bulu (2012) who found that immersion positively affects consumer behaviour.
Flow experience was also found to have a significant effect on players’ intentions to replay a MMORPG. This finding contradicted that of Chang & Zhu (2012) in that flow
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Bader Albatati PhD Thesis October 2017 experience does not influence repeat behaviour. Immersion and social experiences did not show any significant relationship with intention to replay. These findings contradicted other researchers who found that immersion and social experience affects repeat purchasing behaviour (Kao et al., 2008; Bulu, 2012; Xu et al., 2012).
Finally, satisfaction demonstrated a significant relationship with intention to replay. This finding corresponds with that of East & Hammond (1996), that there is a relationship between satisfaction and repeat behaviour. Satisfaction also had a mediating effect between positive feelings and intention to replay. In addition, satisfaction showed a mediating effect on flow experience, social experience and intention to replay. This may indicate that satisfaction has both a direct and indirect effect on intention to replay. Table 5-6 lists all of the hypotheses tested in Study Two.
5.9 Hypotheses Testing in Study Two
The final hypotheses testing in Study Two are shown in Table 5-6.
Table 5-6. List of all Hypotheses Results
Number Hypothesis Result H1 Positive feelings are positively associated with flow experience Supported in game play. H2 Positive feelings are positively associated with immersion Supported experience in game play. H3 Positive feelings are positively associated with social Supported experience in game play. H7 Positive feelings are positively associated with players’ Supported satisfaction. H8 Positive feelings are positively associated with players’ Supported intention to replay the game. H9 Flow experience is positively associated with players’ Supported satisfaction. H10 Immersion experience is positively associated with players’ Not supported satisfaction. H11 Social experience is positively associated with players’ Supported satisfaction.
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H12 Flow experience is positively associated with players’ Supported intentions to replay. H13 Immersion experience is positively associated with players’ Not intentions to replay. Supported H14 Social experience is positively associated with players’ Not intentions to replay. Supported H15 Players’ satisfaction is positively associated with players’ Supported intentions to replay.
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CHAPTER 6 DISCUSSION AND CONCLUSION
6.0 Introduction
The focus of this concluding chapter is on the findings of Study One and Study Two. The data sample in Study One comprised Indian gamers who play MMORPGs. Data collection was done online through the medium of Amazon Mechanical Turk (AMT). The total number of valid respondents of Study One was 319. The sample for Study Two was a cohort of MMORPG players from the US. Similar to Study One, the medium for data collection was through the online portal of Amazon Mechanical Turk (AMT). The total number of valid respondents was
420.
This chapter commences with an extensive discussion of positive and negative feelings and their relationships with flow experience, immersion experience and social experience.
Moreover, it also discusses the effect of positive feelings on the two game outcomes (satisfaction and intention to replay), and the relationships between other game experiences (flow, immersion and social experience) and game outcomes. Lastly, the chapter covers limitations of the studies and future research, together with important theoretical and managerial implications, and ends with a conclusion.
6.1 Discussion
Central to this discussion are the specific objectives of the research. They include the investigation of both positive and negative feelings in relation to the other elements of game experience (flow, immersion and social experiences), and the investigation of positive feelings flow, immersion and social experience on game outcomes. These investigations comprised of five research objectives and each is discussed in turn in the following sub-sections.
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6.1.1 Objective One
The first objective was to investigate the effect of positive feelings on flow, immersion and social experiences separately.
Positive feelings and flow experience
The literature review (Chapter 2) hypothesised that positive feelings have a positive relationship with flow experience as stated in Hypothesis One (H1) (Novak, Hoffman, & Yung, 2000;
Csikszentmihalyi, 1990; Csikszentmihalyi, 1997). The hypothesis was tested on two different samples, which were Indian players in Study One and US Players in Study Two. In both studies, positive feelings were significant with a positive effect on flow experience (p<0.001). These findings suggest that the more players feel positive about the game the more they will be in a flow state and enjoy their experience. Moreover, as both studies (Study One and Study Two) demonstrated a significant positive effect with flow experience it may be concluded that this suggests that there are no effects on the relationship between positive feelings and flow attributable to cultural differences.
In the examination of the first game experience construct of flow in Study One and Study
Two, it was found that a state of flow was achieved by the effect of positive feelings of players such as feelings of fun and escapism. Moreover, positive feelings demonstrated an important role in influencing the specific conditions of flow which was the balance between challenge and skill level. It was found to be more likely for flow to be achieved when the player’s skill level was equal to the game challenges.
In addition, several studies previously linked feelings such as fun and enjoyment to flow
(Gao, & Bai, 2014; Hoffman & Novak, 2009; Bridges & Florsheim, 2008). Consequently, positive feeling was used as a determinant to connect its effect on the state of flow as a component of game experience. In effect, a player’s ability to generate positive emotions
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Bader Albatati PhD Thesis October 2017 derived from confidence in the skills to overcome in-game difficulties, results in enjoyment and maintenance of the flow state. Thus, flow redefines consumer (player) experience as an engaging game with an associated emotional response. In the online shopping setting, flow has proved to be a key construct for a compelling shopping experience (Hoffman & Novak, 1996;
Jiang & Benbasat, 2004). Hoffman & Novak (2009) associated enjoyment and feeling with flow experience for online shoppers both explicitly and implicitly. The findings support previous research by Gao, & Bai (2014) and Hausman & Siekpe (2009) who found that positive feelings
(such as fun, and enjoyment) affect flow experience in an online environment. Last but not least, it has been suggested by Moneta (2004) that national culture may affect flow experience.
However, the significant findings from Study One (the Indian sample) and Study Two (the US sample) may indicate that the relationship between positive feelings and flow experience may not be influenced by culture.
Positive feelings and immersion experience
Another key aspect of game experience examined was the concept of immersion. In the past decade, immersion has attracted the interest of some researchers as a part of consumer online behaviour (Suh & Chang, 2006). The literature review (Chapter 2) discusses the potential influence of positive feelings on immersion. As a result, it was hypothesised (H2) in both studies
(Study One and Study Two) that positive feelings had an effect on the immersion experience of players.
In both studies, positive feelings show a significant positive relationship with immersion
(p<0.001). This can be related to a number of reasons why players play MMORPGs, such as escapism and boredom (Wood et al., 2004), which explain a player’s desire to be immersed in an alternate world in order to feel a sense of enjoyment and happiness. Players who are immersed in the game are also more prone to positive emotions. In other words, the more
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Bader Albatati PhD Thesis October 2017 positive feelings players have, the stronger and longer the duration of the immersion will be
Reid, Geelhoed, Hull, Cater, & Clayton (2005). Moreover, players who have strong positive feelings will be in total immersion whereby they become absorbed in the game dynamics in which players become emotionally invested in the game (Brown & Cairns, 2004). The findings from both studies support other empirical studies within virtual communities which found that enjoyment and positive feeling influence immersion (Koh, Kim, & Kim, 2003; Jennett, et al.,
2008).
Furthermore, components of games such as online game world exploration, story line, avatar design and other aspects of MMORPGs contribute to immersion experience through feelings such as imagination (Douglas & Hargadon, 2001). As a result, immersed gamers will be attached to the game and become more engaged with it. The same is true for MMORPGs, given that games include more features (e.g. world environment, avatar and storytelling) that improve feelings and immersion levels of players. Finally, as positive feeling has demonstrated a significant effect in both studies (One and Two) this may eliminate the notion that culture can have an influence on the immersion experience (Park, Song & Teng, 2011).
Positive feelings and social experience
The last construct of game experience examined is social interaction (denoted as social experience). As discussed in the literature review (Chapter Two), an important aspect of
MMORPGs is that a large number of players play simultaneously. These players can form guilds and cooperate to achieve pre-determined goals. Players regularly compete against each other as individuals or as part of their guild. Alternatively, they can also enjoy simple social experience components such as chatting. These features are identified as the most attractive and fun components of online social experience. Furthermore, the social component has also been identified as a major reason for playing online virtual games (Yee, 2006c). Positive feelings are
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Bader Albatati PhD Thesis October 2017 found to have a strong relationship with social experience (Isen, 1987). As a result, it was hypothesised (H3) in both Study One and Study Two, that positive feelings can positively influence players’ social experiences.
Both Study One and Study Two find that positive feelings (e.g., powerful and fun) are positively related to social experience (p<0.001). The findings suggest that the stronger the positive emotions of a player, the more favourable a player’s social experience will be.
Moreover, social experience may directly shape the overall player experience. As a result, player experience becomes more compelling, which in turn affects the total game experience of players.
Part of the charm of the social experience is the opportunities for co-operation and relationship development between players (Chappell, Eatough, Davies, & Griffiths, 2006).
These interactions are meaningful and emotionally rich. This taps into the basic human need for affiliation and attests to how people can be emotionally stimulated by others (Hill, 1987;
O'connor & Rosenblood, 1996). It is clear from the findings of this study that positive emotions play an important role in the social experience; as in real life, players carry these emotional needs to virtual realities. This study has demonstrated the strong relationship between these positive emotions and social experience in an online game environment. Moreover, these findings are consistent with the findings of Nummenmaa et al. (2012). and Keltner & Haidt
(1999) that social interactions are emotionally focused. Finally, because both studies (One and
Two) detect a significant relationship of positive feelings with social experience, it appears that culture may not have any effect on social experience.
Summary
The results show that positive feelings positively affect flow, immersion, and social experiences.
These findings support the claims of Holbrook et al. (1984), in that feelings play an important
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Bader Albatati PhD Thesis October 2017 role in the context of hedonic consumption such as online games, as well as those of Schmitt's
(1999) where feelings were shown to play a vital role in various types of consumer experiences.
The important role of positive feelings is largely due to the fun nature of multiplayer online role- playing games (Yee, 2006a). The consumption of these games mostly relies on emotion, performance and personality in the experience (Holbrook et al., 1984). Therefore, the hedonic feelings of players (such as imaginative and fun), can be a strong predictor of other game experiences and outcomes.
Several studies have suggested that game experiences may differ from one culture to another (Jeng & Teng, 2008; Park, Song & Teng, 2011). For example, De Mooij & Hofstede
(2010) suggested that feelings may be experienced differentially in different cultures due to cultural values. Both Study One and Study Two find that feelings are important for both Indian and US players. This may be related to the fact that feelings are more universal than they are culturally based (Hirschman & Holbrook, 1982; Holbrook et al., 1984; Holbrook, 2000).
6.1.2 Objective Two The second objective was to investigate the effect of negative feelings on flow, immersion and social experiences separately.
Negative feelings and flow, immersion and social experiences.
Based on the literature review (Chapter Two), it was hypothesised (H4, H5, and H6) that negative feelings would negatively influence players’ flow, immersion, and social experiences.
All of these hypotheses were examined in Study One with the Indian sample.
The findings revealed that in contrast to positive feelings, negative feelings did not show any significant effect on game experience in Study One (p>0.05). These findings contradict some studies which found that negative feelings affected game play experience (Ketelaar &
Tung Au, 2003; Poels, De Kort & Ijsselsteijn, 2012). This may be attributed to the strong
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Bader Albatati PhD Thesis October 2017 entertainment nature of MMORPGs, where most players play for fun, enjoyment, challenge and socialisation (Yee, 2006b). Another reason is that players may have positive and negative emotions at the same time during a game session. Thus, as part of the game appeal is to be challenging enough to stimulate players, these challenges may generate both positive and negative feelings in the players. The negative feelings that are reflected upon players as a result of these challenges may be mitigated by positive feelings.
Furthermore, it may be because players anticipate that any difficulties they encounter will be followed by a sense of relief when they solve the problem. Andrade & Cohen (2007), in their study of people watching horror movies, found that the positive effect occurs very quickly as a result of the ensuing relief after viewing a horror scene. This situation can also occur with gamers. For instance, a player facing a new boss (game opponent) suffers the defeat of their avatar several times, yet in the end victory over the boss is attained. This end result can be a relief mechanism, and joy and satisfaction can replace the initial frustration, anger, or other negative feelings.
Furthermore, Andrade & Cohen (2007) argued that consumers exposed to movies
(videos) experience a state of “captivation”. This is where positive and negative feelings are experienced simultaneously. As a consequence, negative feelings are mitigated by the positive feelings and ultimately get translated as positive feelings. For example, losing the game may be viewed by players as part of the entertainment process that raises the challenge and makes the game more fun. Finally, it has been stated that the experience of more challenging situations can be highly arousing for players, and therefore, can change initial negative feelings to positive feelings. Joy and happiness do not simply arise from victory; rather they are derived from challenging gameplay (Nacke & Lindley, 2008; Ravaja et al., 2005; Ravaja, Saari, Salminen,
Laarni, & Kallinen, 2006; Ravaja, Turpeinen, Saari, Puttonen, & Keltikangas-Järvinen, 2008).
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In contrast to other types of entertainment, MMORPGs comprise powerful aspects that keep players engaged. These aspects range from graphics and sound effects to more interactive qualities such as engaging socially with others. Gamers are provided with different stimuli that prompt different emotions in MMORPGs. The absence of effect of negative feelings can also be explained by MMORPGs. Moreover, the engagement of players with the game occurs in a virtual world where all is imaginary and make believe. In other words, gamers do not suffer actual physical harm. This understanding encourages players to experience negative emotions that can be difficult in reality. According to Aurier & Guintcheva (2014), this can be a means of escape for people who prefer not to take risks in real life. In addition, entertainment enjoyment may not necessarily be part of emotional experiences. However, enjoyment can be in the process of leaving reality and emerging from the experience different from before
(Arnould & Price, 1993; Green, Brock, & Kaufman, 2004). According to Aurier & Guintcheva
(2014), people may purposely seek negative emotions for pleasure. Thus, the findings of this study that show that negative feelings experienced by players have little or no effect on game experience becomes plausible.
Moreover, in psychology there are suggestions that people with negative feelings often seek an outlet to minimise the unpleasantness through play activities (Harreveld, Pligt, &
Nordgren, 2008). Zeelenberg & Beattie (1997) are of the opinion that people who harbour negative feelings as a result of regretful decisions often make adjustments to their behaviour to elevate their feelings. Some scholars believe it to be a mechanism of psychological repair where an individual applies strategies to minimise negative feelings (Gilovich & Medvec, 1995) effectively referred to as “behavioural repair work” (Gilovich & Medvec, 1995). According to
Harreveld et al. (2008), people apply strategies that focus on the beneficial aspects of an experience instead of the harmful aspects. This behavioural strategy finds resonance in the
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Bader Albatati PhD Thesis October 2017 gameplay of MMORPGs, where fun and enjoyment with others far outweigh any negativity associated with losing in the game (Yee, 2006c).
In addition, most players are aware that experiences in the virtual world always come with the opportunity to try again. Most games allow players to save the game and then progress prior to entry to new challenge levels. This type of game feature allows players the opportunity to begin again from the same place rather than lose all current progress. This game aspect can be viewed by players as a “behavioural repair work” strategy where the associated risk is very low.
The findings of the study correspond well with the findings of Verhagen & van Dolen
(2011), who found that online consumer behaviour in fashion goods tend to be dominated by positive rather than negative emotions. Verhagen & van Dolen (2011) claimed that this domination of positive emotions was inherently due to the hedonic nature of the purchase activity. Therefore, the findings of this study suggest that positive feelings are essential in online games and more prominent than negative feelings.
6.1.3 Objective Three
The third objective was to test whether positive feelings influence game satisfaction and intention to replay.
The effect of positive feelings on satisfaction and intention to replay
Consumer satisfaction and intention to replay are believed to be influenced by positive feelings.
The literature review (Chapter Two) discussed the influence of positive feelings on both satisfaction and intention to replay. The relationships were examined in Study Two with the US sample and were reflected by Hypotheses Seven and Eight (H7 and H8). Both hypotheses were found to support the influence of positive feelings on both satisfaction and intention to replay.
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Previous research has found that emotion is a mechanism to deliver customer satisfaction and repurchase behaviour (Schmitt, 1999; Yuan & Wu, 2008). Study Two finds that positive feelings have a positive effect on both satisfaction and intention to replay (p<0.001). It appears that players who have positive feelings of the game play are more satisfied with the game and subsequently, they also have a higher intention to reply the game. This finding corresponds to Li's (2001) virtual store study which found that shoppers’ satisfaction and revisit intention was affected by customers’ positive emotions.
Another explanation for the strong relationship between positive feelings and game outcomes (satisfaction and intention to replay) is the pleasurable aspect of playing games, as most players seek fun from playing MMORPGs. As a result of being in a state of excitement,
MMORPG players experience satisfaction with the games they play (Chen, Siew, Phuah, &
Duh, 2007; Yee, 2006b). Most consumers are influenced by intrinsically pleasure-seeking behaviours (Holbrook & Hirschman, 1982). In addition, Mannell & Kleiber (1997) suggested that consumer feelings are an important part of the leisure experience. This suggests that as hedonic consumption becomes more emotionally based, positive feelings become ever more important in predicting consumer behaviour (Duman & Mattila, 2005).
Moreover, positive feelings (such as fun and pleasure) are found to have a strong impact on consumer behaviour and intention (Lacher & Mizerski, 1994; Hui & Bateson, 1991; Robert
& John, 1982; Wirtz, Mattila, & Tan, 2000). The findings of this study confirm the importance of positive feelings in players’ behaviour with special reference to satisfaction and intention to replay.
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6.1.4 Objective Four
The fourth objective of this study was to explore the effect of flow, immersion and social experience on game satisfaction and intention to replay. Each of these specific experiences were examined separately regarding their effects on the two game outcomes.
The effect of flow experience on satisfaction and intention to replay
It was discussed in Chapter Two that flow experience influence players’ satisfaction and intention to replay. As a result, the two hypotheses (H9 and H12) were examined in Study Two with the US sample. Both hypotheses were supported.
The flow experience of MMORPG players shows a significant relationship with both satisfaction and intention to replay (p<0.05). Participants in Study Two recall their flow experience as an important part of the game experience, affected by their dominant feelings and consequently influencing their behaviour. When player skill levels match the level of in-game challenge, positive experiences are produced that ultimately lead to satisfaction. These findings corresponds with Ghani & Deshpande’s (1994) study that suggest flow experience positively affects consumer intentions.
When a state of flow exists, players may feel good about the game they are playing which can influence a repeat play behaviour. Some studies demonstrated that this flow can result from satisfaction with the environment, location surroundings, and the level of skills that they possess to achieve the challenges of the game (Finneran & Zhang, 2003). In a study conducted by Hausman & Siekpe (2009), it was found that online shoppers who have a more intensified flow experience were more satisfied with the quality of the online shopping features which go to produce a smooth shopping experience. The same conclusions can be drawn from this study, where the more the players are in a flow state, the more they are satisfied with the quality of the
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Bader Albatati PhD Thesis October 2017 game. This relationship can influence and predict future behaviour for playing the same
MMORPG.
In this study, flow experience shows a significant relationship with intention to replay.
These findings contradict those of Chang & Zhu (2012) who found that flow experience does not influence repeat behaviour. This can be related to the fact that players seek challenging games and strive to overcome these challenges by applying their skills in the MMORPG context
(Ghani, & Deshpande, 1994). Furthermore, the challenges that are provided by the game can be an enjoyable task that can push the limits of players and make them more focused on the game.
This focus can eliminate boredom and promote excitement which in turn affects intention behaviour (Finneran & Zhang, 2003; Hsu & Lu, 2004).
It is worth noting that even though flow experience demonstrates a significant relationship to both satisfaction and intention to replay, these relationships are not as strong as the relationship between positive feelings and satisfaction or the relationship between positive feelings and reply intention. This finding may be related to the fact that flow experience can be interrupted by many factors. For instance, a player may run out of places to explore in the game, the more familiar he gets with the environment. Consequently, players gradually lose interest in the game and flow therefore becomes reduced (Hsu & Lu, 2004). As players become more familiar with the game, their level of skill increases and challenges become easier to overcome.
Pace (2004) stated that a web user’s flow experience, which affects website usage behaviour, is more related to the ability to explore the site. Sherry (2004) suggested that, as players proceed with different video games, they build and apply their skills to the unique challenges presented to them. The facilitation of being in a flow state is influenced by the ability of a game to generate new places and challenges to explore (Sherry, 2004). This is why most MMORPGs install new expansions to the game to maintain interest. This can also be related to MMORPGs where
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Bader Albatati PhD Thesis October 2017 players’ intensity of flow can be related to their exploratory behaviour, which can lead to either more or less replay of the game (Webster, 1989).
The effect of immersion experience on satisfaction and intention to replay
It was established in the literature review (Chapter Two) that players’ immersion experience affected both game satisfaction and intention to replay. As a result, both hypotheses (H10 and
H13) were examined in Study Two and were subsequently found not to be supported.
The investigation of the effect of immersion on satisfaction and intention to replay has yielded no significant effect on either of the game outcomes (satisfaction or intention to replay)
(p>0.05). This finding contradicts the findings of previous studies (Suh & Chang; 2006; Kao et al., 2008) which state that immersion is important for customer satisfaction and repurchase behaviour. This study also contradicts the findings of Bulu (2012), who found that immersion and the feeling of presence are an important part of satisfaction in a 3D world.
Immersion did not seem salient in explaining players’ satisfaction or intention to replay the same MMORPG. In other words, players who experienced immersion within the game may not necessarily be satisfied with it. This finding may be explained by the essence of subjectivity in immersion experience, which is simply that different players experience different types of immersion. Player immersion is described in the literature as a feeling or state of mind of being absorbed inside the game world (Slater & Wilbur, 1997). In order to get this feeling, the game needs to be on a certain quality level. These game qualities can range from good graphics and sound effects to story line and degree of realism (Nacke et al., 2009). As a result, players may experience different degrees of these aspects, which result in inconsistencies in immersion experience.
Another explanation lies in the nature of immersion as a feeling rather than an evaluation process (Ermi & Mäyrä, 2005). Thus, people can feel the immersion, but it can be difficult to
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Bader Albatati PhD Thesis October 2017 describe as opposed to evaluate. For example, players may feel immersed in the virtual world but, when asked about what they feel, they relate it to leaving the real world and having the sense of a presence within the online game (Bulu, 2012).
Another explanation for the absence of effect of immersion experience on game outcomes can be related to the habitual game play of gamers. A study conducted by Jolley,
Mizerski & Olaru (2006) found that players’ behaviours are dominant by their habitual gaming.
Players’ frequency of playing and familiarity with MMORPGs, which is related to ‘habit strength’, may influence the effect that immersion experience has on games outcomes
(satisfaction and intention to replay). Habit can reduce the impact of novelty that was deemed as an essential part for immersion experience (Ermi & Mäyrä, 2005). As a result, players become familiar with the game and immersion intensity is reduced. LaBarbera & Mazursky (1983) explained that the more experience consumers have with a brand, the less contribution other factors such as cognitive and hedonic experiences have in the explanation of purchase behaviour. In other words, the more players play the game, the more it becomes habitual. This pattern may affect players’ future experience with the game. Therefore, future studies could investigate the relationship between habitual playing and game outcomes (satisfaction and intention to replay).
Finally, positive feelings have shown a positive relationship with immersion experience, and therefore supports the assertion that immersion is emotion-based rather than cognitive. In view of the findings of both Study One and Two, the conclusion is that immersion experience is important in forming the overall game experience but not important to players’ satisfaction and intention to replay.
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The effect of social experience on satisfaction and intention to replay
The literature review (Chapter Two) discussed the potential influence of social experience on game satisfaction and intention to replay. Study Two examined both hypotheses (H11 and H14) in order to test the effect of social experience on game outcomes. The hypothesis (H11) related to the effect of social experience on satisfaction was supported. However, the hypothesis (H14) that was related to the effect of social interaction on intention to replay was not supported.
In Study Two, social experience has shown a significant relationship with satisfaction
(p<0.05). This finding contradicts the findings of Bloemer, Odekerken-Schröder, & Kestens
(2003) who found no influence of social experience on satisfaction.
One explanation for the significant relationship of social experience with game satisfaction lies in the nature of the experience. According to Zhou et al. (2012), there is no need for virtual world users to base their online interactions on themes already predefined. Instead, they prefer to enjoy the freedom to determine their own cyber experience. This freedom can add novelty and excitement that unexpected things may occur. Moreover, group collaboration and social activities within MMORPGs fulfil a strong need for social bonding; therefore, social experience is an important facet of experience that contributes to the overall game satisfaction.
However, it is notable that the relationship between social experience and satisfaction is not as strong as the effect of positive feelings on game satisfaction. This can be attributed to a number of factors. Social experience may be based on the emotional aspects of human relationships. As satisfaction items tested in this study were more utilitarian-based (e.g., how well players are satisfied with the internet connection and graphics), the social aspect may not make a very strong contribution to game satisfaction (Holbrook & Hirschman, 1982).
According to (Yee, 2006c), some users participate in the virtual environment mainly to make friends and form supportive social networks. Therefore, gamers in virtual environments
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Bader Albatati PhD Thesis October 2017 may be more emotionally oriented than those in the non-virtual environment. Thus, emotions and feelings may be more important than the quality aspects of the environment (such as graphics and internet connection).
Social experience does not haves any significant relationship with intention to replay the same game (p>0.05). This finding contradicts with that of Xu et al. (2012) who found that social experience had a positive impact on social network usage. However, Xu et al.'s (2012) study was on the usage of social networks and the current study was on game play which is a different type of social experience. This finding suggests that the effect of social experience on replay intention is contingent on the type of experience.
Another explanation is that players may encounter negative social experiences which may affect their future replay intentions. Barnett, Coulson & Foreman (2009) surveyed 292
MMO players to find out what angers them in a game. In the responses, 50 per cent of the players cited rude and impolite players as the main cause of their frustration. As a result, players who are frustrated with such encounters often try to find other suitable people to play with or leave the game in pursuit of a new one.
6.1.5 Objective Five
Objective five was to investigate the influence of satisfaction on the intention to replay.
Satisfaction and intention to replay
It was discussed in the literature review (Chapter Two) that satisfaction has a potential influence on players’ intention to replay. Study Two examined Hypothesis Fifteen (H15) that is related to the effect of satisfaction on replay. The hypothesis was supported.
In Study Two, satisfaction demonstrated a significant relationship with intention to replay (p<0.001). This finding supports the claims of Cronin, Brady & Hult (2000) and
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Parasuraman, Zeithaml & Berry (1988) that satisfaction has a positive effect on consumer intention behaviour. An explanation for this significant effect is that player satisfaction is related to pleasure and commitment resulting from the ability of the game in fulfilling and exceeding players’ expectations (Mittal & Kamakura, 2001). Another reason is the rapid improvement in
MMORPGs quality in recent years (Chen, Liu, Hsu & Lin, 2010; Jung, Kim & Lee, 2014). This also supports the notion that improved satisfaction can have an impact on customer retention
(Lin & Wu, 2011).
However, it is noticeable that the relationship between satisfaction and intention to replay is not very strong (e.g., compare to the relationship between positive feelings and intention to replay). A number of studies have detected a similar relationship (Mittal &
Kamakura, 2001; Homburg & Giering, 2001; Kumar, 2002; Seiders, Voss, Grewal & Godfrey,
2005). One explanation may be the familiarity of players with playing the same game. As players play more of the game, they become more familiar with the aspects of the game. Players in an advanced stage would have already explored most of the game, and subsequently become used to the graphics and the mechanics of the game. This situation will result in players becoming less satisfied or willing to replay the game.
This aspect corresponds with the findings of Norman et al. (2000) that consumer satisfaction is weakened when the behaviour is repeated many times. Another possible reason for the weak correlation between player satisfaction and intention to replay a MMORPG may be length of play. Most gamers play the game for long periods of time spreading over several years (Griffiths et al., 2004; Yee, 2006c). For example, most MMORPGs (such as WoW) are continuance games with play stretching over several years. Some studies show that players may play MMORPGs for years with an average play time of more than ten hours per week (Yee,
2006b). LaBarbera & Mazursky (1983) found that the contribution of cognitive factors to the explanation of purchase behaviour is reduced as consumers’ brand experience increases.
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Therefore, the current finding is consistent with previous research in that intention to replay is not necessarily influenced by game satisfaction.
Finally, results of this study demonstrate that satisfaction may be a significant moderator for the relationships between some experiences (flow and social, for example) and intention to reply. In contrary, satisfaction does not seem to have any moderating effect in the relationship between immersion and intention to replay. Future studies may be carried out to understand why satisfaction moderates some experiences and not others.
6.2 Summary and Theoretical Contributions
Some findings of the study can be generalised, meaning that conclusions derived from the research can be, and are applicable to other similar instances (MacCallum & Austin, 2000). The facets of experiences examined in the present study, have important value for researchers who wish to understand hedonic aspects of games experiences on consumer decision making. For example, the research contributions of both studies lie in the fact that they test the relationships of positive feelings with other game experiences and game outcomes in the MMORPG context.
The findings from both Study One and Two suggest that a strong driver of online gameplay may be positive feelings. Gameplay, as a consumption process, has been identified as a type of hedonic experiential behaviour (Holbrook et al., 1984; Schmitt, 1999), especially where players act under the influence of fun, excitement and enjoyment. Positive feelings are established in gameplay, and players’ experiences, satisfaction and intention to replay are increased. Overall, the findings of both studies are consistent with the argument proposed by
Holbrook & Hirschman (1982) and Pine & Gilmore (1998) that hedonic products (such as video games) are strongly influenced by feelings or experience consumption rather than the utilitarian
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Bader Albatati PhD Thesis October 2017 aspects of the games. In these interactions, the decision-making process is stimulated by consumer emotions and experiences with the product.
Study One in this research found that negative feelings had no effect on game experience. As a result of positive experience, negative feelings can be reduced and become insignificant in affecting players’ experience. These negative feelings may be seen as necessary to add to the overall game experience. As discussed in earlier sections, when players are defeated in the game, they may feel more challenged by the game and subsequently become more determined to find and explore new ways to overcome the challenge. This explains why frustration (negative) can be elevated into an enthusiastic feeling (positive) (Andrade & Cohen,
2007). It was argued that negative feelings can be elevated to positive feelings and subsequently act as the reason for consumption of some products such as extreme sports and horror movies.
In the second study, game satisfaction and intention to replay a MMORPG are found to be strongly influenced by positive feelings. The findings support those of Westbrook & Oliver
(1991), in that customer satisfaction is influenced by feelings, especially with regard to hedonic products. Moreover, positive feelings were also found to have an influence on intention to replay behaviour, which further relates to other studies such as those by Lacher & Mizerski (1994) and
Martin, O'neill, Hubbard & Palmer (2008). The power of feelings significantly contributes to decisions about gameplay in the future. This is due to gameplay being fun and enjoyable, which makes it part of the game experience, player satisfaction, and future play intention.
Finally, this study has attempted to answer the research question as to why MMORPG players continue to play. This study extends the study by Meredith et al. (2009) wherein they suggested that virtual experiences can influence gameplay. This project examines major experience elements such as feelings, flow, immersion and social interaction. Some of these experiences have been found to have significant influence on satisfaction and intention to replay.
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Moreover, the project also extends Lacher & Mizerski's (1994) music consumption model, where they argued that their study can be furthered examined in other hedonic products such as video games. In summary, the current project help develop better understanding on game experiences and the influences game experiences have on satisfaction and replay intention.
6.3 Managerial implications
A number of managerial implications are derived from this study. They include game design, emotional appeal, non-cognitive approach, variation, balance, maximisation, positivity, product placement, cultural appeal, market segmentation and advertising.
6.3.1 Game design
The first implication relates to online game design, including its relationship with positive feelings and its use in the amplification of the game experience of the player. Player feelings are important because they influence other major game experience dimensions, such as flow, immersion and social experiences. This suggests that design features of MMORPGs should be tailored on to help players’ positive feelings.
This project also provides game designers with empirical data that explain the importance of the flow, immersion, and social experiences in relation to player feelings. For example, game developers can improve the flow experience by balancing the challenges in the game with the skills of their target players. Game developers can also focus on improving game environment quality, which increases immersion (Yee, 2006c). Finally, social experience is found to be an important attribute that distinguishes MMOs from other games genres (Barnett
& Coulson, 2010). Thus, developers need to enhance this particular experience by providing the right tools and aspects in order to encourage social interactions within the game. All of these findings provide a useful guide for game developers with regards to design features.
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6.3.2 Emotional appeal
As emotional appeal (e.g., playfulness) can play a major role in advertising experiential products
(Holbrook & O'Shaughnessy, 1984), game designers should also take positive feeling factors
(such as fun and excitement) into consideration in their marketing campaigns. Marketers may wish to emphasise factors that make games look fun, and exciting. Doing this will also improve the ability to target consumers in advertising processing (Howard, 1977). For example, showing a player experiencing positive feelings (e.g., excitement, and fun) in an advertisement will make consumers feel more related to the product and hence more receptive to the game advertised.
6.3.3 Advertising
Traditional advertising often utilises a cognitive based approach, for example providing game information. This approach may have minimal brand-building ability in the long term for digital games, because digital games are typically experience products (Clow, 2007). Thus, focus on feelings, flow, immersion, and social experiences will grant MMORPG marketers an edge to build a long term positive relationship with game consumers. In line with MMORPG marketing, findings from this project suggest an experience-based approach in advertising or other marketing programs (Wegert, 2004).
Game marketers can apply experiential aspects in communicating the brand to existing and potential players. For those players who actively search for enjoyment, promoting pleasure- seeking in advertisements is very important. Such consumers may be motivated to explore new
MMORPGs which create a sense of fun and excitement (especially to potential customers). As game experience (e.g., feelings, flow, immersion and social experience) is difficult for consumers to evaluate prior to consumption (Holbrook et al., 1984; Holbrook & Hirschman,
1982), advertising can play an important role in attracting them.
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Marketers should also take into account two other important aspects when formulating advertising strategy. They need to first consider other aspects that influence game purchase, such as game quality and challenge level, as this can appeal to other players beyond the pleasure- seeking segment. Marketers also need to distinguish between current players and potential gamers, as the former may be habitual gamers.
6.3.4 Player retention
Another implication lies in creating a unique and memorable experience for players. Pine &
Gilmore (1999) stated that customers who encounter the exact same experience every time they purchase a product will end up bored with it and may go elsewhere to experience a difference.
They argue that companies need to alter these experiences from time to time in order to keep customer interest and excitement during the consumption and re-consumption. The same notion can be applied to MMORPGs. As stated earlier in the findings, feelings and other experiences are important for achieving player satisfaction.
The market success of an MMORPG depends on repeat play and purchase. Most
MMORPGs adopt a subscription business model, which can be either a monthly fee or other types of payments where players need to buy items and buff ups for their characters within the game. The main point is to keep these players engaged and excited for a longer period of time to increase the opportunity for in-game purchases. This can be through the provision of new game contents. These new game expansions must integrate most or all dimensions of game experience (e.g., feelings, flow, immersion and social experiences). Therefore, it is strongly recommended that game marketers provide a holistic experience for MMORPG players.
6.3.5 Balance and Flow
As discussed previously, the only facet of game experience which showed a significant relationship to both game outcomes (satisfaction and intention to replay), is flow. According to
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Ray (2004), some players will quit playing if they get many punishments and penalties after failing to overcome challenges. When players perceive a game to be well balanced between their own skills and the game challenges, they experience a state of flow. The findings of this study may aid game designers to better implement and assess the quality of game challenges in their game design.
6.3.6 Maximisation
In order to increase the appeal of MMORPG, marketers may maximise game design features such as graphics, sound and other aspects of game quality. These features have the potential to make the difference between a good MMORPG and a great one. The use of higher quality features by MMORPG designers can be the distinction between a compelling experience or an ordinary one (Arnould & Price, 1993).
From a marketing perspective, selling an experience needs to maximise on all levels of experience stages, from the start of customer search for a product or service to the after-sales stage (Pine & Gilmore, 1998). Instead of selling solely on game features, marketers must appeal to a broader range of hedonic offerings in order to attract consumers with a compelling experiential appeal.
6.3.7 Positivity
Positive feelings have been found in this project to be the most influential factor on other game experiences, satisfaction and intention to replay. From a managerial standpoint, this study suggests that positive emotions can be a better indicator of purchase intention of MMORPG than other factors. Video game businesses may find that having fun while playing and wanting a means to escape real life are better forecasters of new games’ success than attitudes or motivations. Moreover, experiencing the game may also be important in marketing communication, as it can engender these feelings.
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A good approach to marketing MMORPGs may rely on having players experience the game first-hand. This can be through a free trial period, where players enjoy playing free of charge for a time. Thus, players get to experience the game first-hand and develop positive emotions in the game. Subsequently, they may continue playing the same MMORPG. In addition, it may be more meaningful for MMORPG marketers to present gameplay in a format that creates a holistic game experience rather than individual utilitarian aspects of the game.
This does not suggest that they ignore the game features, but that they need to incorporate them into the game experience. While each game experience may be unique, the game experience model of MMORPGs developed by this project is still applicable for other forms of online games. This is because most players of online games want to be absorbed in the gameplay, have fun, be challenged and engage with friends.
6.3.8 Product placement
Marketers frequently use strategic product placement in MMORPGs to promote certain products. However, this needs to be done with caution so as not to interrupt game experience.
A number of studies on product placement show that consumers may develop negative attitudes towards product placement as a result of the distraction (Cowley & Barron, 2008).
Another issue with regards to product placement is not linking the product to game experience (e.g., feelings, flow, immersion and social experience). Russell (2002) found that product placement in audio programs that are not connected to the plot has no effect on consumers. Moreover, Nelson, Keum & Yaros (2004) suggested that strong product interaction while playing video games increases an individual’s ability to recall the product at the time of promotion. Marketers must incorporate the product seamlessly within the game so that players engage with the product as part of the game. Further research can certainly look at the link between product placement and various types of experience, such as flow, immersion and social experience.
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6.3.9 Cultural appeal
This project has demonstrated that MMORPGs are enjoyed by consumers from different cultures (India and US). The market share of more of the western MMORPGs is expanding to different nations (Heimburg, 2006). This fact raises the challenge of customising the product to suit local market preferences. The design of effective strategies by marketers to enter new markets is important for game designers. Due to language and cultural differences, meanings can be interpreted differently. Modifications can be made to manipulate the intensity of feeling and to tailor other game experience elements (De Mooij & Hofstede, 2010; Marcus & Gould,
2000). It is important to develop a localised approach, where MMORPGs are designed according to the local language and culture. This localisation approach is beneficial for game designers as it may affect the success of a game in foreign markets.
6.3.10 Market segmentation
One important tool in marketing is segmentation (Kotler & Armstrong, 2010). This project has provided new findings for segmenting the online game market by showing that positive feelings are the most influential aspects of gaming. Game developers who wish to develop strategies and maximise sales should focus on segments that are appeal to hedonic feelings and positive experience. This project also suggests that positive feelings have a positive effect on both game experience dimensions (flow, immersion, social experience) and game outcomes (satisfaction and intention to replay). Thus, consumers who tend to seek pleasure and fun can be viewed as an important market segment for MMORPGs.
Moreover, marketers can segment the market for gamers who are motivated by challenges (being in a flow state). In this project, flow is found to have a significant effect on player satisfaction and intention to replay. Providing players with a level of challenge can be appealing to the particular segments who seek it. Nevertheless, as the effect of flow is not as
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Rather, they may approach it as a mix of two segments (‘pleasure seekers’ and ‘challengers’).
6.4 Limitations and future studies
Despite the research, design and marketing opportunities arising from a better understanding of
MMORPGs and players, this project has several limitations. These include factors such as genre-specificity, data collection method, self-reporting survey, light/heavy player distinction, low average variance, product performance measure, brand engagement, cultural differences and player values. While these limitations appear numerous, they are beyond the scope of the present research.
6.4.1 Genre specificity
The findings of this project relate specifically to MMORPGs, which are a unique type of online game genre with special characteristics that possibly differ from other types of online game
(e.g., online card games) (Chappell et al., 2006). Other genres of online game differ in critical design factors which may affect feelings, flow, immersion and social experience. As a result, generalising the findings of this study to other game genres needs to be done with caution.
6.4.2 Online data collection and control
The fact that the data collection specific to this project was done through an online panel and the use of a self-reported survey raises a number of research issues. All respondents were recruited through the online panel on Mechanical Turk and the researcher was not in physical attendance during data collection. Some respondents may have received a different layout of the questionnaire on their screen, which could render the questionnaire non-standard. Another issue is whether the survey was accessible and readable for all end users (Best & Krueger, 2008).
Since the data collected was self-reported, there was a lack of control over what the reactions of respondents may have been to the questionnaire (Fielding, Lee, & Blank, 2008). As
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Bader Albatati PhD Thesis October 2017 a result, responses to constructs such as feelings and flow may be less than optimum in fully understanding game experience. These factors could be mitigated if new studies were to consider a more engaging panel of players with a more experimental data collection design for the research, such as data collection from players during actual gameplay rather than through recall.
6.4.3 Light/heavy player distinction
This project did not distinguish between heavy and light players (e.g., players who play more than 25 hours a week are considered to be heavy users) (Griffiths et al., 2004b; Poels, de Kort,
& IJsselsteijn, 2012). Therefore, there is a possibility of bias in the responses, as light gamers may experience the game differently to heavy players (Lee, Hu, & Toh, 2000). Future research should examine these two groups in order to better understand the influence of usage on game experience.
6.4.4 Gender differences
Some demographic studies on MMORPGs show that they are enjoyed by both males and females (Griffiths et al., 2004b; Park & Lee, 2011). The difference in gender may yield different game experiences and outcomes. Some marketing studies demonstrated that gender can affect product consumption (Hansen & Møller Jensen, 2009; Tifferet & Herstein, 2012). As a result, future studies need to investigate the role of gender in the MMORPGs experience and consumption.
6.4.5 Product performance measure
Another limitation is that game performance and player engagement with MMORPGs was not measured. More research can be done on design variables characterising interactive environments that influence the immersion and flow state of players (Chou & Ting, 2003; Ermi
& Mayra, 2005). Future research can investigate how game performance (product performance)
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Bader Albatati PhD Thesis October 2017 can affect intention to replay, as previous empirical studies on product performance have shown a strong link between product performance and behaviour intention (Cronin, Brady, Brand,
Hightower Jr, & Shemwell, 1997; Cronin, Brady, & Hult, 2000).
6.4.6 Brand engagement
This project did not examine the influence of any specific game brand of MMORPG, such as
World of Warcraft (WoW). Brand engagement is also an important factor in the gaming literature. Thus, brand engagement or the relationship between a consumer and a brand may influence consumers’ motivation, cognition, emotion and behaviour related to brand purchasing intention (Hollebeek, 2011b; Van Doorn et al., 2010).
A qualitative study on virtual brand communities conducted by Brodie, Ilic, Juric &
Hollebeek (2013) found that brand engagement had an influence on consumer emotion, satisfaction and loyalty. More specifically, more engaged consumers are more likely to be satisfied and loyal to the brand than those that are not. Therefore, future research should also seek to examine whether player engagement with a specific MMORPG brand has a significant effect on players’ satisfaction and intention to replay.
6.4.7 Cultural differences
This project had no specific measures of cultural differences; instead it compared cultural differences based on nationalities. Though the respondents were players of MMORPGs from different nationalities, no basis other than nationality was used to differentiate culture. Future studies may use specific cultural frameworks (e.g., Hofstede’s Cultural dimensions) to examine the influence of these cultural dimensions on game experience.
6.4.8 Values
Finally, this study did not take consumer value into consideration. Consumer values were identified as a means of guidance for people and can act as a motivational goal (Schwartz, 1992).
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Schwartz (1992) identified a number of key human values such as hedonism, achievement and power. These values may have important effects on player behaviour. For example, players who are motivated by the hedonism value, will tend to play for fun and enjoyment, whereas players motivated by achievement, will tend to for completion of the game. Therefore, future studies may investigate the effect of player values on play behaviour.
6.5 Conclusion
Upon analytical inspection of the literature at the commencement of this research, substantial gaps were identified with respect to consumer research on MMORPGs which highlighted a paucity of useful knowledge in the area for both practitioners and academics. This study is one of the few theoretically and empirically-based consumer studies on the emerging MMORPG market to date.
The study used a survey-based research designed in Qualtrics where the sample was collected online from panels in Amazon Mechanical Turk. The project included two major investigations, namely Study One and Study Two. Study One was based on an Indian sample
(n=319). Study One examined feelings and the other three components of game experience
(flow, immersion and social experience). The study had three main objectives. The first objective was to understand positive and negative feelings. The second objective was to evaluate the effect of positive feelings on flow, immersion, and social experiences. Negative feelings were also examined as the third objective in conjunction with their effect on flow, immersion, and social experiences.
The data was analysed using the Structural Equation Modelling method in AMOS.
Major findings of the study were that positive feelings had a positive effect on all other components of game experience (flow, immersion and social); negative feelings did not have
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Bader Albatati PhD Thesis October 2017 any effect on other game experience components. This was attributed to the fact that negative feelings may have been elevated in the presence of positive feelings.
The main objective of Study Two was to examine the relationships among positive feelings, game experience and experience outcomes (satisfaction and intention to replay). The data included a sample of US players (n=425). The study first tested the influence of positive feelings had on players’ game experiences. Later, the relationship of positive feelings with two game outcomes (satisfaction and intention to replay) was tested. Another objective was to test the effects of positive feelings as well as the other three game experiences (flow, immersion and social experience) on game satisfaction and intention to replay a MMORPG. The final objective was to test if satisfaction had any mediating effect between positive feelings and other game experiences and intention to replay. The relationship between satisfaction and intention to replay was examined. Given these objectives, this research has produced several important findings.
Firstly, players’ positive feelings were found to influence both game experience (flow, immersion and social experience) and game outcomes (satisfaction and intention to replay).
Flow experience showed a positive relationship with both satisfaction and intention to replay.
Immersion experience had no significant effect on either satisfaction or intention to replay.
Social experience exerted a significant effect on satisfaction but no effect on intention to replay.
Finally, satisfaction had a significant effect on player intention to replay. Satisfaction was found to mediate the relationships between positive feeling, flow, social experience and intention to replay. These findings suggest that positive feelings (such as fun, imaginative, etc.) are important in developing a positive game experience and satisfaction, which result in players’ replaying the same MMORPG.
Creating a balance between game challenge and players’ skills help players to be in a flow state, which subsequently affects player satisfaction and intention to replay. Socialising
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Bader Albatati PhD Thesis October 2017 with other players and teaming up with them can also affect player satisfaction which, in turn, affects replay intention.
The research findings have several important managerial implications for game marketers and designers. This project provides recommendations for game design, providing players with a memorable experience, and game advertising practice. Games should incorporate aspects which can stimulate positive feelings (such as game environment and avatar design).
Game designers should also carefully decide what skills and challenges are appropriate for each level of the game to help players achieve a state of flow. Appropriate design will lead to players having unique experiences through the generation of positive feelings flow and other aspects.
Positive emotions can also be used to influence satisfaction and generate a positive attitude towards MMORPG communications. Finally, these findings can offer marketers competitive edge in targeting the right audience for their product by focusing on players who are interested in having fun and playing with others.
This thesis represents one of the few early attempts to develop and test an experience- based model related to MMORPG gameplay. It provides both theoretical and practical insights which can assist the development of a more detailed and refined understanding of game experience and outcomes of game experience relative to the online game market.
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APPENDICES
Appendix A: Information Letter, Consent Form, and Online Questionnaire
Information Letter
Project title: Empirical Study of Online Gamers’ Satisfaction, Experiences, Habit and Their Intention to Replay.
Project description: Exploring gamer's opinions on their preferences in games.
This survey asks your opinions about video games. Please answer the questions to the best of your ability. The survey has several questions and your answer to each question is important. Please answer every question.
It is expected that survey will last for approximately 15 to 20 minutes. Participants may stop at any time. Participants’ completion of the survey will be considered as consent to participate without the provision for signature. The survey will be filled on line.
This study is being conducted for research purposes, by Bader Al-Batati, as part of a PhD degree in Marketing, at the University of Western Australia.
Participation in this study is entirely voluntary; however, you need to be 18 years old or older. You may withdraw your consent to participate at any time without reason or justification and without prejudice, in which case your records of participation will be destroyed. Please note that all information will be treated as strictly confidential. No information will be released unless required by law. You are not required to give your name or any identifying information.
Should you have any questions relating to this study or wish to comment on this study, please contact the study organiser, Bader Al-Batati, on [email protected]. Alternatively, you may contact Bader’s supervisors, W/Prof Dick Mizerski, on 64887210 or [email protected].
Thank you for participating in this research project.
W/Prof Dick Mizerski
Approval to conduct this research has been provided by The University of Western Australia, in accordance with its ethics review and approval procedures. Any person considering participation in this research project, or agreeing to participate, may raise any questions or issues with the researchers at any time.
In addition, any person not satisfied with the response of researchers may raise ethics issues or concerns, and may make any complaints about this research project by contacting the Human Research Ethics Office at The University of Western Australia on (08) 6488 3703 or by emailing to [email protected] All research participants are entitled to retain a copy of any Participant Information Sheet and/or Participant Consent Form relating to this research project.
Continue
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Consent Form & Questionnaire
I have read the information provided and acknowledge that my age is 18 or older and any questions I have asked, were answered to my satisfaction. I agree to participate in this study, realising that I may withdraw at any time without reason and without prejudice.
I understand that the information that I will provide will be analysed and used for the project. I agree that research data gathered for the study may be published provided names or other identifying information are not used.
I understand that all information provided is treated as strictly confidential and will not be released by the investigator unless required to by law. I was advised as to what data are being collected, what the purpose is, and what will be done with the data upon completion of the research.
I agree that research data gathered for the study may be published provided our names or other identifying information is not used.
I Agree I do not Agree Have you played any massively multiplayer online role-playing games (MMORPGs) in the last month?
Yes No The following are statements about video game experiences. The response to each statement is numbered from 1 to 5. Please give your opinion to each statement by circling only one answer for each question. For example, the following respondent feels very strongly about video games being relaxing so he circled 5 (strongly agree):
Strongly Disagree Strongly Agree
1 2 3 4 5 I hate losing in the
online game. . I feel mad when I
don’t win. Developing
I feel strong when playing online games. I feel related to the people who play the
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Strongly Disagree Strongly Agree
1 2 3 4 5 same online game as me. I enjoy being a part
of a community. An online game virtual world is the
best place to make new friends. Playing online games is an exciting experience. I feel I am competent enough to meet the high demands of the online game. I do things in the online game spontaneously and
automatically without having to think. I have a strong sense of what I want
to do in the online game. I enjoy finding materials and rare
items in the online game. Playing online games with others is more enjoyable than playing alone. I enjoy the story line within the online game. I felt deeply about the online game that I play. I will feel the experience of the
online game that I play for a while. I “got into” the online game that I play.
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Strongly Disagree Strongly Agree
1 2 3 4 5 When I play a good online game, I feel
preoccupied with the game. When I lose in online games I become upset. When I advance in online games I feel powerful. When I complete quests in online games I feel I gain status among other players. I become angry
when I don’t win. I say positive things about the online games that I play to other people. I recommend online games that I play to someone who seeks my advice. Playing games online gets me away from all the problems that I have. Playing online games makes me
feel like I am in another world. I analyse and think about how to
proceed in the online game. I think about the best way to win the online game. I enjoy taking risks
in online games. When playing online games, I have a feeling of total control over the game.
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Strongly Disagree Strongly Agree
1 2 3 4 5 When I am playing online games, I am not worried about what others may be thinking of me. After playing a good online game, I still fantasise about it. While playing online games I keep moving part of my body (e.g. head or foot). I enjoy sharing my experience of the
online game with other players. Playing side by side with other players is
more comfortable than playing alone. During playing a good online game, I swear. My facial expressions changes
while playing an online game. When I play a good online game, I feel anxious. I become worried when taking on new quests. I get so involved when I play that I
forget everything else. Online games create a picture in my mind. I enjoy doing impossible things in a virtual world. The best online game is the one that challenge my mind
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Strongly Disagree Strongly Agree
1 2 3 4 5 and required thinking to gain victory. I am energetic when playing online games. I am enthusiastic about playing the online game. I am excited when playing online games. I always prefer to play online games with other people than playing against the computer (non- person characters). Developing my character and making it powerful is an enjoyable process. I enjoy helping other players in an online game. I am more at ease playing online games together with other people. When Playing good online games my
heartbeat becomes faster. While playing I yell
from excitement. Playing online games is a fun activity. I enjoy exploring new areas within an online game. I enjoy designing my own avatar in the online game. I have a good idea while I am playing,
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Strongly Disagree Strongly Agree
1 2 3 4 5 about how well I am doing. When playing online games, I become completely focused on the task at hand. After playing, I still think about the online game. After playing, the online game
prompts images in my mind. The way time passes while playing online games seems to be different from normal. Playing online games is extremely rewarding. I felt “carried off” by the online game that I play. I felt as if I were part of the online game. I feel entertained when playing online games. I feel amused when I play a good online game. I feel relaxed when playing online games. I like to play online games with other people. I lose myself in the online game, when I am experiencing it. Experiencing online games with real players is more rewarding than playing alone.
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Strongly Disagree Strongly Agree
1 2 3 4 5 I am annoyed when I cannot finish the online game. I feel frustrated when I cannot
complete the quest in the online game. I encourage friends and relatives to play
the online games that I am playing.
Online Games:
Very dissatisfied Very satisfied
1 2 3 4 5 Overall, how satisfied are you with your experience with playing the online game? Overall how satisfied are you with the online game server? Overall how satisfied are you with the online support in the game? Overall how satisfied are you with the quality of the online
game (e.g. graphics, controllers, and challenge.)? Overall how satisfied are you with log in and log out procedures for the online game? Overall how satisfied are you with the time that is required to download the online game? Overall how satisfied are you with your
performance in the online game?
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Very dissatisfied Very satisfied
1 2 3 4 5 Are you satisfied with the communication tool which is used to communicate with other players? Overall how satisfied are you with the internet connection?
How good are the following factors of an Online-role playing games MMORPG to you?
Bad Good
1 2 3 4 5 Game missions and quests in the online game. The story line of the
online game. Character levelling
and customization. The controller aspects of the online game (e.g. the method that is used to control your character in the online environment). How good is logging in factor in the online game? Ease of learning the
online game. The time that takes to load the online game. Online game graphics.
After I play a good online game:
Very Unlikely Very Likely
1 2 3 4 5 How likely are you to play the same online- role playing game again, if the opportunity arises? I will play the same
online-role playing
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Very Unlikely Very Likely
1 2 3 4 5 game again with my friends. How likely are you to play the same online- role playing game over the next week?
How long in years and months have you been playing MMORPGs?
How long do you usually play any MMORPG during the week? Please answer in terms of hours and minutes.
On which of the following game platforms do you usually play MMORPGs?
Consoles (e.g. Play station, Xbox)
Smart Phones
PC and laptops
IPad
Mobile devices (e.g. Nintendo 3D, PS Vita)
Others (please specify)
Which of the following game genera’s, you prefer to play the most? You can choose more than one.
Role playing games (RPGs). First shooter.
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Online-role playing games (MMORPG). Puzzles. Sports. Others (please specify)
The following are Massively multiplayer online role-playing games (MMORPGs), which game you play the most?
World of Warcraft (WoW) Guild Wars 2 Age of Conan: Unchained EverQuest II(EQ2) League of Legends (LoL) Final Fantasy XIV: Realm Reborn Others (please specify)
What is your gender?
Male Female
What is your country of residence?
USA India Others (please specify)
What is your age in years?
What is your level of education?
Less than high school. High school. Diploma. Bachelor degree
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Master’s degree Others (please specify)
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Appendix B: Factor Analyses Results of Study 1 (Indian players)
This appendix contains all the factor analysis results tables for the Indian Study
Table B- 1. Factor Analysis Results for Powerful feeling
EFA Factor % Total Construct KMO Construct Variable Loadings Variance Reliability Explained (Cronbach’s) α Feeling powerful 0.674 56.046 0.801 0.835 Powerful Feeling Strong 0.763 Feeling Feeling energetic 0.828 Feeling enthusiasm 0.690 Feeling excited 0.777
Table B- 2. Factor Analysis Results for Fun
EFA Factor % Total Construct KMO Construct Variable Loadings Variance Reliability Explained (Cronbach’s) α fun 0.685 52.182 0.815 0.901 new areas 0.703 Fun avatar 0.722 process 0.771 entertain 0.742 amused 0.709
Table B- 3. Factor Analysis Results for Imaginative Construct
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α picture 0.718 53.150 0.779 0.795 impossible 0.705 Imaginative after think 0.730 images 0.760 fantasies 0.731
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Table B- 4. Factor Analysis Results for Sensory Construct
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α move 0.684 52.301 0.694 0.759 faster 0.787 Sensory yell 0.706 facial 0.712
Table B- 5. Factor Analysis Results for Escapism Construct
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α away problems 0.791 61.737 0.689 0.665 Escapism another world 0.806 involved 0.760
Table B- 6. Factor Analysis Results for Negative Feeling
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α upset 0.751 54.420 0.790 0.801 Negative hate 0.716 Feeling mad 0.795 annoyed 0.703 frustrate 0.721
Table B- 7. Factor Analysis Results for Flow
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α competent 0.747 57.017 0.747 0.834 sense 0.752 Flow How well 0.718 focused 0.801
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Table B- 8. Factor Analysis Results for Immersion
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α part of 0.712 53.639 0.780 0.846 deeply 0.749 Immersion feel experience 0.760 got into 0.732 lose self 0.708
Table B- 9. Factor Analysis Results for Social
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α computer 0.675 53.589 0.783 0.896 enjoy others 0.765 Social at ease 0.736 share 0.736 comfort 0.746
Table B- 10. Factor Analysis Results for Satisfaction
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α server 0.769 52.095 0.768 0.848 support 0.723 Satisfaction game quality 0.673 tool 0.788 internet 0.646
Table B- 11. Factor Analysis Results for Intention to replay
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α likely play 0.833 66.486 0.747 0.686 Intention to play again 0.792 replay play next 0.820
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Appendix C: The Reliability (Cronbach's Alphas) for Study 1 (Indian players)
Table C- 1. Item-Total Statistics for Powerful
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted When I advance in online games I feel powerful. 20.56 8.876 .520 .274 .793 I feel strong when playing online games. 20.55 8.481 .617 .393 .771 I am energetic when playing online games. 20.58 8.143 .687 .486 .755 I am enthusiastic about playing the online game. 20.51 9.030 .500 .284 .798 I am excited when playing online games. 20.48 8.810 .635 .412 .769 Playing online games is an exciting experience. 20.42 9.213 .482 .254 .801
Table C- 2. Item-Total Statistics for Powerful after removal of an item
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted When I advance in online games I feel powerful. 16.36 6.394 .504 .260 .788 I feel strong when playing online games. 16.35 6.064 .601 .378 .757 I am energetic when playing online games. 16.38 5.740 .685 .479 .729 I am enthusiastic about playing the online game. 16.31 6.398 .519 .283 .783 I am excited when playing online games. 16.28 6.353 .619 .392 .754
Table C- 3. Item-Total Statistics for Fun
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted Playing online games is a fun activity. 20.29 9.171 .536 .304 .795 I enjoy exploring new areas within an online game. 20.38 9.029 .562 .333 .789 I enjoy designing my own avatar in the online game. 20.48 8.854 .575 .363 .786 Developing my character and making it powerful is an 20.37 8.938 .635 .412 .774 enjoyable process.
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I feel entertained when playing online games. 20.34 8.986 .601 .379 .781 I feel amused when I play a good online game. 20.44 8.738 .562 .337 .790
Table C- 4. Item-Total Statistics for Imaginative
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted Online games create a picture in my mind. 16.01 6.679 .541 .340 .742 I enjoy doing impossible things in a virtual world. 16.00 6.840 .527 .331 .747 After playing, I still think about the online game. 16.03 6.546 .551 .348 .739 After playing, the online game prompts images in my 15.97 6.584 .590 .363 .726 mind. After playing a good online game, I still fantasise about 16.01 6.714 .556 .347 .737 it.
Table C- 5. Item-Total Statistics for Sensory
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted While playing online games I keep moving part of my body 11.75 4.256 .437 .201 .656 (e.g. head or foot). When Playing good online games my heartbeat 11.65 4.065 .554 .307 .580 becomes faster. While playing I yell from excitement. 11.78 4.411 .458 .217 .641 My facial expressions changes while playing an 11.74 4.295 .464 .223 .637 online game.
Table C- 6. Item-Total Statistics for Escapism
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted Playing games online gets me away from all the 7.82 2.218 .510 .265 .590 problems that I have. Playing online games makes me feel like I am in another 7.70 2.323 .531 .283 .563 world. I get so involved when I play that I forget everything else. 7.85 2.420 .473 .225 .635
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Table C- 7. Item-Total Statistics for Negative Feeling
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted When I lose in online games 14.35 10.454 .587 .382 .744 I become upset. I hate losing in the online 14.30 10.153 .544 .335 .759 game. I feel mad when I don’t win. 14.63 9.385 .639 .431 .725 I am annoyed when I cannot 14.15 10.816 .527 .372 .763 finish the online game. I feel frustrated when I cannot complete the quest in 14.26 10.745 .547 .378 .757 the online game.
Table C- 8. Item-Total Statistics for Immersion
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted I felt as if I were part of the online game. 15.81 6.629 .530 .302 .747 I felt deeply about the online game that I play. 15.81 6.547 .578 .345 .732 I will feel the experience of the online game that I play 15.93 6.276 .590 .353 .727 for a while. I “got into” the online game that I play. 15.91 6.664 .561 .319 .738 I lose myself in the online game, when I am 16.13 5.985 .531 .293 .752 experiencing it.
Table C- 9. Item-Total Statistics for Flow
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted I feel I am competent enough to meet the high demands of 16.08 5.920 .536 .301 .744 the online game. I have a strong sense of what I want to do in the 16.07 5.935 .587 .362 .726 online game. I have a good idea while I am playing, about how well I 15.96 6.351 .514 .293 .750 am doing. When playing online games, I become completely focused 16.06 5.940 .605 .379 .720 on the task at hand. When playing online games, I have a feeling of total 16.03 6.175 .523 .287 .747 control over the game.
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Table C- 10. Item-Total Statistics for Flow after removal of an item
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted I feel I am competent enough to meet the high demands of 12.06 3.622 .534 .296 .695 the online game. I have a strong sense of what I want to do in the 12.05 3.762 .541 .305 .690 online game. I have a good idea while I am playing, about how well I 11.94 4.015 .496 .275 .713 am doing. When playing online games, I become completely focused 12.03 3.666 .599 .368 .657 on the task at hand.
Table C- 11. Item-Total Statistics for Social
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted I enjoy being a part of a community. 19.99 9.019 .573 .333 .783 I always prefer to play online games with other people than playing against the 20.05 9.680 .500 .271 .798 computer (non-person characters). Playing online games with others is more enjoyable 19.97 8.776 .621 .388 .772 than playing alone. I am more at ease playing online games together with 20.02 8.902 .579 .342 .782 other people. I enjoy sharing my experience of the online 20.00 8.890 .582 .351 .781 game with other players. Playing side by side with other players is more comfortable than playing 19.99 9.041 .586 .356 .780 alone.
Table C- 12. Item-Total Statistics for Social after removal of an item
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted I always prefer to play online games with other people than playing against the 16.03 6.565 .495 .265 .763 computer (non-person characters). Playing online games with others is more enjoyable 15.96 5.885 .597 .361 .730 than playing alone.
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I am more at ease playing online games together with 16.01 5.956 .564 .326 .741 other people. I enjoy sharing my experience of the online 15.98 5.956 .564 .333 .741 game with other players. Playing side by side with other players is more 15.97 6.062 .574 .341 .738 comfortable than playing alone.
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Appendix D: Covariance’s Modification for Negative Feelings (Indian Players)
Table D- 1. Covariance’s Modification Indices of the First Specified Model of Negative Feeling for India Players
M.I. Par Change e4 <--> e1 7.381 -.104 e4 <--> e2 4.573 -.095 e5 <--> e3 4.855 -.091 e5 <--> e4 45.932 .275
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Appendix E: Factor Analysis related to Study 2 (US Players)
Table E- 1. Factor Analysis for Powerful Feeling
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α powerful 0.777 62.728 0.845 0.809 feel strong 0.739 Powerful energetic 0.775 Feeling enthusiasm 0.842 excited 0.824
Table E- 2. Factor Analysis for Fun
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α fun 0.836 62.557 0.838 0.856 new areas 0.826 Fun avatar 0.656 process 0.807 entertain 0.816
Table E- 3. Factor Analysis for Imaginative
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α picture 0.649 64.035 0.810 0.753 after think 0.863 Imaginative images 0.874 fantasise 0.795
Table E- 4. Factor Analysis for Sensory
EFA Construct Variable Factor Construct KMO % Total Loadings Reliability
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Variance (Cronbach’s) Explained α move 0.642 55.721 0.729 0.750 faster 0.778 Sensory yell 0.796 facial 0.761
Table E- 5. Factor Analysis for Escapism
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α away problems 0.836 64.786 0.722 0.661 Escapism another world 0.834 involved 0.741
Table E- 6. Factor Analysis for Negative Feeling
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α upset 0.854 67.681 0.880 0.828 hate 0.788 Negative mad 0.860 Feeling annoyed 0.802 frustrate 0.807
Table E- 7. Factor Analysis for Immersion
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α part of 0.733 58.422 0.817 0.819 deeply 0.802 Immersion feel experience 0.809 got into 0.775 loses self 0.697
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Table E- 8. Factor Analysis for Flow
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α competent 0.806 60.816 0.782 0.783 sense 0.780 Flow how well 0.800 focused 0.731
Table E- 9. Factor Analysis for Social
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α computer 0.788 62.442 0.848 0.854 enjoy others 0.792 at ease 0.828 share 0.709 comfort 0.828
Table E- 10. Factor Analysis for Satisfaction
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α server 0.773 51.393 0.758 0.802 support 0.735 Satisfaction game quality 0.715 tool 0.734 internet 0.618
Table E- 11. Factor Analysis for Intention to Replay
EFA Construct KMO % Total Construct Variable Factor Reliability Variance Loadings (Cronbach’s) Explained α likely play 0.898 78.306 0.859 0.729 Intention to play again 0.862 replay play next 0.894
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Appendix F: The Reliability (Cronbach's Alphas) for Study 2 (US players)
Table F- 1. Item-Total Statistics for Powerful.
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted When I advance in online 15.61 8.295 .657 .469 .812 games I feel powerful. I feel strong when playing 15.82 8.267 .608 .427 .828 online games. I am energetic when playing 15.61 8.620 .630 .423 .819 online games. I am enthusiastic about 15.26 8.985 .714 .602 .802 playing the online game. I am excited when playing 15.35 8.788 .684 .571 .806 online games.
Table F- 2. Item-Total Statistics for Fun
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted Playing online games is a 17.09 6.586 .701 .539 .790 fun activity. I enjoy exploring new areas 17.06 6.586 .695 .502 .792 within an online game. I enjoy designing my own 17.37 6.442 .511 .270 .854 avatar in the online game. Developing my character and making it powerful is an 17.22 6.484 .678 .464 .795 enjoyable process. I feel entertained when 17.09 6.909 .677 .501 .799 playing online games. .
Table F- 3. Item-Total Statistics for Imaginative
Cronbach's Scale Mean if Scale Variance if Corrected Item- Squared Multiple Alpha if Item Item Item Deleted Item Deleted Total Correlation Correlation Deleted Online games create a 10.67 7.485 .460 .231 .832 picture in my mind. After playing, I still think 10.91 6.084 .712 .576 .720 about the online game. After playing, the online game prompts images in my 11.01 5.875 .731 .576 .709 mind. After playing a good online game, I still fantasise about 11.16 6.124 .619 .405 .767 it.
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Table F- 4. Item-Total Statistics for Sensory
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted While playing online games I keep moving part of my body 9.88 8.186 .419 .179 .728 (e.g. head or foot). When Playing good online games my heartbeat 9.58 7.829 .556 .336 .648 becomes faster. While playing I yell from 10.03 6.959 .579 .353 .632 excitement. My facial expressions changes while playing an 9.24 8.277 .540 .295 .661 online game.
Table F- 5. Item-Total Statistics for Escapism
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted Playing games online gets me away from all the 7.05 3.495 .583 .368 .583 problems that I have. Playing online games makes me feel like I am in another 6.80 3.951 .585 .364 .593 world. I get so involved when I play 7.48 3.788 .472 .223 .724 that I forget everything else.
Table F- 6. Item-Total Statistics for Immersion
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted I felt as if I were part of the 15.33 8.657 .586 .360 .787 online game. I felt deeply about the online 15.34 8.526 .653 .467 .767 game that I play. I will feel the experience of the online game that I play 15.22 8.660 .658 .484 .766 for a while. I “got into” the online game 14.87 9.300 .621 .415 .780 that I play. I lose myself in the online game, when I am 15.36 8.567 .542 .311 .804 experiencing it.
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Table F- 7. Item-Total Statistics for Flow
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted I feel I am competent enough to meet the high demands of 12.17 3.746 .622 .398 .710 the online game. I have a strong sense of what I want to do in the 12.21 3.790 .588 .360 .728 online game. I have a good idea while I am playing, about how well I 12.15 4.021 .618 .383 .717 am doing. When playing online games, I become completely focused 12.39 3.828 .531 .289 .760 on the task at hand.
Table F- 8. Item-Total Statistics for Social
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted I always prefer to play online games with other people than playing against the 15.38 10.245 .655 .437 .819 computer (non-person characters). Playing online games with others is more enjoyable 15.11 11.067 .663 .443 .817 than playing alone. I am more at ease playing online games together with 15.46 10.319 .711 .522 .803 other people. I enjoy sharing my experience of the online 15.26 11.682 .563 .320 .841 game with other players. Playing side by side with other players is more 15.52 9.986 .706 .522 .804 comfortable than playing alone.
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Table F- 9. Item-Total Statistics for Satisfaction
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted Online Games:-Overall how satisfied are you with the 16.04 6.935 .597 .373 .690 online game server? Online Games:-Overall how satisfied are you with the 16.28 6.502 .540 .336 .710 online support in the game? Online Games:-Overall how satisfied are you with the quality of the online game 15.79 7.473 .531 .282 .715 (e.g. graphics, controllers, and challenge.)? Online Games: -Are you satisfied with the communication tool which is 16.03 6.837 .551 .306 .705 used to communicate with other players? Online Games:-Overall how satisfied are you with the 16.00 7.302 .428 .203 .749 internet connection?
Table F- 10. Item-Total Statistics for Intention to Replay
Corrected Item- Squared Cronbach's Scale Mean if Scale Variance Total Multiple Alpha if Item Item Item Deleted if Item Deleted Correlation Correlation Deleted After I play a good online game: -How likely are you to play the same online-role 8.68 2.415 .759 .585 .783 playing game again, if the opportunity arises? After I play a good online game:-I will play the same 8.83 2.217 .699 .489 .839 online-role playing game again with my friends. After I play a good online game: -How likely are you to play the same online-role 8.74 2.251 .749 .575 .787 playing game over the next week?
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Appendix G: Covariances and Correlations for Study 2 (US players)
Table G- 1. Correlation between items For Model One in Chapter 5
M.I. Par Change e4 <--> e2 37.576 -.052 e4 <--> e3 18.401 .061 e5 <--> e3 19.760 .056 e5 <--> e4 24.664 .060
Table G- 2. Correlations between positive feelings constructs
Escape Sensor Imaginative Fun Powerful Escape Pearson Correlation 1 .457** .511** .412** .517** Sig. (2-tailed) .000 .000 .000 .000 N 425 425 425 425 425 Sensor Pearson Correlation .457** 1 .446** .241** .445** Sig. (2-tailed) .000 .000 .000 .000 N 425 425 425 425 425 Imaginative Pearson Correlation .511** .446** 1 .420** .556** Sig. (2-tailed) .000 .000 .000 .000 N 425 425 425 425 425 Fun Pearson Correlation .412** .241** .420** 1 .694** Sig. (2-tailed) .000 .000 .000 .000 N 425 425 425 425 425 Powerful Pearson Correlation .517** .445** .556** .694** 1 Sig. (2-tailed) .000 .000 .000 .000 N 425 425 425 425 425 **. Correlation is significant at the 0.01 level (2-tailed).
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