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Immersion and Valence: An Empirical Study on the Effects of

Negative Valence Immersion within the Movie Industry

Jeremy Fannin University of Amsterdam

MSc Business Administration; Marketing Track Master Thesis 6314M0252Y

26 January 2018

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Statement of Originality

This document is written by Jeremy Fannin who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion

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Abstract

Virtual Reality has been an emerging piece of disruptive technology in consumer markets as of late. However, very little formal academic research has been conducted in regards to immersive technology and consumer behavior. This study delves into consumers’ intentions when adopting this type of immersive experience. Specifically, this study tests those intentions in the context of Virtual Reality movies vs. the Traditional 2D non-immersive movies. Additionally, cognitive defenses (i.e. affect valuation) are explored as possibly effecting those intentions based on the emotional valence of the VR material. The findings prove that a negative Virtual Reality movie produces significantly lower intentions as compared to both a negative Traditional movie and a positive Virtual Reality movie. Additional findings include a strong effect dealing with the preference of the genre of the stimuli. This study holds important implications for both academics and managers as this piece of technology is constantly being utilized within several fields. Within the entertainment industry, this study helps managers adapt their approach of how to position their Virtual Reality material that will result in higher intentions from their target audience. As Virtual Reality is being explored more and more within the academic context, this study helps lay the framework for future research within consumer behavior.

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Table of Contents

1. Introduction 4 2. Literature Review 7 2.1. Virtual Reality 7 2.2. Cognitive Defenses 12 3. Conceptual Framework 15 4. Methodology 15 4.1. Design 16 4.2. Pre-test 16 4.3. Participants 18 4.4. Independent Variable 18 4.4.1. Movie Types 18 4.4.2. Emotional Valence 20 4.5. Dependent Variables 22 4.5.1. Intentions 22 4.6. Other Variables 24 4.7. Procedure 26 5. Results 27 5.1. Descriptive Results 27 5.2. Manipulation Check 28 5.3. Correlation Analysis 30 5.4. Hypothesis Tests 31 5.4.1. Hypothesis 1 31 5.4.2. Hypothesis 2 32 5.4.3. Additional Findings 34 6. General Discussion 36 6.1. Implications 37 6.2. Limitations 39 6.3. Future Research 40 7. Reference List 41 8. Appendix 46

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1. Introduction

Companies can gain and maintain competitive advantages by incorporating dynamic capabilities and continuously innovating or adopting new, disruptive technology (Day 2011; 1994, and Morgan, 2012). Companies that are proactive (addressing latent, future needs) and still meet current demands (i.e. reactive), are the ones that can generate revenue above and beyond the industry standards (Herhausen, 2016). Additionally, companies are suggested to strive to generate blue oceans that create new demand and compete in new market space (Kim and Mauborgne, 2005). Virtual reality has the potential to be that piece of disruptive technology where companies, specifically entertainment production companies, can adopt early on and gain a competitive advantage over the rest of the industry. In 2018, Virtual Reality and Augmented Reality sales are expected to double, reaching totals of $17.8 billion (USD) (Shirer and Torchia, 2017). In regards to movies, global box office revenues increased in 2016 reaching $38.6 billion (USD), while U.S and Canadian box office revenues increased to $11.4 billion (USD) (MPAA, 2017) These two industries combined generate a massive amount of revenue worldwide. Therefore, it is important to determine the implications for production companies who would pursue possibly creating a new experience for their audience.

Movie companies constantly search for the next box-office hit, and constantly strive to set themselves apart from their competitors by advancing their capabilities and knowledge (i.e. Avatar bringing back and improving 3D filming). Throughout the years, movies have adapted and changed with the preferences of the audience watching such as, shortening shots per frame, different patterns of shots to keep the attention of the audience, and including more motion and action for the younger audience (Miller, 2014). Several Hollywood directors have expressed interest in Virtual Reality as a new medium of movies, including Steven Spielberg, who is

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currently working on a Sci-fi VR movie himself (Child, 2016). Even with the early steps of developing a VR movie, Spielberg understands the industry is stepping into unfamiliar territory stating:

“I think we’re moving into a dangerous medium with virtual reality. The only reason I say it is dangerous is because it gives the viewer a lot of latitude not to take direction from the storytellers but make their own choices of where to look. I just hope it doesn’t forget the story when it starts enveloping us in a world that we can see all around us and make our own choices to look at” (Child, 2016).

Even with the acknowledgement and cautionary preparations from directors, very little formal academic research has been conducted in the entertainment industry in regards to incorporating virtual reality as a piece of innovative technology. This technology has become increasingly popular in the retailing and medical (i.e. surgical) contexts, as well as an effective piece of technology used for military training and operations (Goel and Prokopec, 2009; Herpen et al. 2016; Gallagher et al. 2005; Locketz et al. 2017; Seymour et al. 2002; Poushneh and Parraga, 2017; Margee, 2011). Prior research in medicine and retail use VR for purely utilitarian (functional) purposes, stating an increase in consumer purchase intentions in retailing and increase satisfaction in helping with various operations within the medical industry (Goel and Prokopec, 2009; Herpen et al. 2016); Gallagher et al. 2005; Locketz et al. 2017; Seymour et al. 2002; Poushneh and Parraga, 2017). However, more research must be done to determine consumer preferences in regards to purely entertainment purposes of human immersion (i.e. movies). Therefore, one goal of this study is to assess the adoption of VR versus non-VR/ Traditional movies. Several scholars have used various models (i.e. Technology Acceptance Model) to explain and predict behavior of the possible adoption of this technology (Davis et al. 1989; Mathieson, 1991; King and He, 2006; Venkatesh,

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2000; Davis and Venkatesh, 2000). However, most previous applications focus on the utilitarian aspects of technology, mainly information systems (Venkatesh, 2000; Davis and Venkatesh, 2000). With an industry based purely on entertainment, this paper explores the effects of cognitive defenses when determining consumers’ intentions to use such a piece of technology, instead of predicting behavior based on functional determinants.

Several scholars have explored the field of cognitive defenses on a wide variety of topics. In particular, this paper will study two main cognitive defenses when it comes to affect and technology. In regards to realism in technology, the Uncanny Valley is typically used as an explanation for why consumers’ feel uncomfortable when robotics move closer to human likeness on a certain spectrum (Mori, 1970). In this study, the goal is to create an immersive feeling that is too uncomfortable and too real for the participants, hopefully eliciting a similar cognitive defense as those explained in the Uncanny Valley. Additionally, scholars have explained that consumers ideal affect varies from their actual affect (Tsai, Knutson, and Fung, 2006). Essentially, consumers tend to avoid negative emotions or situations because they ideally and actually want to feel positively (Sims et al. 2015). Resultantly, the main goal of this study is to elicit those cognitive defenses to the point that participants are less willing to attend a negative valence movie. Additionally, extensive observation will be conducted to determine if this effect is heightened by the incorporation of immersive technology, such as Virtual Reality. Therefore, the research question for this study that will set a foundation for the rest of the paper is as follow:

What are the limits of VR-based movie immersion – consumer defenses in experiences negative (versus positive) movie scenes? In other words, how does movie scene valence effect consumers’ intentions to adopt a virtual reality movie?

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This paper is organized in the following way. First, a detailed literature review is conducted and presented. The literature review will incorporate various articles of Virtual Reality and Immersive Technology, and various cognitive defenses consumers experience when dealing with negative emotions and realism. Following, the conceptual model used in this study is constructed and discussed in its entirety. Third, the complete methodology is provided including, the pretest, independent and dependent variables, procedure, and design. The results are then discussed, as tests were conducted to search to see if the hypotheses formulated in the literature review can be supported or rejected. Finally, a discussion section concludes this paper, where the limitations, implications, and areas of further research are presented.

2. Literature Review

This following section is organized in a way to establish a clear foundation of previous literature covering the topic of disruptive technology, specifically virtual reality. First, a clear and understandable definition of virtual reality will be presented and analyzed. Additionally, different articles of consumer perceptions of virtual reality in different industries are assessed. Finally, articles covering cognitive defenses when negative information and emotions are presented, as well as the effects of realism in technology.

2.1 Virtual Reality

Virtual Reality (VR) recently has been disrupting several industries with a new form of technology that carries new implications that has not been dealt with before. Even though several scholars have been defining and testing various implications as far back as the 1990’s, little consensus has been reached on the exact implications of this new disruptive technology. Therefore, this area of study is relatively fresh. In the most technical aspect, VR can be defined as “allowing one to create

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a complex immersive environment of high ecological validity (the user is surrounded by the environment), in which the participants are presented with and manipulate a variety of 3D stimuli under controlled conditions” (Kozhevnikov and Dhong, 2012). Additionally, Steuer (1992) was one of the first scholars to attempt to develop a conceptual definition of VR that could be used to analyze this medium and adapt it as technological innovators become increasingly more efficient at utilizing this technology.

Steuer (1992) argued that VR is not just a technological advancement, however, it should be defined as a human experience. VR stimulates several human senses in order to develop a feeling of immersion where people can feel a true presence in a virtual environment (i.e. feeling they are part of the movie) (Magee, 2011). Establishing the human experience aspect to VR adds a component to the definition in which several scholars failed to address, presence. Steuer (1992) defined telepresence as “the experience of presence in an environment by means of a communication method.” Additionally, he states “vividness” and “interactivity” are the two key dimensions of VR. Furthermore, telepresence, depending on the degree of each dimension, will determine the level of immersion the consumer experiences in the virtual environment (Steuer, 1992). For example, a 3D virtual reality ride is highly vivid, making you feel like you actually stepped into that world. However, such rides are relatively low in interactivity, whereas virtual worlds such as World of Warcraft are the opposite (i.e. Low in vividness but high in interactivity). Even though this definition is more than twenty years old, the content and core meaning is relevant for the constant progression of technology advancement. Full human experience is something that has been agreed upon from several scholars in their studies of VR, even if the concept has switched from computer generated environments (Goel and Prokopec, 2009) to a full immersion in a virtual environment with consumer interaction (Herpen, Broek, Trijp and Yu, 2016; Poushneh and

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Parraga, 2017). However, one thing that has varied extensively is the implications and perceptions of consumers about being immersed in a virtual world.

Extensive research of VR focuses around medicine, especially helping doctors diagnose and assist with surgery (Seymour et al, 2002; Gallagher et al, 2005; Locketz et al, 2017), as well as military training and operations (Magee, 2011). However, within the consumer markets, scholars have explored the use of VR and other virtual worlds on consumer perceptions in retailing (Goel and Prokopec, 2009; Herpen et al, 2016; Poushneh and Parraga, 2017). Essentially, they explore the possibilities of using this disruptive technology to enhance consumers purchase intentions before they enter the physical store by creating a virtual store that allows them to replicate the shopping experience. Goel and Prokopec (2009), set up a virtual world in order to attract customers to different stores and increase their real-life willingness to buy and their overall satisfaction. Even though this strategy was not full immersion, it still dealt with a high level of interactivity, as Steuer (1992) stated as one of the important dimensions of telepresence. They found that consumers reported higher trust, better product description, and more informativeness for traditional websites than these virtual worlds (Goel and Prokopec, 2009). In other words, consumers felt uncomfortable in a virtual environment when it came to searching and purchasing real life brands. However, at the time of this study, VR headsets and full immersion to create human experiences was relatively inefficient, even though Virtual Reality has been around for years (VRS, 2017). This study set the precedent for how quickly technology changes, and how important it is to continuously study disruptive technology as new innovation is introduced constantly.

Newer research shifted focus from traditional computer mediated, 3D non-immersive environments to a full consumer immersion by introducing VR and Augmented Reality (AR). Unlike the virtual worlds discussed earlier, AR was found to actually increase user experience by

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increasing customer satisfaction and willingness to pay by offering virtual information and 3D pictures of products that best represents the actual shopping experience (Poushneh and Parraga, 2017). Similar to AR, VR is found to increase that feeling of presence in which normal marketing ads with pictures or videos fail to create (Herpen et al. 2016). Additionally, VR is able to recreate an environment that is comparable to the physical store that allows consumers to replicate in-store behavior through virtual experience, while increasing certain purchases in the lab environment (Herpen et al. 2016). In simpler terms, a Virtual Reality environment is found to be more similar to the physical store itself, as compared to traditional 2D non-immersive ads. In terms of amount purchased, Virtual Reality was found to have mixed results. Certain items (i.e. milk) were found to produce similar amounts purchased as compared to the physical store, while others (i.e. fruit and vegetable) increase in the amount purchased (Herpen et al. 2016).

In regards to new innovations in general, consumers tend to upgrade if they feel the innovation adds monetary value, emotional value, and/or are perceived as higher in usefulness (Tseng and Chiang, 2013). Typically, consumers will avoid buying an upgrade if they perceive the gap between innovation and value too similar (Tseng and Chiang, 2013). In other words, if the consumer does not see the usefulness or thinks the upgrade is not entirely different, they are reluctant to purchase the new innovation of the product. Furthermore, it is found that perceived enjoyment was the most significant factor to influence the consumers’ decision to upgrade (Tseng and Chiang, 2013). 3D movies are typically that next innovation aside from the traditional 2D movie. Immersion in movies comes in various stages (i.e. 2D non-immersive, 3D non-immersive, and 3D immersive) (Kozhevnikov and Dhong, 2012). However, 3D movies have not produced the numbers production companies may hope. Out of the $11.4 Billion total box office sales in 2016 for the US and Canada, only 14% was accounted for by 3D movies (MPAA, 2017). The peak of

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3D movies came in 2010 after the success of Avatar in 2009 (MPAA, 2017; IMDb, 2018). Since then, 3D movies have become stagnant, leaving very little promise in the US and Canada market. In addition, it is often found that frequent moviegoers (79%) own more than four pieces of technology, compared to the 60% of the total adult population (MPAA, 2017). Therefore, frequent moviegoers tend to be more willing to adopt new technology, especially new innovative experiences. Based on the prior research and statistics given by the Motion Picture Association of America, specifically with frequent moviegoers more technological adept, we hypothesize:

H1: Overall, a virtual reality movie will generate a significantly higher level of consumers’ willingness to attend the movie compared to that of a traditional movie.

Little is known how this type of technology can impact hedonic industries such as the entertainment industry (i.e. movies), or the tourism industry (i.e. theme parks or museums). Previous research revolves around the aforementioned industries such as medicine and retail. However, within an industry like entertainment, the desired outcome is not a product itself, but truly the experience. Since VR immerses people into a world to create that human experience, it is important to observe the behavior and intentions of people while using this technology in an industry that is all about entertaining the people. Several models have been theorized and applied to technology, specifically information systems, in order to predict the consumers’ acceptance of the new innovation. Specifically, the Technology Acceptance Model (TAM) is used by several scholars in research of disruptive technology. This model is based on two determinants (usefulness and ease of use) mediated by intentions, resulting in the ultimate behavior. This model states that a consumers perceived ease of use and perceived usefulness will impact their intention of the technology. Ultimately, this model helps predict the consumers’ behavior to use a piece of technology (Davis, Bagozzi, and Warshaw, 1989). Several scholars have expanded this model to

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further and more accurately predict those behaviors (King and He, 2006; Venkatesh, 2000; Venkatesh and Davis, 2000). However, as stated above, this model is typically used in the acceptance of information systems, not disruptive technologies like VR or AI. Therefore, research that is more in line with affect valuation or realism is a more appropriate approach in determining the consumers’ intentions of the acceptance of immersive technology. With that being said, there is a lack of research regarding the moderating and mediating factors of VR specifically when it comes to positive (i.e. surfing with Lilo and Stitch in Hawaii) vs. negative (i.e. Landing on the beaches of Normandy on D-Day) valence VR movies.

2.2 Cognitive Defenses

Naturally, consumers tend to avoid negative experiences or information when using a product or performing a task (Bastian et al. 2015; Sims et al. 2015; Yang et al. 2017). Based on the affect valuation theory, how consumers want to feel will eventually determine how consumers actually feel (Sims et al. 2015; Tsai et al. 2006). In other words, consumers will use certain products, interact with others in a certain way, and may avoid certain situations in order to achieve their goal state of emotional feeling (Bastian et al. 2015; Sims et al. 2015), since it is found that consumers ideal affect often differs from their actual affect (Tsai et al. 2006). Therefore, in certain situations and cultures, consumers tend to strive to maximize the positive while minimizing the negative, essentially eliminating mixed affective experiences (Sims et al. 2015). In simpler terms, American consumers or those of independent mindsets will avoid negative experiences and increase positive experiences because these consumers tend to strive for a positive affect in their daily lives (Sims et al. 2015), or feel pressure from social expectations not to feel negative emotions (Bastian et al. 2015).

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Some cognitive defenses arise when negative information is presented about a company. In a sense, a consumers’ level of involvement with a company can act as a buffer when moderately negative information is brought about against the company itself (Einwiller et al. 2006). For example, if a report comes out against Disney stating moderately negative information (i.e. they discriminate when recruiting their princesses), some customers (i.e. weak identifiers) will have negative responses to these reports and may decrease their purchase intentions. However, those that are highly involved and identify strongly with the company will still develop positive thoughts and positive behavioral intentions in the face of negative information (Einwiller et al. 2006). It is still unknown whether prior involvement with this type of technology (i.e. Virtual Reality), will help overcome the cognitive defenses elicited when a negative valence movie scene is presented, resulting in a higher enjoyment level by the consumer.

The Uncanny Valley, another cognitive defense, is typically associated with the acceptance of technology that is too realistic for the consumer (i.e. robotics). Mori (1970) was the first to introduce this theory with robotics, based on the physical features and movements. He found that as a robots’ appearance moves closer to human-likeness, the likeability increases. However, once the robot’s appearance reaches a certain point on the spectrum, it is regarded as too strange and the consumers often avoid the robots, calling this the “Uncanny Valley.” Other studies have continued based on the ground work Mori (1970) set and expanded the theory into virtual environments. Intuitively, videos of humans are rated more favorably and human-like as compared to both full animation and upper facial removed animation videos, with the latter being rated as the lowest of all three (Tinwell et al. 2011). The stimuli presented in this current study is based on live human actors. Therefore, based on the previous research, one could believe that the uncanny valley will not have an effect on the participants’ cognitive defenses. However, Tinwell et al.

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(2011) continued to include emotions into their study. Findings prove that different emotions shown elicit different levels of uncanniness, specifically fear and happiness being the most uncanny in animated stimuli (Tinwell et al. 2011). These findings lay an interesting path for the overall theme of this study, linking it back to the main research question set earlier.

Even with the prior research on emotions, affect, and uncanniness, little research was conducted on full immersive environments and whether the valence of such an environment will elicit an uncomfortable state in which the consumer will avoid. Additionally, prior research into movies describes a successful movie in terms of star power, famous directors, genre (drama being the largest indicator of a successful movie), and a mixture of these categories, including teamwork between stars and directors (Lash and Zhao, 2016). Furthermore, Kristofferson, Daniels, and Morales (2016) have found that Virtual Reality exhibits significantly higher positive emotional responses. It is also found that technology that immerses or transports the consumer will increase sales for a given product (Escalas, 2004). However, Virtual Reality does elicit higher protective responses than 2D material (Kristofferson et al. 2016), which could affect the consumers’ willingness to attend a negative valence movie. Therefore, based on the previous literature, especially on the affect valuation theory, we hypothesize:

H2a: A positive valence Virtual Reality movie will generate positive effect on consumers’ intentions to attend such a movie.

H2b: A negative valence Virtual Reality movie will generate a negative effect on consumers’ intentions to attend such a movie.

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3. Conceptual Framework

Based on prior literature and developed hypotheses, a conceptual model was produced to set the framework for the following study. The model is organized as follows:

Movie types (i.e. Virtual Reality and Traditional movies) are the independent variable for this study, while the respondents’ intentions to attend these movies are being measured as the dependent variable. It is further predicted that emotional valence (i.e. positive and negative movie scenes) will moderate this relationship. As stated above, it is predicted that Virtual Reality movies, in total, will have a greater effect on consumers’ intentions. Additionally, it is also predicted that a positive valence Virtual Reality movie will generate a positive effect on intentions, while the opposite is true for a negative valence Virtual Reality movie. In the following section, a discussion of how the aforementioned variables were operationalized will be presented.

4. Methodology

In the following section, the methodology used in this study will be discussed. The pre-test design and analysis is covered, as well as the changes reached in regards to the findings during the pre-testing. The design, along with the description of the participants of the main study will then be presented. Additionally, the independent, dependent, and control variables will be acknowledged,

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with the descriptions of how each was operationalized throughout the testing. Finally, the procedure of the main study is formulated at the end of this section.

4.1 Design

The current study is a 2 (traditional vs. virtual reality movies) x 2 (positive vs. negative movie scenes) between-subject factorial design. 50 participants per cell will be used, resulting in a total of 200 participants for this study. Participants will be randomly allocated to one of four conditions, negative virtual reality movie, negative traditional movie, positive virtual reality movie, or positive traditional movie. The independent variables of this study will be movie types with a moderating effect from emotional valence (see 4.4 Independent Variables). The dependent measure of this study will be consumers’ intentions (see 4.5 Dependent Variables). Ultimately, we will be measuring the consumers’ intentions based on different levels of immersion into a movie. We are looking for whether movie scene valence (i.e. concentration camps vs. a hero movie), will elicit any cognitive defenses that will affect the consumers’ willingness to attend a Virtual Reality movie.

4.2 Pretest

A pretest was conducted in order to verify the emotional valence of the stimuli in this study was manipulated and operationalized appropriately (see 4.4 Independent Variables for a detailed overview of the operationalization of the independent variables). 22 participants completed the pretest, and were randomly allocated to the two different conditions (i.e. The Lost Souls and Untold Heroes). The respondents ranged in age from 18 to 30, with 59% being female and 41% being male.

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Emotions is often a complex and dynamic measurement in academic research. Several scholars have attempted to measure emotions in a multitude of ways, including, but not limited to, self-assessment surveys (Richins, 1997; Soleymani et al. 2008), facial recognition (Hildebrandt et al. 2012), and human brain EEG’s (Bekkedal et al. 2011). In the pretest, and in the current study, we used a similar measure to Richins (1997). Two separate methods in which he employed was utilized. First, the respondents were asked to indicate their emotions on a 7-point Likert Scale (extremely negative vs. extremely positive). In addition, the respondents were asked to describe the emotions elicited in an open-ended question. Including this measurement allowed us to determine the precise aspects of the material that elicited the emotions. We also included a measurement of intentions to determine the initial intentions prior to the main study.

According to the results, 72.72% of the participants in the Lost Souls condition found the movie poster and description negative, while all 100% of the participants in the Untold Heroes condition found the stimuli positive. Individual sample t-tests were conducted to analyze if there were any statistical significance between the two conditions. The results showed no statistical difference between both Untold Heroes and The Lost Souls in regards to intentions, emotional intensity, and level of immersion. However, due to the observed directionality, changes were made to tone down the effect of Untold Heroes, in order to allow room for a Virtual Reality condition. The poster was toned down based on the open-ended answers. Several were based on a bright background, therefore, storm clouds were added in order to make the poster darker and less positive in nature. Therefore, the stimuli in this study will be presented as slightly negative (i.e. The Lost Souls) and slightly positive (i.e. Untold Heroes), which will hopefully leave room for the effects of VR.

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4.3 Participants

The subjects of this study are 200 students and faculty members at a Midwest Division II University in the United States. The ages ranged from 18-76, age is discussed further in 4.6 Other

Variables. We focused mainly on students (i.e. 18-25) because according to the Motion Picture

Association of America, this age group went to the movie an average of 6.5 times in 2016, which makes them the most susceptible to new movie releases. Permission was granted by the human resource department of the university and a copy of the survey was presented to the Human Resource Director, in order to gain approval to conduct the study on the various students attending the university. Four professors from the faculty of Economics and Business were asked to help administer the survey to their respected classes. If the students so happened to be in multiple classes with the same professor or with another professor that was administering the survey, they were asked to only participate once. The professors were thanked and offered a gift for their help.

4.4 Independent Variables

The independent variables used in this study are movie types and emotional valence, with emotional valence predicted as the moderator in the relationship between the independent and dependent variable.

4.4.1 Movie types

Qualitatively, Virtual Reality movies and Traditional Hollywood movies vary on several fundamental aspects of filmmaking. Mainly, Virtual Reality movies allow the viewer to be in control of where they look, as compared to Traditional movies that use attention cues (i.e. close up shots) to force the viewer to watch where the action is (Schwartzel, 2016). In other words, Virtual Reality movies must use other cues to draw the audience’s attention toward the necessary

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part of the film. Ultimately, the scripts must be adjusted to view the audience almost as another actor within the film. Additionally, Virtual Reality movies only last a few minutes in length and have slower shots as compared to longer feature length films, due to motion sickness commonly attributed to Virtual Reality experiences (Schwartzel, 2016; Marantz, 2016). Moreover, while Traditional movies are typically shot with several cameras, often with big named stars on elaborate sets and exotic locations, the equipment used in the making of Virtual Reality is significantly different. A 360° camera must be utilized (Millane, 2015). While using this piece of technology, a few things are different from traditional movies. One, while shooting a film in 360°, the crew must not be present behind the camera like that of traditional movies, since the audience is able to see everything around them (Millane, 2015). And two, the location of the camera must be positioned in a way that the audience feels that they are an actor within the movie itself (Schwartzel, 2016; Millane, 2015), unlike the different camera positions of a traditional movie.

Taking into account the budget constraints, time constraints, and practicality of this current study, various movie trailers depicting both a Traditional movie and a Virtual Reality movie, as described above, were unable to be generate. However, the different movie types were operationalized in a variety of ways. Two different types of fictitious movie posters, Untold Heroes and The Lost Souls (Appendix 1.1 – 1.4), were used. Two of the movie posters are presented as a normal traditional movie without any additions on the text itself. The two Virtual Reality posters are presented with an additional banner on top saying “A STUNNING VIRTUAL REALITY EXPERIENCE” in large letters making it noticeable for the participants to see.

Additionally, the descriptions of the movie posters between Virtual Reality and the Traditional movie varied. The movie description of each Virtual Reality condition began as follows (the full descriptions of both movie types can be found in Appendix 2.1 – 2.4):

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Next year, the tear-jerking story of our lifetime is coming to theaters near you, as a much awaited Virtual Reality experience. This story will fully immerse you in the heart of the holocaust (The Lost Souls).

Next year, the heart-warming story of our lifetime is coming to theaters near you, as a much awaited Virtual Reality experience. This story will fully immerse you in the heart of WWII (Untold Heroes).

Including both of these methods (i.e. the clear indication of Virtual Reality in the movie description and the Virtual Reality banner on top of the movie posters), the aim is to encourage the participants to allow themselves to imagine being fully immersed in the movie presented to them. Without the actual Virtual Reality software or the trailers, this study relies on the participants’ imagination abilities to feel the effects of Virtual Reality themselves.

4.4.2 Emotional Valence

When studying emotions elicited from films, scholars typically approached the experiments by presenting the participants a variety of clips from a dataset (Gross and Levenson, 1995; Soleymani et al. 2008). Additionally, scholars have used various audio recordings (Sutherland and Mather, 2012), picture sets (Lane et al. 1999), and the above mentioned video sets (Gross and Levenson, 1995) to manipulate valence and arousal of emotions. Typically, these scholars presented a variety of pleasant and unpleasant stimuli to each subject for a short period of time in order to truly measure the emotions elicited from the stimuli. Once again due to budget constraints and the nature of this experiment (i.e. immersive technology), video datasets and audio recordings will not be used to manipulate emotional valence. An approach similar to Lane et al. (1999) will be utilized,

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such that pleasant or unpleasant pictures will be presented to each subject to elicit an emotional response.

For this study, emotional valence was operationalized in two separate ways. In this study, movie scene valence is considered synonymous with emotional valence and a manipulation check was administered through the pre-test to determine if the manipulations worked (see 4.2 Pretest to see the results). The first effort to manipulate emotional valence was to hint the negative or positive aspects throughout the movie description itself (Appendix 2.1 – 2.4). In order to maintain legitimacy of the study, the genre and scenes were kept as similar as possible. Both of the descriptions are based on the same war (WWII), and the word count was similar (give or take one or two words). This effort to maintain similar descriptions was to allow the participants to develop their own sense of positive or negative emotions, without too much bias. Having only a slightly positive or slightly negative scene, will allow the effects of Virtual Reality to be much more significant as compared to the traditional movie. Essentially, “Untold Heroes” paints the movie as a “heart-warming” story of an American soldier and his war dog. This movie promotes the heroic acts of the war dogs naming them the “untold heroes” of war. Oppositely, “The Lost Souls” depicts a Jewish family fighting to stay alive and stay together as they are being forced into concentration camps by German troops during the Holocaust. The aspects of family and how they fought to keep the meaning of family strong during all the torturous and unspeakable acts done onto them, was included to add a positive light to a topic that is generally negative in nature (see 4.2 Pretest for further results).

Furthermore, even though effort was made in order to maintain as much similarity as possible when developing the movie posters (same reasoning as the movie descriptions and the legitimacy of the study), room was given for a slightly negative and a slightly positive valence within the

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posters themselves. Both posters are darker in image and both include “based on a true story.” With “Untold Heroes”, however, the poster shows a confident American solider standing tall and proud with his war dog. Allowing the participants to see a confident soldier and his dog will hopefully elicit a positive emotion (i.e. proud, motivated, inspired etc.) in each subjects own mind. In regards to “The Lost Souls”, the movie poster is slightly more negative than the aforementioned movie poster. The family in this poster is not smiling and is in black and white. They are depicted as your average Jewish family during the Holocaust. Once again, having a slightly negative image on the movie poster, as well as a slightly negative description, will hopefully elicit a negative mood (i.e. sad, uncomfortable, etc.) in the minds of the participants. These negative emotions will hopefully elicit further cognitive defenses that will shy the participants away of wanting to be fully immersed in a negative movie through Virtual Reality. All four movie posters and descriptions can be found below in Appendix 1.1 – 2.4.

4.5 Dependent Measures

For this study, one main dependent measure was pursued. The following is a description of how the dependent variable was operationalized, and how the variable was eventually measured.

4.5.1 Intentions

Purchase intentions, as well as attitudes, is a typical measurement in marketing research. Several scholars have used purchase intentions to measure the effects of various advertisements (Spears and Singh, 2004; Li et al. 2002), while others simply study the determinants of purchase intentions (Laroche et al. 1996). The current study used a sample of Spears and Singh (2004) scales to measure purchase intentions (unlikely vs. likely), common to the self-report 7-point Likert Scales used in the pretest.

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Each participant was asked to answer the questions honestly based on the emotions they feel when shown the various movie posters and descriptions. Essentially, the participants were forced to analyze the emotions elicited, similar to the affect condition in Shampanier, Mazar, and Ariely (2007) study. In order to have the participants answer based on their emotions elicited from the movie posters and descriptions, the following instructions will be presented prior to the question:

Please carefully read the following movie description of a film that is set to be released early next year. Allow yourself to fully imagine being in a theater near you watching the film. Look at the added movie poster to help with the imaginary in your mind. Please answer the subsequent questions honestly and in full based on your feelings and emotions elicited from the movie presented.

Additionally, it is noted that a discrepancy is found between intentions and actual behavior (Carrington et al. 2014; Sheeran, 2002). Therefore, in order to attempt to close this gap, a method performed by Shampanier et al. (2007) will also be used in this study. In their study, several experiments were conducted to study the theory of zero as a special price. The methods they used ranged from surveys to observations of actual behaviors with similar results throughout the process. However, one particular method they used was to solve the mapping difficulties when weighing two or more options. Often times, people make irrational decisions because it is difficult to map the monetary value of hedonic items (Shampanier et al. 2007). In simpler terms, it is hard to put a price on something that we enjoy. Therefore, Shampanier et al. 2007 offered a multitude of gift cards with varying values in which people could buy for $5, $1, or for free. Zero price effect was still present within this method. The only time that the zero price effect disappeared, was when the participants were asked to force analyze the options at hand. This helped the participants

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generate affect towards each option and determine which option is economically the best fit (Shampanier et al. 2007).

A combination of the above mentioned methods administered by Shampanier et al. (2007), will be used in this study. After the questions that force the participants to analyze their emotions of the presented movie are successfully answered, each participant will be thanked and asked if they would like to sign up for a reward as a token of appreciation for participating in the study. The rewards available for the participants will be either two tickets for the movie presented in their condition, or a $10 Amazon Gift Card ($10 was chosen because the average movie ticket in America in 2016 was 8.65 USD) (NATO, 2018). If the subject is truly wishing to attend the movie, and since the two movie tickets will be economically a better deal than the gift card, they will ultimately choose the movie tickets. However, if the negative emotions elicited truly effected the participants’ intentions, then choosing the gift card will indicate a cognitive defense since the option chosen is economically a worse deal than choosing the two free movie tickets.

4.6 Other Variables

Several other variables are accounted for as they may affect the results of this study, especially variables defined with early innovation adopters. Several scholars have explored the characteristics of those early innovation adopters. Dickerson and Gentry (1983) further supported multiple scholars’ findings indicating that adopters are most likely to be higher educated with higher incomes, compared to what they call “non-adopters.” Interestingly, findings point to working adults as the demographic of innovation adoption, as compared to adolescents who are more susceptible to new market trends (Yee, 2006; Dickerson and Gentry, 1983). Furthermore, if the individual has more experience with a similar product (i.e. technology), the chances of them adopting a new innovation is statistically more significant than those individuals with very little

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experience prior to the introduction of the innovative technology or product (Dickerson and Gentry, 1983)

Therefore, this current study will control for a multitude of those characteristics of early innovation adopters in order to have the results truly show the intended measure. Controlling for age will ensure the results are legitimate. The majority of the ages (i.e. undergraduate students between the ages of 18-24) is below the portion of the demographics considered innovation adopters (i.e. working adults) (Yee, 2006; Dickerson and Gentry, 1983). While it is difficult to truly control for those that are more likely to adopt innovations, this study will measure the participants prior experience level of Virtual Reality on a 7-point Likert Scale (below average vs. above average). This measure will help form the levels of these early innovation adopters. It is predicted that the higher level of prior experience will affect the results in a way that intentions to attend a negative Virtual Reality movie will be significantly higher than those without any prior experience. In other words, it is expected that those with prior experience will fully ignore the uncomfortable feelings elicited in a movie and enjoy the experience either way, since this piece of technology adds a new and exciting experience. Another variable controlled for and maintained is genre, specifically attitude towards the genre. It is recognized that males typically respond more favorably to war movies compared to that of females (i.e. women have a stronger preference for happy mood films than males) (Banerjee, 2008), however, gender will be noted of during the analysis to see if this was statistically proven. Therefore, a measure similar to Spears and Singh (2004) measures on the participants’ attitude toward the genre on another 7-point Likert Scale (i.e. War) (like vs. dislike), will be included. It is predicted that those who enjoy the genre will ignore the valence and enjoy the experience either way, resulting in higher levels of intentions.

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4.7 Procedure

Participants were asked to bring their laptops or cellphones to class the day the professors were told to administer the survey. The professors provided the link at the beginning of each of their respected courses and asked the participants to remain quiet throughout the duration of the experiment. A formal consent was provided at the beginning of each survey with a disclaimer of possible sensitive information. If the participants agreed to continue with the study, they were randomly allocated to one of four conditions (i.e. positive traditional movie, negative traditional movie, positive virtual reality movie, negative virtual reality movie). Each participant was asked to read the descriptions in full and allow themselves to imagine being at the movies during this upcoming film. Once they completed the readings and studied the poster, manipulation checks were included to determine that the correct emotions and levels of immersion were elicited during the readings. Based on those emotions, the participants were then asked to rate how likely is was that they would attend the film that was presented to them.

Several demographic and personally relevant questions were then asked (i.e. age and gender). Measures of prior virtual reality experience, attitude toward the genre, and how often participants typically attend the movies in a given year were included to control for any effects resulting from these variables. Additionally, an attention check was included to make sure the participants were paying attention to the study at hand. The measure to close the intention-behavior gap was finally included in the selection of various gifts. To remain within the ethical standards, participants were then told about the mock question and were told they could enter a lottery to win a $20 Amazon gift card. The winner was picked after all participants completed the study. Finally, the participants were thanked, debriefed, and sent on their way.

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5. Results

This study is a 2 (Virtual Reality vs. Traditional Movie) x 2 (Negative vs Positive movie scene) between-subject experimental design. SPSS was used to conduct the analysis, utilizing various tests to confirm or reject the hypotheses stated earlier (See 2.1 Virtual Reality and 2.2 Cognitive

Defenses). The descriptive results will first be presented and discussed. Following, the results from

both the correlation test and the factorial ANOVA will conclude this section, as well as additional findings from various regression analyses.

5.1 Descriptive Results

Prior to any analysis, several steps were taking in order to clean the data. Missing data was fixed by placing ‘999’ in the empty selections, then identifying them as discrete missing data. Overall, there were only two values missing, both in the behavior category (i.e. would you like the movie tickets or a gift card). Subsequently, values were recoded into the same variable, due to having high values on the lower end (i.e. 1.00  Positive). Therefore, all the values and charts in the following sections are presented as followed:

1-3 = Low (i.e. negative or unlikely) 4 = Neutral

5-7 = High (i.e. positive or likely)

In total, 270 participants completed this study, administered via an anonymous link to faculty and students at a Mid-West Division II university in the United States. After an initial analysis of the attention check, 68 participants failed to answer the section correctly (i.e. Was the movie presented virtual reality or traditional?). Therefore, the final number of participants for this study is n=202. As discussed in the manipulation check below, excluding these participants further validates these

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results. Of this sample size, 63.9% were female (n=129), 34.7% were male (n= 70), and 1.5% didn’t wish to specify (n= 3). The mean age of this study is 31 years old. Additionally, the highest percentage of respondents (35.1%; n= 71) reported that they go to the movies between 2-3 times per year. Furthermore, 61.4% of the respondents (n= 124) reported having a below average level of experience using Virtual Reality technology prior to this study (M=2.96).

5.2 Manipulation Check

Various manipulation checks were conducted in order to determine the validity of the stimuli used during this study. Excluding the participants that failed to recognize whether their movie presented to them was Virtual Reality or Traditional, means that 100% of the participants knew the operationalization of their condition (i.e. Virtual Reality or Traditional movie). An additional manipulation check was conducted to determine whether the movie materials (i.e. description and poster) actually elicited a positive or negative emotion, respectively.

Independent sample t-tests were conducted for each condition to determine the significance of the emotions elicited. The initial t-test, presented in Table 1, resulted in a .000 Sig (2-tailed), meaning that each conditioned related strongly to the emotions elicited during the testing. In regards to the emotions themselves, Table 2 presents the group statistics of the t-tests. For the negative traditional movie (i.e. Lost Souls), 79% of the respondents (n= 43) reported a negative emotion (M= 2.50 for those 34 that stated it being negative; M= 3.36 for overall respondents). For the negative virtual reality condition (i.e. Lost Souls), 82.97% of the respondents (n= 47) reported the stimuli as eliciting a negative emotion (M= 2.21 for those 39 that stated it being negative; M= 3.02 for overall respondents). Turning to the positive conditions (i.e. Untold Heroes), for the traditional movie, 87.5% of the respondents (n= 40) reported a positive emotion (M= 5.77 for those 35 that stated it being positive; M= 5.08 for overall respondents). Finally, for the positive virtual reality condition,

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78.78% of the respondents (n= 33) reported the stimuli elicited a positive emotion (M= 5.81 for those 26 that reported it being positive; M= 5.03 for overall respondents).

Aside from the perceived emotions elicited, an analysis of the responses based on the intended emotions through the manipulation of the stimuli was conducted. An additional independent sample t-test, as seen in Table 3 and Table 4, was conducted to test the valence on the total emotions. Results further prove that the stimuli presented elicited the desired emotions (negative valence M= 3.1875; positive valence M= 5.0556; Sig. (2-tailed) = 0.000). Therefore, after the independent sample t-tests, it is concluded that the stimuli presented was manipulated appropriately, further validating the results discovered in the following subsections.

Table 1: Independent Sample t-tests Results Perceived Emotions

Table 2: Group Statistics of Independent Sample T-Tests between Movie Type and Emotions

Table 3: Independent Sample t-tests of Total Emotions based on Manipulated Emotions

Levene's Test for Equality of Variances t-test for Equality of Means

F Sig. t df Sig. (2-Tailed) MD Std. Error Difference

Emotions Total Equal Variances Assumed 0.483 0.488 -9.679 200 0.000 -1.868 0.193

Equal Variances Not Assumed -9.824 198.653 0.000 -1.868 0.19

Negative or Positive Emotion N Mean Std. Deviation Std. Error Mean Traditional Movie; Lost Souls Emotions Positive 9 5.78 0.667 0.222

Negative 34 2.5 0.707 0.121

Virtual Reality Movie; Lost Souls Emotions Positive 8 5.75 0.707 0.25

Negative 39 2.21 0.833 0.133

Traditional Movie; Untold Heroes Emotions Positive 35 5.77 0.69 0.117

Negative 5 2.6 0.548 0.245

Virtual Reality Movie; Untold Heroes Emotions Positive 26 5.81 0.694 0.136

Negative 7 3 0 0

Levene's Test for Equality of Variances t-test for Equality of Means

F Sig. t df Sig. (2-tailed) MD Std. Error Difference Traditional Movie; Lost Souls Emotions Equal variances assumed 0.615 0.438 12.502 41 0 3.278 0.262

Equal variances not assumed 12.948 13.191 0 3.278 0.253 Virtual Reality Movie; Lost Souls Emotions Equal variances assumed 1.383 0.246 11.212 45 0 3.545 0.316 Equal variances not assumed 12.511 11.381 0 3.545 0.283 Traditional Movie; Untold Heroes Emotions Equal variances assumed 0.305 0.584 9.811 38 0 3.171 0.323 Equal variances not assumed 11.691 5.981 0 3.171 0.271 Virtual Reality Movie; Untold Heroes Emotions Equal variances assumed 13.676 0.001 10.581 31 0 2.808 0.265 Equal variances not assumed 20.631 25 0 2.808 0.136

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Table 4: Group Statistics of Independent t-tests Between Emotions Total and Valence

5.3 Correlation

A correlation test was ran including all the main variables of this study (i.e. Movie Type, Age, Gender, Intentions, Emotional Valence, Immersion, Attitude Toward Genre, Attendance at Movies, Prior Experience with VR). Several of the variables analyzed produced significantly strong coefficients with a medium effect size. Mainly, it is found that Emotional Valence correlated the most with the variables presented, in that Emotional Valence positively and significantly correlates with Intentions (r = .479; p < .01), Immersion (r = .273; p < .01), Attitude toward Genre (r = .314; p < .01), and Gender (r = .233; p < .01). These findings are in line with the hypothesis of Emotional Valence, such that Emotional Valence significantly relates to intentions where the more positive the Emotional Valence, the more likely the participant will want to attend the movie.

However, a contradiction was found with the prediction of Intentions between Movie Type. Surprisingly, Movie Type resulted in a lack of significant correlation with all other variables presented in the analysis, including Intentions and Emotional Valence. The lack of correlation does not support Hypothesis 1, however, more tests (i.e. Factorial ANOVA) were ran and will be discussed in the following section. Another important finding regards the control variables for this study. Out of the four control variables (Age, Gender, Genre, Experience), Attitude toward Genre had the strongest effects with the other variables analyzed. Attitude toward Genre was shown to be positively and significantly correlated to Age (r = .200; p < .01), and Gender (r = .321; p < .01). Additionally, Genre was positively and significantly correlated to Intentions (r = .348; p <

Valence N Mean Std. Deviation Std. Error Mean

Emotions Total 1 112 3.1875 1.442 0.136

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.01). Therefore, with the small to medium effect sizes produced by Attitude toward Genre, it seems to be important that we did control for Attitude toward Genre in case there is an unwarranted effect on Intentions while proceeding with the following analyses. However, it is also important to recognize that correlation does not always lead to causation in certain situations. The full results are shown in Table 5 below. In the following section, the main hypotheses and effects are fully discussed in detail.

Table 5: Means, Standard Deviations, and Correlations

* = p < .05 ** = p < .01

5.4 Hypotheses tests

To test the hypotheses set at the beginning of this study (See 2.1 Virtual Reality and 2.2

Cognitive Defenses), a variety of tests were conducted to determine the significance of the main

effects and interaction effects. To fully reject or support any of the following hypotheses, a full analysis was taken into account including, the above mentioned correlation analysis, estimated margin of means, and univariate tests.

5.4.1 Hypothesis 1

Hypothesis 1 proposes that virtual reality movies will lead to a higher intention to attend as compared to a typical traditional movie. To test the hypothesis, a univariate test was conducted as part of the overall Factorial ANOVA (discussed in Hypothesis 2a & b). In line with the correlation Table 5 : Means, Standard Deviations, Correlations

Variables M SD 1 2 3 4 5 6 7 8 9 10 1. Age 31.02 15.04 -2. Gender 1.68 0.55 0.092 -3. Mood 2.27 0.797 -0.126 0.114 -4. Type of Movie 1.48 0.5 -0.051 0.007 0.042 -5. Intentions 3.6 1.85 -0.033 0.061 0.076 0.049 -6. Emotions 3.98 1.65 -0.087 .233** 0.023 0.108 .479** -7. Immersion 3.28 1.13 -0.047 0.122 0.011 -0.067 .275** .273** -8. Attitude toward genre 3.42 1.7 0.200** .321** .260** -0.046 .348** .314** 0.111 -9. Attendance at movies 2.63 1.00 -0.245** -0.067 0.017 -0.028 -0.017 0.083 -0.094 -0.12 -10. Prior experience with VR 5.04 1.64 0.065 0.087 -0.105 -0.026 -0.085 0.085 0.096 0.056 -0.084

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-analysis, the pairwise comparisons found that Traditional movies (M = 4.510) and Virtual reality (M = 4.373) movies did not significantly differ from each other in regards to overall intentions to attend the given movie (p = .571). The results of the Univariate tests show that Movie Type (i.e.

Virtual Reality vs. Traditional) equates to F(1, 194) = .323, n2 = 0.002, p = 0.571. Therefore,

based on both the univariate test and correlation analysis, results find that Hypothesis 1 was not supported by the outcomes of this study.

5.4.2 Hypothesis 2

With Hypothesis 1 not supported, an analysis to test Hypothesis 2a & b (a negative (positive) virtual reality movie will lead to lower (higher) level of reported intentions), was conducted. The univariate test found that there was a significant main effect of Emotional Valence on overall

Intentions, F(1, 194) = 8.589, p = .004, n2 = 0.042. Pairwise comparisons show that Intentions

significantly differ from the positive and negative emotions, which was intended during this study. In other words, positive movies lead to significantly higher intentions as compared to the negative valence condition. Thus, both Hypothesis 2a & b are supported based on the initial analysis of the results shown through the factorial ANOVA.

Further analysis shows that there is a non-significant interaction effect between Movie Type (i.e. Virtual Reality vs. Traditional) and Emotional Valence (i.e. positive vs. negative) on participants

reported level of intentions, F(1, 192) = 1.403, p = 0.238, n2 = 0.007. However, the significance

level decreases greatly when observing the interaction between Valence and Type. Therefore, with directionality taken into account (as seen in Figure 1; Valence 1.00= Negative, 2.00= Positive), an analysis of the compared means of the main effects was observed. In regard to the positive emotions elicited, the mean for the Traditional movie resulted in M= 4.66, while for the virtual reality condition resulted in M= 5.00. Not that significantly different in the results, however, a

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slight increase is noticed based on the means. On the other hand, movies associated with negative emotions produced means of M= 4.309 for traditional movies and M= 3.824 for virtual reality movies. In other words, a negative virtual reality movie produces significantly lower intentions as compared to a negative traditional movie. This is in line with the predicted outcome of this study, as presented in Hypothesis 2b. Therefore, based on directionality, we provide further support for both Hypothesis 2a & b.

Figure 1: Estimated Marginal Means

1.0 = Negative Valence

2.0 = Positive Valence

Even though there was a non-significant interaction effect, as well as no support for Hypothesis 1, further analysis based on the empirical evidence found in the means of both the traditional and Virtual Reality negative movies, was conducted. An additional independent sample t-test to determine whether the means between the Movie Type and Emotional Valence significantly differ from each other, was ran. In other words, the results are aimed to show whether the means of the

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negative and positive Traditional movie differ from each other, as well as the means of the positive and negative Virtual Reality movie. Accordingly, the means produced from the Virtual Reality condition are found to significantly differ (Sig. (2-tailed) = .003), whereas the means of the Traditional movie condition do not significantly differ (Sig. (2-tailed) = .285) (See Appendix 3.1). These results are in line with the initial pretest. The hopes were to prove that the intentions of a traditional movie will not significantly differ. Results support this claim. Additionally, Virtual Reality seems to enhance the perception of valence within each condition. Even though Hypothesis 1 cannot be supported (Movie Type does not significantly differ from each other), Virtual Reality is still found to either enhance the intentions of the participant or significantly reduce the intentions of the participants. Therefore, results provide support for both Hypothesis 2a & b, and show promise in regards to the effects of Virtual Reality. The full results can be found in Table 6 below.

Table 6: Factorial ANOVA Results

5.5 Additional findings

An additional effect found was in regard to the aforementioned Attitude toward Genre. As mentioned previously, Attitude toward Genre has a significant and positive correlation with Emotional Valence (r = .314; p < .01) and Intentions (r = .348; p < .01). In other words, a more

SS DF MS F Sig. Age 3.616 1 3.616 1.257 0.006 0.264 Gender 2.05 1 2.05 0.713 0.004 0.4 Genre 85.806 1 85.806 29.833 0.133 .000 Experience 4.723 1 4.723 1.642 0.008 0.202 Type 0.928 1 0.928 0.323 0.002 0.571 Valence 24.705 2 24.705 8.589 0.042 0.004 Valence*Type 4.034 1 4.034 1.403 0.007 0.238 Error 557.994 194 2.876 Total 4590 202

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positive emotion is related to a more positive attitude toward the genre (i.e. War). Additionally, when controlling for Attitude toward Genre in the Factorial ANOVA to test the hypotheses, a

significant effect was found for the relationship tested, F(1, 194)= 29.833, n2 = 0.133, p = 0.000.

Moreover, the covariate of Experience (i.e. prior level of experience with Virtual Reality

technology), resulted in an effect of F(1, 194) = 1.642; n2 = .008; p = .202. Even though the results

for Experience is not significant, the results are still promising has having an effect on the relationship between Emotional Valence, Movie Type, and Intentions.

Based on the results from the Factorial ANOVA as covariates, multiple regression analyses were conducted utilizing PROCESS by Andrew F. Hayes. Model 3 and the Johnson-Neymen technique was used for both Experience and Genre to determine an additional moderation effect between the relationship of Type x Valence with the dependent measure being Intentions. In regards to Prior Experience, there was no significant turning point. Therefore, it is found that the higher level of Experience does not equate to a higher level of Intentions. However, an interesting find comes in regards to Attitude toward Genre. Based on the results from the same model 3 and Johnson-Neymen test, an additional moderation effect was found. The higher the Attitude toward Genre, the more significant the effect on the initial moderation effect of Emotional Valence. In other words, a strong Attitude toward Genre will lead to higher intentions regardless the Emotional Valence of the stimuli. Additionally, even though the significance level is above .05, a moderately

strong three-way interaction effect was found (r2 = .0083; p = .1631). The promising three-way

interaction p-value means that intentions are effected by the relationship between Valence x Type x Genre. For example, if a positive virtual reality movie with a high Attitude toward Genre will result in higher Intentions. Oppositely, a negative traditional movie with a higher Attitude toward Genre will again result in higher Intentions. Even though these results were not predicted prior to

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the experiment, they are still interesting findings that hold significant implications on their own. The results of the regression analysis can be found below in Appendix 4.1 and 4.2.

6. General Discussion

The results of this study are three fold. First, it is found that the Movie Type (i.e. Virtual Reality or Traditional Movie) does not differ in regards to the overall Intentions produced. Both the Virtual Reality condition and the Traditional condition resulted in relatively the same means. Therefore, the results were unable to provide support for Hypothesis 1. The differential effect came when Emotional Valence was added into the equation. Therefore, the second major finding of this experiment was presented and tested from Hypothesis 2a & b. While a positive and negative Traditional movie do not significantly differ from each other in terms of intentions, a positive and negative Virtual Reality movie do significantly differ from each other. A positive Virtual Reality movie results in higher intentions to attend such a movie, while a negative Virtual Reality movie elicits some cognitive defense where the participants will report a significantly lower intention to attend. This is the main and most important finding of this study. It proves that Virtual Reality does, when coupled with Emotional Valence, have an effect on consumers’ preferences of movies, especially when dealing with the negative valence condition. While consumers are highly interested in attending a positive Virtual Reality movie, that interest is diminished when a negative movie is presented. The reason why is still unanswered based on the results of this study. Assumptions and speculations could be made, such as consumers are reluctant to be immersed in a negative movie based on the uncomfortable feeling elicited when dealing with realism or even consumers prefer happy-mood and high arousal movies (Banerjee et al. 2008) and that result is just further enhanced when immersive technology is added. Whatever the case may be, it is

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important to further research the psychological foundation as to why consumers may be less willing to be immersed into a negative environment through Virtual Reality.

The third additional finding in this experiment came in regards to the three-way interaction effect of Valence x Type x Genre. Accordingly, if the consumer as a high interest or enjoyment of the genre presented, then it no longer matters whether the movie is positive or negative, as well as Traditional or Virtual Reality. This finding is slightly obvious in nature, however, it still holds a tremendous amount of weight within this piece of literature. The effects of a negative Virtual Reality movie can be overcome by essentially targeting those that are deeply intrigued in the genre that is presented. Furthermore, based on the results of this study, it is concluded that if a production company desires to produce a Virtual Reality film, a positive film will elicit higher intentions as compared to a negative film. In addition, the audiences’ attitude toward the genre will help ease those effects of a negative Virtual Reality film. Therefore, all three factors (Valence x Type x Genre) must be taken into account by the production company, especially when pursuing a Virtual Reality film.

6.1 Implications

This study proves to provide several managerial and theoretical implications. Theoretically, this study extends the literature of Virtual Reality, which is not well explored within the academic community. Other scholars have explored the positive and negative effects of Virtual Reality (Kristofferson et al. 2016) prior to this study. However, this study extends the prior knowledge with the addition of Emotional Valence. It pursues the cognitive defenses when immersed into a virtual environment, whether that be positive or negative. Prior research has explored the effect of realism in robots (Mori, 1970) and the affect valuation theory (ideal and actual affect) (Tsai et al. 2006). This study, however, incorporates realism to the extent in which it may make people feel

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