Overcoming uncertainty : the influence of stars on video game reception

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Overcoming uncertainty

The influence of Stars on video game reception

By Naomi van Hattem

Student Name: Naomi van Hattem

Student Number: 10631259

Supervisor: Frederik Situmeang

Date: 28-07-2015

Msc Business Administration – EMCI Track

First Full Thesis Draft

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Statement of originality

This document is written by Student Naomi van Hattem who declares

to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is

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 of the work, not for the contents.

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Thesis Abstract

Purpose: The purpose of this thesis is to show what the use of celebrities in games contributes to the success of games. Success of games is expressed through both revenue and through customer and expert reviews. This provides new information for marketers in the gaming industry and offers new insights for research into the

marketing of successful games and other experiential products, as success of an experiential product like games are difficult to predict by both developers and gamers. Proposition: this research proposes that the appearance of a star (from either outside the game industry: a pop culture star or from inside the industry: a developer star) has a positive influence on reviews. The influence is stronger for consumers than for experts and the influence of different types of stars differ.

Design: The research is based on quantitative analysis of the appearance of a star in combination with both consumer and expert game reviews. Information on reviews and success of games is taken from (http://www.vgchartz.com and

http://www.metacritic.com) and tested through a two-stage statistical model.

Results: the test results showed that both pop culture stars and developer stars did not have a significant effect on either game sales or reviews from critics or customers.

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Introduction

Over the last few years the gaming industry has grown to be a mature market with a mainstream appeal. (Gottschalk, 1995; Binken & Stremersch, 2009, p.92) The surge in popularity of playing video games has led to a growth of the gaming industry at an incredible rate. Growth is most obviously observable when we look at the revenues that are being made in the game industry over recent years. The revenue of the global traditional video game market in 2012 (excluding mobile games on smart devices) contributed to $57.2 billion (Nayak, 2013), but grew to an impressive $67 billion in 2013 (Kamenetz, 2013).

The popularity of gaming as entertainment is only expected to grow as people are being brought up in an environment that moves away from passive entertainment like television and radio to the interactive entertainment of playing (computer) games (Rodgers 2002 in Chaney, 2004, p. 37).

Yet, games are an experiential product that makes it hard for customers to gauge quality before consumption (Basuroy, Desai and Talukdar, 2006), and causes difficulty in predicting whether a game will be a commercial success for producers (Hennig-Thurau, Houton, Heitjans, 2009; Suárez-Vázquez, 2011).

One method used in the creative industries to try to appeal to the preference of customers and to lessen the uncertainty associated with experiential products, is through the use of celebrities or “stars” as a signal of quality. Signaling theory proposes that if a customer has limited information available to weigh the quality of an experiential product before consuming it, the customer will focus on signals of quality outside the scope of the product to measure the quality (Spence, 1973 in Situmeang, Leenders, Wijnberg, 2014).

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While these signals can take many forms like special effects or sequels (Gemser, van Oorstrum, Leenders, 2007; Basuroy, Desai, & Talukdar, 2006; Situmeang, Leenders, Wijnberg, 2014), this research will focus on the influence of stars (and their star power) as a signal of quality.

The definition of “stars” I will be using for this research is based on the definition provided by Gottschalk (1995): “characters whom players recognize from previous encounters on the multiple screens of the media-scape.” They will therefore

be taken from both inside, and outside the gaming industry

The ability for customers to recognize and recall a star, and their function as a signal of quality is the main focus of this research. The previous encounters are therefore stars that spill over from movies, television of other entertainment the customer is accustomed to. This can take the shape of famous people, franchises, television or animation stars spilling over in the game industry, or from inside the game industry by the use of renowned game developers.

Considering stars generate more than $190 billion a year to the entertainment industry (Kowalczyk & Royne, 2013, pp. 211–212) much more should be researched about its moderating factors on revenues. Specifically for the game industry the use of stars makes for more revenue in the industry and added realism to certain games that can increase customer happiness. (Yang, Roskos-Ewoldsen, Dinu, & Arpan, 2006, p. 143) Since many customers have strong emotional attachments with celebrities, managers can make use of consumer’s obsession by tying them to their product (Flora 2004 in Kowalczyk & Royne, 2013, p. 211).

This research tries to close gaps left in contemporary research on the effect of stars on reviews and the role stars play in the marketing of video games. While scholars have focused their research on stars and star power in the movie industry,

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this has yet to happen extensively for the game industry. Up until today the only research on stars directly related to sales in the gaming industry is conducted by Binken and Stremersch (2009) Yang, Roskos-Ewoldsen, Dinu, & Arpan, (2006) and Farrand et al. (2006), however, they all take a different look at the role of stars in the game industry.

The research by Binken and Stremersch (2009) is specifically designed to look at the effect stars have on hardware sales, in other words the sales of the

accompanying console. This is because Bincken and Stremersch define superstars as software titles that cause a sudden spike upwards to the sale of the hardware (for example Super Mario 64 for the Nintendo 64), and not stars in the literal sense: like people/developers or brands. Farrand et al. (2006) also focus on stars in the gaming industry, but take another approach. They focus their research on gaming celebrities through the examination of game character Lara Croft and the idea that video game characters by themselves can also become stars later, shown through their growing franchise or game characters endorsing a product. (Farrand, Nichols, Rowley, & Avery, 2006, p. 12) While it is interesting to note whether game characters can become a brand endorser themselves, this has no direct answer to the effect stars have on the game industry itself.

Also, this research will also try to fill the gap that exists on the effect of reviews. While scholars have taken an interest in the effect of reviews on consumer behavior in the creative industry in general (Larceneux, 2001) and for its separate niches like the movies industry (Dempster, 2006; Gemser, Van Oostrum, & Leenders, 2007) and theatre (Desai & Basuroy, 2005; Shrum, 1991), the effect reviews have on customers and their buying behavior has only been limitedly researched for the gaming industry. Current research has concentrated on other signals of quality of

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games, like word of mouth and sequels (Zhu and Zhang, 2010, Situmeang, Leenders, Wijnberg, 2014) this research would add to that by looking whether game producers can influence game reception by using a star. Closing these gaps, this research fills this existing lack of information within academic research concerning the gaming industry. This thesis therefore tries to answer this main question:

What is the effect of stars in the game industry?

The questions I am looking to answer during this research are if using celebrities actually make games more popular? Is fame a good indicator of potential success of a game?

The research starts off with a literature review, which shows what other researchers have found on these topics within the creative industry, the influence of stars and their star power. This review will lead to the identification of the gaps within the research, which I will answer through my own proposed hypotheses in the

theoretical framework. In the method section I will show the research design and method used to answer the proposed hypotheses. Results of the quantitative tests are discussed in the discussion section including the implications and limitations of this research. The results section shows all previous findings and concludes this thesis.

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Literature review

In this literature review I will try to define the elements of my research question, showing the gap within the existing literature about stars and their influence on

customers and my own research that will be conducted in accordance with these gaps. Since the early beginning of video gaming, celebrities have played a role in bringing in customers (one of the earliest examples in the gaming industry would be Mike Tysons Punch Out in 1987 for the NES) even though the celebrities themselves mainly starred in other parts of the entertainment industry.

Scholars have found that there are signs that the use of a celebrity name or brand may have a positive effect on buying behavior considering the social role stars fulfill. As is found by multiple scholars (Lindenberg, Joly, & Stapel, 2011; Rindova, Pollock, & Hayward, 2006) there is a certain obsession with celebrities, which caused by several social goals that celebrities fulfill for the customer: “A celebrity helps fulfill various behavioral goals, which (…) include meeting an audience’s needs for gossip,

fantasy, identification, status, affiliation, and attachment.” (Rindova et al., 2006, p.

51) Rindova concludes that celebrity therefore is a characteristic of the relationship of the actor with their audience, rather than a characteristic of the star/actor themselves. (Rindova et al., 2006) This identification and projection of goals onto celebrities by audiences is what leads them to form cliques and one of the consequences is that audiences talk about products affiliated with the star. (Suárez-Vázquez, 2011)

This word-of-mouth could be an indication that stars would have a positive effect on the sales of products. This is also shown throughout other research about stars because adding to the social and behavioral goals, from the customers’ perspective, the use of stars brings them security.

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The use of a star in a product answers to a customers’ expectation about quality (Wilcox, Roggeveen, & Grewal, 2011). As Suárez-Vázquez notes for the movie industry, just the fact that a celebrity chooses to appear in a film is indicated by costumers as confidence in the quality of the film. (2011, p. 120) If a customer

believes that a celebrity “might actually use” or believe in the endorsed product, sales are already increasing just thought the effect of the celebrity tie. (Creswell 2008 in Kowalczyk & Royne, 2013, p. 211; Suárez-Vázquez, 2011)

This reducing of risk and emotional attachments of audiences to stars is one explanation of why celebrities can be found in more industries still and this is tied to the complications that are inherent to the game industry and other experiential goods.

Experiential Goods

Games are experiential products, which brings along complications in terms of

assessing quality by the customer. A typical experience product to Basuroy, Desai and Talukdar is “characterized by a product-quality information asymmetry between firms and consumers”. (2006, p. 287) This means that an experiential product is judged by a customer through its enjoyment value, but the quality of the product is hard to

measure by them pre-consumption. (Basuroy, Desai and Talukdar, 2006, p. 288) That is to say, a customer buying a game will not know if the game will be entertaining without trying it, so by buying the game a customer risks dissatisfaction if he/she judged the game incorrectly.

Dealing with uncertainty in judgment is not one-sided as it is also a problem for the producers, as they cannot consistently predict payoff of the production of their experiential product. (Hennig-Thurau, Houton, Heitjans, 2009) As from an example in the movie industry given by Suárez-Vázquez (2011), directors and producers will

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also find it difficult to measure what will become a commercial success, as high investments in production and marketing do not equal high quality rating by the customers. (Suárez-Vázquez, 2011, p. 119)

For developers the information asymmetry means that they have to pick what information to give to the customers, as the provided information may win the customer over. In order to satisfy the needs of different customer groups, game developers therefore feel the need to differentiate and innovate their games.

Innovation in the creative industries is tricky to accomplish, since the products that are offered in this industry are experiential in nature. (Orth & De Marchi, 2007; Tschang, 2007)

As is said by Lampel et al. (2000, p. 266 in Tschang, 2007, p. 990) “while customers expect novelty in cultural goods, they also want novelty to be assessable and familiar.” This notion of assess-ability and familiarity brings difficulty for game developers as they can only see what has worked in the past, but this cannot predict if a new project will be successful at all. (Tschang, 2007; Walls, 2010) Therefore there are certain dimensions and expectations about new games that a developer needs to address. This feeling of familiarity of course could explain why developers choose to use stars that they know are already successful.

As is explained by Tschang (2007, p. 990) about the entertainment industry in general: “Entertainment products and services are notorious for having 1) a hits-oriented nature (i.e., a small subset of products responsible for generating the larger

proportion of the total revenue); 2) a short product lifecycle in the marketplace and 3) difficulties in predicting product acceptance.”

This is true for the gaming industry especially, where because of the

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the difficult technology and time involved with creating a new game and need to appeal to customer demands, leads to “short product life cycles and intense inter- and intra-generational rivalry.”(Clements & Ohashi, 2005, p. 516)

Tschang (2007, p. 989) states that even though firms within creative industries are object of continuous change and renewal, they are also maturing as other

industries. This maturing leads to a more market-driven perspective on product decisions. Also, since in the game industry “the value of the product to an individual increases with the total number of users”(Clements & Ohashi, 2005, p. 515), it is key for game developers to find out what or who is popular with the largest crowd, as this will thus increase the desirability of their product.

Especially the third characteristic of the nature of entertainment products mentioned by Tschang (2007) – the difficulty of predicting what innovations the customers are going to accept – is what influences decision-making. This nature of the game industry influences the decision-making on the developers’ side, as well as on the consumers’ side. There are however methods that both developers and

customers use to infer quality and to steer decision making, one of those methods is signaling.

Signaling Quality

The signaling theory by Spence (1973 in B.I. Situmeang, A.A.M. Leenders, & M. Wijnberg, 2014) provides an explanation on how customers make a decision if there is limited information available to them to evaluate a product. It proposes that if the customer has a low ability to evaluate a product, the customer will focus on the other occurring signals that do not come from the product or producer itself. The

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his/her own ability to evaluate the product. (Spence, 1973 in Situmeang, Leenders, Wijnberg, 2014, p. 1469)

“Signaling properties” reduce uncertainty about an experiential product. For the movie industry these properties can take the form of “popular stars and directors with mass appeal, special effects, exotic locations and/or elaborate sets” (Gemser, van Oorstrum, Leenders, 2007, p. 47) Basuroy, Desai and Talukdar (2006), add sequels and advertising expenditure as instruments of signaling. Situmeang, Leenders and Wijnberg add past evaluations of customers and experts to the list of potential signals of quality, as they can act as a guide to the product performance (2014, p. 1469).

However, signals can also be a liability if they contradict one another. This signal “noise” in for instance customer reviews can cause confusion. (Situmeang et al., 2014, p. 1471) Therefore a signal should not be too conflicting to the customer, as this could affect judgment negatively. The question then arises whether using popular stars as a signal causes more clarity or confusion to the buyers of video games.

The use of stars as a signaling tool.

As stated before, the success of a game is difficult to predict by developers, therefore they are prone to making conservative changes to the product in order not to defer consumers from buying an “unfamiliar” product. (Tschang, 2007, p. 990) Tschang (2007) also proposes that because the gaming industry is maturing, this lead to more market-driven developments. Developers have already stated to sell in-game space to develop more revenue and to lessen costs that come from the development and

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For the gaming industry one other example is the use of in-game advertising. From a marketing perspective, in-game advertising like brand placements are a useful tool, since gamers have shown to have no negative view towards brands appearing in games. (Yang et al., 2006)

This openness to brand placements by customers has also been supported by Chaney, Lin and Chaney (Chaney et al., 2004), as they found that billboard ads in games have higher recall than those in sports events, showing that it is an effective environment to use for promotional purposes. Marketing of a game which has

recognizable characters or franchise makes sure that on the developers side less effort has to be put in the marketing of a game (Chaney et al., 2004) and is already

commonly done in the movie industry, as it allows producers to track consumer patterns and successful movies with a star featured in it(Albert 1998 in Suárez-Vázquez, 2011, p. 123), this allows developers to know what was a commercial success in the past and how to apply that previous success to new projects. (Caves 2000, p 371 in Walls, 2010, p. 265) This is in the interest of the company because a star or celebrity must of course generate more value than their actual cost. (Knittel & Stango, 2014, p. 22)

One could propose that the sell of in-game space to companies, and tying their name to a game kills two birds with one stone as the developer increases revenue, while the customers see a brand they are familiar with and do not feel opposed to.

One argument in favor of this proposal is that it is already proven that customers in some cases like to see familiar brands. Seeing familiar brands and people offers the advantage of added realism to sports games. As is stated by Nelson (2002): “In video games, brand placements are aided by consumer identification with

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famous sports figures, such as Tiger Woods in Cyber Tiger or Michelle Kwan in Michelle Kwan Figure Skating”. (in Yang et al., 2006, p. 144)

The use of celebrities as endorser, spokesperson or as advertising vehicle is widely accepted as a method to get customers interested in the products to be sold. (Farrand et al., 2006, p. 12; Kamins, Brand, Hoeke, & Moe, 1989, p. 4)

Already many scholars have looked at the role celebrities’ play in the

entertainment industry and/or their influence on customers, most focus on stars in the form that we know them most of: movie stars.

Stars in the movie industry may serve as a double indication of quality, if a star is in a film it shows that the star has confidence in the quality of a film. A director can also show his confidence in a film by committing to a star, as hiring a star might be expensive and therefore a high-risk undertaking. (Ravid, 1999, p. 456, Suárez-Vásquez 2011)

However, stars are also examined for the role they play outside their own industry. They refer to celebrities moving outside their own industry to venture into others as a form of brand extension (Hennig-Thurau, Houston, & Heitjans, 2009; Kamins et al., 1989). Choi and Berger (2010) even go as far as to say that the

appearance of celebrities outside their traditional workplace (movies for instance), is a form of mission creep where celebrities gain influence over popular opinion.

Celebrities are filling more roles than the usual pitch of products, but also act as their own brand with brand extensions in other industries. (Kowalczyk & Royne, 2013, pp. 211–212)

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Reviews as a reassuring measure.

As experiential products are bought for the experience and pleasure, customers rely significantly more on recommendations than for other product categories. (Zhao, Yang, Narayan, & Zhao, 2013, p. 154)

As we have seen, one way customers can decrease their uncertainty about the quality of a product is through the formation of cliques or fan-bases around a star. Another source of reliable information is through expert and customer reviews. Once again, like in the case of stars, the customers are looking out for the appearance of what or in this case whom they know, as it acts as a reassuring signal of quality. This reassuring effect to both the use of stars and the use of reviews is reinforced by Suárez-Vázquez (2011, p. 131): both “reviews and stars serve as ‘search criteria’” by which a customer can assess the risk of consuming an experiential product”.

Larceneux (2001) gives two reasons for a consumers’ sensitivity to criticism and willingness to seek out other opinions. On the one hand consumers’ willingness to look out reviews depends on their ‘self-expertise’, or in other words: the belief in their (in)ability to judge the quality of a product themselves, and on the other hand is based on their perceived credibility of a critics’ opinion. (Larceneux, 2001, p. 68)

That is why consumers read reviews to try and assess the quality of an

experiential product beforehand. (Larceneux, 2001; Suárez-Vázquez, 2011; Tschang, 2007; Wilcox et al., 2011)

In the creative industries, there is a separate and special role for the expert or professional review. For instance in the movie industry, scholars have proven that professional critics can influence and predict box office revenues and shape and influence customer decision-making. (Ravid, Wald, & Basuroy, 2006)

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The influence on decision-making happens through several channels, in one way because customers seek after them as a knowledgeable and independent source of information and in another way because game developers can use that independent position to build reputation. (Larceneux, 2001)

Critics influence consumers in a way that consumers are looking for critics with similar tastes, this way they act as a predictor of the quality of a product. (Basuroy, Chatterjee, & Ravid, 2003, p. 116)

On top of that, there is a trend were consumers mistrust conventional forms of advertisement. Critics offer customers a source of information independent of the producer. They can both promote a product and build reputation, which is convenient for producers since they can use these reviews in a promotional campaign.

(Larceneux, 2001, pp. 60–62)

However, this role of the expert reviewer is under scrutiny as the impact of critical opinion is different for every customer (Larceneux, 2001),

Consumers no longer believe that critical reviews tell all or reflect their own personal opinion and the role of critical reviewers in the gaming industry is therefore not yet set in stone.

As is mentioned by Moon et al. (2010, p. 109) in relation to the effect of critical reviews in the movie industry, there is an ongoing debate on what role critical reviews play on box office revenue. Whether they are predictors of box office

success, they influence consumer behavior through positive word of mouth or both. Even though consumers can obtain useful information from critics and they are definitely useful to an extent, they still tend seek out the opinion of fellow consumers, states Moon et al (2010, p. 108), since consumer opinion can be at odds with the preferences and opinions of critics.

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Research by Zhao et al. (2013, p. 153) adds that consumers now have online access to multiple reviews posted by several users, which has shown to impact product sales. Consumers seek out other customers’ opinions more and more on online review forums, causing a high connectivity between customers and greatly impacting the effect of word of mouth. (Moon et al., 2010, p. 108) Situmeang,

Leenders and Wijnberg (2014) add that it is also more common for groups of users to form communities around the brand or series they like and therefore have a sense of responsibility to the community. They propose that this feeling of responsibility lower ambiguity and other signal noise and is therefore a positive factor in buying decisions. (2014, p. 1473) While the research of Situmeang et al. was focused on the role of past evaluations on the purchase of sequels, one could propose that there is a similar trend of communities based around stars, and that consumer reviews therefore weight heavier in the mind of the consumer while buying a game.

This research proposes that the use of stars overall has a positive effect on customer and expert reviews alike.

As this research has found, one of the changes that are “easily” recognized by the customer and easily incremented by the developer is the use of a star in a game.

All of this suggests that there does not have to be a negative connotation for stars appearing in a game, the boundaries of this claim have not been researched.

Even if celebrities are used, as is found in a study related to the use of

celebrities in the movie industry by Elberse (2007), there is not enough evidence that concludes that stars add more value than they capture. In the case for the movie industry, where more research is done on the topic of star power, the results are conflicting. (Basuroy et al., 2003, p. 106)

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However, because of the aforementioned emotional and behavioral importance of stars to consumers, I suspect that stars have a stronger effect on customer reviews than on expert reviews, as they are more likely to form cliques or follow popular “stars”, while experts offer a more critical view of the offered material.

There is also the assumption that even though stars take away uncertainty, these stars still have to adhere to certain expectations. This statement can be linked to Tschang’s notion that innovation must still be familiar in order to be accepted by customers. (Tschang, 2007)

Desai et al. (2005, p. 207) have found that there is a condition to the acceptance of brand extensions. As they have found for the movie industry, highly popular movie stars are likely to make consumers expect a high quality film, however, the new movie must be of a genre a star is usually associated with. Desai et al.

conclude that consumers’ reliance on star power varies depending on genre familiarity. (Desai & Basuroy, 2005, p. 210)

Focusing on the idea of familiarity in innovation by Tschang (2007) and the signaling theory by Spence (1973 in B.I. Situmeang et al., 2014), this research tries to shine a light on the effect of stars on reviews and game sales from two perspectives: stars from inside the gaming industry and outside the gaming industry. This is done to see whether both experts and customers rely on familiar measures to gauge quality.

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Theoretic Framework

This research wants to show whether it is true for the game industry that stars bring in more revenue and cause more positive reviews. As is shown in the literature review, there are multiple clues that customers react positively to stars in games. On top of this, there has not been any research about the efficiency of the use of a star in the gaming industry. Do stars actually make it easier for developers to sell games, and is this reflected into the sales of games? Also, since it is proposed that customers react positively to stars in a game, can the same be said for experts?

To look more into these questions I offer these hypotheses:

H1a: Pop Culture Stars have a positive effect on game sales. H1b: Star Developers have a positive effect on game sales

Since customers also tend to form cliques around stars, I propose that the effect of stars on customers is stronger than on experts, who have to look at games from a professional angle and therefore might not be led by fandom as much as regular customers. This is why this research proposes the next hypothesis:

H2: Pop Culture Stars have a positive effect on reviews made by customers (2a) and critics (2b)

H2c: the positive effect of stars on reviews is stronger for customer reviews. H3: Developer Stars have a positive effect on reviews made by customers (3a) and critics (3b)

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Data and research design

The research starts of with an explanation of the industrial setting, followed by the explanation of the retrieval of the used sample and finishing with the method used to conduct the research. In the research design the measures to calculate stardom and star power are shown.

Industrial Setting

Game industry

Providing there is a multitude of game producers and to avoid confusion about which part of the game industry I am talking about I would like to provide an

inclusive definition that I will be referring to in the rest of my research.

The definition of game industry I will use is best described by Arakji and Lang (2007, pp. 198–199): The video game industry is “divided into PC games and console games. The former are played on regular home computers, whereas the latter

need specialized hardware such as PlayStation (Sony), Xbox, (Microsoft) or Wii (Nintendo) in addition to a TV or display screen.”

This definition and the parameter of this research therefore includes games played on consoles and pcs but excludes mobile games, as this is in line with most other academic research on the game industry. (Arakji & Lang, 2007; Gottschalk, 1995; Tschang, 2007)

As is stated in the introduction, gaming is turning into one of the most important forms of entertainment for the mainstream market. (Gottschalk,

1995;Binken & Stremersch, 2009; Fattah and Paul 2002 in Chaney, Lin, Chaney, 2004) As is shown the gaming industry is now good for $67 billion in revenues (in 2013). (Kamenetz, 2013) and new data shows the ‘nerdy’ male stereotype of gamers

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is now no longer accurate as both genders and every age group are using computers and playing games (Fattah and Paul 2002; Greenspan 2003a; Greenspan 2004 in Chaney et al., 2004, p. 37). Therefore the importance of this growing popularity of games is not only more revenue in general, but to marketing managers the new customer groups that use gaming as a leisure activity offer new opportunities to reach target audiences (Chaney et al., 2004, p. 37). This growing popularity and the

resulting new customer groups also mean that developers need to shift their focus on the desires of these different customer groups as they have different wants and needs that need to be addressed.

Adding to that is that my choice for the gaming industry is based on the fact that not only its popularity is gaining more mainstream players, the gaming industry is now also maturing (Tschang, 2007) which means the decision-making might become more market driven. In that case it would be essential to know what the big public likes and dislikes, especially because investing in a star costs a lot of money but it has not been proven if this method will even work for the game industry.

Stars and Star Power

Defining what makes a star is difficult as it can be viewed from several angles, however I will try to add my own definition by looking at what other researchers have found on the topic, and what they concluded to be the ingredients that make a star.

Most research has been done on the topic of stars in the movie industry. The way these researchers define who is a star or not is mostly based on whether an actor/actress or director has won an award or not. (Ravid, 1999; Basuroy et al., 2003) Wallace, Seigerman and Holbrook, define their set of movie stars by the fact if they are alive during the test and have featured in at least 7 films during their careers

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(1993, p.5) Suárez-Vázquez (2011), measures her stars through a publishing of “10 moneymaking stars of 2013”, measuring her test by choosing two films, from which one has a star (Jim Carrey) and one that does not have a star. Ravid (1999) offers a different definition to a star in a research setting. And defines stars as cast actors who have won Best Actor/Actress or a Director winning an Academy Award. He broadens his classification by taking both award winners and nominations, as this measures recognition value on a broader scale.

Unfortunately, these interpretations from the movie industry are however not as applicable to the stars in the game industry, as contrary to the definition of stars in other experiential product categories like the movie industry - where the star is almost always a person - this is not always the case in the game industry. This is also stated by Clark and Horstmann (2013, p. 18) in that fashion, a star does not necessarily take the shape of a person, but can also be a “well-known creative element that also achieves value enhancement and/or represents the low-cost means of ensuring brand recall”. This is also the stance that this research takes and this is also reflected in the definition of recognizable characters in video games by Gottschalk (1995):

“characters whom players recognize from previous encounters on the multiple screens of the media-scape.” Therefore the ability for customers to recognize and

recall a star, and their function as a signal of quality is the main focus of this research. Star type can be divided into two groups: one from inside the gaming industry and one from the outside.

The outside game stars (or Pop Culture Stars as I will be referring to them from now on) is based on the original entertainment platform a game came from, these can come from (1) movies, (2) television/animation, (3) celebrity endorsement, (4) product/toy franchise (5) sport.

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Game titles that have a name in the title of the game, of either a famous franchise or person. The people featured in the titles may vary from famous athletes (like Tiger Woods) or franchises (Disney) some even have double franchise names (Lego and Lord of the Rings).

For the titles to be selected, there needed to be at least one link to a movie (i.e. Shrek the Game), a famous person (50 Cent Blood in the Sand) or a franchise. This also means that within a gaming series some games could be stars while others could not. For example Guitar Hero was not picked as a star, but Guitar Hero featuring Green Day was picked as a star game.

The game titles serve as a visual clue for the selection of the Pop Culture

Stars. Walls (2010, p. 262) uses DVD titles as a measure for the market success of

information feedback, or in other words “the transmission of information that affects demand” in his case for the movie industry. This research builds on Walls framework by researching information (or signal effect) on game titles.

The choice for using the effect of titles is that since both DVD’s and video games are sold under the premise of using them at home at the customers own equipment, and that the amount of information available to customers at the sale of dvds and games can be viewed as similar, this research focuses on the measure of game titles for the market success of games.

In research in the gaming industry game titles are also often used as a variable to test the hypotheses (Clements & Ohashi, 2005; Binken & Stremersch 2009). Since customers are looking for signals, the first visual cue is both the title and the

packaging itself. Since this research focuses on database research and creating a database of game packaging is time consuming (and not to mention a grey area of

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what characteristics in packaging are important) this research focuses on the importance of game titles to customers.

Developer stars are picked through another measure. This research also considers developers as stars, as a star from inside the gaming industry.

However the difficulty in using developers is that award shows like at E3 give developers “Lifetime Achievement Awards”, which makes it difficult to measure at what point their games became stars and therefore which games to take into the sample. Research from metacritic.com shows that a small percentage of game producers and their developers are responsible for most of the successful games.

Therefore the developers are chosen not by any awards as would be done in academic research on stars in the movie industry. They are chosen for their critical acclaim measured by the positive reviews they have gotten from experts present in the database. Developers that have more than 10 high scoring games (8 or higher) in the database were used as a star developer. The only developers that made the cut were 1) Capcom 2) EA 3) Rockstar 4) Ubisoft.

Star power is explained as “a star being a key design component responsible for attracting large audience or loyal fans” and in movies is an easy “formula” for

bringing in audiences. (Wallace, Seigerman, Holbrook, 1993, p. 2) Even though it is explained as an “easy” formula, what the opinions are divided on what actually makes a star “powerful”.

In terms of star power, the added value of a “star” is that they “possess unique and superior attributes or skills that command a disproportionately large payoff”

(Rosen 1981 in Binken & Stremersch, 2009, p. 89). The definition of star power provided by Rosen (1981 in Binken & Stremersch, 2009, p. 89) is a very broad

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are in the game industry. In terms of star power, I would like to suggest the ability for a star to bring in more revenue (in amount of games sold) and more positive reviews from customers and critics alike as the disproportionately large payoff.

Genre and Rating

The genre and rating of a game will be used as dummy control variables. The rating for genre is taken from the official ESRB website, and the rating E and E10+ were combined as to portray the youngest demography next to Teen and Mature.

The genres were chosen as they were found to be the most popular genres according to Situmeang et al., the Action genre was chosen to offset this demography (B.I. Situmeang et al., 2014, p. 1474).

Definition of the Variables

ExpSC Expert evaluation of a game (1-10) UserSC User evaluation of a game (1-10) Sales Sale of a game (per Million copies) Sequel Dummy Variable if a game is a sequel Mature Dummy Variable of game rated Mature Teen Dummy Variable of game rated Teen E_Rate Dummy Variable of game rated E/E10+

FPS Dummy Variable if game genre is First Person Shooter Sports Dummy Variable if a game genre is Sports

Action Dummy Variable if game genre is Action Racing Dummy Variable if game genre is Racing

Star_Pop Dummy Variable of a game having a pop culture star Star__Dev Dummy Variable of a game being produced by a Star

Developer

Prob_StarP Probability of a star featuring in a game, derived from the binary logit model.

Prob_Dev Probability of a star developer, derived from the binary logit model

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The Sample

The sample of games used for the research is obtained from two online databases. (1) http://www.vgchartz.com provides the sales variable in number of copies sold and (2) http://www.metacritic.com is a video games database more often used in gaming industry research ( Situmeang et al., 2014; Hennig-Thurau et al., 2009) for both customer and critic game review scores.

The games used as a sample are games published on the market between 2005 and 2015. This timeframe is chosen since the website vgchartz.com did not exist before 2005; therefore the sample is capped at that point as there is no suitable information available on total number of copies sold before that time.

The sample is appropriate for the research question because the sample ensures a cross-sectional overview of both sales and customer and critic reviews combined, allows not only to show if but also under what circumstances star power has an effect and since the sample of both sales and reviews is taken out of a period of 10 years, this creates a reliable and numerical setting to accurately test the effect of star power.

The complete sample of games used for the research finally consisted of n = 1422 games, of which the full information can be found in graph 1 below.

1: Thesis Sample

N Min Max Mean Std. Dev.

Ex_SC 2667 1.16 9.80 6.5778 1.50663 Us_SC 2118 .29 10 7.3223 1.40301 Sales 2137 .01 81.00 1.2661 3.10208 Star_Pop 3843 0 1 .208 .4061 Star_Dev 3851 0 1 .08 .266 Valid N (listwise) 1422

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Method

All data is first collected in Word Excel, after which the data was put through testing in SPSS 20 for Mac. The games in the sample are connected to a

corresponding number, resulting in a Series ID. If and which number the game is within a series is coded by using a game sequence ID.

This is followed by the categorization of Sales (number of copies sold in Millions), Genre (definitions taken from metacritic.com), Version (first or sequel), Developer, Console, Review Score, and Star Type. The categorization on which Star Type (Binken & Stremersch, 2009; Farrand et al., 2006; Gottschalk, 1995) is based can be found in the operationalization of stardom earlier in the paper.

After categorization the method used to conduct the research is largely based on the work by Ho et al. (2009) who use a “two-stage instrumental variable estimator modified for a discrete endogenous decision variable” which resembles this research setting. (Mroz, 1999 in Ho et al., 2009, p. 170)

The method is chosen simply because in the case of this research there could also be synchronous characteristics occurring with the use of a star that are not accounted for, that may influence game reviews and sales.

This stage is followed by the 2nd stage in which the estimated probability score of the first step is merged into the equation to predict whether there is an effect of “star power” (Ho et al, 2008, p. 178) within the gaming industry and further; if and/or how the appearance of a star within the game influences the customers perception of the game.

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2: Descriptive Statistics and Correlation Matrix

Mean SD Sales Ex_SC Us_SC SEQ Mature Teen E/E10+ Sports Racing FPS Action Star_ Pop Star_ Dev Sales 1.266 3.102 Ex_SC 6.578 1.507 .268* * Us_SC 7.322 1.403 .067* .565** SEQ .676 .468 .099* * .122** .029 Mature .13 .340 .056* * .097** -.005 .055** Teen .23 .419 -.049* .029 .055* .055** -.213** E/E10+ .58 .494 .000 -.108** -.041 -.130** -.460** -.636** Sports .13 .334 .088* * -.002 -.060** .062** -.135** -.081** .187** Racing .06 .245 .005 -.010 .000 -.025 -.103** -.079** .146** -.100** FPS .05 .221 .082* * .090** -.007 .048** .275** .033* -.254** -.089** -.061** Action .20 .400 .002 -.073** .012 -.017 .160** .030 -.140** -.191** -.130** -.116** Star_P op .208 .406 .023 -.109** -.122** .075** -.107** .016 .081** .202** .048** -.091** .109** Star_D ev .08 .266 .096* * .133** .054* .067** .093** .029 -.079** .047** -.028 .017 .039* .051** ** Significant at 0.01 level (2 tailed)

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Descriptive and Correlation Matrix

The correlation matrix in table 2 shows all means, standard deviations and correlations between the used variables. Even though correlation does not mean causation, and therefore some caution should be taken when deriving results from the correlation matrix, some preliminary relations between variables can be observed through its observation. One of the first things that can be observed is that the Pop Culture Star variable is not significantly correlated with Sales (r = .023 p > 0.05) while Star Developers are positively correlated with Sales (r = .096 p < 0.01)

This could be preliminary evidence that stars from popular culture do not have a positive influence on game sales figures but that Star Developers might have other characteristics that do cause sales to rise.

Both Pop Culture Stars and Star Developers are correlated to Expert Score, however, this correlation is negative for Pop Culture Stars (r = -.109 p < 0.01) and positive for Star Developers (r = .133 p < 0.01) showing that experts react more favorably to games by Star Developers. The same can be said for User Score, which is negatively correlated with Pop Culture Stars (r = -.122 p < 0.01) and positively with Star Developers (r = .054 p < 0.05)

Interestingly to note is that both star types are positively correlated with sequels, indicating that there is a possibility that stars are more likely to either make or appear in a sequel. In terms of rating both stars have a positive correlation E-rated games, Popular Culture Stars are negatively correlated to Mature rated games (r = -.107 p <0.01) and Developer Stars are positively correlated (r = .093 p < 0.01) but there is no correlation found for Teen rated games, which is interesting because one could argue that Teens are more susceptible to the appearance of stars. The

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Pop Culture stars except First Person Shooters, which are negatively correlated (r = -.091 p < 0.01).

Binary Logistic Regression

In order to test the hypotheses first we have to check the probability that a game has a star. Running these tests will control for endogeneity, - the attributes that are not accounted for in the model that may potentially influence either the number of copies sold or the reviews of the game -, by using a variety of exogenous variables. (Ho et al, 2008, p. 172, Situmeang et al, 2014, p. 1476)

Since having a star appear in a game is a binary variable (either Yes or No), the probability is tested through a binary logistic regression. The binary logistic regression does not only calculate the odds under which a binary variable occurs, but also calculates estimation values of the probability of a star occurring, which is later added to the regression model which tests the research hypotheses.

The binary logistic regressions also have to be tested on “goodness of fit” of the model by the Hosmer and Lemeshow test. The preferred outcome for the Hosmer and Lemeshow test is non-significance, as its purpose is to show whether the model has better fit than a model with no predictors. If the test statistic is greater than 0.05 we can assume that there is a non-significant difference between the observed values and the values predicted by the model. This means we can use the Binary Logistic Model outcomes to infer probability.

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3.Hosmer and Lemeshow Test

Chi-square Df Sig.

11.310 8 .184

4. Binary Logistic Model: Star Pop

Variables B SE Exp(B) Sales .000 .017 1.000 Ex_SC -.117* .056 .889 Us_SC -.103 .058 .902 SEQ .308* .150 1.361 Mature -1.646* .811 .193 Teen -.355 .793 .701 E/E10+ .009 .791 1.009 Sports 1.666** .181 5.293 Racing .543* .256 1.721 FPS -.079 .382 .924 Action 1.328** .173 3.774 Nagelkerke R Squared .242 ** Significant at 0.01 level * Significant at the 0.05 level

The first binary logit model tests the odds ratio which determines what circumstances a Pop culture Star features in a game or not. One of the first things we can observe is that the addition of several independent variables to the model is significant. Interesting is that Expert Score is significantly negatively related to the appearance of a star in a game (B = -.117 p < 0.05) and that therefore the odds of a star being in a game diminishes with a more positive review score by experts (by factor .889) this is in accordance with the prediction in the correlation model. The same can be said for Mature games, which is also negatively related to the odds of a star appearing in a game. (B = 1.646* p < 0.05) The genre of a game also plays a role in the probability of a star appearing in a game. For both Action (B =1.328 p < 0.01) and Sports games (B = 1.666* p < 0.01) the model shows a positive significant

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5. Hosmer and Lemeshow Test

Chi-square Df Sig.

10.445 8 .235

6. Binary Logistic Model: Star Developer

Variables B SE Exp(B) Sales .023 .018 1.023 Ex_SC .214** .076 1.239 Us_SC -.023 .079 .978 SEQ .207 .194 1.230 Mature -.690 .889 .501 Teen -.527 .880 .590 E/E10+ -.721 .878 .486 Sports .698** .241 2.011 Racing .252 .365 1.287 FPS .443 .322 1.557 Action .551** .218 1.735 Nagelkerke R Squared .045 ** Significant at 0.01 level * Significant at the 0.05 level

The binary logit model shows a slightly different story for Star Developers. A game having a good Expert Score increases the odds of a star featuring a Star

Developer (B = .214 p < 0.01). The odds for a Star Developer also increase if a game is an Action game (B = .551 p < 0.01) or a Sports Game (B = .698 p < 0.01)

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7. Multiple Linear Regression Models: Pop Stars

Game Sales Expert Reviews Customer reviews

Beta VIF Beta VIF Beta VIF Beta VIF Beta VIF Beta VIF

Sales .175** 1.037 .216** 1.026 -.044* 1.105 -.073** 1.098 Ex_SC .369** 2.195 .337** 1.598 .194** 2.267 .818** 1.223 Us_SC -.084* 2.089 -.111** 1.665 .174** 2.026 .532** 1.190 SEQ .037 1.457 .061* 1.116 .253** 1.309 .078** 1.110 .234** 1.344 .068** 1.112 Sports -.153 13.538 1.238** 9.969 1.136** 10.857 Racing -.025 1.469 .001 1.080 .212** 1.364 .004 1.080 .181** 1.402 -.015 1.080 Action -.107 7.412 -.005 1.474 .758** 6.078 -.085** 1.461 .918** 5.659 .206** 1.402 Mature .138* 4.976 .064 1.754 -.566** 4.245 -.053* 1.753 -.716** 3.923 -.200** 1.691 E/E10+ .037 2.246 .066* 1.757 .254** 2.096 .027 1.761 .301** 2.057 .108** 1.743 Prob_Pop .305* 26.927 .101* 2.780 -1.744** 19.896 -.121** 2.766 -1.788** 20.325 -.336** 2.602 Star_Pop -.001 1.226 .000 1.225 .008 1.226 .001 1.225 .008 1.226 .002 1.225 R Square .098 .092 .096 .091 .574 .571 .420 .416 .523 .520 .404 .400 Adjusted R Square ** Significant at 0.01 level * Significant at the 0.05 level

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8. Multiple Linear Regression Model: Developer Stars

Game Sales Expert Reviews Customer reviews

Beta VIF Beta VIF Beta VIF Beta VIF Beta VIF Beta VIF

Sales -.321** 1.743 -.128** 1.476 .064* 2.280 .005 1.525 Ex_SC -.741** 4.027 -.255** 2.945 .865** 2.070 .768** 2.095 Us_SC .046** 1.617 .005 1.609 .265** 1.250 .406** 1.107 SEQ -.217** 1.215 -.109** 1.161 -.175** 1.161 -.117** 1.137 .086** 1.311 .060** 1.173 Sports -.778** 2.774 -.577** 2.397 .139** 4.127 Racing -.144** 1.201 .020 1.078 -.101** 1.195 .028 1.076 .021 1.248 -.007 1.079 Action -.560** 1.920 -.202** 1.333 -.474** 1.450 -.275** 1.165 .238** 2.547 .163** 1.352 Mature .110** 1.336 .092** 1.335 .109** 1.301 .127** 1.299 -.100** 1.348 -.092** 1.334 E/E10+ .384** 1.694 .110** 1.350 .241** 1.722 .019 1.368 -.094** 2.016 -.039 1.366 Prob_Dev 1.534** 5.053 .770** 2.381 1.153** 3.417 .686** 1.853 -.447** 10.111 -.266** 3.173 Star_Dev -.008 1.028 -.004 1.028 -.006 1.028 -.004 1.028 .002 1.028 .001 1.028 R-Squared .563 .560 .344 .340 .811 .809 .672 .669 .383 .379 .379 .375 R-Squared Adjusted ** Significant at 0.01 level * Significant at the 0.05 level

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Testing the hypotheses

The probability scores of both Pop Culture Star and Developer Star are inserted into multiple linear regression models to test the effects of added variables. For all dummy variables one variable was removed to avoid perfect multicollinearity as is similar to the approach made by Ho et al (2009), I removed Teen for the Rating dummy variables, and First Person Shooter for the game genre. Even though this is done, still some variables show multicollinearity. Since all VIF’s ideally should be below 5 to have no multicollinearity, both Sports and Action variables and the probability measure calculated by the binary logistic model showed signs of severe multicollinearity, and more action needed to be taken.

One remedy for multicollinearity is to remove one of the offending variables to see if this improves the VIF. This is done for the Sports variable, after which we can observe that all VIF have dropped below 5 and are all therefore within acceptable range. Doing this also has its downsides since all control variables serve to explain part of the model, and this change had quite strong consequences, however from this second model we can at least infer about our hypotheses.

As we can see from the regression models of Popular Culture Stars, hypothesis one can be completely rejected for both Pop Culture Stars and Developer Stars, as there is no significant effect of Stars on the sell of a game and therefore it cannot be proven that stars have a positive effect on game sales. (Star_Pop Beta .000 p > 0.05; Star_Dev Beta -.004 p > 0.05)This is unfortunate as the probability score f of both Pop Culture Stars and Developer Stars show significant probability in combination with Sales. (Prob_Pop Beta = .101 p < 0.05; Prob_Dev Beta = .770 p < 0.01)

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And while the probability of having a Popular Culture Star is significant, its effect is negative for User Score meaning the probability of a Pop Culture Star being in a game has a negative influence on User Score. (-.336 p < 0.01)

Hypothesis 2a, 2b and 2c as well as 3a, 3b and 3c, can also be rejected, as neither Star Developers as Pop Culture Stars showed to have any relevant significant effect on both customer (Star_Pop Beta .002 p > 0.05; Star_Dev Beta .001 p > 0.05) and critical reviews (Star_Pop Beta .001 p > 0.05; Star_Dev Beta -.004 p > 0.05), this is unfortunate as both Pop Culture Stars and Developer Stars showed some promising probability in the binary logistic model to appearing in a game with good expert reviews.

Because of these results a final statement cannot be made whether who is affected more by stars, since both show no significant influence on either sales, reviews by critics or reviews by customers.

Discussion

In the discussion section the results of the analyses are discussed and the answers to hypotheses constructed in the beginning of the research are addressed. Following, the academic and managerial implications flowing from these answers are discussed, as are the strengths and weaknesses of this research. In the conclusion all findings of this research are surmised and thus finishes the thesis.

Academic Implications

This research has several implications for further academic research on the topic. This research was the first to look at both outside and inside the game industry for stars, building on both research from the gaming industry on stars (Farrand et al.,

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2006) and outside, from for instance Signaling theory (B.I. Situmeang et al., 2014), this paper offers an approach to stardom not yet seen in the research of the game industry. The research moved away from traditional sources of stardom as was found in the movie industry through the research of awards and created its on by connecting a large sample from a respectable source to materials from the entertainment industry.

The research was the first in its kind because it tried to look at the purpose of stars in the game industry and the role they could possibly fulfill.

The rejection of the hypotheses means that even though much research has been done on a topic in one part of the entertainment industry, the results can differ tremendously. There was evidence from other academic research that stars would have an influence on the customer. Stars could increase customer happiness (Yang et al., 2006, p. 143), fulfill behavioral goals (Lindenberg et al., 2011; Rindova et al., 2006) and customers would also be willing to see stars outside their normal market as they are also not opposed to brand placements in games (Yang et al., 2006). Using celebrities as endorser, spokesperson or as advertising vehicle was established as a way to get customers interested in the products to be sold. (Farrand et al., 2006, p. 12; Kamins et al., 1989, p. 4) However this research concluded that there was no provable connection between stars and any favorable outcome like increased sales or better reviews and therefore adds a new viewpoint to the discussion, for both research on stardom in general as applied to the entertainment industry.

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Managerial Implications

The results of this thesis can be used by managers in the game industry to assess how to bring a game to the market, especially if they are thinking to use a star as a marketing tool. It is important for marketing managers and game developers to know how their customer is going to react to new ideas, especially since this paper established that innovation should be familiar to the customer.

The conclusion managers and producers could take from this research is that the use of a star may not result in any added benefits to the company. Now this new evidence leads to the conclusion that it adding a link to a star might not reave the benefits like good reviews by either critics and customers and it definitely does not guarantee additional revenue, managers would be wise to research their decision thoroughly.

Strengths and Weaknesses of research design

Conducting the research the way this research does, has both strengths and weaknesses. The strength of the research design is that this research opens up a new dimension in game research that is not yet explored. There is little to no research done previously on the use of stars within games, and therefore it offers valuable

information for game developers and marketers alike.

Also, because we have accounted for endogeneity with the two-stage model we can also estimate that endogeneity is addressed sufficiently and that the results coming from the test are reliable.

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While the results from the tests are inconclusive and lean more to the negative side of the spectrum, this too brings valuable information to researchers and game developers alike as to the added value of stars in this industry.

There are also some limitations to the way the research is conducted. When choosing the Star Developers, this research focused on their success measured by their expert score. There are multiple ways of measuring whether a developer can be called a star, as their star power could also be based on whether the directors get an award as is done in most research on stars in the entertainment industry. This research did not take this route as the only awards given to developers at the moment of

writing the thesis were “lifetime achievement awards” to certain members of a developing team. This makes it difficult to measure at what moment a developer becomes a star and what games to be included in their “star oeuvre”.

Focusing on expert score therefore seems like a logical decision, but this could work against the smaller game developers or popular indie developers to be included in the model. Adding to this is the fact that this research uses games from 2005 onwards, however there are of course developers who have made a big name for themselves before that time and therefore may not be included in the Star Developer variable while they might deserve to be. This is something that could be added to future research to get a more complete picture of the role of developers and their star-power.

Another limitation is the choice for research through a database. There is no real contact with the reviewers and, as is stated in a similar test by Situmeang et al, (2014, p. 1483) due to high anonymity of the Internet, we cannot know for sure if their review reflects their buying behavior. It would be good to have a research where these two factors were connected.

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This would also take away the problems connected to using databases like Metacritic. The use of review websites makes the repeatability of this study harder as the reviewer can take away or change the tone of their review, and reviews are added every day which changes a games average review score

The way the stars are chosen might also create a limitation, since the focus lies on titles as a visual clue but this has not taken into account any other visual clues that might be present on the packaging. Taking this into account might give a richer view on how consumers make their final buying decisions.

Also, this test does not take into account the effect the advertising of a star within a game has had on the evaluations. If the star is heavily marketed, for instance in other markets, this also may influence the effect of the star appearing within a game. (Situmeang et al, 2014, Ho et al, 2008) The research does not take into account the revenue of a developer. If a developer has more money, then the chance of using software to put in a famous face might go up. This would go hand in hand with licensing; sometimes authenticity is not possible due to pricing or other agreements made by the original company (think pro-soccer players in Pro Evolution Soccer looking different from their EA FIFA counterparts). Adding to that, many studios might choose bigger stars for certain projects that are already known to pull in high revenues, or that stars may choose the most promising projects (Elberse, 2006, p. 4).

Also in addition, many more added variables could be measured together with a star, as to see whether some moderating factors might still have an effect on game revenues and reviews.

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Conclusion

This research has been trying to find the influence of star power on reviews and sales of games based on the Signaling theory developed by Spence (1973). The game industry was chosen because of its growing importance as mainstream

entertainment and the problems game developers face as they create games that are experiential in nature. The problem of games and experiential products are that by only being able to assess the quality of the product after purchase, it makes it difficult for consumers to predict what product on offer they are going to like, on top of that developers have problems forecasting whether a new venture will be successful or not.

This research proposed that customers use the appearance of stars in games as a predictor of quality. The fact that they were used as a signal of quality would result in better scores of sales and positive reviews from both experts and customers. Before the actual testing of the hypotheses, I proposed that customers are looking for new products under the condition that they fit their existing frame of reference – they are recognizable as being of good quality.

Stars from both pop culture and from within the gaming industry were chosen to create the broadest base for research on the influence of stars. What the results show is that both types of stars had no connection to either of the dependent variables and therefore all of the hypotheses in the research were rejected.

However, some possibly important factors were left out of the equation, like developer revenue and the choice for database research over customer contact. This is were other scholars might find another gap to challenge the outcome of this paper.

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Overcoming uncertainty : the influence of stars on video game reception