External Information Search on the Home Video Gaming Market

(1)

External Information Search on the Home

Video Gaming Market

Arjan Zijlstra S1414305 Date 16-09-2010 Supervisor Dr. Wander Jager Abstract

Present research was executed to research differences in the external information search between hardcore gamers and casual gamers. The results of this research indicated that hardcore gamers were more involved than casual gamers and, therefore, spent more effort

searching information before purchasing a video game and are willing to pay for this information. Casual gamers spent less effort searching for information, and were more

dependent on marketing activity for information about video games. The information acquired by the hardcore gamers is mostly found in dedicated magazines, tv-shows, and websites. Differences in susceptibility towards normative influence between these groups

were not found, but were indeed present in different age groups, as younger gamers were more susceptible to normative influence than older gamers. Also, network effects became

(2)

1. Introduction

Over the last decades, the video game industry has faced an enormous growth of sales. It is still a relatively young market, with the first video games available for mass consumption just three decades ago. Since then, much has changed. In the late 1970’s people played Space Invaders with just a two directional and a fire button, while at this moment, immense virtual worlds can be explored with sceneries that are beyond the fantasies of many. The industry has evolved from mere PC games to special gaming consoles, increasing in performance with every new generation. Having arrived at the seventh generation of gaming consoles, gaming design has delivered stunningly beautiful 3D worlds in which the gamer can experience several adventures in an alternate reality. In this industry, some games become an unexpected success, while others fail. So far, it has been hard to put the finger on the exact reason why a video game becomes a success. The target consumer base consists of a diverse group of people, which have different norms, needs, and motivations. By carrying out this research, the aim is to gain more insight in the factors which drive the success of a video game at different consumer groups.

The giant steps taken in the gaming industry are the result from technological advances that have increased the performance of the consoles and the imagination of the developers of video games. In 2004, the size of the gaming industry in the U.S. was a massive $10.3 billion (www.businessweek.com). This shows that the gaming industry is thriving and can already be considered as a part of today’s and tomorrow’s culture. With the gaming industry still growing each year, especially the future value of games should be taken very seriously.

In what is called the seventh generation of gaming consoles, the competition in the gaming console market is heavier than ever. Nowadays there are three home gaming consoles which make up the larger part of the console sales. These home video gaming consoles are Sony’s Playstation 3 (PS3), Microsoft’s X-Box 360 (X360), and Nintendo’s Wii.

(www.consumersearch.com, www.wikipedia.org). All of these have different characteristics, just

(5)

Table 1 (Information from McMillan & Gaczka, 2009; blog.nielsen.com)

Console Nintendo Wii Playstation 3 X-box 360

Performance *** ***** *****

Motion Control ***** *** -

Online Gaming * **** ***** (not free)

Game Selection **** **** *****

Console usage *** ***** *****

Price €199 (bundle) €299 €199 - €299 (bundle)

Wi-Fi offered offered not offered

Compatible with: none CD, DVD, Blu-ray CD, DVD

Backw. Compatibility Fully Partially Fully

Age segment Male 6-11 Male 14-18 Male 12-17

Women 25+ ***** = Very good (industry leader) * = Very bad (lowest in industry) - = Not available

The characteristics of the different consoles imply differences in user profiles. The consoles which deliver the highest performance, such as the PS3 and the X360, attract the segment of ‘hardcore gamers’, who are engaged console users, while the Wii offers entertainment for the whole family. The Wii appeals the most to males between six and eleven years old, and women between 25 and 34. The Wii users are similar to what literature (e.g. Bosser & Nakatsu, 2006) refers to as casual gamers, those who want quick entertainment with less immersion. The potential market of these gamers is very large (www.wikinvest.com). Hardcore gamers spend more time on their consoles and less time to other entertainment such as television. This group can be split up in X360 and PS3 users, who have different average ages. The X360 is mostly used by males between twelve and seventeen years of age, while most users of the PS3 are between eighteen and 24. The Wii is the most sold video game console on the market. However, this does not mean that it is used most frequently. In fact, the X360 and the PS3 exceed the Wii in total playing time (McMillan & Raczka, 2009).

(6)

multiplaying options. In this way, social value of video games increased. Knowing that group membership is a determinant of behaviour (Merton & Ross1, 1949), and the fact that visible goods are more susceptible to social influence (Bearden & Etzel, 1982), social influences in the video game industry are expected to have risen over the years. Previous research has shown that the video game industry shows an exceptionally strong relationship between game quality and game sales (Binken & Stremersch, 2009), which leads to evidence that a video game is a product which quality is heavily discussed among experts and consumers. Two categories of gamers are identified by the industry (www.wikinvest.com) and literature (Bosser & Nakatsu, 2006), hardcore gamers and casual gamers. In this study, it will be researched whether the information searching behaviour of the two categories differ and if different marketing strategies are desirable for the two customer segments.

The results of this research are useful as an indicator of the buying behaviour of consumers in the gaming industry. Furthermore, further scientific research can benefit by using the results of this study, while video game publishers might gain insight in the external search behaviour of the consumer. First and foremost, insight is given in the motivations of different types of consumers within the industry. By specifying the searching behaviour of different consumers, game developers can perform the most effective way to pursue the customer to purchase their games. It is also important to know at which point in time specific customers need to be addressed. For example, early adopters are often highly influential people who use independent information channels, while later adopters are more dependent on their social environment to gain information (Rogers, 2003). As hardcore gamers spend more time playing games, similarities arise with the group of earlier adopters. I will assume that hardcore gamers are the early adopters of the video game industry. This is because of an increased knowledge about video games, which is caused by more external search effort, they should be more confident about their choices. As this group is more aware of the activities in the video game market, they are aware of the newest video games to be released. The results of this study are also useful for scientific purposes, as information about the video game industry is lacking. As a result, differences between the video game industry and other entertainment industries can appear and subsequently be analyzed.

1.1 Past research in similar industries

(7)

the factors that drive the diffusion of a video game and its different users. Because the lacking of dedicated literature, the forming of hypotheses will be somewhat tentative. That is why the nature of this research will be explorative, with hypothesis partially based on previous research in comparable industries.

The larger part of research on success factors in the entertainment/arts sector can be found in the motion picture industry. While not being similar in consumption patterns, there are similarities between the video game and the motion picture industry which lead to evidence that information search is also similar. Movies are viewed in public, or can be bought/rented for home use often via similar online or retail channels in which video games are purchased (e.g. Free Record Shop, www.amazon.com). Especially with the increasing usage of the Internet, the influence of opinion leaders and word-of-mouth (WOM) is increasing. Both products are heavily discussed in online message boards (e.g. www.imdb.com, www.gamesforum.com), and many websites and magazines have professional reviewers employed to publish judgments on recently released products (e.g. www.ign.com, www.gamespot.com, www.rottentomatoes.com). In the movie industry, research has shown that WOM and reviews have a significant impact on box office revenues (Duan, 2008; Liu, 2006; King, 2007). However, evidence from the video game industry considering WOM is lacking.

Within the video game industry, network effects are clearly present (Shankar & Bayus, 2003). Gaming consoles which have greater installed bases have an advantage over their competitors. Yet, network effects do not only occur in large customer bases, but are also related to network strength. In this way, a console with a smaller installed base can compete with consoles with larger customer bases (Shankar & Bayus, 2003).

(8)

1.2 Rogers’ Innovation Diffusion Theory

A key focus of diffusion theory (Rogers, 2003) is the way in which information about an innovation is transmitted to or within the social system (Mahajan et al., 1990). Rogers (2003) distinguishes between five groups which have different propensities to adopt an innovation. Ordered from high time to adopt to low time to adopt, the groups are ordered as follows: Innovators, Early adopters, Early Majority, Later Majority, and Laggards. Each of these groups has its own personality, which are displayed in Appendix B. As these groups have different personality characteristics, part of this research is dedicated to research which product information search channels they use.

For the hardcore gamers, gaming is a significant part of their lives. As they are to a higher extent involved, and make up for the more knowledgeable part of the gaming market (e.g. Moorthy et al. 1997), I expect that hardcore gamers are the first ones to buy recently released games, and as such, are the early adopters of the gaming industry. The casual gamers play games purely out of relaxation, they want quick entertainment with less immersion (Bosser & Nakatsu, 2006). As opposed to the group of hardcore gamers, I expect them to perform less effort on information search. As they are not following developments in the gaming market, I assume casual gamers to be later adopters of recently released games. The differences in distributions in purchasing behaviour of an innovative product of the two groups of gamers is shown in figure 1. (The purpose of this figure is only to give an indication on the shape of the distributions and is not based on empirical data.)

(9)

Regardless of the adoption stage, Rogers (2003) mentions five qualities which determine the success of an innovation in general. This theory can also assist in the understanding of the success of a video game. In the following segment, the qualities which cause the success of an innovation will be discussed regarding video games.

First, an innovation must be perceived better than the product that is used (positive relative advantage). This can explain the success of sequels. For example, in the Fifa-soccer franchise, each year a new version is released with updated squads, better gameplay and graphics. Because the relative advantage is clearly visible, the sales numbers are high each year. The second quality, compatiblility with existing norms and values, is the degree to which an innovation can be assimilated with a potential adopter’s life. In the video gaming industry, this is an essential quality, as games need to be playable on the console that is owned by the potential adopter. As such, diffusion can be inhibited by consumers still playing games on an obsolete console. Also, diffusion is dependent on the console on which a game is released. If a game is compatible with more than one console, diffusion is more likely to be larger than with exclusive releases.

(10)

1.3 Consumer external information search

In many cases, the buying process of a certain product begins with an external search to obtain information about the product that is to be purchased (Beatty & Smith, 1987) A vast amount of research has been dedicated to the area of external information search. (e.g. see Guo, 2001). Usually, information search has two phases, internal search and external search. Internal search refers to the acquisition of information from sources inside of the memory, while external search is the acquisition of information from sources outside of the memory (Guo, 2001). Research on external search has been performed on different sorts of products, such as cars (e.g. Kiel & Layton, 1981), and TV’s (Duncan and Olshavsky, 1982), and bread (Moore & Lehmann, 1980).

Findings of these studies were that consumers can be classified as low information seekers, high-information seekers, and selected information seekers (Kiel & Layton, 1981). Kiel and Layton (1981) also found that high self-confidence, low price, negative attitude towards the shopping process were related to low information-seeking. Duncan and Olshavsky (1982) found a relationship between marketplace beliefs (e.g. on advertising, salespeople, perceived ability to judge) and information search. In the case of bread, Moore & Lehmann (1980) found that time pressure (negative), and product knowledge (positive), were related to time spent on information search.

Information search can be stimulated positively by decreasing search cost and increasing benefit of search (Guo, 2001). Decreasing search cost, for example, can occur when information is easily accessible. Increasing benefit occurs, for example, when a cheaper alternative can be found while increasing search. Some variables, such as complexity of alternatives, can increase cost, but also increase benefit, because an informed choice can make a substantial difference. If the benefit outweighs the cost, there will be a positive relationship between the variables and the search effort (Duncan & Olshavsky). On the contrary, there are also many variables which impact external search negatively. For example, brand loyalty (Jacoby et al.) and brand satisfaction (Punj & Staelin, 1983), decrease search effort because it lessens increased benefits of search.

(11)

1.4 Involvement

People who are highly involved with a product, have an increased likelihood to spend more time gathering information about the product of interest (Moorthy et al. 1997). Product involvement leads to a more extensive decision-making process including greater deliberation and search. The benefit of search is driven by how a consumer perceives the uncertainty in his or her choice environment, the importance of the product to be purchased, and the risk aversion (Moorthy et al. 1997).

Laurent and Kapferer (1985) distinguish between four antecedents which exert influence on involvement. From their research, the first antecedent was product importance and perceived importance of the consequences of a mispurchase. Second, the subjective probability of a mispurchase. Third, the hedonic value of the product class, and fourth, the symbolic or sign value of the product class.

Knowing this, it seems logical that people who devote more attention to the information gathering, are less vulnerable to extensive marketing campaigns and are more interested in objective judgments from experts. As they have higher knowledge, by means of information searching, marketing campaigns will not provide extra incentives for buying a video game. In this way, especially for the adolescent segment, the large amount of information that is to be found on the internet will be used frequently. When involvement is high, adolescents use online word-of mouth frequently (Wang & Chang, 2008). Just as WOM is perceived as credible and trustworthy (Liu, 2006), Social Influence (Bearden & Etzel, 1982) and Critics/Opinion Leaders (Rogers, 2003) are perceived as objective sources. As marketing activity is performed by the developers themselves, these marketer dominated sources are perceived as less credible (Hoyer and MacInnis, 2007). Because people with high involvement are, to a higher extent, searching for product characteristics (Cialdini & Goldstein, 2004), I expect this holds true for the video game industry.

H1: Hardcore gamers are to a higher extent involved when purchasing a video game and therefore spend more effort searching for product information

1.5 Social influence on consumer behaviour

(12)

influence on different products, Bearden and Etzel (1982) refer to a video game as a privately consumed luxury. Because consumption of this product is not visible to others, the consumers are less susceptible to social influence when making the purchasing decision. More than 20 years later the situation has changed significantly, as game consoles nowadays are multimedia stations which are also used as a socializing medium. Because of this, the concept of the video game as a privately consumed luxury is outdated. A shift has taken place from a privately consumed luxury towards a publicly consumed luxury, which leads to evidence that that the purchaser of a video game gets more susceptible to social influence (Bearden et al. 1982; Batra et al. 2001).

A closely related stream of research is specified as Word of mouth (WOM). WOM, or “buzz,” involves informal communication among consumers about products and services (Liu, 2006). WOM has been recognized as one of the most powerful marketing tools since the beginning of human society (Duan, 2008), this is especially the case for experience goods (Godes & Mayzlin, 2004). WOM affects consumer purchasing behaviour, especially in Internet retail sites (Chevalier, 2006). WOM is usually perceived as useful and credible and trustworthy (Liu, 2006). A McKinsey & Company study found that 67% of the sales of consumer goods are based on WOM (Liu, 2006).

In the film industry, which has similar WOM-communication channels as the video game industry, WOM plays a particularly important role. Awareness must be built and consumers need information when deciding whether to purchase a product they do not know well. In this market, hundreds of new films are released annually in Hollywood and by independent filmmakers. In general, it is believed that WOM strongly influences people’s movie selection (Liu, 2006). De Vany and Walls (1999) suggest that it is film quality that determines the success of a movie. After reviewing a huge dataset containing 2.015 movies, they conclude that after the movie comes out, the audience decides its fate. This implies that film quality is a huge determinant of film success. Based on this information, I assume that video game quality can be a marketing tool for itself, as the information search channels for movies and games are similar, reaching the consumer via WOM and critical reviews. Delre et al. (2008) found that imitation and coordinated consumption are key to a film becoming a hit. The greatest effect, that of coordinated consumption, can be explained by the high amount of social influence in the motion picture industry. A movie is in most cases consumed in the company of some friends/family, which stimulates conformity behaviour. (Delre et al., 2008).

(13)

reduce complexity and most of all, the product gets observable for the masses through discussion (Rogers, 2003). Also, the network effects that will be mentioned further on, can increase trialability of a video game when several members of social networks share the same video game.

1.5.1 Normative influence

The term normative influence derives from norms, which are society’s collective decisions about appropriate behaviour. Normative influence implies that consumers will be punished or rewarded by their social environment for following norms and expected behaviours (Burnkrant & Cousineau, 1975). McGuire (1968, as in Batra, 2001) characterized influenceability as a consequence of, and as being dependent on, personality variables such as self-esteem or anxiety, as these variables make a person more influenceable.

The types of social influence can differ between products and occasions. Previous research has shown that people differ in their susceptibility to normative influence (SNI) (Orth & Kahle, 2008). A person’s social identity may shape their SNI. More specifically, people with a less complex social identity exhibit a higher SNI (Orth & Kahle, 2008). People with complex social identities are often associated with higher relative importance of fulfillment, self-respect, and sense of accomplishment (Orth & Kahle, 2008). These values are referred to as internal values. External values tend to relate positively to SNI. Examples of these values are sense of belonging, security, being well respected, warm relationships with others, and also fun/enjoyment values (Homer & Kahle, 1988).

(14)

casual gamers are less involved, and search for quick entertainment (Bosser & Nakatsu, 2006), I expect casual gamers to be less affected by normative social influences when purchasing a game. This expectation also rises from the fact that hardcore gamers are part of smaller networks, which have higher network strength (Shankar & Bayus, 2002). Furthermore, as adolescents are more susceptible to normative peer influence (Mangleburg et al. 2004), I expect the effect of normative influence to be stronger with the younger (perhaps X360) users of video games (See table 1)

H2: Hardcore gamers are more susceptible to normative social influence than Casual gamers

H2b: (Pre-) Adolescent video game users of the hardcore gamers segment are more susceptible to normative influence than adult hardcore gamers

1.5.2 Informational influence

Informational influence is driven by a desire to form accurate interpretations about reality in order to make more informed decisions and behave correctly (Cialdini and Goldstein, 2004). Reference groups and other sources of influence can exert informational influence. Informational influence is taken into account when utilities of the product need to be acquired, with the goal of making effective decisions (Cialdini & Goldstein, 2004, Rosen and Olshavsky, 1987). Informational influence is important in a sense that it effects the amount of time and effort consumers devote to information search and decision making.

(15)

experiences rather than the using this information in the purchase stage. For casual gamers, however, I expect they do consult their social environment for product information, as they have less expertise.

H3: As they have less expertise, casual gamers use informational social influence to a greater extent in their purchase decisions than hardcore gamers.

1.5.3 Network effects

Network effects occur when a consumer’s utility of using a product increases with the number of other consumers using the product (Katz and Shapiro, 1985). For example, the utility of Microsoft Word increases if your social environment uses the same word processor, because file exchange gets more convenient. A video game system consists of the console (hardware) and the game (software), which is the most commonly discussed source of indirect network effects (e.g. Katz and Shapiro, 1994; Church & Gandal, 1992). In the video game market, a great benefit can arise when people within a social network share the same gaming console. In this way, games can be exchanged and the owners can engage in (online) multiplayer gaming. Literature supports that indirect network effects occur in the video game market (Clements & Obashi, 2005). In this way, the console is not per definition bought because of peers owning the same console, but information is exchanged between the members of the social network about the product (Birke, 2009). Shankar and Bayus (2003) found that network effects are stronger in smaller networks than in larger networks, which implies that these effects should be stronger for X360 and PS3 users, as these networks are smaller compared to the Nintendo Wii.

H4a: The value of video games increases with the number of people within a social network owning the same console

H4b: Network effects are stronger in the hardcore gaming segment.

1.5.4 Word-of-Mouth outside the direct social environment

(16)

internet, WOM does not diminish fast over time and distance. The emergence of social networking sites has changed the ways in which information is transmitted and transcended the initial limitations of WOM (Liu, 2006).

Online WOM offers both challenges and opportunities to retailers. On the one hand, it reduces the ability to influence consumers through traditional marketing and advertising. On the other hand, it provides a convenient way to reach large numbers of customers. Hence, WOM is seen as a new marketing tool (Duan, 2008). A unique aspect of WOM is the positive feedback mechanism between WOM and retail sales. In this way, positive consumer reviews of a certain product result in more sales, and negative consumer reviews result in a sales decline (Duan, 2008).

On the internet, WOM can take many forms, including online reviews, discussion boards, chat rooms, blogs, wikis, and others. For creative goods such as books, games, music and movies, WOM social contacts transmit consumers’ appraisals at a very low perceived cost to them, giving WOM its importance for a creative good’s ultimate success.

Early adopters are less influenced by opinions of peers than later adopters, and are enthusiastic about trying new products (Rogers, 2003). Because earlier adopters are more venturesome than later adopters, I suspect a greater influence of word-of-mouth in later adopters. However, earlier adopters are more active on forums on the internet, sometimes even before the release of the actual game (e.g. http://www.gtplanet.net/forum ). Rogers (2003) distinguishes between 3 phases that occur before the actual purchase takes place: Knowledge, Persuasion, and Decision. At the knowledge stage, the consumer is getting aware of the product for the first time. In the persuasion stage, the consumer is actively seeking information about the product. Finally, at the decision stage the consumer makes a decision whether or not to buy the product. Knowing this, I expect early adopters to be more active in the spreading and searching of information at the first two stages of the purchasing decision. As, in the case of video gaming, the later adopters are often older (more financial abilities), less involved, and have lesser expertise, and consequently need less persuasion for buying a video game and therefore make decisions based on positive or negative WOM.

H5: Casual gamers value WOM information to a higher extent than hardcore gamers.

H6a: Hardcore gamers are more actively involved in WOM activities than Casual gamers.

(17)

1.5.5 Opinion Leadership and Critical Reviews

Opinion leadership is most often identified with the diffusion of innovations model (Rogers, 2003). An opinion leader is someone who acts as an information broker between the mass media and the opinions and behaviors of an individual or group. Opinion leaders have some position, expertise, or firsthand knowledge that makes them particularly important sources of relevant and credible information, usually in a specific domain (Rogers, 2003). They assist with the process of reducing uncertainty for a specific product in their social network (Rogers, 2003) In general, the quality of artistic products is evaluated differently between experts and ordinary consumers (Holbrook, 1999).

Many people qualify as opinion leaders, King and Summers (1970) measured that 21% of 976 respondents did not qualify as an opinion leader in any of six different product categories. They also found that there is an overlap between opinion leadership across different product categories, and that his overlap is the greatest with product categories which involve similar interest. These findings suggest a generalized opinion leader across consumer products (King and Summers, 1970).

Opinion leaders can also emerge outside the direct environment, for example via an online review. In this way, the information processes one way. Reviews from critics, for example, on web-sites or specialized magazines, can influence sales of consumer products for several reasons. First, reviews are widely available, for example in newspapers, websites and magazines. Second, reviewers view themselves as an advisor in the purchasing process. Finally, critical reviewers are, in most cases, objective (King, 2007). Duan and colleagues (2008) argue that online reviews are influencers and also influenced by sales. They found that the content of critical reviews has little persuasive effect on consumer purchasing decisions. However, the volume of critical reviews resulted in an increased awareness of the product, which caused an increase in sales. In this way, visibility of the video game gets increased, which can speed up the diffusion (Rogers, 2003). Critics can also give insight in the relative advantage of the video game, they might even reduce the complexity of a video game (Rogers, 2003). Distributors are correct to be worried about the critical rating, because in the movie industry, a 20 out of 100 point increase in review score was associated with extra income of $26 million (King, 2007).

(18)

demanding artwork (Holbrook, 1999). These are usually the aspects of a video game which attract the hardcore gamer, as opposed to the casual gamer, which looks for mere entertainment. The casual gamer, as a later adopter, usually follows trends if risk is minimalized and a fear of not fitting in with the majority starts (Rogers, 2003). As such, the decisions of the later adopters are influenced by product evaluations of the earlier adopters.

H7: Hardcore gamers gain more knowledge from video games through expert reviews/independent sources than casual gamers

H8: Hardcore gamers view themselves, to a higher extent, as opinion leader than casual gamers.

1.6 Marketing activity

In the video game industry, it is often the case that games with positive reviews by both consumers and critics do not top the sales charts. Especially in the short term, a bad video game can yield excellent sales results if it is supported by an excellent marketing campaign (Hennig-Thurau et al. 2006). For example, in 2008 I compare two games that were exclusively published for the Nintendo Wii console, Metroid Prime 3 (MP3) and Mario & Sonic at the Olympic games (MSO). MP3 got an overall review score of 90%, while MSO scored 67%. The overall review score was an average score derived from several leading computer magazines and websites. The obvious superior quality of Metroid Prime 3 did in fact not result in higher sales volume. MP3 sold a mere 1.1 million units, which was massively outperformed by MSO, which sold 3.4 million units (www.edge-online.com). These sales numbers contradict heavily with research outcomes which state that the quality-sales relationship in entertainment industries is very strong (Binken & Stremersch, 2009).

(19)

balance between rationalization and creativity. Rationalization is defined as the predominant focus on business interests or productivity-oriented production processes, usually at the expense of creativity, which is defined as the continual development and renewal of intellectual property (Tschang, 2007). In general, developers need to find a balance between innovativeness and marketing activity to sell the maximum amount of units (Tschang, 2007).

More money spent on marketing can increase awareness of a product, and subsequently result in more exposure. Also, product distribution and shelf-placement affect exposure. The more widespread the brand’s distribution (the more stores in which it is available), the greater the likelihood that consumers will be exposed to the actual product (Percy & Elliot, 2005). Thus, the main role of marketing activity is increasing observability of the innovation. If marketing activity includes demo releases, the relative advantage and compatibility become more clear, while the trialability increases (Rogers, 2003).

Because games do not usually involve brand attitudes (except in some cases with sequels to previously released games), the main effect for advertising will be awareness. A video game is in most cases a new product, which is obsolete in a few months in the highly innovative video game industry. For new products, marketing skills and proficiencies are related to new product success (Calantone et al., 1996). Proficiency of marketing activities includes understanding of customer needs and behaviour, the competitive situation, consumer testing and monitoring market launch (Calantone et al., 1996). More money spent on advertising for entertainment products can serve as a quality signal, which increases sales (Basuroy et al., 2006). As mentioned before, marketing activity is most important at the knowledge stage of the innovation-decision process (Rogers, 2003).

Earlier adopters have more exposure to mass media channels than later adopters, which results in the fact that mass media channels are more important to the earlier adopters than the later adopters (Rogers, 2003). However, as previous segments have shown, hardcore gamers are more active in their information search. Knowing this, persuasion by marketing activity should not be likely. As casual gamers are less involved, I expect marketing effort to have a more direct effect on purchase decisions in this group, as they are easier to persuade. Besides the direct effect on purchase intention, adverting can also increase awareness of a product (Percy & Elliot, 2005). As they are active information seekers, hardcore gamers might become aware of soon to be released video games. As such, the hypotheses are formulated as follows:

(20)

H10: For Casual Gamers, marketing activities are more important at the decision stage, than for Hardcore Gamers.

1.7 Conceptual model

The conceptual model, which summarizes the hypotheses formulated in the former section, can be found in figure 2.

Figure 2, Conceptual Model

Casual Gamers Hardcore

Gamers

High involvement, increases in: - Normative influence

- WOM-activity

- Marketing influence in the knowledge stage

- Use of expert reviews - Perceived knowledge

Low involvement, increases in: - Susceptibility to informational influence

(21)

2. Method

Data Collection

Past research on external search has been employed with different methods, such as a survey (Beatty & Smith, 1987), field experiment (Moore & Lehmann, 1980), laboratory experiment (Brucks, 1985), interview (Newman & Staelin, 1971), and protocol and analysis (Bettman & Park, 1985). The data for this research was collected by means of a survey which was handed to respondents via personal delivery or via an online survey tool. The survey was administered among owners of three different gaming consoles, which were the Nintendo Wii, Playstation 3 and the X-box 360. All of these respondents were of the Dutch nationality. First, the survey was administered among the direct social environment of the researcher by sending the link to the online questionnaire via e-mail. The online survey was further dispersed by looking for online gaming communities, and placing the link to the survey on the websites which hosted these communities.

The survey gained much response from the websites, but less than half of the respondents failed to complete the questionnaire, this was for the largest part due to the length of the questionnaire. For this reason, printed questionnaires were also administered to people within the direct social network of the researcher who worked with large amounts of people (e.g. teachers, football coaches) to ensure a higher response rate.

Before releasing the questionnaire, pre-testing was executed to ensure the measurement scale was understandable for younger respondents. After testing the questionnaire on several young respondents, feedback was acquired about several parts which were too complex. After the acquisition of this feedback, the questionnaire was redesigned. No complaints regarding excessive complexity were encountered afterwards.

Measurement

(22)

determined with the goal of eventually researching differences between gamers who possess different demographic attributes.

As a very broad concept, many questions were dedicated to the measurement of social influence. A measurement scale developed by Jager (2010) was translated and used to measure ingoing normative and informational social influence, as well as outgoing normative and informational social influence. The goal of this scale was to gain insight in which respondents were opinion leaders and those who were followers. All of these questions were measured on a seven-point Likert scale.

Social influence that was not exerted within the direct social environment was also taken into account when making the questionnaire. Several questions were developed to measure respondents attitude towards different mediums which disperse information about video games (e.g. online critics, television shows, magazines). The extent to which consumers let these mediums influence their purchase decision was also measured to find possible differences between the groups of gamers. All but one question were measured on a seven point Likert scale. Network effects were measured by asking respondents about a maximum of three people within their social network that also own a gaming console. The respondent was asked to identify these people and give an estimate of how much time they spend on their gaming console. In this way the importance of video games within a specific network should be identified and measured. Furthermore, some questions were dedicated to research whether it was important to respondents if their social network had the same gaming console and if the value of their console would increase if this was the case. Respondents were asked to answer the questions in this segment on seven-point Likert scales.

To research whether advertisements have different impact on different gamers, a measurement tool was used which was developed by Ducoffe (1996). As the survey mainly measures attitude towards video game advertisements, some questions were added to research the extent to which advertisements influence purchase intention on casual and hardcore gamers. To study the effect of sequels on information search, some questions regarding the use and external search habits of sequels were added to the survey. These questions were also to be filled in on a seven-point Likert scale.

(23)

3. Results

Questionnaire demographics

Table 2 displays the demographics concerning the questionnaire that was dispersed. A total of 134 questionnaires was completed by the respondents. The percentage of men was much higher than women. As the Wii is the most sold console, it is not surprising that the larger part of the respondents did own a Wii. Fortunately, a substantial amount of owners of every console was reached, so every console had a representative sample.

Table 2: Questionnaire statistics.

Item Frequency Mean Minimum Maximum

Age 134 19.4 10 51

Wii owners 65

PS3 owners 55

X360 owners 45

Completed Education: Sex:

Elementary 29 M = 102; F = 30 VMBO/MAVO 33 HAVO 15 VWO 5 MBO 31 HBO 15 WO 5

(24)

Table 3: Console statistics

Console Age Time spent per week Money #games p/year Wii 19.22 (18.74) 8.95 hours (7.28) €174,71 (120.78) 5.43 (4.26) PS3 19.98 (20.90) 9.77 hours (8.22) €193.60 (92.87) 5.11 (3.72) X360 19.91 (18.84) 11.49 hours (9.81) €230.95 (109.96) 5.90 (3.54)

Between the users of different consoles, there were no significant differences concerning involvement and external search effort. However, there are some small differences between the Wii and the other two consoles. As can be seen in table 4, users who own a single X360 or PS3, are slightly more involved and spend slightly more time searching for external information. However, these differences are not significant.

Table 4. Involvement Item Involvement Ext. search effort

Wii 4.42 4.30

PS3 4.56 4.68

X360 4.70 4.61

Identifying the sample and Hypothesis 1

(25)

Table 5: Correlations with the Involvement variable

Correlations Pearson Correlation Score (Involvement)

Money Spent 0.428*

Time Spent 0.412*

Number of Games Purchased 0.346* * Significant at the 0.01 level

H1: Hardcore gamers are to a higher extent involved when purchasing a video game and therefore spend more effort searching for product information

To further define the type of gamer concept, the Money Spent and Time Spent Gaming variables were standardized and combined to result in a specific gamer score. The median was identified to split the database in two separate groups: Hardcore gamers and Casual Gamers. To test whether these categorization of respondents was correct, the difference in involvement was measured between these groups by using an independent samples t-test. Respondents who dedicated more money and time to video games scored higher (5.02) than those who spent less time and money on video games (3.97, T = -4.702; df = 91,581; p = .000).

By having these concepts defined and tested, H1 can be researched. Differences of the two gamer groups in external search effort, which was the average of two items combined with the involvement variable (The two items combined with ‘involvement’ gave a Cronbach’s Alpha of .680, without ‘involvement’ Cronbach’s α was .574), were also tested by using an independent t-test. The hardcore gaming segment had an average score of 5.12, the casual gaming segment 3.62, which indicated a significant increase of external search effort if a respondent was qualified as a hardcore gamer. The T-value in this case was 9.068 (df = 101,25; p= 0.000), which makes Hypothesis 1 accepted.

Social influence (H2, H2b, and H3)

(26)

social influence can be split up according to the factor loadings. These are presented in bold in table 6. Factor 1 includes two variables which score high on the same factor. Because these variables are necessary for measuring different hypotheses, they will be split up in informational and normative influence. These will be tested later on in the segment ‘opinion leadership’.

Table 6: Factor analysis social influence constructs

Questions F1 F2 F3 Questions F1 F2 F3 INI Q1 .109 .811 .116 ONI Q1 .773 .249 .103 INI Q2 .228 .714 .166 ONI Q2 .681 .427 .019 INI Q3 .199 .847 .229 ONI Q3 .666 .527 -.014 INI Q4 .147 .787 .224 ONI Q4 .733 .290 .219 INI Q5 .301 .623 .385 ONI Q5 .550 .525 .143 INI Q6 .395 .386 .562 ONI Q6 .753 .392 .067 INI Q7 .246 .738 .157 ONI Q7 .751 .341 .092 INI Q8 .521 -.067 .410 ONI Q8 .723 .129 .371 III Q1 .271 .466 .571 OII Q1 .745 .330 .159 III Q2 .141 .101 .781 OII Q2 .765 .021 .354 III Q3 .218 .401 .607 OII Q3 .747 .154 .350 III Q4 .164 .256 .761 OII Q4 .794 .050 .296

H2a: Hardcore gamers are more susceptible to normative social influence than Casual gamers

H2b: (Pre-) Adolescent video game users of the hardcore gamers segment are more susceptible to normative influence than adult hardcore gamers

(27)

H3: As they have less expertise, casual gamers use informational social influence to a greater extent in their purchase decisions than hardcore gamers.

The same method, the independent samples t-test, was used to test differences between hardcore gamers and casual gamers concerning III. The mean of the hardcore gamers (3.39) was higher than the casual gamers (3.10). As this difference was not significant (T = .981; df = 121.277; p = .330), hypothesis 3 is rejected.

Network effects (H4 and H4b)

To test whether network effects affect the value a respondent gives to a video game, the first thing to be found is which variables measure video game value. A factor analysis was used to split up the ‘involvement’ questions in different concepts. At the first glance it already becomes visible that the questionnaire contains different types of questions. Some questions concern feelings associated with games, while others concern the relative importance of games. By using a factor analysis, it will be investigate whether these categories exist. This categorization will subsequently be used to measure the value of video games.

Table 7: Factor analysis Video game value

Item Factor 1 Factor 2 Factor 3

Belangrijk .641 .381 .236

Betekent veel .720 .345 .265

Waardevol .454 .488 .414

Heb ik baat bij .550 .468 .354

Kan me veel schelen .606 .394 .284

(28)

In table 7, the three factors which had an eigenvalue higher than 1 are presented (Eigenvalues: F1 = 9.999; F2 = 1.792; F3 = 1.087) from the involvement measurement scale. Each factor has some items which load to a higher extent. These items are highlighted in the table. As mentioned before, a clear distinction can be made between the first two factors. While the highest loading items in Factor 1 are more resembling to relative importance or value of games, the highest loading items in Factor 2 are connected with feelings associated with video games. Factor 3 is mixed up out of different items, and therefore will not be considered at further testing.

Part of the questionnaire was dedicated to identifying gamers within the social network of the respondents and owners of the same gaming console. In this way, research could be executed whether the value of video games increases if there are more owners of the same console in the social network of the respondent.

Figure 3: Mean scores Game Value and Game Feeling

H4a: The value of video games increases with the number of people within a social network owning the same console

(29)

Value and Game Feeling. As can be seen, the value of the game increases slightly as the number of friends with the same gaming console increase. To a higher extent, Game feeling increases as the number of same consoles in the social network increases. When tested with a one way ANOVA test, the results were unexpected. As stated in the hypothesis 4a, it was expected that Game Value would increase as the social gaming network increases. In the ANOVA test, the differences between the means of the groups were not significant, the means can be found in figure 3. The results of the ANOVA test indicated that the differences between groups were not significant (F = .619; df = 123; p = .604). However, the difference of the feelings associated with games between the groups were significant (F = 2.844; df = 126; p = .041) at the 95% confidence interval. This means that if there is an increase in similar consoles in the social network, game feelings increase significantly, but game value does not. As such, hypothesis 4a is rejected.

H4b: Network effects are stronger in the hardcore gaming segment.

To test the differences between hardcore gamers and casual gamers concerning network effects, respondents were asked about the importance of their social network owning the same games or consoles. Four items were dedicated to this subject. A factor analysis was executed to test whether the questions measured the same construct. The results of the factor analysis can be found in table 8. As a result, question 1 was not used for further testing.

Table 8: Factor analysis network effects Item Factor loading

Q1 .336*

Q2 .817

Q3 .830

Q4 .714

*Will not be considered in further testing

(30)

Further testing (one-way ANOVA) on network effects revealed other insights on the gaming behaviour of people with more of the same consoles in their direct environment. The results of this test showed that an increase in similar consoles in the social environment resulted in an increase in social gaming such as multiplayer gaming (F = 3.567; df = 130; p = .016). Furthermore, outgoing informational influence was also higher as similar consoles in the social environment increased (F = 4.884; df = 131; p = .003).

Word of Mouth (H5, H6a & H6b)

H5: Casual gamers value WOM information to a higher extent than hardcore gamers.

In the questionnaire, four questions were dedicated to the measurement of the construct WOM. To test whether the items on the questionnaire were reliable, a reliability analysis was executed, which score indicated (Cronbach’s α = .861) that the four questions were a reliable measure of the construct. Knowing this, the differences between hardcore gamers and casual gamers considering WOM can be measured by using an independent t-test. The t-test indicates that the contrary of hypothesis 5 is true, the average score of the hardcore gamers (3,64) is higher than the casual gamers (2,77) (T = -3.076; df = 99.304; p = .003), and hypothesis 5 is rejected. Knowing this, WOM is more important to hardcore gamers than casual gamers in influencing the purchasing decision.

H6a: Hardcore gamers are more actively involved in WOM activities than Casual gamers.

H6b: Casual gamers let, to a larger extent, their decision whether to purchase a video game depend on WOM, than Hardcore Gamers

(31)

hypothesis 6a can be accepted. Furthermore, the extent to which WOM influences purchase intention was also measured by means of an independent t-test. In this case, the mean of the casual gamer group was lower (2.61) than the hardcore gamers group (3.21) (T = -1.752; df = 103,848; p = .081). As this difference is not significant, hypothesis 6b is rejected, as it cannot be confirmed that casual gamers let their purchase decision to a higher extent depend on WOM. However, a tendency is present of WOM influencing purchase intention to a larger extent in hardcore gamer groups.

Opinion leadership (H7 & H8)

Hypothesis 7 states that hardcore gamers gain more knowledge from expert sources from specialized magazines, websites and television programmes. Each of these mediums had seven items (Magazines = MQ; Websites = WQ; Television = TVQ) on which the construct was built. A factor analysis was executed to test whether the items did in fact measure the construct. The results of this factor analysis are presented in table 9. Three factors were identified in the dataset, the factor scores which relate highest to each of the mediums are shown in bold. It becomes clear that factor 1 loads highest on the items measuring websites, factor 2 highest on television, and factor 3 highest on magazines (Eigenvalues: F1 = 9.837; F2 = 3.069; F3 = 1.924).

Table 9: Factor analysis opinion leadership Item Factor 1 Factor 2 Factor3

(32)

TVQ7 .032 .624* .046 *Will not be considered in further testing

H7: Hardcore gamers gain more knowledge from video games through expert reviews/independent sources than casual gamers

The items which scored highest on Factor 1 will be combined to the variable Websites, the same will be done with Factor 2 (Television), and Factor 3 (Magazines). The items on which these variables are composed can be found in table 9. By using an independent t-test, the differences between the hardcore gamers and the casual gamers were measured. Hardcore gamers had a higher mean score on Magazines (HG: 4.23; CG: 3.23; T = -3.299; df = 103.869; p = .001), Websites (HG: 4.88; CG: 3.45; T = 2.470; df = 102.619; p = .000), and Television (HG: 4.23; CG 3.16; T = 3.382; df = 100.367; p = .001). This means that hardcore gamers score significantly higher on information gained from expert sources, which implies that hypothesis 7 is accepted.

H8: Hardcore gamers view themselves, to a higher extent, as opinion leader than casual gamers.

To find out whether outgoing social influence was higher with hardcore gamers, the first thing that was executed was a factor analysis. Outgoing social influence can be normative and informational, that is why this distinction has to be tested with a factor analysis. The factor analysis was executed earlier in this chapter (Table 5), and indicated that all items related to the construct, but no distinction could be made between normative and informational social influence. Because of this, there will only be one construct Outgoing Social Influence, which is composed of the items presented in bold in table 6, loading highest on Factor 1.

As the items are clear, an independent t-test can be executed to test whether differences exist between casual gamers and hardcore gamers, regarding outgoing social influence . The results show that outgoing social influence is higher for hardcore gamers than casual gamers (HG: 2.69; CG: 1.92; T = -2.996; df = 104; p = .003). This implies that both outgoing social influence is significantly higher at the hardcore gamer group, and that hypothesis 8 is hereby accepted. Marketing activity (H9 & H10)

(33)

measured the construct marketing activity in the right way. The results can be found in table 10. As can be seen, the items which loaded highest on the construct measured attitude towards marketing activity (Q1-Q8). These items were combined into one variable called Marketing Activity Attitude. The difference between hardcore gamers and casual gamers regarding marketing activity attitude were tested by using an independent t-test. The t-test found no significant differences in Marketing Activity Attitude between casual gamers and hardcore gamers (HG: 3.09; CG: 2.80; T = 1.080; df = 98.391; p = .283).

Table 10: Factor analysis marketing items

Item Factor score Item Factor score

Q1 .851 Q2 .862 Q3 .824 Q4 .774 Q5 .759 Q6 .785 Q7 .771 Q8 .834 Q9 .755* Q10 .581*

*Will not be considered in further testing (Q9 not because factor score, but because of it measuring knowledge stage susceptibility)

H9: For Hardcore Gamers, marketing activities are most effective in the knowledge stage of the purchase-decision process

H10: For Casual Gamers, marketing activities are more important at the decision stage, than for Hardcore Gamers.

Hypothesis 9 and 10 basically state that marketing activity has different functions at different stages for casual gamers and hardcore gamers. It was presumed that marketing activity was more persuasive at the knowledge stage for hardcore gamers, and more persuasive at the purchase stage for casual gamers (Hypothesis 9 was measured by using the item Q9, Hypothesis 10 by using the item Q10). The independent t-test found that there were no significant differences concerning marketing effectiveness in the purchasing stage (HG: 2.62; CG: 2.44; T = -.508; df = 100.650; p = .612). In the knowledge stage, differences were more visible (HG: 3.88; CG: 3.22; T = -1.928; df = 103.201; p = .057), but not significant at a 95% confidence level. However, the last result gives an indication that differences between hardcore gamers and casual gamers concerning marketing activity in the knowledge stage are present.

Searching behaviour.

(34)

total of 100%, over eight different information sources. The results can be seen in table 11. Two differences between casual gamers and hardcore gamers are significant. Hardcore gamers use magazines more often, while casual gamers rely on marketing activity of the developers when purchasing a video game. Some differences that were also notable but not significant were found in websites and online forums, which are to a higher extent used by hardcore gamers. No differences were found at the buying channels of the two groups.

Table 11: searching behaviour CG and HG

Item Mean CG Mean HG T P df

Info Magazines 5.35 13.58 (9.11) -2.278 .025* 104 Info Websites 16.67 27.00 (21.41) -1.902 .060 103.279

Info TV 7.96 5.73 (7.27) ..750 .450 80.146

Info Not Spec. 5.20 1.90 (3.29) 1.550 .120 59.662 Info Forum 3.96 8.62 (6.31) -1.875 .064 83.385 Info Demo 10.87 15.19 (11.95) -1.096 .276 97.183

Info Adv 8.31 2.52 (5.97) 2.141 .035* 104

Info Social 29.17 22.58 (27.77) 1.119 .266 100.061 *significant on a 95% confidence interval

(mean of the entire dataset)

Additional findings

(35)

Table 12: Searching behaviour Early adopters and Later Adopters

Item Mean LA Mean EA t p df

Info Magazines 6.60 14.88 -2.353 .015* 130 Info Websites 21.97 20.71 .2.474 .795 94.246

Info TV 5.92 10.44 .260 .178 55.546

Info Not Spec. 4.14 1.46 -1.365 .050* 116.164

Info Forum 6.07 7.00 -1.983 .656 108.840

Info Demo 11.21 13.90 -.709 .481 65.664

Info Adv 6.96 3.34 1.588 .115 122.140

Info Social 31.26 18.24 2.237 .027* 130

Age 20.34 17.93 2.282 .025* 103.525

*significant on a 95% confidence interval

Many tests were executed to find differences between customers who spent a lot of effort searching the cheapest alternatives. However, no notable differences were found between budget gamers and non-budget gamers. Also, there were not any age differences between the different gaming consoles, all the means were approximately nineteen years of age.

Finally, it was tested which games are preferred by which groups of gamers. As can be seen in table 13, hardcore gamers score significantly higher on shooters and action games, and consider gameplay, total game duration and online options more important than casual gamers. The casual gamers score higher on music and sports games, however, these scores are not significant.

Table 13: Independent t-tests gaming preferences

Item Mean CG Mean HG t P df

Graphics 5.22 5.79 -1.895 .061 104 Gameplay 5.76 6.48 -2.914 .004* 104 Duration 4.56 5.40 -2.538 .013 101.996 Online options 3.22 4.40 -2.875 .005** 103.075 Offline multiplayer 4.07 4.31 -.624 .534 101.175 Action 4.32 5.29 -2.758 .007** 101.622 Adventure 4.13 4.73 -1.473 .144 101.931 Shooters 3.69 5.37 -4.095 .000** 103.538 Music 2.87 2.35 1.268 .208 101.995 Race 3.94 4.24 -.817 .416 101.769 Sport 4.91 4.24 1.571 .119 100.771 Strategy 3.38 3.51 -,331 .742 100.866

(36)

Age differences

The tests that were executed to test differences between hardcore and casual gamers were also tested on age groups. A notable finding was that younger gamers had much higher (4.49) scores on game value than their older counterparts (3.52) (T = 3.871; df = 118.735; p = .000). Involvement as a whole was also higher in the younger group (4.90) than the older group (4.16) (T = 3.279; df = 110957; p = .001). Some interesting scores were also found in the external information searching. Younger gamers use more magazines, more television, and less non-specialized media. Both groups use social information to a large extent, older gamers 31.09% and younger gamers 22.87% of their external search time. However, this difference is not significant. Table 12 shows that early adopters are younger than later adopters. After testing differences specifically between age groups, the results showed that adolescent gamers played more shooters (Mean Yng: 5.00; Mean Old: 4.12; T = 2.195; df = 126.975; p = .030) and less strategy games (Mean Yng: 2.92; Mean Old: 3.70; T = 2.220; df = 125.665; p = .028) and gave more value to online options (Mean Yng: 4.60; Mean Old: 3.38; T = 3.280; df = 124.905; p = .000) than older gamers.

Summarized: Accepted Hypotheses

H1: Hardcore gamers are to a higher extent involved when purchasing a video game and therefore spend more effort searching for product information

H2b: (Pre-) Adolescent video game users of the hardcore gamers segment are more susceptible to normative influence than adult hardcore gamers

H4b: Network effects are stronger in the hardcore gaming segment.

H6a: Hardcore gamers are more actively involved in WOM activities than Casual gamers.

H7: Hardcore gamers gain more knowledge from video games through expert reviews/independent sources than casual gamers

H8: Hardcore gamers view themselves, to a higher extent, as opinion leader than casual gamers. Summarized: Rejected Hypotheses

(37)

H4a: The value of video games increases with the number of people within a social network owning the same console*

H5: Casual gamers value WOM information to a higher extent than hardcore gamers.**

H6b: Casual gamers let, to a larger extent, their decision whether to purchase a video game depend on WOM, than Hardcore Gamers***

H9: For Hardcore Gamers, marketing activities are most effective in the knowledge stage of the purchase-decision process

H10: For Casual Gamers, marketing activities are more important at the decision stage, than for Hardcore Gamers.***

*Not significant for video game value, however, video game feelings increase as number of consoles within social network increase (significant on a 95%

confidence interval)

**Contrary is the case, significant on a 95% confidence interval

(38)

4. Discussion

As a result of present study, clear distinctions became clear when analyzing the acquired data. As was expected (Moorthy et al., 1997), people with a higher involvement score spent more time searching external information. Scores on external information search were moderate to high, which indicates that video games are a moderate-involvement product. The questionnaire also revealed that consumers, both hardcore and casual gamers, use a wide array of information sources. Regardless of the involvement that is associated with a product, Guo (2001) argues that information search increases when information is easily accessible. In the video game market this is unarguably the case as there are countless websites, magazines and television programmes dedicated to the subject. Interesting findings became clear in the area of external search. Casual gamers and hardcore gamers differ on a few sources when searching information on video games. Hardcore gamers use more expert sources, such as magazines and websites, while casual gamers spend less effort on external search. Casual gamers can, to a higher extent than hardcore gamers, be pursued to buy a video game by being influenced by marketing activity.

One of the main reasons for the execution of this study was the probability of the outdating of the video game as a privately consumed luxury (Bearden & Etzel, 1982). However, the test results indicated that (online)multiplayer options are still the least important quality indicator when purchasing a game, even in the hardcore gamer group. Conventional game qualities such as graphics and gameplay were more valuable to casual and hardcore gamers. As is consistent with Batra et al (2001), who stated that more publicly consumed products increase normative social influence associated with a product, the scores on outgoing and ingoing normative influence were very low. No difference was found between hardcore and casual gamers. However, there was a significant difference between younger gamers and older gamers. This finding is consistent with the research of Steinberg and Monahan (2007), who find that normative social influence is strongest in early adolescence, around the age of fourteen, and decreases afterwards.

(39)

when playing a video game. The more pleasurable sensations might come from the fact that people with larger networks spend more time gaming online, which can give another dimension to video games. Griffiths et al. (2003) give insight in the motivations of online gamers. They find that the favourite aspects of online games include interaction and creation. These are, according to the respondents, to a lesser extent present in offline games. Also, people who are gaming with other people in their network, enjoy more feelings of challenge and competition compared to people who spent less time gaming with other people (Jansz & Tanis, 2007).

The findings considering online gaming revealed one interesting difference between two groups. Younger gamers value online gaming options significantly more than older gamers. This result can be explained by two reasons. First, it is possible that a new generation of gamers is emerging. Younger gamers, in this case, could be more technologically advanced and use video games to satisfy other needs than older gamers (e.g. challenge and competition, see Jansz & Tanis, 2007), who grew up playing console games which could not be played online and stick to this conventional way of gaming. Second, age itself can be a factor which relates to online gaming. As such, online gaming could be a quality indicator which loses its appeal after adolescence.

External Information Search on the Home Video Gaming Market