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The likeliness of developers engaging in creating innovative games; when do developers in the video game industry approach the resource dependence theory or prospect theory to explore their products?
Abstract: The topic of this research is about innovation in the video game industry. This paper researches when developers choose to engage in product innovation. Two main theories (resource dependence theory and prospect theory) are examined in a literature review in order to understand the likeliness of either product exploration or product exploitation. Based on these findings hypotheses are drawn and tested in a qualitative research. It extends previous literature by linking the theories to the video game industry. By making use of a database, I show that expert and consumer evaluations play part in defining when it is likely for developers to use product exploration as a strategic formula. This
strategy can sustain or create competitive advantage in the economic environment.
Jorrit Kalkman 10113665 June 2014
Supervisor: F. Situmeang Strategic Marketing in the video game industry Thesis and Seminar Business Studies
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Inhoud1. Introduction ... 3
2. Literature review ... 6
2a. Resource dependence theory ... 7
2b. Prospect theory ... 10
3. Theoretical framework ... 13
3a. Expert/critic evaluations ... 14
3b. Consumer Evaluations ... 16
3C. Sales ... 17
4. Methodology ... 18
4a Evaluations variables ... 18
4b. Sales data variable ... 19
4c. Innovation Evaluation variable ... 20
4d. Operationalization of variables ... 20
4e. IMB SPSS Statistics ... 20
4f. Model specification ... 21
5. Results ... 22
6. Discussion ... 24
6a. Managerial Implications ... 26
6b. Limitations & future research ... 27
7. Conclusion ... 28
8. References ... 29
9. Appendix ... 34
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9B. Bootstrap Analysis Figures ... 34
9D. Appendix ... 45
9E. Appendix ... 59
1. Introduction
The game industry or video game industry is vast and has grown tremendously (Williams, 2002). It is now a multi-billion dollar industry (Hartmann and Klimmt, 2006) and many households have brought a home video game console to their home to enjoy video games. Playstation 3, having the biggest market share among the three top consoles in 2013 (52,5%), is the market leader in the so-called ‘console war’ against Nintendo and Microsoft. Owners of a Playstation 3 are able to play thousands of different games on their game console (VGchartz). These games are going through different stages before it reaches the consumer; they have to be developed and/or published, distributed and sold (Johns, 2006). All these games, which can be categorized in many different genres, give the gamer
different experiences. Clearly, with all these games being made, it can be particularly hard for developers to bring something new to the table for consumers. They might struggle to let consumers experience new concepts captured in games since the game industry is under heavy competition.
Therefore developers might not always succeed in developing a new, innovative game. If they do not, it means that the consumer would be less motivated to buy the game resulting in lower revenues for all stakeholders of the particular game. In that case the developer might be better off producing a sequel of an earlier produced game. This sequel generally has less new-to-the-world concepts and drives on concepts used earlier in its prequel. There is usually an assumption that the basic premise will be similar to the previous
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game (Becker, 2007). If consumers have proven to “like” its original version, it can be a far safer choice for the developer to develop this sequel rather than engaging in developing a game that is new-to-the-world. Hence, in order to succeed in creating an innovative game, a creative team not only has to have the capabilities to do so, but also the willingness to incorporate uncertainty and take risk when leaving their current business.
According to Williams (2002), developers usually contribute creative elements that often lead to innovation. This innovation can transform the company into the desired future state (Rank et al, 2004). But since all stakeholders are creating the environment, the
stakeholders can also contribute to the capabilities. And even more, they can determine the possibility to innovate; when stakeholders other than the developer are against creating an innovative game, the ultimate decision whether to engage in innovation or not might not solely be up to the developers.
This vision is supported by the resource dependence theory (RDT). It tells us that the outcomes are accounted for by the context in which the organization is embedded (Pfeffer, 1978). Organizations are interdependent and must transact with elements of the
environment. The more resources an organization (or in this case the developer) has, the less (inter)dependent it is. And the more independent it is, the more likely it is it will engage in developing an innovative game. And independency will likely result in the development of more innovative games. This type of behavior, which is actually risk-seeking, can be seen as product exploration where the developer explores new, inventive ways to play a game. When a developer uses product exploration as a strategic choice, it is trying to create an innovative game. Even more, exploration creates skill that enhances innovation whereas exploitation supports the competence to drain the buffer created once exploration was there. However, when a developer chooses to apply product exploitation as a strategic choice, it deliberately focuses on using existing features and characteristics in order to procure revenue. Overall, the results reveal pragmatic decision making balancing the benefits of superior strategic position against the risks of jeopardizing viability (Voss et al, 2008).
In contrast to the RDT is the prospect theory (PT). This theory states that a person (or in this case; the developer) is risk averse if he prefers the certain prospect to any risk
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regarding risky or precarious choices (Kahneman, 1979). If this risk aversion can be applied to developers, it is not likely that a big developer with plenty resources will produce an innovative game when there are still existing games to be exploited. Only when a developer is already under threat (and risk) it will seek other ways to generate revenue, such as being innovative.
Previous research regarding those two schools of thought has been linked to organizational strategy and its economic environment (Pfeffer, 1978). However, it has not been directly linked to the likeliness of product exploitation or exploration in the game industry. This is the research gap that I would like to address in this research. The research problem here is that, to my knowledge, it is not clear for a developer when to engage in either product exploration or exploitation. The effectiveness of either is dependent on different situations of the firm’s environment (Danneels, 2011). My aim is to extend the existing knowledge within the limits of the assumptions, to specify when it is wise and likely for a developer to explore instead of exploit.
Better yet, in this paper I am going to research when it is likely for developers to engage in making an innovative game. To do so I will first provide an overview of different schools of thoughts on when it is likely for organizations to engage in innovation. Theories are discussed to explicate, predict and interpret phenomena regarding these concepts. Next, I will link this literature to the game industry in the theoretical framework chapter .
In the theoretical framework chapter I will demonstrate the relevance of the theories on concepts and relate them to the topic at hand; namely the video game industry. By doing so it will become clear what key variables I will put to use in order to address the research problem. The purpose of this is to identify means for prescribing or evaluating the research problem. Additionally, I will set up the hypotheses that I want to put to the test.
Thereafter, the methodology and research design are explained and used to test the hypotheses that are derived from previous findings. I will present how I will conduct my research and of what tools I will make use to tackle the research problem. The database of Playstation 3 games, handed to me by professor M. Situmeang, will be used to conduct quantitative research as a scientific method to analyze the data. With the program SPSS, particular algorithms and formulas are tested to proof significance for my hypotheses.
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In the results my findings are analyzed and I will present my findings and confirm validity of them. In the discussion I will talk about what the managerial implications are and what advise I will give to developers, helping them to make future decision making more successful. In the section ‘limitations and future research’ I will reflect on the boundaries of the research gap addressed and what can be potential subjects for future research to expand the knowledge of this paper. Lastly, the conclusion will round up the basic concepts and once more explicate them in short, leaving the reader notified of what is learned and what problems are tackled.
2. Literature review
Today industries engage in creativity and intellectual capital in order to create new wealth through innovative offering. The business models mostly discuss knowledge-intensive creation of mass-produced products and services based on transforming inventions into innovations (Salimaki, 2012). Salimaki states that with stable competition and with a market that can be anticipated with a high level of certainty, firms mostly need to arrange resources to ensure a maintained competitive advantage through resource efficiency and incremental innovation.
However, the game industry is not a market that can be anticipated with a high level of certainty. Therefore it is questionable whether the competitive advantage should be incremental to innovation. There is a maturing trend with product designs becoming well established as genres and consumers and publishers desiring increasingly innovative games (Tschang, 2007). At the same time consumers (gamers) want more of the same if they like a certain concept. These demands are contradicting. Even to conceive of new development in response to market information and prediction of the future is a creative act (Andrews, 1971). The firms cannot survive by responding to every environmental demand. The
interesting issue then becomes the extent to which organizations can and should respond to various environmental demands (Pfeffer, 1978).
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2a. Resource dependence theory
In the game industry, virtually all organizational outcomes are based on interdependent causes or agents (Pfeffer, 1978). That is, since “interdependence exists whenever one actor does not entirely control all of the conditions necessary for the achievement of an action or for obtaining the outcome desired from the action.” It characterizes that these agents, or firms, together shape the outcome. The identification of this dependency can be seen in various ways. One of these is the outcome interdependence, whereas the outcome of firm A is interdependent with the outcome of firm B. The behavioral choices made, which lead to the outcome, are independent in this case. However, when we speak of behavioral
interdependence, the actions of firm A are dependent on the actions of firm B. A good example of this is a poker game; in a poker game there is a certain amount of money (the outcome) that can be won. The amount of money won by player A will depend on the actions taken by player B.
A further distinction between dependencies can be made whether the actors are having a symbiotic or competitive relation. In the last case it means that it is only possible to have a higher outcome when the outcome of the competitor is lower. In contrast to this is the symbiotic relation where both parties can help each other with increasing their outcome. In terms of human ecology, competitive relationships exist when the actors each require identical resources for survival. Symbiotic relationships involve one actor using the by-products of the other, or in other words, using different resources (Pfeffer, 1978).
It is clear that dependence is meaningful to a firm because of the impingement it has on the capacity to reach the desired outcome. And like all processes in ecology,
interdependence is an effect of how the environment naturally unfolds. The same can be said for the economic environment because similar processes unfold. Companies have to cope with this by gathering the proper resources required for survival (Sherif, 1963).
Organizations have to acknowledge and act upon environmental demand in order to survive. Keeping in mind that the environment is under constant fluctuations in demand, it is
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Unfortunately, these demands cannot always be met. The question then becomes, interestingly enough, “to what extent the organization should respond in order to meet demands. The outcome in this might differ from one firm to another. If so, they are
deliberately creating different products, based upon their view of how to put their available resources to best use.
It seems that interdependence is inevitable and there are three factors that determine the dependence of one firm to another. Firstly, the value of the resource.
Secondly, the degree to which the group has discretion over the resource allocation and use. Lastly, the extent to which there are few alternatives for the resource. There are also
conditions leading to more independence, like strategic resources, who promote
independence (Blau, 1964). It is generally assumed that there are certain determinants who secure independence. Or, by owning the resource the company will make itself more
independent.
These definitions can be linked to strategy analysis in the video game industry; game developers must position themselves and their resources in such a way that their qualities are being exploited to the fullest in order to create a competitive advantage. This is a corporate strategy, which is phrased in terms of the resource position (strengths and
weaknesses) of the firm (Wernerfelt, 1984). This is a resource-based view. It views resources as a basis of power, since the resources of company A are often wanted by competitor B, but competitor B is unable to procure these. When company A structures these resources
properly in their company it will change the outcome, or in this case, the product (game). This view holds that firms with an ‘above average’ strategic performance are likely to have gained their sustainable competitive advantage because of the core competences of their resources (Slack et Al, 1995). These resources play a crucial role in the performance of organizations, especially in regards to the rarity of these resources. Organizations depend on valued resources to sustain ongoing processes and long-term viability (Christensen & Bower, 1996). Rare and valued resources, which are central to creating and sustaining competitive positions, are scarce and unique (Voss, 2008).
Proponents of this view argue that it plays a crucial role in allowing organizations to innovate by permitting them to experiment with new strategies and innovative projects that might not be approved in a more resource-constrained environment (Cyert & March, 1963).
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A big supporter of the resource dependence theory is Eric van Hippel, as he explains that ”innovation is an economic event that tends to occur where economic logic meets
institutional skill where the opportunity both to generate and to capture value through technology exists, innovation is likely to happen (Hippel, 1988)”. In addition, van Hippel tells us that the most likely source of innovation is with consumers, so all firms need to address the precise category of product, process, or service being considered (in our case product). This is just one example of a resource an organization can posses to enlarge its number of resources.
Threat rigidity also brings up what problems arise in uncertain conditions; “Efficiency concerns are manifested in the tightening of available budgets, increased
emphasis on cost cutting, and intensification of efforts to insure accountability. These effects are often brought about by a severe decline in performance and a reduction in slack
resources within the organization” (Staw et al, 1981). These conditions are said to eliminate the use of creative or novel strategies in decision making. This will make the firm focus on the things they do well. It is commonly assumed that methods of coping with adversity are appropriate and increase the survival prospects of the organization or protect local interests. In other words; when an organization is under threat, it might not have much monetary power compared to their competitors, thus the organizations can experience high dependency and posses a low number of (powerful) resources. The organization is then inclined to primarily focus solely on the things that kept their business alive in the first place.
One might conclude, from a resource-based perspective combined with the resource dependence theory, that high resources in combination with low dependability will provide a game developer with the best conditions to incorporate risk and thus their positive attitude towards creative games. This is because a game developer with high resources already has a competitive advantage and has room (in terms of utilities as well as monetary power) to try out something new that can possibly increase the competitive advantage and thus revenues. From this theory it is argued that a developer with the most resources (and less dependency) is most likely to be expected using product exploration as a strategic advantage. Conversely, for a company to cope with adversity, it doesn not have many resources and is rather dependent, it would not have the proper environment to explore (and take risk), and
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therefore use product exploitation as a corporate strategy. Threat rigidity supports this last statement.
2b. Prospect theory
Developers are constantly trying to meet the demand of the gamer by producing games they desire. This demand is always evolving and/or changing and gamers expect to experience something new in a game. But not only the newness of the game is what they are interested in. They also want the game to have a nice playability, short loading times, a good story line, nice graphics, etc. All these factors need to be taken into account by the developers.
Obviously, they cannot always deliver all these desirable characteristics.
So when developers create a completely new game, it is not clear yet how well the game will be evaluated by the gamers, as well as the critics. Especially innovation can be rather tricky for developers to incorporate in the game they are developing, since innovation is inherent to something they have not done before. Therefore, with innovation comes uncertainty. And uncertainty is a possible risk which companies want to reduce.
The prospect theory (Kahneman & Tversky, 1979) views peoples’ readiness to
incorporate this uncertainty (it could also be referred to as ‘the certainty effect’). This could mirror the developers’ willingness to produce highly creative, innovative games. It might appear that developers are making rational choices regarding when to engage in innovation.
However, the expected utility theory (EUT) states that the decision maker chooses between risky or uncertain prospect theory tells us otherwise (Viner, 1925). But before going into deeper detail about this theory, one has to understand the EUT first. That is why I am going to explain the basics of this theory first.
The prospect theory builds upon the expected utility theory which is applied as a descriptive model of economic behavior (Vinerprospects by comparing their expected utility values, i.e., the weighted sums obtained by adding the utility values of outcomes multiplied by their respective probabilities (Mongin, 1997). This systematic generalization of this theory can be seen in the sequences of Bernoulli’s experiments. “Expected values are computed by multiplying each possible gain by the number of ways in which it can occur, and then
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dividing the sum of these products by the total number of possible cases where, in this theory, the consideration of cases which are all of the same probability is insisted upon” (Bernoulli, 1954).
To give a clear example; a person is asked at what price he would value a lottery ticket. This ticket has one percent chance of winning a thousand dollars. The chance of winning nothing at all is ninety nine percent. Corresponding with the EUT this person would say the sum of each possible gain is one thousand. Subsequently, there are a hundred considerations of the same probability, whereas ninety nine of those are losing. To calculate the break-even point (the point one would expect to make either a profit or a loss) he would divide the winning sum of these products (1000 dollars) by the total number of cases (100 different percentages representing 100 different lottery tickets), which is 1000 dollars /100= 10 dollars. This person would value, according to the EUT, the lottery ticket at 10 dollars. To maximize the expected monetary value, one would expect to buy the ticket anywhere under 10 dollars, and don’t buy it if the ticket costs over 10 dollars. Corresponding with this it would seem that developers would always choose to embrace product exploration
whenever the expected utility (revenue) is higher than the risk to lose monetary value. Yet, this is not always the case.
This is where prospect theory comes in. Prospect Theory (PT) talks about how people tend to have an aversion against risk. They value 100% certainty disproportionally.
For example, if someone can choose between 10 Euro guaranteed, or 80% on 15 Euro, one is more inclined to choose the first option than to choose the first option in the next two examples: choose between 10% on 10, or 8% on 15. In both propositions, there is an equal loss of certainty as 80 to 100 is the same as 8 to 10, except the numbers are divided by ten. The difference is that in example 1 the first option reaches 100% certainty in winning the amount. Most people value this certainty so high, they will pursue this amount of certainty for themselves disproportionally against other possibilities. Hence the evidence of positive behavior towards certainty.
Mathematically this decision-making is irrational, but prospect theory proves risk aversion in choices involving sure gains and to risk seeking in choices involving sure losses (Kahneman, 1979). In other words, the more resources a firm has, the less likely it is that it would engage in product exploration and the more likely it is it would use product
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exploitation. Because threats carry the potential to erode the organization’s strategic position, managers should increase investments in innovative competencies to counter threatening environments and their unpredictable outcomes (Voss, 1998). Even so, resource scarcity should drive change (Sherer et al, 2002)
It is believed that this strategic behavior is reflected in activities that support product exploitation. This suggests that exploration is in the long-term vital for an organization since its buffer from the previous exploration is finite.
At the time the buffer is completely drained, demands for exploration have come to arise. This period of time is called organizational slack. But what is the relationship between organizational slack and exploration? Opponents of slack argue that slack diminishes
incentives to innovate and promotes undisciplined investment in R&D activities that rarely yield economic benefits (Jensen, 1986). Steady support for this particular theory is to be found in literature by Voss (2008).
It would imply for the game industry that the leading companies would actually not engage in risk taking since they are the companies that are most certain in their very existence. This theory would argue the opposite behavior of the resource based theory; it are not the biggest companies engaging in innovation and creativity, but the companies that experience uncertainty.
A logical explanation supporting this theory is that companies who are dealing with great uncertainty need drastic change and are more willing to take risks in order to change their current status, leading to try to make completely new, innovative and creative games.
Thus far the distinct differences of both approaches have been addressed to
differences in the field of strategic management and industrial organization whereas profit differentials among firms have been linked to the ownership of particular resources. On the other hand, the industrial organization literature links profit differentials among industries to the existence of barriers to entry (Mol et al, 2005).
This distinction of these two theories is not new. Yet, in this paper I try to link these approaches to the game industry, and more specific, to the industry of Playstation 3 games. Before I do so, I will make clear in the theoretical framework some variables that need to be investigated. These variables will be of concern since they are applicable to the video game industry, and also relate to the theories.
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3. Theoretical framework
In the creative industry, where the video game industry takes place, it can be of particular importance for developers to look at evaluations of both critics and consumers to stimulate or stipulate purchase intentions. Reviews have become a major information source for consumers and marketers regarding product quality (Hu, 2008). This word-of-mouth (WOM) communication is considered a valuable marketing resource for consumers and marketers and a reliable and effective metric for measuring customer loyalty with critical implications for a product’s success. Thurau’s (2009) assumption is that when the evaluations of a
certain product are positive, it is likely the next product of that organization will be regarded in the same way. This can be seen this way because an organization has found a way of producing heuristic, feel-good products. He also states, in the case of the movie industry, that the long-term success depends on bringing people into theaters when a movie opens. If they don’t, they will not have any kind of effect thereafter. Naturally, to get the theater filled with people, advertising (for example by advertising critics’ evaluations) needs to take place.
Both positive and negative reviews are correlated with weekly box office revenues over an eight-week period. WOM information (especially over the internet, where WOM has the ability to spread fast over different groups and can reach many people) offers significant explanatory power for both aggregate and weekly box office revenue, especially in the early weeks after opening (Hu, 2008). This exhibits the power evaluations have over the
performance of a company and its future performance. Particularly in the creative industry where product qualities are difficult to ascertain before consumption, these valuators help the consumer to set up expectations and identify product attributes before deciding to buy (Situmeang, 2013).
In this paper, I am going to look at what signals are important factors for organizations to either support the RDT or the PT. Signaling theory (Spence, 1973) explains behavioral
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of, all available signals become all the more important since they are the only signals to evaluate by.
The signals I am going to conduct research on are: - Positive critic (expert) evaluations
- Negative critic (expert) evaluations - Positive consumer evaluations - Negative consumer evaluations - Sales
- Innovativeness evaluations.
Please note that the first four signals are interrelated to sales, like so (figure 1):
Figure 1
All consumer and expert evaluations relate to sales. If negative, sales will also have a negative effect. And when positive, sales are likely to increase.
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Evaluations of critics are seen in countless products. The fact that critics are experts in a certain domain, gives validity to their findings in that domain. Their findings could represent a group as a whole, or it could represent what the probability is that the group that is interested in that domain, would find. In other words, some critics could represent viewers’ preferences, and some people could consider critical reviews when deciding whether to see a film (Eliashberg, 1997). At high levels of quality, the market success of a movie increases with the critics’ ratings. It is expected, as critics are experts in a certain field, that they can assess quality. Considered from the perspective of signaling theory, expert evaluations signal the quality of the product being evaluated, which in turn reduces consumers’ uncertainties regarding the product (Situmeang, 2013). This is because expertise is a function of what is known (epistemic expertise), and it has the capacity to provide strong justifications for a range of propositions in a domain (the video game industry in our case) (Weinstein, 1993).
In this paper I will review critic scores on Playstation 3 games as being positive or negative. This outcome will be related to the number of positive or negative words
consisting in the text. This will not be related to a digital number, as one might expect (for instance, any critic score above 55 is positive on a scale from 1 to 100, and any score under is negative). How I have come up with the outcome of either these positive or negative
evaluations will be explained in the methodology sector.
These findings could mean that positive evaluations of games from experts could mean that the developer, responsible for the game, has the right resources to produce a good game. With respect to the RDT it would imply that developers receiving good critic evaluations have more resources to invest in producing an innovative game. Developers receiving high critics’ evaluations will be more likely to produce an innovative game in the future.
Of course, as discussed before, the PT contradicts this assumption. If high critic evaluations signals quality, it would mean the organization has the capabilities (and thus resources) to produce a qualitative good game. The prospects seem good for this firm, so there is no need to change that. According to PT this company will less likely take any risk, and produce an innovative game in the future.
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H1a: If critic/expert evaluations are positive, it will be more likely that the company responsible for developing that game will produce an innovative game in the future. H1b: If critic/expert evaluations are positive, it will be less likely that the company responsible for developing that game will produce an innovative game in the future.
H1c: If critic/expert evaluations are negative, it will be more likely that the company responsible for developing that game will produce an innovative game in the future. H1d: : If critic/expert evaluations are negative, it will be less likely that the company responsible for developing that game will produce an innovative game in the future.
3b. Consumer Evaluations
The value, as perceived by customers can be reflected in customer evaluations (Riel, 2001). If consumers, after having consumed the product, rate the product good or positive, it means that they were satisfied with the product. The signal that positive evaluations give to developers is of high importance since the consumers are the customers that create revenue for the company. Moreover, consumers react more favorably to an element of the
marketing mix for the brand than they do to the same marketing mix element when it is attributed to a fictitiously named or unnamed version of the product or service (Keller, 1993). The same marketing mix element could refer to an extension of the brand like a sequel of a game, whereas the unnamed version could relate to a new (innovative) game. This embraces the PT perspective, because a firm might not feel the urge to produce an innovative game if the customers are reacting positive and prospects look good. RDT, in contrast to this, proposes that a developer might do the opposite to try to create even more competitive advantage than it already does. Therefore, I would like to propose the following hypothesis:
H2a: If consumer evaluations are positive, it will be more likely that the company responsible for developing that game will produce an innovative game in the future.
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H2b: : If consumer evaluations are positive, it will be less likely that the company responsible for developing that game will produce an innovative game in the future.
H2c: If consumer evaluations are negative, it will be more likely that the company responsible for developing that game will produce an innovative game in the future.
H2d: If consumer evaluations are negative, it will be less likely that the company responsible for developing that game will produce an innovative game in the future.
3C. Sales
Sales data is one of the most important variables to take into account for companies that pursue profit. Sufficient sales data enable reliable predictions for future product success. Given the high failure rate of new products, as well as the often large investments in new product introduction, it is critical to assess the success potential of an innovation as early as possible in order to avert preventable financial losses and concentrate resources on the support of innovations that have a high chance of success. Although there is a school of research focused on early assessment of a product’s likelihood of success, with few exceptions, there has been relatively little success in transforming sales data into valid measures for predicting post-launch success (Garber, 2003; Golder and Tellis, 1997).
I would like to see if the effect of positive sales data on the likeliness of producing an innovative game can either support the RDT or the PT. Therefore, I propose the following hypothesis:
H3a. The better sales data a developer has on their games, the more likely it is that this developer will produce an innovative game in the future.
H3b. The better sales data a developer has on their games, the less likely it is that this developer will produce an innovative game in the future.
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4. Methodology
This research is a quantitative study. This is an empirical investigation widely used in different sciences. The quantitative research will be deductive, since it will test the hypotheses manifested in previous explained findings. All data used is part of a bigger database handed to me by professor Situmeang. This database has evaluations of data of both consumers as well as critics on most games that have been released on most consoles. As explained in the theoretical framework I was particularly interested in the evaluations as well as the sales for these games. In order to guarantee validity for my testing of the
hypothesis I needed not only to get the exact data for as many games as possible (as the sheer size of a sample can enlarge the trustworthiness of the results), but I wanted to make sure that the coverage of the games was representative for the hypotheses opposed. To achieve this, I chose to narrow the sample down to the statistical variables of Playstation 3. Playstation 3 (ps3) is a good platform to test hypotheses since a lot of games have been released on this platform. Also, Playstation 3 is a console that, compared to other consoles, has the most processing power (except for the next generation consoles Playstation 4 and Xbox One). Additionally, the more power a console has, the more widespread use and variety can be applied in games. And as such, this power could be used to exploit innovation skills. Therefore ps3 seems the best console to test the two schools of thoughts regarding innovation on. In this database there are 2449 ps3 games reviewed by critics as well as consumers. This sample of games is a good representation of all the games, with all its genres, produced on ps3 and this sizable amount is sufficient to provide statistical power.
4a Evaluations variables
To make a distinction of the evaluations in whether they’re either positively charged or negatively charged, I made use of a dictionary found on all positive words and all negative words. The “positive-word-dictionary” consisted of 2006 words. The “negative-word-dictionary” consisted of 4783 words. This list was not applicable to test matching either positive or negative on the critics and consumers evaluations, because in the jargon of the
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video game domain words that appear negative could be used in a positive way (this game is “ridiculous” or “breathtaking”). Furthermore, to test all these words in all words of the evaluation would be a too intensive calculation for any personal computer (PC) with normal to high processing power. This would have resulted in continuously crashing of the PC when trying to make the calculations.
The ambiguous character of the words and restraining processing power of my PC lead me to narrow down the list of the positive words as well as the negative words. Eventually, I chose to narrow down the number of positive words from 2006 words to 292. And the number of negative words I narrowed down from 4783 words to 417 words. The list that is narrowed down for positive and negative words can be found in Appendix C and Appendix D.
These dictionaries could be applied to the texts consumers and experts had written to see if there were more matches in the positive wordlist or the negative wordlist. The result was the 4 variables:
- Critic positive evaluation - Critic negative evaluation - Consumer positive evaluation - Consumer negative evaluation.
4b. Sales data variable
To acquire the sales data, I utilized the global sales data available on VGchartz.com which contained sales data for the same games as the experts and critics had evaluated on. VGchartz.com is an independent website providing individuals with sales data in the game industry. The website is broadly accepted as having the networks to gather this information adequately and has no interest in cheating with their results. This is why the data used is applicable for my research and qualifies for rendering the required data.
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4c. Innovation Evaluation variable
The last variable I am using to test my hypotheses is the innovation evaluation variable. To create this variable I summarized all words, with the help of the Oxford Dictionary that are synonyms or antonyms of the word “innovation”. This variable contains 17 words. To see an overview of these words, see Appendix E. Similar to the expert and consumers evaluations, this variable is matched with all its containing words with the words of the consumers as well as expert evaluations.
4d. Operationalization of variables
To match all positive words to the evaluation of the expert, Microsoft Office Excel 2007 is used. All 2449 Playstation 3 games (n=2449) with its evaluations were placed, starting in row A2, giving a total number of 36913 rows. Next, all positive words were placed in columns, creating a table for Excel to calculate on. The total matches per word were computed in the table by using the formula for Excel to match words. The last column at the far right end was given the name “Total”. The total matches of all words per evaluation were computed.
This tactic was repeated for all variables, making the variables ready for further research on SPSS.
4e. IMB SPSS Statistics
Through the use of IMB SPSS Statistics (SPSS), the standard for statistical software, I will put my variables to the test to see what hypotheses (based on the theory) are supported. Together with professor Situmeang I worked out the methodology. We used the logit regression function (or logistic regression function). This function can predict probability by looking at dependant binary variables. By analyzing the regression of binary logistics we were able to see if a firm will probably explore or exploit in the future.
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To define exploration and exploitation we assumed that every produced sequel is an act of product exploitation, because existing concepts are refined. In contrast, every new game will be regarded as product exploration, because it explores new concepts used in the game. This is an example of innovation.
All variables are analyzed in the logit regression analysis. Next, to ensure that the analytical model is reliable and produces accurate results, the variables have undergone a bootstrap. Both the outcomes of the logit regression analysis and the bootstrap will be shown and discussed in the results.
4f. Model specification
This study estimates the occurrence of product exploration in the video game industry. The variables tested are treated as dependent variables and through logit regression analysis is estimated if the RDT or the PT is likely to find support. The main function in this study is the function of sales performance, positive consumer evaluations, negative consumer
evaluations, positive expert evaluations, negative expert evaluations, consumer
innovativeness scores, expert innovativeness scores. Mathematically the function can be written as follows:
(1) ( , )=
0+ 1Global, + 2ConsumerTotalsAvgOfPositive, + 3ConsumerTotalsAvgOfNegat ive, + 4CriticTotals.AvgOfPositive, + CriticTotalsAvgOfNegative, + 6ExpInnv, + 7ExpInnv, + 8 , + 9ConsumerTotalsAvgOfNegative, ∗Global, + 10 ConsumerTotalsAvgOfPositive, ∗Global, + 11CriticTotalsAvgOfPositive, ∗Global, +CriticTotalsAvgOfNegative, ∗Global, +
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5. Results
To see if any of the hypotheses can be maintained, a significance level of P<0,05 is maintained (table 1). The probability factor B must is positive (B > 0) when product
exploration is likely and negative (B < 0) when it is less likely to explore. The higher B is, the more likely it is to explore. Even so, the lower B is, the less likely it is to explore.
In the outcome of the logit regression analysis the variable ‘Critic Positive’ showed no significance (P=0,324). So H1a and H1b are not supported.
In the outcome of the logit regression analysis the variable ‘Critic Negative showed significance (P=0.14) and the probability of exploring is B=-.406. This means that if experts give negative evaluations, developers are less likely to act on product exploration. This is consistent with hypothesis H1d and is supported. H1c can be rejected.
In the outcome of the logit regression analysis the variable ‘Consumer Positive’ showed no significance (P=0,624). So H2a and H2b are not supported.
In the outcome of the logit regression analysis the variable ‘Consumer Negative’ showed significance (P=0,018) and the probability of exploring is B=0.173. This means that if consumers give negative evaluations, developers are likely to act upon product exploration. This is consistent with H2C and is supported. H2d can be rejected.
In the outcome of the logit regression analysis of the variable ‘Global Sales’ showed to be extremely significant (P=0,000) and the probability of exploring is B=0,926. This means that when the global sales are good, it is very likely that developers will use product
exploration. This is consistent with H3a and is supported. H3b can be rejected. In the outcome of the logit regression analysis the variable ‘global sales with
consumer negative’ showed to also be extremely significant (P=0,000) and the probability of exploring is B=0,797, which is very likely. For this finding no hypothesis has yet been
established in this paper. In the discussion I explain what this outcome can mean, like inductive research does.
In the outcome of the logit regression analysis, the variable innovation on both experts and consumers showed no difference. Therefore no support can be inducted from these results.
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Model Parameter Probability StE. Wald Sig. Exp(B)
___________________________________________________________________________ Average Score Consumers -0,041 0,038 1,138 0,286 0,96 Average of Consumer Positive -0,018 0,037 0,241 0,624 0,982 Average of Consumer Negative 0,173 0,073 5,575 0,018 1,189 Average Score Critics -0,001 0,005 0,038 0,845 0,999 Average of Critics Positive 0,098 0,099 0,972 0,324 1,103 Average of Critics Negative -0,406 0,165 6,073 0,014 0,666
Global Sales 0,926 0,107 74,901 0 2,524
Expert Innovativeness -0,081 0,049 2,734 0,098 0,922 Consumer Innovativeness 0,017 0,01 3,144 0,076 1,017 Global Sales with Consumer
Negative 0,797 0,201 15,725 0 2,218
Global Sales with Consumer Positive -0,293 0,214 1,868 0,172 0,746 Global Sales with Expert Negative -0,062 0,145 0,18 0,672 0,94
Global Sales with Expert Positive -0,104 0,076 1,88 0,17 0,901 ___________________________________________________________________________ Table 1, the results of the logit regression analysis. The probability to explore can be seen in the second row (B). In the sixth row (Sig.) the significance. See appendix A for all figures.
The bootstrap analysis (table 2) proved the logit regression analysis was valid because all significant variables (and unsignificant variables) proved to be consistent with both findings.
However, there were some differences: in the bootstrap, the variable ‘Critic Negative’ was slightly less significant than the logit regression analysis (P=0,016 compared to P=
0,014).
The variable ‘Consumer Negative’ also showed to be less significant in the bootstrap (P=0,028 compared to P=0,018).
The variable Global showed to be still extremely significant losing its significance in the bootstrap only by 0,001 (P=0,001 compared to P=0,000).
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_________________________________________________________ Average Score Consumers -0,041 0,04 0,316 Average of Consumer Positive -0,018 0,047 0,687 Average of Consumer Negative 0,173 0,084 0,028 Average Score Critics -0,001 0,005 0,838 Average of Critics Positive 0,098 0,108 0,334 Average of Critics Negative -0,406 0,171 0,016
Global Sales 0,926 0,152 0,001
Expert Innovativeness -0,081 0,049 0,087 Consumer Innovativeness 0,017 0,01 0,041 Global Sales with Consumer
Negative 0,797 0,293 0,003
Global Sales with Consumer
Positive -0,104 0,139 0,159
Global Sales with Expert
Negative -0,062 0,195 0,758
Global Sales with Expert Positive -0,293 0,29 0,318 ______________________________________________________
Table 2, bootstrapping analysis. See Appendix B for all figures
6. Discussion
The findings of this study show that when expert evaluations are negative, it is not likely the developer will engage in product exploration. This strategy underlines the perspective of the resource dependence theory; steer away from taking risk by staying in the same business concepts. If a developer is signaled by experts that it is not performing well, it apparently will not produce an innovative game. It will rather focus on the resources that they have and the things they do well.
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Also supporting the RDT is the outcome of the sales. If sales are good, a developer would expect to have the proper resources to explore and create an innovative game. Thus far, this analysis has proven to be most significant, giving the strongest support for the RDT.
In contrast to this, another finding can be linked to the prospect theory, namely the outcome of the negative consumer evaluations. It seems here that when a developer is presented with bad reviews from consumers, he will react by applying product exploration tactics. In other words, if prospects don not look good triggered by consumers, the
developer will try something in order to meet their demands.
Lastly, a combination of both theories can be found in the combination of sales and negative consumer reviews. If prospects are good (like sales in this case), RDT contemplates that a developer will explore. Following this argumentation, if prospects are bad (like negative consumer reviews), RDT would suggest not to explore. This is not true; this is exactly what PT says. Apparently both theories can be found in this type of behavior. It is up to others to find the underlying reason.
Why likeliness of the negative evaluations on critics and experts differs in the way developers choose to approaches different theories is yet to be explained. This study only examined these effects. It seems that developers value negative critic evaluations different than consumer evaluations. What this means is perhaps to be conducted research in a qualitative manner, to pinpoint the views of these developers on the differences.
It is also not clear why developers use product exploration when sales are good but consumer evaluations are negative. This decision-making can be supported by either theory, depending on what variable is taken perspective on. If we look at the sales, which are going well, and it is likely they would explore, RDT is supported. But at the same time, if we look at the negative consumer evaluations, in combination with the likelihood to explore, one would think the developer is using PT to approach this matter. This entails some confusion, and also here further research is to pinpoint what the underlying idea behind this behavior is.
All in all there have been four significant findings in the results, supporting both the hypotheses. Two of which support the RDT, one of which supports the PT, and one finding actually supported both theories. These findings seem to be pretty well divided among the two theories, suggesting not one of those is to be ruled out when taking the video game industry into consideration.
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6a. Managerial Implications
The research captures the probability for game developers to explore (or, if not, to exploit). Consistent with the findings I can safely define different managerial implications for game developers. These implications refer to the need for exploration. If the need is low, it would mean the certain developer probably will make a clever strategic move when it decides to move business away from exploiting existing concepts and starts to explore. If the need is low, the developer will be advised to stay in the same business leaving game exploration as it is.
In the findings it became clear that a developer is likely to start exploring when consumers were dissatisfied with the products they were currently putting on the market. Apparently, developers take the negative feedback from consumers serious and want to make a shift in the kind of products they are developing. Assumable is that developers take into account that consumers are the ones that create revenue, so it is important to listen to the feedback they are giving you; if they are not satisfied, then try something else. Game exploration is the result of this reasoning. Managers are advised to take up on this reasoning if they want to imitate the tactics currently handled in the game industry. More explicitly; if consumers report negative feedback in their reviews on your product, then the developer should not try to create more revenue in producing a sequel, but he should try to develop an entire new game.
However, when experts give bad reviews and their feedback is negative, it is proven to be less likely to explore. It seems wise for game developers to improve the reviews by improving their current business. This is a different approach than when consumers are not ‘happy’ with the product (and write negative feedback in their reviews). The implication in this situation is that game developers are advised to develop the same kind of games of better quality in their sequel, as expert reviews signal quality. A reason for this difference could be that the developer has room for improving the quality of their product, and therefore does not want to leave its current business yet. Perhaps, when the game
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developer feels it has delivered a qualitative good game but consumers still are not satisfied with it, only then it will start to explore.
The most obvious implication for game developers is due to the finding of the likelihood of exploring when global sales are good. In this case, it is very probable the developer would explore. This implication is a big supporter of the RDT, as monetary power is a powerful resource that encourages game exploration in order to find (and sustain) competitive advantage. Not only do good sales reflect on money as a resource, it reflects that the firm already has good resources altogether to develop a game that is globally sold well. Game developers are thus advised to indeed expand their business if they have a choice (made available by their resources) to do so. In short; a developer should try to produce entire new games when the sales scores are positive. And the more positive, the more he should develop an entire new game.
6b. Limitations & future research
This paper has addressed when developer would start to explore and searched for correspondence with either the RDT or the PT. I have made use of Playstation 3 data for considerable reasons (earlier explained), but it does limit my research. To create a better understanding of the implications the research needs be expanded towards other platforms as well. Different conditions are plausible for platforms with different characteristics and processing power.
To find prove for either the RDT or the PT I have taken a closer look at the sales scores of the games and the consumer and expert evaluations of these games. Also, I have shed light to whether innovativeness scores are of significance in whether a developer will be likely to explore or exploit. These are the variables I used to reflect on the resources of a developer. I have made clear that monetary power (gained from good sales) is a powerful resource. This paper also pointed out that if a developer has good sales it possesses the proper resources to create a game consumers are satisfied with (since positive reviews correlate with positive sales data). The question remains what the ‘proper resources’ are.
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Therefore, extended research on what resources are of importance in product exploration has to be done. Resources such as access to information, group cohesiveness, slack, technological power, knowledge, strategic decision making etc. all need to be taken into account to fully understand under what condition a developer is likely to explore or not. It is possible that not possessing a critical resource could limit the developer to take further action towards innovation, regardless of the other assets it has. If so, once the resources are identified, the hypotheses that have been rejected or that are not supported in this paper can be explained through the presence of this particular resource.
Additionally, all identified resources in future resources should be combined as variables in a regression analysis to gain knowledge on what combination of resources are creating the environment a developer will be innovative. It may be clear that one certain resource alone has either a positive or negative impact on product innovation, but a
combination of resources together could counter each other’s positive are negative attitude
towards product exploration, resulting in a different pattern as expected when tested apart. Furthermore, this paper has not paid attention to the different genres that exist
within the game industry. Some genres can be more susceptible to innovation than others. For instance, in the sports genre it could be increasingly difficult to innovate since this genre is based on existing concepts. This automatically puts limits to what can be applied and changed in these games, since it has to stay a sport from ‘the real world’ with its own set of rules and procedures . Even so, in the role playing game genre (what is based on character building) it could be less difficult to innovate since the limitations are as far as the
imagination of the developer goes. Now, if a developer has the right set of conditions to create an innovative game, but thus far only produced exclusively sports games, it might not be wise to innovate for them. Future research need to be conducted to see what genres seem more plausible to innovate for.
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The RDT and PT are thoroughly explained and linked to the video game industry.
This study demonstrated that not just one of the two theories, resource dependence theory or prospect theory, is applicable for the video game industry. It seems that different factors can lead developers to choose between those approaches of business strategy. They can both be used as strategic decision-making.
In my hypothesis I have found support that both RDT and PT can foresee how likely it is that a developer would engage in producing an innovative game. Thus far, the most evident support found for the RDT is global sales numbers. When global sales are good, it is very likely that this particular developer will be creative and develop an innovative game. RDT is also supported whenever consumer’s reviews are negative. In contrast to this, PT is supported when reviews by critics are negative.
It seems that either theory can find support in different sets of conditions. It remains to be seen what theory is practiced under different conditions, to get a better understanding of when developers choose to explore their product. Innovation seems a concept that needs more attention of how and when it should be used as a tool to improve business in the economic environment in the video game industry.
My intention to close the research gap (linking both theories to the game industry) has, as expected, not fully been closed. However, extended literature on previous literature is delivered. Its findings leads challenges to other scientists to conduct further research as to why and when developers engage in product exploration.
8. References
30
Becker,K. (2007). Pedagogy in Commercial Video Games. Games and simulations in online learning: Research and development frameworks, pp. 21-47.
Bernoulli, B. (1954). Exposition of a New Theory on the Measurement of Risk. Econometrica, Volume 22, Issue 1, pp. 23-36.
Blau, P.M. (1964). Exchange and Power in Social Life. New York: Wiley, Pp. 119-125.
Christensten, C. M., Bower, J. L. (2001). Organizational actions in response to threats and oppurtunities. Academy of Management Journal, pp. 197-218
Danneels, E., Sethi, R. (2011). New product exploration under environmental turbulence. Organization Science, 22(4), 1026-1039.
Eliashberg, J., Shugan, S.M. (1997). Film critics: influencers or predictors? Journal of Marketing, 61, pp. 67-78
Garber, T., Goldenberg, J., Libai, B., Muller, E. (2003). From Density to Destiny: Using Spatial Dimension of Sales Data for Early Prediction of New Product Success. Retrieved from: www.complexmarkets.homestead.com/files/SpatialFinalNovember2003.pdf
Golder, P. N., Tellis, G. T. (1997). “Will it ever fly? Modeling the Takeoff Really New Consumer Durables. Marketing Science, 16, pp. 256-270.
Hartmann, T., Klimmt, C. (2006). Gender and computer games: exploring females’ dislikes. The journal of Computer-Mediated Communication, 11, pp. 910-931.
Hennig-Thurau, T., Houston, M.B., Heitjans, T. (2009). Can good marketing carry a bad product? Evidence from the motion picture industry. Marketing letters, vol. 17, pp. 500-513
31
Hippel, von H. (1988). The sources of innovation. Oxford university press.
Hu, N., Ling, L., Zhang, J. (2008). Do Online Reviews Affect Product Sales? The Role of Reviewer Characteristics and Temporal Effects. Information Technology and Management, Vol 9, Issue 3.
Johns, J. (2006). Video Games Production Networks: Value Capture, Power Relations and Embeddedness. Journal of Economic Geography, pp. 151-180
Kahneman, D., Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, Vol. 47 No. 2 pp 263-292
Keller, K.L. (1993). Conceptualizing, Measuring and Managing Customer-Based Brand Equity. Journal of Marketing, 57(1), pp. 1-22.
Monging, P. (1997). Expected utility theory. Handbook of Economic Methodology, pp. 342-350
Pfeffer, J., Salncik, G. R. (1978). The external control of organizations: A Resource Dependence Perspective. New York: Harper & Row.
Rank, J., Pace, V.L., Frese, M. (2004). Three avenues for future research on creativity, innovation, and initiative. Applied Psychology, 53(4), pp. 518-528
Riel, A. C. R., Liljander, V., Jurriëns, P. (2001). Emerald Article: Exploring consumer evaluations of e-services: a portal site. International Journal of Service Industry Management, 12 (4), pp. 359-377.
Salimäki, M., Karjalainen, T. (2012). International design business management. IDBM Program, Aalto University. Vol. 2. Retrieved from:
32
Sherer, P.D., Lee, K. (2002). Institutional change in large law firms: A resource dependency and institutional perspective. Academy of Management Journal, 45(1), pp. 102-119
Sherif, M., Sherif, C. W. (1953). Groups in Harmony and Ten- sion: An Introduction to Studies in lntergroup Relations. New York: Harper and Row.
Sherif, M., Harvey, 0.J., White, J, W.R., Sherif, C. W. 1961. Intergroup Cooperation and Competition: The Robbers Cave Experiment. Norman, OK: University Book Exchange.
Spence, M. (1973). Job Market Signaling. The Quarterly Journal of Economics, 87(3), pp. 355-374
Staw, B.M., Sandelands, L. E., Dutton, J. E. (1981). Threat Rigidity Effects in Organizational Behavior: A Multilevel Analysis. Administrative science quarterly. Vol. 26, No. 4. Pp. 501-524
Tschang, F. T. (2007). Balancing the Tensions Between Rationalization and Creativity in the Video Games Industry. Lee Kong Chain School of Business, Singapore Management
University, Singapore 178899, Republic of Singapore. Volume 18, issue 6, November-December, pp. 989-1005
Viner, J. (1925). The utility concept in value theory and its critics. The Journal of Political Economy.
Voss, G. B., Sirdeshmukh, D., Voss, G. Z. (2008). The Effects of Slack Resources of Environmental Threat on Product Exploration and Exploitation. The Academy of Management Journal, Vol. 51, pp. 147-164
33
Wernerfelt, B. (1984). A resource-based view of the firm. Retrieved from:
http://www1.ximb.ac.in/users/fac/Amar/AmarNayak.nsf/dd5cab6801f17235852564740053 27c8/456e5a8383adcf07652576a0004d9ba5/$FILE/RBV.pdf
Williams, D. (2002). Structure and Competition in the U.S. Home Video Game Industry. The International Journal on Media Management, Vol. 4, pp. 41-54.
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9. Appendix
9A. Logit Regression Analysis Figures
Kolom1 B S.E. Wald df Sig. Exp(B)
ConsumerTotalsAvgOfScore -0,041 0,038 1,138 1 0,286 0,96 ConsumerTotalAvgOfPositive -0,018 0,037 0,241 1 0,624 0,982 ConsumerTotalAvgOfNegative 0,173 0,073 5,575 1 0,018 1,189 CriticTotalsAvgOfScore -0,001 0,005 0,038 1 0,845 0,999 CriticTotalsAvgOfPositive 0,098 0,099 0,972 1 0,324 1,103 CriticTotalsAvgOfNegative -0,406 0,165 6,073 1 0,014 0,666 Global 0,926 0,107 74,901 1 0 2,524 ExpInnv -0,081 0,049 2,734 1 0,098 0,922 Consinnv 0,017 0,01 3,144 1 0,076 1,017 gl_cn 0,797 0,201 15,725 1 0 2,218 gl_cp -0,293 0,214 1,868 1 0,172 0,746 gl_en -0,062 0,145 0,18 1 0,672 0,94 gl_ep -0,104 0,076 1,88 1 0,17 0,901
9B. Bootstrap Analysis Figures
Kolom1 B Bias
Std. Error
Sig.
(2-tailed) Lower Upper
ConsumerTotalsAvgOfScore -0,041 0,002 0,04 0,316 -0,122 0,042 ConsumerTotalAvgOfPositive -0,018 -0,003 0,047 0,687 -0,114 0,07 ConsumerTotalAvgOfNegative 0,173 0 0,084 0,028 0,011 0,338 CriticTotalsAvgOfScore -0,001 0 0,005 0,838 -0,01 0,009 CriticTotalsAvgOfPositive 0,098 -0,017 0,108 0,334 -0,135 0,297
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CriticTotalsAvgOfNegative -0,406 -0,011 0,171 0,016 -0,756 -0,1 Global 0,926 0,068 0,152 0,001 0,738 1,318 ExpInnv -0,081 -0,002 0,049 0,087 -0,179 0,009 Consinnv 0,017 0 0,01 0,041 -0,002 0,038 gl_cn 0,797 -0,016 0,293 0,003 0,24 1,314 gl_cp -0,104 -0,061 0,139 0,159 -0,518 0,029 gl_en -0,062 -0,019 0,195 0,758 -0,45 0,299 gl_ep -0,293 -0,021 0,29 0,318 -0,893 0,246 9C. Appendix Positive words used: Accomplish Achieve Adept Admirable Adorable Advanced Advantage Amaze Amazing Amiable Amity Amuse Appreciate36
Aspire Arouse Ascend Astonish Attentive Attraction Attribute Authentic Award Awesome Beatific Beatify Beatitude Beauteous Beautiful Beautify Benefaction Beneficial Benefit Benevolent Beauty Beloved Best Better Betterment Bless Blessed Blessing Blossom Bonus Boost37
Bright Brighten Brilliant Capable Celebrate Charm Classy Comfort Comfortable Compliment Confidence Congratulate Considerate Constructive Content Contribute Correct Courage Creative Credit Cute Decency Decent Delight Desirable Dynamic Ecstasy Effective Efficiency Elegant Elevate38
Eligible Emphasis Emphasize Emphatic Enchant Endear Endearment Endeavour Energetic Energize Energy Enhance Enjoy Enlighten Enterain Entertainment Enthusiasm Enthusiastic Exceed Excel Excellence Excellent Excite Expert Expertise Exquisite Extraordinary Fabulous Fame Fancy Fantastic39
Fascinate Favourite Felicity Fine Finesse Fitting Flamboyant Flexible Forward Freedom Fruitful Fulfil Fun Funny Gallant Generate Generous Genius Genuine Gift Gifted Glad Glorious Glory Good Goodness Gorgeous Grace Graceful Grandeur Grateful40
Great Happily Happy Harmonious Harmonize Heaven Heavenly Hilarious Hilarity Hip Holy Honest Honestly Honesty Honour Honourable Humorous Humour Ideal Ideally Immense Impress Impressive Improvement Incredible Ingenious Innovate Innovative Inspiration Inspire Inspired41
Interest Interested Interesting Joy Joyful Juicy Kudos Liberate Liberation Likable Like Liking Lovable Love Lovely Loving Luxurious Luxury Magnificent Magnitude Majesty Marvellous Miraculous Miraculously Neat New Newly Nice Nicety Nifty Noble42
Notable Nurture Opportune Opportunity Original Originality Outstanding Passion Passionate Perfect Perfection Pleasant Pleasurable Plus Positive Potential Practical Precious Productive Progress Promote Promotion Prosper Pure Purify Purity Real Reliable Remarkable Reward Rewarding43
Rich Richly Satisfaction Satisfactory Satisfy Sincere Special Spectacular Splendid Steady Succeed Succeeding Success Successful Super Superior Supreme Sweet Swell Swift Sympathetic Sympathise Sympathy Tact Tactful Thank Thanks Thankful Thorough Thoroughly Thoughtful44
Thrill Thrive Timeless Top Triumph Triumphant Trustworthy Trustworthiness Ultimate Unequalled Upstanding Useful Usefulness User-friendly Valuable Venerable Viable Viability Vivid Wholehearted Win Winner Winning Winsome Wonder Wonderful Worthwhile Worthy Wow45
9D. Appendix
Negative words used: Abandoned Abused Afraid Aggravated Aggreessive Anger Angry Anguish Annoy Annoyed Anxious Anxiously Artificial Ashamed Assaulted Atrocious Atrociously Awful Awfully
