a Abstract
There are many elements that needs to be considered when gamifying a business. The paper aims to answer the following research question ‘To what extent does Collaboration as game design affect the Attitude of users, therefore affect Continuance of Intention and Word of Mouth ?’ The study does so by investigating the effect of Collaboration has on Attitude of Users and whether this relationship is mediated by Progression Reward, and moderated by Types of Gamer. The study predicted that a significant relationship exists between all
variables, and the effect of Collaboration on Attitude is explained by Progression Reward and strengthen by Types of Gamer. The study concluded a significant relationship did exist between Collaboration, Progression Reward and Attitude, as well as the mediation effect of Progression Reward. The study found no significant relationship for Types of Gamers and the moderation effect. Attitude had a significant positive effect on Continuance of Intention and Word of Mouth.
Keun Sang Oh 10839194 26/06/2018
University of Amsterdam
Collaboration in Online
Gamification: Empirical Study on
Steam Trading Cards
b Statement of Originality
This document is written by Student Keun Sang Oh who declares to take full responsibility for the content of this document
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
c TABLE OF CONTENTS 1. INTRODUCTION... 1 2. THEORETICAL BACKGROUND ... 3 3. THEORETICAL MODEL ... 6 3.1 COLLABORATION ... 6 3.2PROGRESSION REWARD ... 7 3.3TYPE OF GAMER ... 8 3.4ATTITUDE ... 9
4. DESIGN AND SAMPLE ... 11
5. ANALYSIS AND PREDICTIONS ... 15
6. RESULT... 22
7. DISCUSSION ... 26
7.1 SUMMARY ... 26
7.2 KEY FINDINGS ... 26
7.3IMPLICATIONS ... 30
7.4 REPLICATIONS AND EXTENSIONS OF RESEARCH ... 31
8. CONCLUSION ... 35
9. REFERENCE ... 36
1 1. Introduction
Marketing is all about creating values for consumers (Kotler & Keller, 2016, p.27). Kotler, et al (2016, p.27) defines marketing as “the art and science of choosing target markets and getting, keeping, and growing customers through creating, delivering, and
communicating superior customer value.” It is becoming ever more important for consumers that value creation evolves along this development of technology. (Parasuraman & Grewal, 2000). One strategic option that integrates technology while keeping in line of the marketing purpose is online gamification. Online gamification is a relatively new concept. Deterding et al. (2011) defines gamification as “the use of game design elements in non-game context” with the purpose of increasing consumer engagement, promoting loyalty, building stronger brand awareness, and motivating consumers while giving a sense of purpose or ownership. Cleary, online gamification is becoming an opportunity to deliver superior customer value. There are abundance of research that links the relationship between some of gamification features to the marketing outcomes. Garm & Laucassen’s (2014) interview with marketing executives on the potential of gamification, all agree that gamification can improve
engagement, brand loyalty and brand awareness. Furthermore, four distinct elements of gamification – story, mechanism, aesthetic and technology – could influences the marketing outcomes. These studies however do not provide any conclusions whether these effects exist.
Both research explore the potential of gamification, however do not provide with
empirical evidence that suggests that gamification truly improves marketing outcome. Many of papers that studied the effect of gamification on the marketing outcomes were descriptive in nature, therefore were not able to infer on the effect of gamification. (Hamari, Koivisto, Sarsa, 2014)
Quantitative and qualitative analysis has been performed on the effect of gamification in the online academic dissemination (Kuo & Chuang, 2016). The study investigated whether
2 gamification increased user visits and engagement (Kuo, et al, 2016). The study found that gamification can attract, motivate, engage and retain users (Kuo, et al, 2016). Though the results suggests that gamification has to potential to benefit organizations, the study does not specify the design of gamification (Kuo, et al, 2016). The study integrates various design however fails to isolate a specific game element to the success of gamification.
Empirical study by Hamari & Koivisto (2013) do isolate a specific game element, namely the effect of social motivation has on attitude and in turn on marketing outcomes. The study is limited to just one aspect of game element (Hamari, et al, 2013). The lack of empirical studies on the effectiveness of gamification and its element suggests further research is needed.
This paper addresses the lack of empirical research by conducting a quantitative analysis of collaboration, a game element of gamification and marketing outcomes. This leads to the following research question: To what extent does Collaboration as game design affect the Attitude of users, therefore affect Continuance of Intention and Word of Mouth? Additionally, the paper investigates other factors that potentially influence the existing effect. Clear
understanding of the influence of collaboration as a game element has managerial
implications. It allows managers in deciding whether to add collaborative features to online gamification. Furthermore, it may provide a direction for which an existing online
3 2. Theoretical Background
It is informative to break down the definition of gamification by Deterding et al. (2011) Unlike gaming or playing, which the focus lies on creating content that entertains the users, gamification is a secondary feature that incorporates game interface design patterns (badge, leaderboard, level), game design patterns and mechanics (time constraints, limited resources, turns), game model (challenge, fantasy, curiosity), game design principles and heuristics (goal oriented, diverse method of play) to increase engagement, joyfulness and user experience of the main feature. (Deterding, et al, 2011).
Although Deterdings et-al’s definition of gamification is convincing and can be applied, the definition simply adopts the systematic perspective to games. Huotari & Hamari (2012) argue that within the context of the service marketing, game services are only
completed through the participation of players. As participation by player is a necessity, the value of the service is determined by the subjective assessment of the player, therefore the definition of games and what constitutes playing games are unique to each individual
(Huotari & Hamari, 2012). By this principle, gamefulness encompasses broader meaning than defined by Deterding et-al, allowing proliferation of designs and method (Huotari, et al, 2012). The focus shifts from designing gamification purely by gaming elements in non-game context to more customer centric design that can be applied in any context (Huotari, et al, 2012). This gives Gamification a new definition, “a process of enhancing a service with affordance for gameful experiences in order to support user’s overall value creation.’ (Huotari, et al, 2012). Due to the subjectiveness of game, the game elements are boundless and can no longer be based on a concrete set of mechanics (Huotari, et al, 2012). Rather it should be taken as an attempt to increase the gamefulness of a player (Huotari, et al, 2012). This definition is more relevant to marketing concept, in that the focus now lies on
4 discovery service app is not a gamified service nor a core service. However, the app can gamify itself, through certain features that enhances the value of core services such as restaurants or bars.
From this definition, Hofacker et al. (2016) applied the Elemental Tetrad Model in Gamification. The model consists of “four elemental design characteristics that interrelate and create a cognitive and affect ecosystem around the theme of a game”, the four elements being; story, mechanics, aesthetic and technology (Hofacker, et al, 2016). Within each element are distinct features that promotes user engagement. The Story is concerned with the narrative of the game. It gives meaning to the users and provides context in which users can immerse themselves into (Hofacker, et al, 2016). The Mechanical aspect explores how the rules and structure of the game, for example incentives, rewards structure, and way of progression (Hofacker, et al, 2016). It provides a guideline to users on how the game is played. A meaningful game must have a clear relationship between player actions and game outcome (Salen & Zimmerman, 2004). The Aesthetic encompasses how the narrative is told. It deals with the appearance of the game and most importantly, whether it is text oriented or visual oriented. Lastly, the elemental tetrad model suggests Technology. Technology is concerned in the platform at which gamification is played out (Hofacker, et al, 2016).
The Elemental Tetrad Model is more aligned to the definition of gamification by Deterding et al. It manipulates game elements to engage users to the gamified business. Nicholson (2015) suggests ‘recipe’ for meaningful gamification that is matches the definition by Huotari & Hamari. The idea behind it is that there are limitation of controlling game elements (Nicholson, 2015). Reward system are effective in developing engagement for a task that lacks initial motivation and therefore improve short term performance of
gamification (Nicholson, 2015). However, the study stipulates that in long term, the engagement developed from extrinsic reward diminishes and therefore the user loses
5 motivation (Nicholson, 2015). To create a meaningful gamification, the study suggests 6 elements that are relevant to games (Nicholson, 2015). Play is concerned with the freedom to explore without repercussion (Nicholson, 2015). Exposition is creating story that individuals can emphasize in real world setting and giving users Choice by allowing them to dictate how to play the game (Nicholson, 2015). Information is concerned with developing game designs and display concepts that allows users to learn and expand (Nicholson, 2015). Engagement provides users with the opportunity to develop and enhance oneself and Reflection is about helping users find their preferences (Nicholson, 2015). Integrating these six components provides users with intrinsic motivation that engages users to gamified business in the long term and clearly focused on the subjective matter of individual (Nicholson, 2015). Nicholson (2015) suggests to develop gamification that is meaningful to the individuals. Furthermore, the study suggests that this concept should not be limited to itself, rather it should be integrated with other concepts such as reward system to amplify the engagement of users (Nicholson, 2015). Using badges that allows users to set goals on the way to earning a reward should limit the diminishing effect of reward (Nicholson, 2015). The study concludes by suggesting to remove reward in the long run to maintain high engagement throughout the whole process (Nicholson, 2015).
6 3. Theoretical Model
3.1 Collaboration
Collaboration is a process framework, in which users repetitively interact formally or informally in forms of negotiation, development of commitments, and achieving those commitments (Thomson & Perry, 2006). In aspect of games or gamification, collaboration is an exchange of information and cooperation with other users to progress in the game
(Ducheneaut, Moore, Nickell, 2007). Progression of the user depends on the level of collaboration, whether the user frequently collaborates with other users and community or has minimum interaction. An exploratory research into the effect of collaboration on board games found that collaborative games tempt users to act competitively while at the same time fostering a cooperative environment to win games (Zagal, Rick, Hsi, 2006). Games can be won simply by luck or careful management of resources, however winning is more likely through collaboration (Zagal, et al, 2006). Specifically, active communication with other users and forgoing an individual benefit for the greater good of the group (Zagal, et al, 2006). Online gamification can foster this similar environment through promoting competitiveness among users through ranking features, however at same time support collaboration through greater benefits of cooperation. Combining both factors, it becomes evident that collaboration will quicken the progression at the same maintain quality through competitiveness.
H1: Higher level of Collaboration leads to better Progression Reward Oldenburg & Brisett (1982) examined the effect of sociability and concluded that
sociability is ‘the sheer pleasure of the company of others.’ Sociability exists in ‘third places’, in which people can naturally interact with other people (Oldenburg, et al, 1982). It provides people with conscientiousness, emotional expressiveness and a varied perception (Oldenburg, et al, 1982). More recent research have linked ‘third place’ to communism, identity signaling, and self enhancement (Ward, Walker & Ostrom, 2007) (Berger, 2014). However,
7 Oldenburg’s ‘third places’ is limited to physical environment. Steinkuehler & Williams (2017) explored whether this ‘third place’ could exist in the virtual world. The study
indicated that as the virtual world are structurally identical to the real world in terms of social capital, it can therefore facilitate the same effect as Simmel’s ‘third place’. Another research on the Mass Multiplayer Online Games (MMOG) indicates that virtual meeting place “provided human touch to game’s universe, provided entertainment for others, and established a real relationships with other regular present in the same virtual space”
(Ducheneaut, Moore & Nickell, 2007). Similar by nature, online gamification can replicate the same effect as MMOGs and facilitate the ‘third place’. The facilitation of a virtual ‘third place’ in online gamification will positively affect the attitude of the user through higher sense of satisfaction, and a sense of community and belonging
H2: Higher level of Collaboration positively influences the Attitude of user 3.2 Progression Reward
Rewarding users for achieving certain level or progression is critical. It provides users with motivation to continue playing and a set of goals to reach for. It is important to align rewards to tasks appropriately and proportionately. An explanatory research looked at the relationship between the task and reward interdependencies in Massively Multiplayer Online Role-Playing Games (MMORPGS) (Choi, Lee & Choi, 2007). The two interdependencies are task interdependency, defined as the degree to which the individual’s performance is
dependent on other users, and reward interdependencies as degree to which other user’s performance affect the reward an individual receives as well as distribution of the group reward (Choi, et al, 2007). The research found that high level of task interdependency and reward interdependency had a significant positive result in terms of fun, better experience flow, and one’s subjective measure of performance (Choi, et al, 2007). This clearly indicates that, being rewarded is likely to create a positive and fun experience, as well as being
8 appropriately rewarded for one’s performance in the group (Choi, et al, 2007). The similar design of MMORPGs and online gamification, the paper predicts that progression rewards will encourage a fun environment and enhance experience flow. Furthermore, a positive review one’s own performance is likely to encourage users to continue playing.
H3: Progression Rewards positively influence the Attitude of user
Combining the effects of Collaboration and Progression Reward on Attitude, it can be deduced that through collaboration, users will be able to progress faster and therefore receive more rewards. As these users are being rewarded more often and are given more benefits, the attitude of the users will be more positive than the users who do not collaborate with others and therefore receive less progression rewards.
H4: Progression Reward mediates the relationship between Collaboration and Attitude.
3.3 Type of Gamer
Type of Gamer is defined as how an individual classifies oneself as gamer. These gamers are differed by their play style and are motivated differently. According to the BrainHex Model, there are seven types of gamers; achievers, conquerors, daredevils, masterminds, seekers and socializers, and survivors (Orji, Mandryk, Vassileva & Gerling, 2013). Each type of gamers are motivated by different aspect of the game (Orji, et al, 2013). For example, Achievers are goal-oriented, in that they are motivated by the rewards and gain satisfaction through the completion of objectives (Orji, et al, 2013). Conquerors are challenge oriented. They are motivated by being challenged and feel euphoric after overcoming those challenges (Orji, et al, 2013). Socializers find enjoyment in games by interacting with other players. They hold trust as the most important factor in gaming (Orji, et al, 2013).
As each type of gamer are motivated by different factors, certain type of gamers are more likely to find the gamification design more appealing than others. For Achievers, the game
9 design of Steam Trading Cards will be more appealing because they are reward for
completion of a challenge, i.e. have a full collection from a game. Furthermore, they are more likely to find the level system more appealing as it allows them to set a goal.
For Socializers, the ability of the users to trade cards with other users function as a pseudo-community. They have to find the users with the cards they desire, and are able to progress faster than other users who do not interact with other users. Furthermore, the nature of trading requires certain degree of trust, which is an important factor for Socializers. The paper predicts that, due to the nature of Steam Trading Card requiring collaboration and rewarding users for reaching a certain level, Achievers and Socializers will have a positive effect on the attitude
H5a: Users that are Achievers will have a positive Attitude H5b: Users that are Socializers will have a positive Attitude. .
In addition, not only will the users have positive attitude, the paper predicts that the effect of Collaboration on Attitude will be stronger for certain type of gamers. For Achievers, Collaboration will allow them to progress faster and achieve their goal efficiently. Therefore, through collaboration, their attitude should be stronger than other types of gamers. In terms of socializers, the ability to trade and share information in a community should be a strong incentive for socializers.
H6a: The effect of Collaboration on Attitude will be stronger for users who are Achievers H6b: The effect of Collaboration on Attitude will be stronger for users who are
Socializers 3.4 Attitude
The paper defines attitude as the overall evaluation of the service (Ajzen, 1991). The attitude may be positive, in that the user are satisfied with using the service, while a negative attitude means that the user is dissatisfied with the service provided. It is important that the
10 service provides users with positive attitude. Having positive attitudes have shown to
promote Word of Mouth and are like continue using the service. Berger and Milkman (2012) found that positive content were more likely to promote WOM compared to negative content. Furthermore, their analysis revealed that higher level of arousal or activation had stronger WOM effect than less activating or arousing emotions (Berger, et al, 2012) Therefore, the paper predicts
H7: Attitude positively influences the intention to suggest the service to others (WOM) Attitude also influences the loyalty of the customer. It is generally believed that positive attitudes are likely to yield a stronger customer loyalty as users are satisfied with the service being provided. According to Oliver (1999), attitude influences the affective loyalty. Affective loyalty is the second phase of loyalty developed, it is concerned with “liking or attitude toward the brand has developed on the basis of cumulatively satisfying usage occasion. (Oliver, 1999)” From this, the paper hypothesize that
H8: Attitude positively influences the intention to continue using the service (Loyalty) The full illustration of the model can be found on Fig.1
Progression Reward Collaboration Type of Gamer Attitude Continuance of Intention Word of Mouth Fig. 1 Theoretical Model
11 4 Design and Sample
The paper investigates how the collaboration as a game design influences the Progression Reward and Attitude, and how those outcome can be used to predict the user’s intention to continue using it and their intention to suggest it to other people. Steam Trading Cards are ‘virtual cards earned by playing games on Steam that can be combined into game Badges’ (Steam). In more detail, users can earn cards by completing certain achievements from games purchased on Steam platform. Maximum of 3 cards are given by each game, and depending on the game 6 cards are available for users to collect per game. The only method to earn the remaining three cards is to collaborate and trade with other users. It is not a necessity for users to complete the whole set to get rewards, however completing the entire set yield better reward for the user. Earning Steam Trading Cards allows users to achieve higher level, gain badges and achievements that provides them with benefits of discounts, exclusive features such as larger friend list and flairs, and recognition among other users.
From the data, quantitative analysis through regression will be performed to find relationship between the variables. Total of four models will be construct, in which total of ten regression analysis will be performed. First model will be on the effect of mediating variable, progression reward has on the relationship between collaboration and attitude. Second and third model will look into the moderating variable, type of gamer, and its effect on the strength of relationship between collaboration and attitude. Lastly, fourth model will look into the direct relationship between the Attitude and Continuance of Intention, and Word of Mouth
The data was gathered through an online survey carried out on the Reddit forum called /r Steam. The survey was open to all subscribers of /r Steam and was available on the forum for two weeks. Permission from the forum moderators were needed and were granted.
12 Before submitting the survey to the /r Steam, the survey went through a trial of 5 participants. The trial survey suggested to add introduction and instruction to the survey itself instead on the putting it on the post of r/ Steam. Furthermore, a question was added on the Attitude section concerning the user’s Attitude towards the Steam Trading Cards community.
A total of 495,226 users are subscribed to the /r Steam on Reddit. Of those users, 92 users participated in the survey. Of those 92 participants, 53 users completed the survey correctly, yielding a completion rate of 57.60%. The participants were mostly male (73.6%) and majority of the participants were in between the age group 16 – 25 (50.9%).
Attitude
Attitude (α = .691) is the mediator variable. Attitude was measured with a seven item scale developed by Arjen (1991) and Ajren & Fishbein (1977). The seven items were asked to the users of Steam Trading Cards. An example item from attitude is: ‘I am satisfied with using Steam Trading Cards’ Scales ranged from (1) strongly disagree to (7) strongly agree. One items score was reversed due to the phrasing of the question. The item was ‘there are things in Steam Trading Cards that I strongly dislike’. Score of (7) means strongly agree and (1) is strongly disagree. A high score in attitude means that the users have positive attitude towards Steam Trading Cards.
Continuance of Intention
Continuance of Intention (α =0.691) is the dependent variable. Continuance intention was measured by four item scale. The scale was developed by Vankatesh & Davis (2000), Mathieson (1991) and Bhattacheriee (2001). The four item scale were asked to the users of Steam Trading Cards. An example of continuance intention is ‘I predict that I will play Steam Trading Cards more frequently rather than less frequently’ Scales ranged from (1) strongly disagree to (7) strongly agree. One item had to be reversed due to the phrasing of the question – ‘I intend to continue using Steam however not use Steam Trading Cards’. For this question,
13 a score of (1) was assigned to strongly agree while score of (7) was assigned to strongly disagree. A low score in continuance intention signifies that the users are more likely to continue using Steam Trading Cards while using Steam.
Word of Mouth
Word of Mouth (α = .720) is the dependent variable. Word of Mouth is measured by three items scale developed by Goyette, Ricard, Bergeron & Marticotte (2010). The three item scale were asked to the users of Steam Trading Cards. An example of Word of Mouth is ‘I would strongly recommend people to use Steam Trading Cards.’ Scales ranged from (1) strongly disagree to (7) strongly agree. People who scored high on Word of Mouth are more likely to suggest Steam Trading Cards to other people and speak about Steam Trading Cards in general conversation.
Collaboration
Collaboration (α = .705) is the independent variable. There were no clear measures of collaboration developed by relevant previous research. The study therefore designed 4 items to measure the degree or the frequency of collaboration among users in Steam Trading Cards. The two items gave option and two question allowed participants to fill in themselves. The purpose of the open question was to understand the absolute frequency of collaboration. An example item is ‘I find collaborating/trading feature of Steam Trading Card important’ Scales ranged from (1) strongly disagree to (7) strongly agree. High score in collaboration means that the participants finds collaborating and trading feature important and trades frequently when using Steam Trading Cards.
Type of Gamer
Type of Gamer is a mediating variable. Participants were given a list of options that describes a type of gamer. Total of six types (achievers, conquerors, daredevils, masterminds, seekers and socializers, and survivors) were described to the participants and were told to
14 select only one that best describes the participant. The types of gamer were based on the description provided by Orji, et al. (2013).
Progression Reward
Progression Reward (α = .635) is a mediating variable. No clear measures of
Progression Reward was available from antecedent research. The paper designed three item scale to measure the perceived progression reward. An example item is: ‘I find rewards given for my Steam Level adequate. (Non-tradable items i.e. profile showcase and extra friend list slot)’. Scale ranged from (7) Extremely Adequate and (1) extremely inadequate. High score in Progression reward means that users of Steam Trading Cards find progression reward as highly balanced and sufficient
15 5. Analysis and Predictions
In the testing of the 6 hypothesis, ten regression analysis will be conducted, therefore yielding total of four models. The first model will test the three hypothesis
H1: High level of Collaboration leads to better Progression Reward H2: High level of Collaboration positively influences the Attitude of user H3: Progression Rewards positively influence the Attitude of user
H4: The effect of Collaboration on the Attitude of user is influenced by the Progression Reward.
Model 1 (Fig. 2) requires total of four regression analysis. First regression analysis will be performed on the link between the independent variable Collaboration and Attitude. Then, second regression analysis will be performed on the relationship between the
independent variable Collaboration and mediating variable Progression Reward. Third regression analysis will be conducted on the relationship between the Progression Reward and Attitude. Lastly, regression analysis will be performed to determine if Progression
Reward do mediate the relationship between Collaboration and Attitude. For mediation effect to be present, first, the three previous regression analysis must be positively significant. Once this condition is met, regression analysis on the mediating effect can be performed. The
Progression Reward
Collaboration Attitude
16 regression analysis will be performed on relationship between Collaboration and Attitude and including variable Progression Reward.
The study expects to find all three regression analysis significantly positive. As all three regression analysis are significant, the study will perform the mediation analysis and expect to find a significant mediation effect.
The second phase of the study investigates the relationship between Collaboration and Attitude, and how that relationship is moderated by type of gamer. To investigate the effects of moderator has on the existing relationship between Collaboration and Attitude, the model will be sub divided into two separate models. The models will be divided as following.
Model 2 (Fig. 3) will test the individual effects of Collaborations, Achievers and Socializers has on the attitude therefore total of two additional regression analysis will be performed. The variable Achievers and Socializers will be transformed into a dummy
variable so that categorical regression analysis can be done. Users that describe themselves as Achievers will be given the value of 1 while other user types will be given the value of 0.
Attitude
Socializers Achievers
Collaboration
17 This will be replicated with Socializers, but instead Socializers will take the value of 1 while others take the value of 0. The following hypothesis will be tested
H5a: Users that are Achievers will have a positive Attitude H5b: Users that are Socializers will have a positive Attitude. .
The study expects to find Achievers and Socializers having a significant positive relationship with Attitude.
Model 3 (Fig. 4) looks at the specific interaction between the Collaboration,
Achievers, Socializers and Attitude. This will be done by testing the interaction effect of the independent variable collaboration and moderator variable Achievers or Socializers on the variable Attitude. This yields total of two regression analysis for this model. To study this effect, the data first has to be centralized. This means that the average score of Collaboration (3.163) will subtracted from each individual score of collaboration. Furthermore, to study the interaction effect, the dummy variable Achievers and Socializers will be multiplied by the
Attitude
Socializers Achievers
Collaboration
18 standardized collaboration score. Then, this interaction variable will be regressed against Attitude. From this, the following two hypothesis will be tested out.
H6a: The effects of Collaboration on Attitude is strengthened by users being Achievers
H6b: The effects of Collaboration on Attitude is strengthened by users being Socializers
The study expect to find a significant positive result for both Achievers and
Socializers, meaning that both variables strengthen the relationship between Collaboration and Attitude.
The fourth model (Fig. 5) for this study will investigate the direct effect the variable Attitude has on the two marketing outcomes, namely, Continuance of Intention and Word of Mouth. The following two hypothesis will be tested.
H7: Attitude positively influences the intention to suggest the service to others (WOM) H8: Attitude positively influences the intention to continue using the service (Loyalty)
Continuance of Intention
Word of Mouth Attitude
19 The study predicts that users with positive attitude are more likely to continue using the Steam Trading Cards, therefore have a positive relationship between Attitude and Continuance of Intention. Furthermore, users with positive attitudes are more likely to talk and suggest Steam Trading Cards games therefore predict a positive relationship between attitude and Word of Mouth.
In the Table 1 the means, the standard deviation and the reliabilities of all variables are presented. Test reliabilities is concerned with the consistency of the data and its
importance is explained by two reasons. First, reliability provides a measure of the extent to which measurement error is prevalent in the test. Second reason is concerned with the validity of the data. Validity, according to Wells & Wollack (2003), is defined as ‘the extent to which the inferences made from a test is justified and accurate’. Gliem & Gliem (2003) suggests that a reliability coefficient of 0.70 or higher is acceptable. From Table 1, it is apparent that most Cronbach Alpha coefficient is approximately 0.70 apart from progression reward. This indicate that the study scale has an acceptable level of reliability in terms of Cronbach Alpha. Also relevant to the validity and reliability of the data is the normality test. Normality test determines if the data set follows a normal distribution, in other words, the population in which the sample is taken from is normally distributed. This study uses
Shapiro-Wilk test to determine the normality of the data. The test revealed that Collaboration (W (53) = 0.960, 0.076), Progression Reward (W = 0.960, df = 53, p > 0.05), Attitude (W = 0.965, df = 53, p > 0.05), Continuance of Intention (W = 0.963, df = 53, p > 0.05), and Word of Mouth (W = 0.957, df = 53, p > 0.05) accepts the null hypothesis with p value greater than 0.05. This means that all five variables are normally distributed.
Table 1 also contains the relevant correlation coefficient. The Pearson Correlation coefficient is a measure of the strength of the linear relationship between two variables. The coefficient takes values between -1 through 0 to +1 (Mukaka, 2012). A negative correlation
20 would indicate that a larger number of one variable would yield a lower number of the other variable (Mukaka, 2012). A positive correlation would mean that an increase in one variable would yield a relative increase in the other variable (Mukaka, 2012). However, with Pearson Correlation, it must be noted that an increase in one variable does not explain the cause of the increase in the other (Mukaka, 2012). It acts as an indication of the direction the relationship and whether the correlation is significant (Mukaka, 2012)
The correlation analysis reveal that Continuance of Intention have a significant positive correlation with Word of Mouth (r (53) = 0.515, 0.000), Attitude (r (53) = 0.764, 0.000), Progression Reward (r (53) = 0.507, 0.000) and Collaboration (r (53) = 0.418. 0.000). Translating these correlation coefficient in the context of Steam Trading Cards, an increase in Word of Mouth, Attitude, Progression Reward and Collaboration increases the Continuance of Intention. By Wiersma (2003) rule of thumb of interpreting the size of the correlation coefficient, Attitude has a high positive correlation while Collaboration has a moderate positive correlation.
Word of Mouth have a significant positive correlation with Attitude (r (53) = 0.510, 0.000) and Collaboration (r (53) = 0.357, 0.009) while a no significant positive relationship with Progression Reward (r (53) = 0.260, ns). Putting into context, increase of Attitude and
Collaboration will increase Word of Mouth. Word of Mouth and Progression Rewards are not correlated with each other.
Attitude has both significant positive relationship with Progression Reward (r (53) = 0.575, 0.000) and Collaboration (r (53) = 0.601, 0.000). Lastly, Progression Reward has a significant positive correlation with Collaboration (r (53) = 0.538, 0.000)
21 Table 1. Descriptives and correlation between the variables (Cronbach's Alphas on diagonal)
M SD 1 2 3 4 5 1 Continuance of Intention 3.448 1.330 (0.691) 2 Word of Mouth 4.076 1.288 0.515* (0.720) 3 Attitude 3.703 1.013 0.764* 0.510* (0.691) 4 Progression Reward 3.289 1.330 0.507* 0.260* 0.575* (0.635) 5 Collaboration 3.613 1.340 0.418* 0.357* 0.601* 0.538* (0.705) Note. N = 53 *p < 0.01 (2-tailed)
22 6. Results
The first model explores the effect of Progression Reward as a mediator on Collaboration and Attitude. The first hypothesis is concerned with the direct effect of Collaboration has on the Progression Reward. This effect is tested through a regression analysis between the two variable. Supporting this study’s hypothesis with an explained variance of 28.9%, a significant positive relationship was found between Collaboration and Progression Reward (β = .538, 0.000, R2=0.289). This indicates that users that trade and share information with other users positively influences the perceived and attainment of Progression Reward. Due to a significant relation, hypothesis H1 is supported.
The second hypothesis is concerned with the effect of Collaboration has on the Attitude. Aligned with the prediction of this study, with an explained variance of 36.2%, the regression analysis revealed that the relationship between collaboration and Attitude is positively significant (β = .601, 0.000, 0.362). It is worth noting that the unstandardized total effect of this relation was 0.455. This positive relationship between Collaboration and
Attitude justifies our prediction that users that collaborate with other users in terms of trading or sharing information experience positive attitude towards Steam Trading Cards. Therefore, hypothesis H2 is supported.
The third hypothesis investigates the relationship between the mediating variable Progression Reward and Attitude and is done so by a regression analysis. The result of the regression analysis supports the prediction made about the two variables, with an explained variance of 33.1%, in that a significant positive relationship exists between Progression Reward and Attitude (β = .575, 0.000, R2=0.331). The significant positive results
demonstrate that having Progression Rewards positively influences the Attitude of the users in Steam Trading Cards.
23 Furthermore, the paper regresses the mediating variable Progression Reward on the relationship between Collaboration and Attitude. It must be noted that as the three previous hypothesis all had a significant positive relationship, it meets the condition for Progression Reward to act as a mediator. The result of the regression is aligned with the prediction of the paper. With an explained variance of 45.1%, the regression analysis demonstrates that the effect of effects Collaboration had a significant positive relationship to Attitude
(β = .441, 0.002, R2=0.451), however a significant positive correlation also existed for the relationship between Progression Reward and Attitude (β = .354, 0.006, R2=0.451). Although both results are significant, the effect of Collaboration has on Attitude when Progression Reward is included is smaller than the effect of Collaboration on Attitude without
Progression Reward. This is indicative of a partial mediation instead of complete mediation. Translating in context of Steam Trading Card, Progression Reward partially explains the relationship between Collaboration and Attitude. Further analysis of this variable reveal that the unstandardized direct effect of Collaboration on Attitude is 0.3105 (0.1218; 0.4993) and the indirect effect of Collaboration on Attitude is 0.1441 (0.0288; 0.2762). Both at 95% confidence interval. Therefore, H4 is partially supported. All four regression and their statistics can be found in Table 2.
The second and the third model looks at how Type of Gamer effects the strength of the relationship between Collaboration and Attitude. Specifically, the study is interested in users that define themselves as an Achiever or a Socializers. The second model explores the independent influences of Collaborations, Achievers and Socializers on Attitude. The hypothesis on Collaboration and Attitude (H2) is supported in the previous section on the mediating model.
The fifth regression analysis involves the direct influence of Achievers on Attitude. Contradicting the prediction made in this study, the regression analysis reveal that there is no
24 significant relationship exists between users that define themselves as an Achievers and Attitude (β = -0.136, ns, R2=0.418). The non-significant result indicates no conclusion can be drawn about Attitude from users that are Achievers. Therefore H5a is rejected.
The sixth regression analysis explores the direct influence of users that are Socializers on Attitude. Despite the prediction that Socializers will have positive Attitude, the regression analysis rejects this prediction as no significant relationship was found between Socializers and Attitude (β = 0.166, ns, R2=0.0.418). This means that no conclusion can be drawn about Socializers and their attitude towards Steam Trading Cards, rejecting H5b. All regressions of second and third model can be found on Table 3.
The third model investigates the interaction effect of the variable Type of Gamers, specifically interaction of Achievers and Collaboration on Attitude, and the interaction of Socializers and Collaboration on Attitude. Contrary to the prediction, the result shows that in this sample, there is no significant relationship between Achiever, Collaboration and Attitude (β = -0.009, ns, R2=0.419). This mean that no significant conclusion can be drawn about the strength of the relationship of users that are Achievers on the relationship between
Collaboration and Attitude. This leads to the rejection of H6a
The eighth regression analysis is concerned with the effect of user type Socializers has on the strength of the relationship between Collaboration and Attitude. Similar to the Achievers, the results of regression analysis rejects the predictions of this study, in that no significant relationship was found (β = -0.031, ns, R2=0.419). This mean that no conclusion can be drawn about the impact of users that are Socializers on the relationship between Collaboration and Attitude. Nothing can be said about whether Socializers strengthen the relationship between Collaboration and Attitude. This leads to the rejection of H6b.
Lastly, the fourth model investigates the direct relationship of Attitude towards the two marketing outcomes, Continuance of Intention and Word of Mouth. The ninth regression
25 analysis looks at the relationship between the Attitude and Continuance of Intention. Aligned with the prediction on relationship, the regression analysis supports the hypothesis that a significant positive relationship exists between Attitude and Continuance of Intention, with the explained variance of 58.4%. (β = 0.764, 0.000, R2=0.584). The significant positive result means that users with positive attitude are likely to continue using Steam Trading Cards when using Steam. Furthermore, it is an indication that they are likely to play as often or more than the usual amount. Therefore hypothesis H7 is supported.
The last regression analysis explores the direct effect of Attitude on Word of Mouth. The regression analysis yielded an expected result, in that a significant positive relationship exists between Attitude and Word of Mouth, with an explained variance of 26.0% (β = 0.510, 0.000, R2=0.260). The result is an indication that users with positive attitude have spoken frequently of Steam Trading Cards with other people, and that they are likely to suggest the usage of Steam Trading cards to others as well. This results supports the Hypothesis H8. The results can be found on Table 4.
26 7. Discussion
7.1 Summary
This study aimed to contribute to the literature concerning Collaboration, with the effect of mediating variable Progression Reward, and moderating variable Type of Gamer, have on the Attitude of the user in online gamification. Furthermore, the study examined whether Attitude effected the user’s Continuance of Intention and Word of Mouth. Many studies have focused on investigating and questioning various effects of game element on the gamification model but provide inadequate a quantitative evidence supporting a significant relationship. Furthermore, there is a lack of understanding on how collaboration as a game element and the importance of progression reward on promoting positive attitude. The results from this study fills the research gap by providing a quantitative evidence through regression analysis that Collaboration do influence the Attitude of the users and this effect is mediated by progression reward. In addition, the study supports the general consensus on the effect of positive attitude on the marketing outcomes, i.e. loyalty and WOM. This study clearly answers the research question: To what extent does Collaboration as game design affect the Attitude of users, therefore affect Continuance of Intention and Word of Mouth?
7.2 Key Findings
7.2.1 Mediation Effect of Progression Reward
In general, the results were aligned with predictions. From the first model, the study found a significant positive relationship between Collaboration, Progression Reward and Attitude, therefore accepting H1, H2 and H3. The significant result allowed further investigation into the mediation effect, which found a significant, but a partial mediating effect of Progression Reward on the relationship between Collaboration and Attitude.
Though the results are significant, it does seem that Collaboration does not involve sociability and therefore replicate the ‘third place’ environment described in MMORPGs
27 (Ducheneaux, et al, 2007). Rather, the results indicate that the nature of Steam Trading Cards is not to foster and build a social support. The Collaboration element acts as a practical tool that helps users achieve their goal efficiently. This explanation is further supported by findings that users in Steam Trading Cards found recognition among other users important, contradicts the idea of building social support. Some research through class ranking system in schools suggests that ranking students by their performance made student view other students not as friends or potential collaborators, but as a threat and an obstacle to their success (Kohn, 1992).
These findings are more aligned to the example of a board games, where the purpose of fostering collaborative environment is to promote cooperation while encouraging
competitiveness within the group to win the game. This explanation is why Progression Reward partially mediates the relationship between collaboration and attitude. It can be understood that Collaboration acts a practical tool to achieve higher level and therefore attain better intrinsic (through recognition of user) and extrinsic (through discounts and coupons) rewards for the user. The attainment of these rewards in turn stimulate positive emotions and therefore give positive Attitude towards Steam Trading Cards.
7.2.2 Moderation Effect of Type of Game
The second and the third model yielded unexpected results. Both type of gamers, Achievers and Socializers had no significant relationship to Attitude. Furthermore, the regression analysis on interaction effect of the Type of Gamer revealed that users that are Achievers or Socializers do not moderate the strength of the relationship between
Collaboration and Attitude, rejecting H5a, H5b, H6a and H6b.
The fact that the results did not confirm H5a, H5b, H6a and H6b could be attributed to the nature of online gamification itself. As defined in this study, online gamification borrows the gaming elements of games to increase user engagement, loyalty and brand awareness of
28 the main objective (Deterding, et al, 2011). As online gamification is not the main purpose but a tool that enhances experience, users may find collaborating or achieving goals not as significant as they would in actual gaming (Deterding, et al, 2011). This may explain why users being Achievers or Socializer, a type of user described in actual gaming, had no significant effect on the Attitude in online gamification. Furthermore, the types are differentiated by motivating factors (Orji, et al, 2013). It may simply be that in online gamification, not much motivation is needed as frequency of playing is not as frequent and for long duration. Furthermore, the complexity of online gamification is relatively simple compared to real gaming therefore not requiring much effort or motivation in participating. Segmenting users based on their Type of Gamer is simply not significant in Steam Trading Cards. As users are indifferent in the types of gamer in Steam Trading Cards, it makes sense that being a certain Type of Gamer does not strengthen the relationship between
Collaboration and Attitude.
7.2.3 Direct Effect of Attitude on Marketing Outcomes
Lastly, the fourth model found expected results. Attitude of the users did positively influence the user’s Continuance of Intention and are more likely to talk about Steam Trading Cards, accepting H7 and H8.
The results are aligned with the explanation provided by Berger, et al, (2012) in that positive content promotes Word of Mouth. Users of Steam Online Trading Cards that had a positive attitude were more likely to spread the use of Steam Trading Cards and suggest the usage to other non-users as well. However, it must be noted that a plausible alternative explanation exist. The increase in Word of Mouth can be influenced by the network effect. According to the theory of network externalities, the network effect is defined as the benefit that arises when the network of users and compatible products grows. (Lin & Bhattacherjee, 2008). The direct network externalities arises when the physical number of users increases
29 (Katz & Shapiro, 1985). As the number of users increase, the existing users have more
opportunity to interact, communicate and trade with other users, therefore increase network utility. (Lin, et al, 2008). This may be the case for Steam Trading Cards. The increase in users allows more opportunity for existing users to trade cards with other users and provide
opportunity to trade diverse cards with other users. Indirect network externalities are accompanying benefits that arises from larger network (Lin, et al, 2008). Indirect network externalities in Steam Trading Cards will drive down the prices of certain cards and therefore make trading more economical. Furthermore, the increase in demand of use of Steam Trading Cards has prompted users to develop bots that help users trade more efficiently and reducing transaction costs
The network effect also applies to Continuance of Intention. As user base gets larger and more active, users have an incentive to continue using Steam Trading Cards and reap the benefits of Steam Trading Cards when purchasing and playing through Steam. Furthermore, the survey indicates that users that intend to continue using Steam are likely to continue using Steam Trading Cards as well. The network effect will make trading easier will reduce the search cost.
Overall, these findings are broadly consistent with the existing research on online gamification. The purpose of game mechanism in online gamification is to provide guideline to users on how to play the game and how to get the most value by controlling the reward structure and way of progression (Hofacker, et al, 2016). Along with other factors in the elemental tetrad model, the ultimate aim is to increase user engagement (Hofacker, et al, 2016). This clearly aligns with the findings of this study, as the result from the first model clearly indicates Collaboration and Progression Reward act as a way of controlling users and ultimately increase user engagement through Continuance of Intention and Word of Mouth.
30 7.3 Implications
For several years, gamification has been a trending topic in marketing. (Hamari, et al, 2014). It is a tool to which marketing managers can increase user engagement and enhance positive patterns when using the service (Hamari, et al, 2014). It is expected that over 50% of organizations will gamify their business (Hamari, et al, 2014). As there is a large flux of interests in gamification, it becomes crucial for business management to effectively gamify their business. As there are various mechanism, story, and other factors that influence the effectiveness of implementation, this study provides an indication to business management, a possible direction in gamifying their business. As the study indicates that Collaboration directly influences the Attitude of the user positively, business management can integrate the collaborative features when gamifying their business. It also allows marketing managers to build on features that amplify the collaboration aspect of gamification. Combining this study’s result with the result on the effect of social motivation (Social influence, recognition and reciprocal benefit) has on gamification, it becomes clear that interaction with other users are imperative in gamifying business (Hamari, et al, 2013). Interaction with other users’ acts as a tool for progression therefore providing extrinsic motivation while it can also act as a community that provides social support and sense of belonging i.e. intrinsic motivation. As studies indicate that extrinsic motivation are useful in short term while the efficiency drops in long run, intrinsic motivation can last longer and therefore overcome the shortfall of extrinsic rewards. (Benabou & Triole, 2003). Collaboration feature can be used to attract users to the gamified business and engage in short term while the social motivating factors can be utilize to make users commit to the gamified business in the long run. Furthermore, the study found that implementing a reward system that proportionately reward users will help build customer engagement and enforce positive pattern in use (Deterding, et al, 2011).
31 The non-significant result of the Type of Users can be an indication to business
management that segmentation on customer characteristics is not necessary. Being a certain type of person does not influence the attitude toward the gamified business. However, it must be noted that the type of gamer specifically focus on the motivating factors of the person in gaming environment. Other criteria of characteristics may potentially be useful when segmenting the customer base. This result is an indication to managers that gaming characteristics are not essential in gamification. As gamification borrows the concept of games, it may refer individuals to believe that gaming characteristics may be important in gamification, but to the contrary, gaming characteristics do not influence the attitude of the user.
Lastly, this study further supports the idea that positive attitude builds customer loyalty and Word of Mouth, specifically in online gamification. The direct relationship between the variables suggests that business managers need to focus on building positive attitude on their gamified business in attempt to increase customer engagement and loyalty.
7.4 Replications and Extensions of the Research
The nature of the research being a survey, it was inevitable that some problems arose. Due to confusion of instruction and other reasons, some participants did not complete the survey. This problem translated into lower level of response rate and some incompleteness. However, to limit these problems as much as possible, the survey did go through a trial of 5 participants which gave suggestions that greatly improved the instruction on the survey. In the future, implementing a small reward for completion of survey may increase the number of completion.
The survey also faced some biases. The first bias is towards the opinion on Steam Trading Cards. The paper purposely avoided using the official Steam Trading Cards forum to minimize any bias that may arise. As the official Steam Trading Cards community is
32 designed for trading among users, it is more likely that users will have a favorable opinion towards collaboration on the Steam Trading Cards forum. Furthermore, it is likely that there will be limited number of users that do not trade or collaborate at all due to the purpose of the forum. Alternatively, Steam forum on Reddit is community for any Steam related topics, therefore has more general users. Steam forum is more likely to have Steam Trading Cards users that do not collaborate or trade with other users. Furthermore, as Steam forum has more diverse users with various , it has greater external validity as it is more representative of the general population.
Despite this effort, the forum is still designed for users to interact and share information with other users. This means that users in this forum has a favorable opinion towards interaction as they are voluntary participating in the community. This will also influence the Word of Mouth as they are already actively sharing and talking about Steam related features with other people. Furthermore, users in this forum may already have a favorable opinion towards Steam and Steam Trading Cards which may skew their attitude positively. To limit this bias, future studies can directly contact users that are registered to Steam Trading Cards. As users that participate in Steam Trading Cards have to be registered, instead of posting a survey online, the future research can directly send messages to users for participation of the research. This method would avoid using the community which may cause biases and therefore obtain unbias sample of data, yielding greater internal validity. This would require longer data collection period and permission from Steam.
There are certain aspect of this research that merits future investigation by different sample of online gamification. Results of this study has high transferability, in that business model of Steam is evident in other industries. Steam acts as a platform for which video games developers can sell their video games to users online, acting as an e-retailer (Dinsey, Naim & Potter, 2004). This model is evident in various industries. In shoe industry, Zappos.com acts
33 as an online retailer that allow shoe manufacturer to sell their product online. Amazon.com is a massive online retailer that provides a platform in which manufacturer can connect to consumers. For digital goods, example can be Netflix, which provides users with movies and TV series from various studios and channels. All these business model earn revenue by collecting commission on sale of the product and is classified as E-Shopping (Disney, et al, 2004). The similarity in the structure of the business model would allow the findings to be translated and transferred into the other industries fairly well. Other industries can adopt a similar concept of Steam Trading Cards that promotes collaboration through progression reward therefore enhancing customer engagement and loyalty. Future investigation can verify whether the findings of this study do in fact transfer to other industries with gamified
business.
One aspect that the paper fails to address and is a possible area of future research is the whether the success of Steam Trading Cards directly translates to the increase in
marketing outcomes of Steam such as frequency of use, loyalty, Word of Mouth and number of purchases. This study establishes that two features of Steam Trading Cards positively influences the attitude and this in turn promotes further use of it. This establishment does not necessarily translate to increase in the marketing outcomes of Steam. Investigating into this direct relationship between Steam and Steam Trading Cards would allow deeper
understanding into the effectiveness of gamification of business. As the purpose of Steam Trading Cards is to promote further use of Steam platform, a further investigation into this relationship will determine whether Collaboration in gamified business translates to better performance of the main business. Using different method of investigating would also make an interesting future research. By using the raw data from Steam and Steam Trading Cards, it would be possible derive factual statistics that support or rejects the findings of this study. As all transaction and trades in Steam Trading Cards are recorded in database, it will be possible
34 to see whether the number of trades do influence the marketing outcome. Furthermore, it will be interesting to find the direct relation between Collaboration in Steam Trading Cards to the attitude towards Steam platform.
Most importantly, further research is needed in studying the mediation effects of Collaboration on Attitude. The result of the study indicate that Progression Reward partially mediates the relationship between Collaboration and Attitude. This is an indication that other factors may mediate the relationship between the two variables. This suggest that further research should be carried out that can identify other variables that potentially explain the partial mediation of the relationship, for example social interaction. Though unlikely in Steam Trading Cards, several studies do indicate that interacting with other people promote positive attitude. A definitive study can determine whether this is may be the case. It would also be interesting to investigate whether other characteristics of individuals moderate the relationship between Collaboration and Attitude. An example can be user’s preference towards online platform. Users that are comfortable using online platform are likely to find collaborating feature in Steam Trading Card easier to use therefore are more likely to have a positive attitude compared to users who have unfavorable opinion towards online platform.
35 8. Conclusion
The study has addressed the question of whether Collaboration as a game element influence the Attitude of users, and whether the relation is mediated by Progression Reward or moderated by the Type of Gamer. Furthermore, the study investigated whether this positive attitude influences the Continuance of Intention and Word of Mouth. The study strongly support the conclusion that Collaboration does influence the Attitude positively and that this relationship is partially mediated by Progression Reward. However, the study found no significant relationship between Type of Gamer. Despite the result, it is unclear from this study that gamification serves as a positive influence on the business. However, it does provide a starting point for further research into Collaboration as game element and its potential influence in gamification.
This brings the question should all companies integrate Collaborative feature into their gamified business? This study suggests it should, however, managers must be aware of several other influential factors that may interact with collaboration that may potential amplify or hinder the effect. Furthermore, some businesses may not simply bind well with the collaborative feature. For example, it is easy to implement a collaborative in online gamification as there are no physical boundaries blocking the interaction of individuals. This may not be the case for gamification that operates offline. Inserting a collaborative feature where it is difficult to meet other users may dissuade them in further use. Managers should understand the environment surrounding the business and after a thorough research, implement a gamified business that best serves their need.
Word Count: 9,806
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39 Appendix
41 Table 5: Survey Items
Variable Survey Item Loading Construct Source
Collaboration
How often do you collaborate/trade with other people?
0.705 I find collaborating/trading feature of Steam Trading Cards important
Approximately how often do you collaborate/trade each month?
Approximately how many times a month do you participate in Steam
Trading Cards
Progression Reward
I find rewards given for my Steam Level adequate. (Non-tradable items i.e. profile showcase and extra friend list slot)
0.635
I find Coupons/Profile Background/Emoticons given for crafting badges adequate.
The rewards given by playing Steam Trading Cards is an important factor of playing.
Type of Gamer Please select ONE Type of gamer that best describes you
Attitude
All things considered, I find using Steam Trading Cards to be a wise thing to do
0.691 Arjen (1991), Arjen & Fishbein (1977)
All things considered, I find using Steam Trading Cards to be a good idea All things considered, I enjoy playing Steam Trading Cards
All things considered, I find using Steam Trading Cards to be favourable I am satisfied with using Steam Trading Cards
There are things in Steam Trading Cards that I strongly dislike I find Steam Trading Cards Community (Steam Forum or Reddit) an important aspect of Steam Trading Cards.
Continuance Intention
I intend to continue playing Steam Trading Cards rather than stop playing it.
0.691
Vankatesh & Davis (2000), Mathieson (1991), Battacheriee (2001)
I intend to continue using Steam however not use Steam Trading Cards. I predict that I will be playing Steam Trading Cards more frequently I intend to play Steam Trading Cards as often as I used to.
42 Word of Mouth
I am proud to say to others that I use Steam Trading Cards.
0.720
Goyette, Richard, Bergeron & Marticotte (2010)
I would strongly recommend people to use Steam Trading Cards I spoke of Steam Trading Cards to many individuals.
