The effect of Crisis Communication and Community Response on
Consumer Sentiment and Brand Reputation in the Video Game
Industry
Master’s Thesis
Graduate School of Communication, University of Amsterdam,
Master’s in Corporate Communication,
Student: Conor McKenna, 12489875 Supervisor: Toni Van Der Meer Date of Completion: 26th-06-2020
ABSTRACT
Crises are a fact of life for organisations, but the vast combination of factors that can amount to a crisis mean that not every industry can approach crisis management from the same perspective. This study sheds light on the under researched field of crisis communication in video games and the bandwagon effect that gamers can inadvertently cause. A fictional gaming company based upon real world crisis communication methods are the subject of the investigation. Based upon a survey-based experiment utilising the responses of 211 participants, the findings suggest that gaming organisations brand reputation is affected by both crisis response strategy and the response of its community, but these factors do not interact with each other. Additionally, while the type of gamer was not significant overall, publisher perception plays a crucial role in consumer sentiment. With these findings, this study provides a starting point for further exploration into crisis communication in the video game industry.
Keywords: crisis communication, community response, bandwagon effect, gamer type,
INTRODUCTION
“In the 21st century, a social media savant can do more harm than a trial attorney.” – Jonathan Bernstein of Bernstein Crisis Management Inc.
The way in which online gaming companies communicate to their consumers and playerbase can have immediate and more pronounced impact than other industries. A poorly managed crisis can result in stakeholders severing ties and spreading negative impressions of the organization(Coombs, 2007). Gaming companies and organisations are especially vulnerable to crisis scenarios because the online and media-centric nature of their business can leave them vulnerable to rapidly developing social media narratives that can be driven by irrationality (Vignal Lambret & Barki, 2017). This study attempts to further explore the role crisis communication plays in protecting gaming companies from reputational and financial loss, and how the actions of the community may further exacerbate negative affect of crises on brand reputation and consumer sentiment.
Crisis communication is an essential tool for organisations to de-escalate crises and to help stakeholders make sense of these crises. However new consumer technologies have changed the arena in which companies, and gaming companies in particular, must focus their efforts. Research indicates that consumers prefer to express opinions on social media or online discussion communities and almost 90% of consumers can be influenced by word of mouth on online communities (Chuang, 2020). Video game companies must account for how social media communities form around most of their successful products/games. Within these communities, negative sentiment can create a feedback loop that can lead to an increase in user activity (Cheng, Niculescu-Mizil & Leskovec, 2014) promoting further negative feedback. Crisis management within social media networks is effectively a public arena and organisations must take great care to justify their decisions with rationales consistent to the good of the wider public and stakeholders (Patriotta, Gond & Schultz, 2011). Crisis Communication ,Organisations are expected to use their social media to rapidly respond to crisis’s (Gruber, Smerek, Thomas-Hunt & James, 2015) or they risk facing both reputational damage which will be either accelerated or decelerated depending on the suitability of their crisis response strategy (Kim, Park, Cha & Jeong, 2015).
Gaming companies in particular are at risk from negative bandwagons affecting their reputation and sales. Bandwagon effects are a sociological phenomenon that implies that individuals will join what they perceive to be the dominant position or majority view (Schmitt‐Beck, 2015).
Bandwagon effects are have been hypothesised to affect election forecasts (Henshel,& Johnston, 1987). Recent studies have indicated that bandwagon effects now take place in social media networks and can amplify the rate at which information is diffused across networks (Wang, & Zhu, J. 2019). The same principal applies to gaming communities, where negative sentiments can rapidly become the dominant position and players can become negative towards the company by becoming exposed to a dominant negative position.
Online communities can be extremely beneficial to video game companies as online gaming communities with a high degree of social capital and networking promote loyalty to those communities (Hsiao & Chiou, 2012). Unfortunately, online communities resemble a double-edged sword as users tend to pay more attention to negative reviews and sentiment (Yang, Mai & Ben-Ur, 2012). Gaming companies therefore have a particular vested interest in having effective crisis communication strategies in an online medium, to prevent online crisis’s snowballing into a bandwagon effect.
A recent example of a large gaming company falling victim to a social media bandwagon concerning is Electronic Arts after the launch of the popular Star Wars Battlefront 2. Consumers expressed disappointment and irritation that several characters such as Darth Vader had effectively been paywalled by microtransactions in the game. EA’s community team responded by suggesting that they were trying to provide players with “a sense of pride and accomplishment” by unlocking them. The response was generally perceived as condescending and dismissive and ultimately resulted in the EA community team having the most downvoted comment ever on Reddit ("Seriously? I paid 80$ to have Vader locked? : StarWarsBattlefront", 2017).
Impersonal corporate responses to crises can be the cause of corporate reputation being damaged further (Jahng & Hong, 2017) and EA’s failure to enact appropriate crisis communication in an environment where the primary influencers of their E-reputation (Dutot & Castellano, 2015) were the main audience led to the stock price dropping by 7 percent (Kim,2020) and a loss of confidence from Investors. Alternative crisis response strategies focussed on validating concerns and offering compensation to victims could have blunted angry sentiment towards EA (Coombs & Holladay, 2005), however external pressure from stakeholders can increase time pressure on an organisations PR professionals (van der Meer, Verhoeven, W.J. Beentjes & Vliegenthart, 2017), which may lead statements being rushed or
inappropriate to the crisis. This can cause further outrage from stakeholders as mismatched communications will only cause the crisis to spiral farther out of control,
With this context, the aim of this study is to examine (using an experiment) the effectiveness of crisis communication strategies specific to online gaming companies, how these strategies impact the community response to the crisis and how the interaction of these factors ultimately impacts the brand reputation and consumer sentiment of the company.
RQ: What kind of crisis response strategies can online gaming companies make use of to protect their brand, and how does the community response to these strategies affect brand reputation and consumer sentiment?
THEORETICAL FRAMEWORK Crisis Literature
Crisis’s are a matter of when not if for Corporations. Coombs (2007) defines a crisis as “the perception of a sudden and unpredictable event – and can disrupt an organisations operation and poses a financial and reputational threat”. During a crisis, an organisations goal should be to change attributions of the crisis, change how the organisation is perceived because of the crisis and reduce the reputational damage suffered because of the crisis (Coombs, 2007). A corporation’s reputation is a collective construct that forms an aggregate of stakeholder’s perspective of the organisation (Fombrun, Gardberg & Sever, 2000) and so despite its necessity to an organisations legitimacy and performance it is still fundamentally an abstract and intangible asset (Wæraas & Byrkjeflot, 2012) . Further research into the concept of corporate reputation has suggested that it is a construct that combines emotional appeal and rational appeal (Fombrun, Gardberg & Sever, 2000, pg. 14).
Different types of crises represent different levels of threats to an organisation’s reputation. Attribution theory suggests that crises that have an unstable cause (or good organisational performance history) with “strong internal control and weak internal locus” (low intentionality) have the lowest public attributions of crisis responsibility(Coombs & Holladay, 1996). Crisis Communication Literature
Robust crisis communication strategies are invaluable assets to organisations seeking to protect themselves from reputational, operational, or financial damage incurred from a crisis. Crisis
response strategies do this by shaping perceptions of a crisis and the organisation itself (Coombs & Holladay, 1996). By changing perceptions of a crisis, organisations can reduce stakeholder attributions of responsibility and prevent an unfavourable narrative gaining traction amongst the public or stakeholders. Because organisational crisis communication is such an invaluable asset, scholars have developed many different models and methodologies for tackling crises which will be explored here.
Situational Crisis Communication Theory is the core framework for the crisis communication strategies explored in this experiment. SCCT is built upon Attribution Theory, which explores stakeholders need to assign blame and understand the cause of crises (Coombs, 2007). SCCT builds on rhetorical crisis response strategies by providing an experimental framework to test the effectiveness of these strategies in protecting organisational reputation (Coombs, Frandsen, Holladay & Johansen, 2010). SCCT is a robust framework for crisis communication managers to identify the correct response strategy and it is the main foundation for the theoretical framework of this study.
SCCT explores three distinct levels of crisis responsibility, which are based upon attributions of crisis responsibility determined by the type of crisis type (Coombs, 2007). The victim cluster has the weakest attributions of crisis responsibility and is focused upon natural disasters and other environmental factors that an organisation could not reasonably have anticipated. The accidental crisis cluster has minimal attributions of organisational responsibility and explores crises such as human and technical errors, where the event is considered accidental. The intentional crisis cluster has very strong attributions of crisis responsibility and generally concerns organisational misdeeds or malpractice. The advantage of grouping crises into clusters is that management teams can formulate and draft response strategies for each type of cluster instead of trying to prepare plans for every conceivable type of crisis(Coombs, 2004). The context of this study will be within the intentional crisis cluster, this is because the cause of the crisis relates to predatory monetisation practices, which must be deliberately developed and implemented by an organisation, so they are an intentional organisational misdeed. The intentional cluster also raises stakes for organisations as the more stakeholders attribute a crisis to a corporation, the greater the threat to the organisation’s reputation (Coombs and Holladay, 1996,2001). In the intentional cluster attributions of organisational fault or accountability are very high amongst stakeholders (Coombs & Holladay, 2002). Based upon previous research (Coombs, 1995), transgressions or intentional crises are best countered with
a Mortification strategy that do not deny the crisis and attempt to repent for the crisis is some way.
SCCT posits that crisis managers should match the organisation response with the level of reputational threat (Coombs, 2007). SCCT has also faced criticism, with researchers making the point that while SCCT provides a solid foundation for crisis communication, it does not adequately account for the role of social media (Roshan, Warren & Carr, 2016), which only became widely used after SCCT had been developed. Therefore, although SCCT offers a robust foundation upon which to build crisis communication strategies, it requires modification by authors to be implemented properly in a context where social media is relevant.
Secondary Crisis Communication builds upon SCCT by accounting for the effects of different media and mediums on crisis communication (Schultz, Utz & Göritz, 2011). Some PR practitioners advocate for having crisis communications drafted well in advance of a potential crisis, as allowing narratives to form on social media will be extremely difficult to correct (Claeys & Opgenhaffen, 2016). By drafting communications in advance of crises, gaming organisations can rapidly address stakeholders directly and reduce the chances of social media bandwagons forming.
This study follows the foundations laid out by previous studies into crisis communications and the following hypotheses are expected.
H1: Compensation-based crisis response strategies will have a positive effect on Brand Reputation in comparison to Denial and Commitment response strategies.
H2: Compensation-based crisis response strategies will also have a positive effect on Consumer Sentiment in comparison to the Denial and Commitment strategies.
Bandwagon and Social Media Literature
A bandwagon is the key theory justifying the presence of the second independent variable community response. It has been observed in social science research that the opinions of others can alter our own opinions subconsciously, for example an election forecast might cause you to reconsider who you vote for(Henshel & Johnston, 1987). This phenomenon can also apply to an organisation or its products (Schultz, Utz & Göritz, 2011), where negative sentiment can cause a negative feedback loop on social networks, which provide a channel for rapidly spreading fads (Li & Bernoff, 2010). Even without pre-existing negative sentiments, social networks and social media amplify an organisations visibility and subsequently increase the
risk of problems escalating into a full-blown crisis (Coombs & Holladay, 2012). With this in mind, the maintenance of relevant social networks and the timely dissemination of crisis response strategies to these communities is paramount to protecting an organisations reputation.
Social media networks have amplified bandwagon effects, as they provide an interpersonal echo chamber where sentiments are formed and adopted as a collective narrative. This is partly due to social media networks being primarily made up of networks of individuals who are familiar with each other offline, (Fu, Tao & Seng, 2012) suggesting that social media is primarily used to strengthen offline connections rather than form new ones. This in turn supports assertions that in online networks, participants make mental shortcuts to come to conclusions (Wang, C., & Zhu, J. 2019) as opposed to attempting to consider all available information comprehensively or objectively. Additionally, on social media sites, individuals are influenced by the reaction of others, and will post comments based upon how they perceive existing posts (Wang, Ming-Lai, Wang & Wu, 2015).
The Social-Mediated Crisis Communication model (Jin & Liu, 2010) provides insight into how the digital nature of Gaming companies and their players have a more impactful online relationship than traditional brands. If we assume that gaming companies and players have a fundamentally similar relationship to a blogger-follower relationship (given that gaming companies publish content that gamers pay to view), there is an implication that when trustworthiness towards companies is high, argument quality has a greater impact on brand attitudes (Jin & Liu, 2010). This implication furthers the importance of establishing crisis communication plans for social media networks as research into SMCC suggests that people who learn about a crisis through social media are more likely to seek further information about the crisis on social media (Austin, Fisher Liu & Jin, 2012) as opposed to traditional media sources. The role of social media in the dissemination of information has serious consequences for organisations as internet users rarely verify information online and if they do they generally verify less consequential facts (such as verifying if the information is current and not the authors qualifications) (Flanagin & Metzger, 2000).
Crisis response strategies for social media platforms have a demonstratable impact in calming sentiments and preventing the escalation of stakeholder emotions, if executed correctly. A study previously found a corporate executive publicly apologising via social media “significantly and immediately lowers the amount of negative sentiments in Twitter and also
increases the level of factual tweets compared to opinion tweets” (Kim, H., Park, J., Cha, M., & Jeong, J. 2015). It was also noted that there was comparatively a very marginal increase in positive sentiment tweets but the effectiveness of appropriate social media strategies to quell negative sentiment and prevent a bandwagon effect from occurring is invaluable for corporations at risk from this such as gaming companies.
The bandwagon effect will be examined by using a key metric for video game performance (number of players) and examining if participants have negative or positive perceptions of the game based upon its number of players.
H3: The positive effect of Compensation based response strategies versus denial strategies will be enhanced by exposure to a positive (non-boycott) community response.
Gamer Type
Understanding the profile of the gamers who buy and play their games is hugely important for gaming companies, as it helps them to tailor make games to a specific audience and thus sell more games. This study attempts to determine if the different categories of gamers also react differently to crisis communication strategies. Previous research into the topic of player profiles and typologies have indicated that there are some fundamental differences in how people play and interpret games that are based upon cognitive and personality differences (S Ferro, Walz & Greuter, 2013). In the context of crisis communication, it would be advantageous for gaming companies to know if their player base will be more or less receptive to specific response strategies, and previous research has indicated that different gamer types interpret branding and marketing differently in video games (Butcher, Tang & Phau, 2017). This study seeks to explore whether the differences in how players perceive branding and marketing around games may also apply to how they perceive crisis communication. Research into gaming habits has indicated that men are more likely to play games than women and were more likely to spend a greater length of time playing than women (Ogletree & Drake, 2007). No research exists into the area of how gamers interpret communication strategies, however the amount of time spent playing video games has been associated with an increase in social media and computer literacy (Appel, 2012), Therefore, this study will aim to categorise gamers into gamer types by grouping them into non gamers, casual gamers or avid gamers, with play time and frequency of play being the determining factors. To expand upon previous research this study expects the following hypothesis.
H4: Exposure to a Compensation based approach will lead to more positive Brand Reputation than Denial or Commitment approaches, and the effect will be strongest for avid gamers. Perception of Organisations
The area of pre existing organisational reputation is well documented. Pre-existing perceptions of an organisation can positively or negatively influence how an organisation fares during a crisis. Coombs (2007) describes a favourable pre-crisis reputation as “a buffer against the reputational damage lost during a crisis”. Organisations with more positive perceptions by stakeholders may therefore resume normal operations sooner than organisations with lower pre-crisis reputation.
Organisational perception by the public is in large part determined by an organisations crisis history and practices and companies that attempt to mitigate reputational damage through CSR (corporate social responsibility) practices might even see these efforts backfire against them (Shim & Yang, 2016), if the organisation has previous intentional crisis history or immoral business practices. An organisations reputation can promote positive behavioural intentions such purchase intention (Coombs & Holladay, 2001), therefore it is imperative for organisations to match their crisis response and CSR efforts to the attribution level and severity of the crisis.
In contrast however, consumers aggregate perception of an industry, and how this perception influences their behavioural and purchase intentions has not received the same level of scrutiny. This study attempts to expand upon knowledge of consumers aggregate perception of industries by examining how the aggregate perception of video game publishers affects participants brand reputation and consumer sentiment. Research into the video game industry has repeatedly shown that there the perceptions of video games by critics and by consumers are not in-sync (Cox & Kaimann, 2015), and that perceptions of the video game industry and what its core standards should be vary between consumers in different countries (Kitami,Saga & Matsumoto, 2011). The implementation of publisher perception was therefore an interesting theoretical addition to this study. How consumers perceive an industry as an aggregate will affect their behavioural and purchase intentions towards organisations in this industry which leads to the following hypothesis.
H5: Exposure to a Compensation based approach will lead to more positive Brand Reputation than Denial or Commitment approaches, and the effect will be strongest for respondents with a positive perception of publishers.
The final hypothesis will explore how the effect of crisis communications are moderated by both gamer type and publisher perception. Community response will not be taken into account for the final hypothesis as the interaction effect between crisis communications and community response will already be explored in hypothesis three. The focus of the final hypothesis is identifying if there are any key differences in how different demographics of a player base will interpret crisis communication, and the community response is not relevant for this specific hypothesis.
H6: Compensation based strategies will lead to higher Brand Reputation and Consumer Sentiment versus denial strategies and this effect will be strongest for avid gamers with a positive perception of game publishers.
Hypotheses
The Hypotheses are summarised below in this conceptual model.
Fig. 1: Conceptual Model, Crisis Response Strategies and Community Response.
METHOD Sample
In total, 330 adults responded to the experiment. Of these 330 participants, 118 were excluded because they did not finish the survey. Another participant was excluded because they were under the age of 18. Analyses were conducted on the remaining 211 participants.
The majority (73%) were men (N= 154). The participants ages ranged from 18 to 70 years with a mean age of 32.34 (SD = 10.72). Participants who held master’s degrees and bachelor’s degrees made up the largest groups at 39.3% of responses each (N=83). 10% had some college experience, 7.1% were high school graduates and participants who had less than a high school education made up the same as participants who held doctorates at 1.4% each (SD = 0.98). Experimental design
Participants were randomly assigned to the following: six conditions were arranged in a three (Crisis Response Strategy: Denial vs. Compensation vs. Commitment) by two (Community Response: Boycott vs No Boycott) between-subjects factorial design. To test the hypothesis the experiment was run on an independent measure’s methodology, with each participant being exposed to one of three Crisis Response Strategies and one of two Player Counts.
For the condition Crisis Response Strategy Condition the Participants were spread across the 3 conditions relatively equally (1 = 29.4%, 2 = 38.4%, 3 = 32.2%). This was the same for the PlayerCount condition (1 = 47.9%, 2 = 52.1%).
Procedure
The experiment was taken online and was distributed by an anonymous link via email, WhatsApp and Facebook messenger. The survey began by presenting the respondents with informed consent. Next demographics such as age, sex and education level were filled out. The two moderator variables were then measured, the first being gamer type and the second being perception of publishers. The respondents were then asked to closely read the following statement which was background information about the fictitious company that the conditions would revolve around. The respondents were then exposed, firstly to a crisis response strategy on behalf of Tang Howard, rainbow games fictional CEO. The company was described as a successful Amsterdam based gaming company with a specialisation in online gaming, and that they were currently experiencing a crisis related to unpopular monetization practices. They were then exposed to graphic which indicated how many people were currently playing a game the crisis revolved around. The respondents were randomly assigned to one of six different experimental conditions, where the type of crisis response strategy and community response was varied across conditions. After exposure to the experimental conditions, the respondent’s reactions were measured with a questionnaire, concerning the brands reputation and their own consumer sentiment. The experiment ended with a manipulation check for both main independent variables, followed by a debrief about the fictional character and company.
Stimuli
IV 1 Crisis Response Strategy
Crisis Response Strategy was manipulated by adapting coombs SCCT to be closer to the context of communications from gaming companies over social media. The main implication of SCCT strategies were kept intact but the context was shifted to reflect the unique context of video game companies, the denial strategies were kept identical to coombs interpretation, rebuild strategies were formulated as “commitment” on the developers end to fix the issues in the game, and a compensation response strategy was utilised in place of diminish type strategies to more accurately represent gaming companies past responses to crises. Therefore, the three levels of Crisis Response Strategy were denial, compensation, and commitment. Utilising a fake twitter account, a fictitious CEO either denied the crisis “We strongly deny allegations that in game purchases influence gameplay”, offered compensation “all paid items will be reverted and the transactions will be refunded to the accounts they were paid from” or promised ongoing commitment to improve the game “forward your complaints suggestions and any feedback at all to darkscenesdeveloper@rainbowgames.com”. The respondents saw a
screenshot of the fictitious tweet without any other information (i.e.: no likes, retweets). To keep factors consistent across the three conditions, the tweets all came from the same account, with the same generic profile picture. The language of the tweets was also kept as similar as possible across the conditions, with the same corporate tone of voice. The three response strategies can be seen below.
Fig 2: Crisis Response Strategies, Denial, Compensation and Commitment
IV 2 Community Response:
The Community Response condition was manipulated by creating two different scenarios where fictitious data indicated how many players the Dark Scenes game had. The Boycott condition illustrated an enormous drop off in number of players over a 5-month period, simulating the real-life phenomenon of dissatisfied players abandoning a game. In contrast the non-Boycott condition showed a player count that rose at an exponential rate over the 5-month time, indicating that there was a positive feedback loop which led to more and more people buying and playing the game over time. The two community response strategies can be seen below.
Fig 3: Community Response variables, Boycott and No Boycott. Observed Variables
Dependent Variable 1 Brand Reputation
To assess the participants' evaluation of Brand Reputation, a five-item scale was used. Participants indicated their opinion on Rainbow Games reputation by selecting “Strongly disagree” (1) to “Strongly agree” (7) with the statements on a 7-point Likert scale. Example questions include, “I admire and respect Rainbow Games” or “I feel like Rainbow Games has been transparent with their fans”.
To determine if the new variable would be reliable and consistent, before it was computed, a reliability analysis and factor analysis were conducted to establish internal consistency and reliability of the scale. The reliability analysis resulted in a Cronbach’s alpha of (α = .87) so the five items were highly related. For the factor analysis an Oblimin rotation was used as it was expected the 5 items would be highly related. A principal component analysis showed the 5 items formed a unidimensional scale. The factor analysis resulted in KMO (.86) and Bartlett’s test (p < .001). There was one item with an eigenvalue above 1 (eigenvalue 3.451) which accounted for 69.01% of the variance. The five items were combined into a single scale (M = 4.11, SD = 1.31).
To assess participants consumer sentiment (or their attitudes towards the quality and worth of Rainbow Games products) a four item scale was used which asked participants questions such as “the number of players an online game has is an indication of its quality” and participants indicated their opinion by selecting “Strongly disagree” (1) to “Strongly agree” (7) with the statements on a 7-point Likert scale.
A principal component analysis showed the 4 items formed a unidimensional scale. The factor analysis resulted in KMO (.785) and Bartlett’s test (p < .001). A reliability analysis indicated a Cronbach’s alpha of (a = .856). The four items were then computed into a single scale (M = 3.63, SD = 1.35).
Moderator 1 Gamer Type:
To determine the participants gaming habits a 3-item scale was used. Participants indicated their habits by selecting a condition from ranging from “Daily” (1) to “Never” (5) on statements such as “How often do you play video games”.
A principal component analysis showed the 3 items formed a unidimensional scale. The factor analysis resulted in KMO (.57) and Bartlett’s test (p < .001). A reliability analysis indicated a Cronbach’s alpha of (a = .65). The three items were then computed into a single scale.
Moderator 2 Publisher Perception:
To assess the participants' preconceived perception of video game publishers, a three-item scale was used. Participants indicated their feelings towards video game publishers by selecting “Strongly disagree” (1) to “Strongly agree” (7) with the statements on a 7-point Likert scale. Example questions include, “I believe that game publishing organisations have a good balance between financial interests and publishing great games”.
A principal component analysis showed the 3 items formed a unidimensional scale. The factor analysis resulted in KMO (.61) and Bartlett’s test (p < .001). One item had an eigenvalue above 1 (1.89) and accounted for 62.81% of the variance. A reliability analysis indicated a Cronbach’s alpha of (a = .7). The three items were then computed into a single scale (M = 3.28, SD = 1.23). Manipulation check
To first test the strength of the manipulations, a pre-test was conducted using an anonymous link with 28 participants. The majority of participants in all 3 groups interpreted the manipulation successfully over both conditions. For the main experiment, to check whether
respondents had successfully understood the manipulation of the two independent variables, they were asked to indicate whether “did Tang Howard take responsibility for the issues that caused a negative player experience?” and “Did the number of players decrease significantly in the 5 months since launch?”. Both of these statements were measured on a 7-point Likert scale ranging from “Strongly agree” (1) to “Strongly disagree” (7).
A chi-square test for association was conducted for Crisis Response Strategy and Player count on the manipulation check variables. All expected cell frequencies were greater than five. For Playercount, χ2(6) = 124.3, p = < .001, 90.1% recognised a decrease in player count, and 62.8% recognised that the player count did not decrease.
For Crisis Response Strategy, 3 expected cell frequencies were below 5, and Fishers exact test could not be computed so Mantel-Haenszel test (Linear by Linear association) was used as a last resort. For Crisis Response Strategy, χ2(1) = .872, p = .350, 64% misinterpreted the Compensation condition, 69.1% misinterpreted the Denial condition and 60.3% misinterpreted the Commitment condition. Reaching out to participants after discovering this misinterpretation revealed that many participants had forgotten the condition by the time they reached the manipulation check.
Randomization Check
To check if the participants were distributed evenly over the CRS and PlayerCount conditions, a randomization check was conducted using chi-square analysis, One-way ANOVA, and independent t-tests. Both Genders were exposed relatively equally across the three different crisis response strategies, X2(4) = 1.68, p = .793. Both Genders were also equally exposed to the two different player counts X2(2) = 3.69, p = .115.
One-way ANOVAs were conducted to prove that there were no significant differences between the groups who were exposed to the CSR conditions across age (M=32.33 SD =10.72 ), (F(2,205) = .423, p = .656), and education (M=4.13 SD = .97 ), (F(2,206) = .676, p = .51). For the Playercount condition an Independent t-test showed that there were no significant differences between the PlayerCount condition and age (t(.21) = .364, p = .719, 95% CI [-548; .782]) A one-way ANOVA showed that the participants educational level did not differ significantly over age either (F(1,237) = .3.279, p = .068), in the Boycott (M=4.21 SD = .954) or Non Boycott (M=3.98 SD = 1.02 ). The participants are balanced across the six experimental conditions.
RESULTS
Effect of Crisis Response Strategies on Brand Reputation and Consumer Sentiment
H1: States that compensation-based crisis response strategies will have a positive effect on
Brand Reputation in comparison to Denial and Commitment response strategies. A one-way ANOVA was conducted to test this relationship. The assumption that there would be equal variance in the conditions was met (Levene’s F (2,208) = .913; p = .403).
The results (F (2,208) = 24.58, p = .001). showed that there were significant differences between compensation-based strategies (M = 3.48, SD = 1.08) in comparison to Denial (M = 4.81, SD = 1.18) and Commitment (M = 3.83, SD = 1.3). Post Hoc analysis showed that There was a statistically significant mean difference between the Compensation and Denial groups of -1.22 (95% CI, -1.69 to -.73), p <.001, and between Commitment and Denial groups of -.99 (95% CI, -1.45 to -.52), p <.001. The relationship between compensation and commitment was not statistically significant. The first Hypothesis is therefore confirmed.
H2: States that compensation-based crisis response strategies will also have a positive effect
on Consumer Sentiment in comparison to the Denial and Commitment strategies. A one-way ANOVA was conducted to test this relationship. The assumption that there would be equal variance in the conditions was met (Levene’s F (2,206) = .2.62; p = .074).
The results (F (2,206) = .216, p = .806). showed that there were no significant differences between compensation-based strategies (M = 3.71, SD = 1.19) in comparison to Denial (M = 3.55, SD = 1.47) and Commitment (M = 3.63, SD = 1.37). Crisis response strategies therefore have no statistically significant effect on consumer sentiment, so the second hypothesis is not supported.
Interaction effect of Crisis Response Strategy and Community response on Brand Reputation.
H3: To test if the positive effect of Compensation based response strategies is improved by
exposure to a positive (non-boycott) community response, a two-way ANOVA was conducted. Firstly, the assumption equal variances between groups was met as Levene’s test was not significant F (5,205) = 0.64, p = .67. The results of the Two-way ANOVA revealed a significant interaction effect was not found, (2,205) = .245 p= .783, η2 = < .002. There was a significant main effect of both Crisis Response Strategy (as discussed in Hypothesis one) and Community Response F (1,205) = 5.88 p = 0.16, η2 = .028.
Therefore Hypotheses 3 is rejected, there is no statistically significant interaction effect of Crisis Response Strategy and Community Response on Brand Reputation.
Moderation effect on Crisis Response Strategy’s main effect on Brand Reputation.
H4: Hypothesis 4 posits that exposure to a Compensation based approach will lead to more
positive Brand Reputation than Denial or Commitment approaches, and this relationship would be moderated by Player type, with the effect expected to be strongest for avid gamers. A two-way ANOVA with Crisis Response Strategy and Player Type as independent categorical variables and Brand Reputation as dependent variable was conducted. Firstly, the assumption equal variances between groups was met as Levene’s test was not significant F (8,210) = 1.773,
p = .084. The results of the Two-way ANOVA revealed that a significant interaction effect
between Crisis Response Type and Gamer Type and was not found, F (4,210) = .2.156, p= .075, η2 = .039, and the size of the effect was very small. CRS (η2 = .16), as mentioned in H1, had a large and statistically significant effect on Brand Reputation, Gamer Type (η2 = .02) had a small effect size on Brand Reputation, but it was not statistically significant. Therefore Hypotheses 4 is rejected, there is no statistically significant moderation effect of Gamer Type on the relationship between CRS and Brand Reputation.
H5: Hypothesis 5 posits that exposure to a Compensation based approach will lead to more
positive Brand Reputation than Denial or Commitment approaches, and this relationship would be moderated by Publisher Perception, with the effect expected to be strongest for people with a positive perception of publishers. A two-way ANOVA with Crisis Response Strategy and Publisher Perception as independent categorical variables and Brand Reputation as dependent variable was conducted. The results of the Two-way ANOVA revealed that a significant interaction effect between Crisis Response Type and Publisher Perception and was not found,
F (4,210) = .1.51, p= .2, η2 = .028, and the size of the effect was very small.
Interaction effect of Crisis Response Strategies and Moderators
H6: Hypothesis 6 posits that Compensation based strategies will lead to higher Brand
Reputation and Consumer Sentiment and this effect will be strongest for avid gamers with a positive perception of game publishers. To test this hypothesis, a two-way MANOVA was conducted. The assumption of equal variance was met for DV1 (F (25,183) = 1.058, p = .396, and DV2 (F (25,183) = .876, p = .638.
There was no statistically significant interaction effect between Crisis Response Strategy, Gamer Type and Publisher Perception on the combined dependent variables, F(14, 364) = 1.312, p = .197; Wilks' Λ = .906. There were statistically significant effects on the combined dependent variables for IV1 F(4,364 ) = 10.643, p < .001, Wilks' Λ = .802, partial η2 = .105, and for Moderator 2 (Publisher Perception) F(4,364 ) = 3.233, p = .013, Wilks' Λ = ..933, partial η2 = .034.
Follow up univariate two-way ANOVA’s were run and the main effect of CRS with both moderators had a statistically significant effect on Consumer Sentiment F(7,183) = 2.254, p =.032, , partial η2 = .079 but not for Brand Reputation F(7,183) = .618, p =.740, partial η2 = .023.
To investigate further, Tukey pairwise comparisons were run for the differences in mean consumer sentiment score on Publisher Perception. There was a statistically significant mean difference between the negative publisher perception and positive publisher perception of .825 (95% CI, .267 to 1.37), p = .002, and between negative publisher perception and the neutral publisher perception of .6 (95% CI, .054 to 1.15), p = .027. There is no statistically significant positive interaction for Compensation strategies with avid gamers who have a positive perception, so Hypothesis 6 is rejected.
Discussion
The main aim of this paper was to apply traditional crisis communication methods to the context of the gaming industry and provide insight into how crisis response strategies, compounded with the effects of a community response affect a gaming companies brand reputation and the sentiment of its customers. This study did not find that there was a significant interaction of crisis response strategies and community response on brand reputation and consumer sentiment, but there were still some interesting findings that might offer a starting point for future research.
Firstly, in line with previous studies (Coombs, 2008), compensation-based strategies were proven to be the most effective crisis response strategy for preserving brand reputation when compared to efforts to rebuild or deny the crisis. There was however no support for compensation-based strategies having a similar positive impact on consumer sentiment. Previous studies have indicated that withn the video game industry, brand image is a less important factor in determining purchase intention (Kitami, Saga, , & Matsumoto, 2011) than other factors such as gameplay.This could suggest that perhaps in the gaming industry, brand
reputation is not as strongly associated with behavioural intentions on the part of consumers as it is with other industries. Crisis response strategies and community responses both influenced brand reputation, but they did not have an interaction effect, which might indicate that future research should treat these situations as scenarios that are separate from each other.
The predicted bandwagon effect, whereby participants exposed to a negative player count would in turn develop negative sentiments, was not found. This was surprising as previous research has shown that gamers are fickle and highly influenced by the actions of others in the community (Butcher, L., Tang, Y., & Phau, I. 2017) and therefore a strong interaction between these two conditions was expected, as both were shown to have effects individually. One potential reason why the effect was not as strong as anticipated could be explained by the bandwagon effect is more pronounced in interpersonal networks (Wang & Zhu, 2019), where network participants can decide amongst themselves what they think is news. Participants being exposed to the negative player count individually, without a chance to interact with others may not have reacted as strongly as they would have if they could react collectively via social media.
However, future research should focus upon the interplay between Crisis Response Strategies, Publisher perceptions and Gamer Types, as a significant relationship was found between these factors. Potentially this could imply that regardless of the brand reputation of gaming companies, Avid Gamers with an overall positive view towards gaming publishers would still be willing to purchase their products. Publisher Perception by itself also had a positive effect on consumer sentiment, perhaps implying that players who are more optimistic about gaming publishers in general will be more willing to tolerate a brand and purchase its games.
Practical implications
The findings of this study have practical implications for communications professionals in the video game industry. If video game companies find themselves in a crisis scenario the communication professionals should adhere to established foundations upon which to build their crisis communication efforts such as SCCT. This study adds credibility to Coombs assertion that rebuild strategies are most effective for crises in the intentional crisis cluster. Gaming companies should also factor in that crises that negatively affect the organisations brand reputation may not adversely affect consumer sentiment to the same extent. Although
further research is needed in this area, this assumption is backed up by a study from Kim, Park, Cha & Jeong, (2015), which asserted that “public apology from the company significantly lowers the expression of negative purchase intentions in tweets”. Therefore, gaming companies should continue to adhere to the guidelines of SCCT as a foundation to build response strategies from.
Gamer types may not influence their interpretations of crisis communications to the degree that this study anticipated, however future research into this area could expand upon this by incorporating a more complete player typology or profile, by expanding the number of items used to formulate a scale.
Publisher perception is an area that future researches should address further as it had outcomes for both brand reputation and consumer sentiment. Gaining future insight into how gamers perceive the industry as a whole would be beneficial in giving gaming organisations inspiration to enact policies to set themselves apart from competitors and stand out further.
Theoretical Implications
This study brings attention to crisis communication in the gaming industry which up until now has been relatively under investigated despite being a high-risk industry for online crises. Firstly, this study reinforces the existing literature by reaffirming the assertion that response strategies are situation specific, where rebuild strategies are most effective at countering crises in the intentional cluster (Coombs, 2007).
The anticipated bandwagon effect amongst participants exposed to the negative player count was not found, this can be explained by the lack of opportunity for the participants to communicate amongst a network, which previous studies have found to be a focal point for bandwagon effects (Wang & Zhu, 2019). Future studies should therefore focus upon the bandwagon effect specifically in the arena of social media, as this study finds little evidence that a bandwagon effect will be pronounced outside of a social network. This is in line with findings of previous studies which have shown that a key indicator of a bandwagon’s effectiveness is the number of early participants (Wang, Ming-Lai, Wang & Wu, 2015). Future studies could build upon this study by incorporating bandwagon stimuli (such as player counts from this study) into a social network to determine if negative stimuli results result in respondents a negative bandwagon effect and vice versa.
Limitations
This study advances upon crisis communication literature but there were some key limitations that need to be addressed. The main issue is the niche nature of the study topic, which is gaming, may have affected the ability of the author to maintain a representative sample via the convenience sampling method used over Facebook. 30.6% (N= 100) of respondents identified as non-gamers meaning they almost never played video games for any length of time. This likely affected the credibility of the scale for Gamer Type with an unanticipated distribution towards non gamers who may have been disinterested in the subject. In hindsight, the convenience sampling methods used may not have gathered a representative sample, and future research should focus upon more video game centric social media networks, for example r/gaming on reddit.
Secondly, while psychological profiles of gamers have been studied extensively, very little of this research relates to communication and its interpretation by gamers. If gamer type were to be studied in future studies as a potential moderator, the scale would need to be reworked or adapted to enhance its credibility. This could be achieved by expanding from a 3-item scale to a 5-item scale and including questions that measure the respondent’s behaviours such as “is video gaming your primary hobby?”
Thirdly, the stimuli utilised for the community response condition were too limited in scope to demonstrate the expected interaction effect. Bandwagon effects are primarily observed in interpersonal and social media networks, and the bandwagon condition was shown as stimulus in a closed environment. This study hypothesised that when respondents were shown a dominant negative position that they would then adopt a similar negative sentiment. In hindsight, this study was limited by its scope and means of the author to incorporate social media analysis for bandwagon effects. Future studies should aim to further explore the bandwagon effect in gaming centric social media networks, as opposed to testing individuals with bandwagon stimuli in a closed network.
Conclusion
This study builds upon previous crisis communication research by providing support for established crisis communication literature and its suitability for the gaming industry. While this study failed to identify a significant predicted bandwagon effect it does break new ground
in the video game industry by showing that gaming companies must account consumers pre-existing perception of publishers as an aggregate when formulating crisis communication strategies. This study has been explorative in nature and future research should be concerned with expanding upon the findings of this study, notably by further researching the pre existing perceptions consumers have of game publishers and how these perceptions interact with crisis communication.
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