Over the past few decades, video games have become a popular avenue for dissemination of information and publicity about video games is word-of-mouth sharing. Thus, it is imperative for researchers to examine the social, and individual processes that guide this behavior. Focusing on prerelease sharing behavior specifically, this pair of studies aimed to identify predictors and dimensions of video game information sharing, and explore the impact content characteristics have on individuals' likelihood to share game information either online or to friends. A pretest survey (n = 577) investigated the prominence word-of-mouth communication has on video game information sharing amongst college students, finding it to be the most common method of sharing and obtaining game information. The main experiment (n = 227) examined participants' intent to share information after being exposed to a video game trailer manipulated to emphasize variations of core video game characteristics (control, narrative and mechanics) and emotional appeals (control, awe and hedonic). There were no findings suggesting the manipulations had a significant effect on participants' intent to share; however, those who reported first-person shooters as their favorite genre reported significantly more intent to share. Conceptualized through Social Exchange Theory and Social Identity Theory; the experiment also explored motivations to share based on exchange value, and gamer identity. Implications for our understanding of motivations to share game information based on social networks and content characteristics are discussed. / Master of Arts

Viral marketing allows companies to promote their products and services using very small budgets and the viral ad can be considered the holy grail of digital marketing. In this light, the aim of this study is to discuss the main factors in the existing literature that are considered to boost virality and articulate a summarized Virality Theoretical Model. The empirical study included in this thesis involves the monitoring of an actual ad and an assessment of whether it went viral or not and if it follows the guidelines of the Virality Theoretical Model. The empirical study showed that the ad did not go viral and did not include and display all the attributes proposed by the Model. This further indicates, in regards to theory as well as for marketing executives and advertising practitioners, that virality is a complex phenomenon that depends on several different factors involving both content characteristics and dissemination.

Subjective video quality and video decoding complexity are jointly optimized in order to determine the video encoding parameters that will result in the best Quality of Experience (QoE) for an end user watching a video clip on a mobile device. Subjective video quality is estimated by an objective criteria, video quality metric (VQM), and a method for predicting the video quality of a test sequence from the available training sequences with similar content characteristics is presented. Standardized spatial index and temporal index metrics are utilized in order to measure content similarity. A statistical approach for modeling decoding complexity on a hardware platform using content features extracted from video clips is presented. The overall decoding complexity is modeled as the sum of component complexities that are associated with the computation intensive code blocks present in state-of-the-art hybrid video decoders. The content features and decoding complexities are modeled as random parameters and their joint probability density function is predicted as Gaussian Mixture Models (GMM). These GMMs are obtained off-line using a large training set comprised of video clips. Subsequently, decoding complexity of a new video clip is estimated by using the available GMM and the content features extracted in real time. A novel method to determine the video decoding capacity of mobile terminals by using a set of subjective decodability experiments that are performed once for each device is also proposed. Finally, the estimated video quality of a content and the decoding capacity of a device are combined in a utility-complexity framework that optimizes complexity-quality trade-off to determine video coding parameters that result in highest video quality without exceeding the hardware capabilities of a client device. The simulation results indicate that this approach is capable of predicting the user viewing satisfaction on a mobile device.

Purpose: The purpose of this paper was to extend the understanding of content marketing, content characteristics and its effects on attitudes towards the advertisement and advertising value on social networking sites. Methodology: This study had an explanatory research design with a quantitative research approach. The data used in this study was gathered through an online questionnaire. Conclusion: The results of this study shows that there is a difference between different SNS and the effects that arises from different content characteristics in SNS advertisements. Different SNS can not be treated the same, these differences need to be addressed when communicating and creating advertisements and when being present on different SNS. Implications: This study contributed to the field by providing additional information on how advertising value is affected by attitudes towards the advertisement. The results of this study also show what effect the different content characteristics have on affective and cognitive responses, and gives information regarding what type of content marketers should emphasize, depending on which of the two SNS investigated in this study they are currently operating on.