Intention to upload video content on the internet: The role of social norms and ego-involvement

Park, Namkee; Jung, Younbo; Lee, Kwan Min

doi:10.1016/j.chb.2011.05.006

Cited by 44 publications

(30 citation statements)

References 49 publications

(65 reference statements)

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“…Based on the limited available literature, social influence factors such as perceived social norms were found to have an impact on individuals in their innovation adoption behavior involving green electricity (Ozaki, 2011), video content online uploading (Park, Jung, & Lee, 2011) and push-to-talk (one-to-one and one-to-many), instantaneous mobile voice communication service (Dickinger, Arami, & Meyer, 2008). Even so, the effect of social influence factors on ICT adoption decisions has yet to be fully vetted.…”

Section: Literature Reviewmentioning

confidence: 98%

Exploring Podcast Adoption Intention via Perceived Social Norms, Interpersonal Communication, and Theory of Planned Behavior

Mou¹,

Lin

2015

Journal of Broadcasting & Electronic Media

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Previous research has not linked the perspectives of social influence, interpersonal discourse, and behavioral theory to study new media diffusion. The current study integrated these perspectives by incorporating theory of planned behavior with the concepts of normative beliefs and interpersonal communication to explain podcast-adoption intention. A cross-sectional sample responded to a survey online. Results show that descriptive norm and perceived control were significant predictors of attitude; injunctive norm was a significant predictor of perceived control. Social discourse, perceived descriptive norm, and attitude had a direct effect on podcast-adoption intention; perceived control and injunctive norm had an indirect effect in contrast.

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Section: Literature Reviewmentioning

confidence: 98%

Exploring Podcast Adoption Intention via Perceived Social Norms, Interpersonal Communication, and Theory of Planned Behavior

Mou¹,

Lin

2015

Journal of Broadcasting & Electronic Media

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“…In this article, we first update an idea we brought up in 2005—the distinction between collective norms and perceived norms—that has been the subject of a number of articles (Alexy & Leitner, ; Carcioppolo & Jensen, ; Chia, ; Jensen & Bute, ; Lippman & Campbell, ; Mabry & Mackert, ; Park, Jung, & Lee, ; Reeves & Orpinas, ; Shulman & Levine, ; Storey & Kaggwa, ). Conducting a brief review of the literature on the use of the theory of normative social behavior (TNSB; Rimal & Real, ), we then categorize the growing list of moderators in the relationship between descriptive norms and behaviors.…”

mentioning

confidence: 99%

A Re-Explication of Social Norms, Ten Years Later

2015

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We revisit some ideas from our previous article on social norms by conceptualizing norms as dynamic entities that both affect and are affected by human action; elaborating on the distinction between collective and perceived norms; summarizing key findings from studies that have adopted the theory of normative social behavior (TNSB) and thereby proposing guidelines for further expanding the purview of the TNSB; discussing the attribute-centered approach as a framework for focusing on behavioral characteristics; and highlighting areas for further inquiry into social norms.

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“…Bornoe and Barkhuus [6] study motivations for video microblogging, and discover that the main goals of bloggers are self-expression, entertainment and self-presentation. Park et al [7] conduct surveys to investigate factors that are associated with users' intentions in uploading videos to the Internet. Their results reveal that, in particular, ego-involvement (e.g., self-presentation) is associated with users' attitudes toward uploading behavior.…”

Section: Related Work a User Intentmentioning

confidence: 99%

“…The approach follows a methodology similar to one that has proven effective in earlier work on the discovery of search intent classes for video search [1]. Our approach is superior to conventional methods such as those using transaction log analysis [8], interviews [4] or surveys [7] because it exploits evidence from a very large user population to directly access spontaneously expressed information about why users upload videos. In a further step, we use a crowdsourcing user study, which gives us access to a large number of users, both to refine initial classes discovered through social-Web mining and to annotate videos based on our uploader intent typology.…”

Section: Related Work a User Intentmentioning

confidence: 99%

Uploader Intent for Online Video: Typology, Inference, and Applications

Kofler

Bhattacharya

Larson

et al. 2015

IEEE Trans. Multimedia

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We investigate automatic inference of uploader intent for online video, i.e., prediction of the reason for which a user has uploaded a particular video to the Internet. Users upload video for specific reasons, but rarely state these reasons explicitly in the video metadata. Information about the reasons motivating uploaders has the potential ultimately to benefit a wide range of application areas, including video production, videobased advertising, and video search. In this paper, we apply a combination of social-Web mining and crowdsourcing to arrive at a typology that characterizes the uploader intent of a broad range of videos. We then use a set of multimodal features, including visual semantic features, found to be indicative of uploader intent in order to classify videos automatically into uploader intent classes. We evaluate our approach on a dataset containing ca. 3K crowdsourcing-annotated videos and demonstrate its usefulness in prediction tasks relevant to common application areas.

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Intention to upload video content on the internet: The role of social norms and ego-involvement

Cited by 44 publications

References 49 publications

Exploring Podcast Adoption Intention via Perceived Social Norms, Interpersonal Communication, and Theory of Planned Behavior

Exploring Podcast Adoption Intention via Perceived Social Norms, Interpersonal Communication, and Theory of Planned Behavior

A Re-Explication of Social Norms, Ten Years Later

Uploader Intent for Online Video: Typology, Inference, and Applications

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