Understanding the adoption of location-based recommendation agents among active users of social networking sites

Zhu, Dongqing; Chang, Ya Ping; Luo, Jian Jun; Li, Xin

doi:10.1016/j.ipm.2014.04.010

Cited by 56 publications

(42 citation statements)

References 27 publications

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“…Information systems theories such as the technology acceptance model are often used as the theoretical bases. Zhu et al (2014) found that perceived usefulness is an important factor affecting SNS user adoption of location-based recommendation agents. Jin (2013) noted that perceived ease of use, perceived usefulness and perceived playfulness affect a user's intention to continue using Facebook.…”

Section: Research Model and Hypothesesmentioning

confidence: 99%

The effect of network externality on mobile social network site continuance

Zhou

2015

Program

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The effect of network externality on mobile social network site continuance Tao Zhou Article information:To cite this document: Tao Zhou , (2015),"The effect of network externality on mobile social network site continuance", Program: electronic library and information systems, Vol. 49 Iss 3 pp. 289 -304 Permanent link to this document: http://dx. (2015),"Epistemological and ontological spirals: From individual experience in educational innovation to the organisational knowledge in the university sector", Program, Vol. 49 Iss 3 pp. 266-288 http:// dx.If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to examine the effect of network externality on users' continuance of mobile social network sites (SNS). Design/methodology/approach -Based on the 230 valid responses collected from a survey, structural equation modeling was employed to examine the research model. Findings -The results indicated that network externality, which includes referent network size and perceived complementarity, has a significant effect on perceived usefulness and flow. Privacy concern affects perceived usefulness, flow and privacy risk. These three factors determine continued use. Originality/value -Previous research has focussed on the effects of motivations such as perceived value on user adoption of SNS. The effect of network externality on user continuance has seldom been examined. This research tries to fill the gap.

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Section: Research Model and Hypothesesmentioning

confidence: 99%

The effect of network externality on mobile social network site continuance

Zhou

2015

Program

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“…Initiators or consultants implementing the LARSMA system or other similar systems should emphasize the gap in the flow of advertising services between advertisers and consumers and the importance of using geographical data. Initiators or consultants explain that the employed LARSMA with a mechanism of recommending advertising messages can help target mobile consumers by considering their location and personal preferences with an acceptable privacy risk (Zhu et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Location-aware service applied to mobile short message advertising: Design, development, and evaluation

Kao

et al. 2015

Information Processing & Management

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“…By studying the previous recommendation algorithms about the similarity calculation of the friends and the target user, most of them [15,16,21,22] just simply compute a direct similarity based on Pearson correlation coefficient, and there is no distinction between direct similarity and indirect similarity. Wang et al [19] find out that lots of mentioned video recommendation algorithms ignore the attributes of recommendation so that the accuracy of the recommendation results are not very satisfactory.…”

Section: The Proposed Computing Methods Of Similarity Between Usersmentioning

confidence: 99%

“…By studying the previous recommendation algorithms about the similarity calculation of the friends and the target user, most of them just simply compute a direct similarity based on Pearson correlation coefficient, and there is no distinction between direct similarity and indirect similarity. Wang et al .…”

Section: The Trust Friends Computing Modelmentioning

confidence: 99%

A video recommendation algorithm based on the combination of video content and social network

Cui

Dong

et al. 2016

Concurrency and Computation

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Summary Recently, social network has been one of the biggest information exchange platforms of the Internet. Moreover, the users in social network used to watch videos through social network application. To provide a proper recommended video list, the video recommendation algorithm for social network is becoming a hot research issue. On one hand, more and more researchers introduce the concept of trust into video recommendation algorithms. However, most of them only select the trust friends based on the similarity and neglect the characteristics of social network. On the other hand, most previous video recommendation algorithms are only based on the number that a video is viewed to evaluate a video's quality. They do not make good use of the social relationship in social network and the video's reputation. This paper mainly focuses on the challenge that the effectiveness and performance of current video recommendation algorithm in social network cannot satisfy the users. In this paper, we propose a novel video recommendation algorithm based on the combination of video content and social network. Our proposed algorithm consists of the trust friends computing model and video's quality evaluation model. The trust friends computing method takes into account similarity between users, interaction between users, and the active degree of a user. In our video's quality evaluation model, we combine the acceptance ratio of a video with a video's reputation. The video can be given an appropriate rating score through this model. We design corresponding trust friends computing algorithm and video recommendation algorithm respectively for two proposed models. Our integral video recommendation algorithm consists of these two algorithms. The experimental results indicate that the performance and effectiveness of our algorithm are better than those of two classical video recommendation algorithms (i.e., user‐based collaborative filtering algorithm and TBR‐d algorithm), in terms of precision, recall and F1‐measure. Copyright © 2016 John Wiley & Sons, Ltd.

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Understanding the adoption of location-based recommendation agents among active users of social networking sites

Cited by 56 publications

References 27 publications

The effect of network externality on mobile social network site continuance

The effect of network externality on mobile social network site continuance

Location-aware service applied to mobile short message advertising: Design, development, and evaluation

A video recommendation algorithm based on the combination of video content and social network

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