Social activity and structural centrality in online social networks

Klein, Andreas; Ahlf, Henning; Sharma, Varinder M.

doi:10.1016/j.tele.2014.09.008

Cited by 43 publications

(18 citation statements)

References 54 publications

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“…Klein et al summarized that the personal activity status of key members was highly correlated with their structural centrality measures. Therefore, friends might always make similar choices and behave similarly [27]. Zhang et al designed the MMSN architecture, including privacy of the content query, trust-based service evaluation, and privacy-preserving content filtering, to decrease the conflict between sharing and privacy [73].…”

Section: Social Network Based Mobile Payment Analysismentioning

confidence: 99%

Influencing Factors Analysis for a Social Network Web Based Payment Service in China

Rong²,

Chen³

et al. 2018

J. theor. appl. electron. commer. res.

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WeChat payment has recently become a popular mobile payment service in China by bundling with WeChat, the most popular social network service in China. It is interesting to investigate the reasons for its popularity. In this research, we applied the technology acceptance model to predict its acceptability and to identify variables attributing to its popularity. In addition to the primary explanatory variables, i.e., Perceived Ease of Use and Perceived Usefulness, the proposed framework is further extended to include the constructs of Social Interaction, Trust, Perceived Enjoyment and Use Context. The results indicated that the proposed model is able to explain the variance in a user's intention to use WeChat payment service. We hope this study can provide insights into understanding the adoption behaviour of socially aware mobile payment services.With the development of information technology, E-commerce has become an important part of modern economic industries and a magnificent growth has been observed in recent years. Many companies added E-commerce models to their operations and began innovative marketing and sales programmes via the Internet [3]. Moreover, handling financial transactions over the Internet has also greatly changed people's lifestyle, especially in the payment field.Currently, online mobile payment has become one of the most important smartphone services in E-commerce [41], since mobile payments can lead to customer satisfaction [41], cost reduction [67], competitive advantage [25] and firm growth [71]. Mobile payment enables users to check account balances, transfer money, pay bills and conduct financial management at any time from anywhere [43]. Due to the advances of mobile Internet technology, and the rapid growth in the number of mobile users, making payments for goods, services, and bills via mobile devices is now playing an increasingly important role in our daily life [54]. For example, according to the data of the recently released report, The First Quarter of 2017 China's Third Party Mobile Payment Market Monitoring Report, China's third-party mobile payment market size of 18.80912 trillion yuan increased 46.78% [61].Although Alipay is maintaining the largest mobile payment platform in the China, WeChat payment's market share has grown from 10% to 40% in the past three years, and it is close to that of Alipay. Tencent released WeChat payment in 2013 and bundled it with WeChat 5.0 so that users could simply click on the item links in the chat window to easily complete payments [8]. Moreover, through the red envelope promotion, WeChat payment attracted more than 8 million users, giving away 3 billion RMB on Chinese New Year's Eve in 2014 [9]. In 2017, the total number of WeChat red envelopes sent and received was 46 billion, up 43.3% from the previous year [60].Compared with Alipay, WeChat payment is obviously different due to its social factors. WeChat is currently the most popular mobile social network service application in China. Its number of monthly active users has increased to ...

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Section: Social Network Based Mobile Payment Analysismentioning

confidence: 99%

Influencing Factors Analysis for a Social Network Web Based Payment Service in China

Rong²,

Chen³

et al. 2018

J. theor. appl. electron. commer. res.

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“…Some researchers have also used ‘in-degree’ to identify hubs in the online discussion network [ 39 ]. Empirical results show that degree centrality is the most effective indicator of the structural importance of a participant in the social network [ 40 ]. Moreover, the node with the high ‘betweenness centrality’ has the high power of diffusion in the network [ 41 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

Variations in power of opinion leaders in online communication networks

2018

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Online social media has completely transformed how we communicate with each other. While online discussion platforms are available in the form of applications and websites, an emergent outcome of this transformation is the phenomenon of ‘opinion leaders’. A number of previous studies have been presented to identify opinion leaders in online discussion networks. In particular, Feng (2016 Comput. Hum. Behav. 54, 43–53. (doi:10.1016/j.chb.2015.07.05210.1016/j.chb.2015.07.052)) has identified five different types of central users besides outlining their communication patterns in an online communication network. However, the presented work focuses on a limited time span. The question remains as to whether similar communication patterns exist that will stand the test of time over longer periods. Here, we present a critical analysis of the Feng framework both for short-term as well as for longer periods. Additionally, for validation, we take another case study presented by Udanor et al. (2016 Program 50, 481–507. (doi:10.1108/PROG-02-2016-001110.1108/PROG-02-2016-0011)) to further understand these dynamics. Results indicate that not all Feng-based central users may be identifiable in the longer term. Conversation starter and influencers were noted as opinion leaders in the network. These users play an important role as information sources in long-term discussions. Whereas network builder and active engager help in connecting otherwise sparse communities. Furthermore, we discuss the changing positions of opinion leaders and their power to keep isolates interested in an online discussion network.

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“…Users on the Web 2.0 have found it easier to know who knows what through social media. However, it is a burden to contact people who possess the desired knowledge via their personal ties [60]. Many social bookmarking systems, which help users to manage and share interesting information, as well as blogging systems, which aim to post online journals to express their opinions, offer users this special kind of online sociability without any need to ask a consent to be followed.…”

Section: Network Ofmentioning

confidence: 99%

Recommendations Based on Social Links

Lee

Brusilovsky

2018

Social Information Access

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Abstract. The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues and the future direction. Among several branches of social recommendations, specifically, this chapter focuses on recommendations, which are founded on users' self-defined (i.e., explicit) social links and target to suggest desirable items, not social links of interests. This chapter provides a brief overview over the technical needs for social link-based recommendations and the studies explaining the viability of users' social networks as useful information sources from a social science point of view. Then, the in-depth understanding of existing recommendations based on users' social links will be addressed. Lastly, various issues and future directions of social link-based recommendation research are put forward. IntroductionThe remarkable popularity of social media encourages web users to participate in various online activities and to generate data on an unprecedented scale. Given exponentially growing social media data volumes, personalized recommendation technologies have proven the effectiveness as a solution of the information overload problem; have offered positive user experiences with more relevant contents; and have presented competitive advantage to the social media business [119]. As predictive analysis of online users' tastes, recommendation technology adaptively filters out unnecessary data and selectively chooses presumably favorable information according to users' preferences. At odds with the original principle of personalized recommendations, which is to give users control of information access, however, the typical and most popular collaborative filtering recommendation technology performs all steps autonomously and allows no room for users to get involved in information personalization of their own. The lacking user involvement causes several problems (illustrated in Section 12.3.1), and many researchers have called attention to solutions to improve the quality of typical recommendation technology and to cope with the relevant problems. Among the several streams of research to develop the solutions, one important evolution is to take advantage of users' self-defined online social networks and proposes to generate recommendations based on users' social linkssocial link-based recommendations. The idea to utilize users' online social links as a foundation of recommendation is undoubtedly spurred by the recent phenomenon of online social networks.Compared with the era when web users stayed in isolation, a number of social media systems have been adapting online sociability, helping users to find people of interest, and as well as encouraging them to socially associate with people of interest. In fact, web users' active and eager participation on social media are intrinsically motivated by not only personal desire for information management and knowledge acquisition but also social desire for engagement and communication with...

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Social activity and structural centrality in online social networks

Cited by 43 publications

References 54 publications

Influencing Factors Analysis for a Social Network Web Based Payment Service in China

Influencing Factors Analysis for a Social Network Web Based Payment Service in China

Variations in power of opinion leaders in online communication networks

Recommendations Based on Social Links

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