scholarly journals A Study on the Millennials Usage Behavior of Social Network Services: Effects of Motivation, Density, and Centrality on Continuous Intention to Use

2021 ◽  
Vol 13 (5) ◽  
pp. 2680
Author(s):  
Gwijeong Park ◽  
Fangxin Chen ◽  
Le Cheng

Whether in terms of social media platforms, mobile pay apps or an increasing acceptance of RFID chips in humans, technology has transformed everyday life for consumers. Social networks have experienced enormous growth as online personal networking media. Social exchange theory (for motivation and social reward) and theories of collective action can be applied in order to understand how an individual’s behavior may exert effects on or receive influences from other users with regard to the continuance usage intention of social apps. First, this study aims to examine behavioral characteristics of the Millennials, and takes flow and social reward systematically so as to explore SNS users’ continuance based on SNS characteristics. Targeting Millennials SNS users, this study empirically examines users’ continuance intention at individual level and simulates users’ continuance behavior at group level, which are expected to be influential as a next generation of purchasing group, focusing on social network services (SNS) usage. Second, this study tries to suggest strategic implications by identifying key factors that dominate SNS users’ behavior in the process of experiencing SNS. For the empirical purpose, this study analyzes the relationship between SNS characteristics (motivation to use, density, and centrality) and usage behavior (flow, social reward, and continuous intention to use). As a result, each construct of motivation to use SNS, SNS density, and SNS centrality are positively linked with flow. Motivation to use SNS and SNS centrality are positively associated with social reward, however, SNS density does not have a significant effect on social reward. In addition, flow and social reward turn out to have positive linkage with continuous intention to use respectively. The findings of this study are expected to provide implications for researchers and operators in related fields to identify various factors that explain the SNS usages of the Millennials, especially the major factors that sustain SNS involvement and activities. This study can enrich both SNS continuance theory, and help SNS operators to manipulate resources effectively to attract and retain users.

2010 ◽  
Vol 1 (2) ◽  
Author(s):  
Chen Ming Hung

The management of the social networks of individual technological innovation has been hampered by the lack of a comprehensive typology for categorizing of social networks. Based on social support and social exchange theory, this study develops a social networks typology that identifies three constructs type of social networks. This study also point to limitation with the measurement of individual innovation and outline the five constructs design to overcome these limitations. The study then examines the relationship between social networks and technological innovation. The results suggest that technological innovation was significantly influenced by the informal centrality and tie strength of all three social network types. Furthermore, the relationship can be explaining more variance by adding specificity asset as positive moderate variable. The theoretical framework of this study brings informal social network phenomenon into technological innovation management in individual level. Both the typology for conceptualizing the nature of social networks and the constructs for scaling the measurement of technological innovation of this study provide a solid foundation on exploring the application in new contexts.


2021 ◽  
Vol 11 (6) ◽  
pp. 2530
Author(s):  
Minsoo Lee ◽  
Soyeon Oh

Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.


2014 ◽  
Vol 71 (6) ◽  
pp. 2035-2049 ◽  
Author(s):  
Feng Jiang ◽  
Seungmin Rho ◽  
Bo-Wei Chen ◽  
Xiaodan Du ◽  
Debin Zhao

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