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Social Networking Sites as Business Tool: A Study of User Behavior

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Business Process Management

Abstract

Social Networking Sites (SNS) are second generation web applications allowing the creation of personal online networks; the social networking domain has become one of the fastest growing online environments connecting hundreds of millions of people worldwide. Businesses are increasingly interested in SNS as sources of customer voice and market information but are also increasingly interested in the SNS as the domain where promising marketing tactics can be applied; SNS can be also used as business process management (BPM) tools due to powerful synergies between BPM and SNS. Marketers have various options: SNS can be engaged as tools of customer engagement, social interaction, and relationship building but also as channels of information, collaboration and promotion. Understanding the adoption motives and adoption process of these applications is an essential step in engaging the SNS as part of the marketing toolbox.

In order to analyze the factors influencing the acceptance and use of SNS in The Netherlands a Technology Acceptance Model (TAM) was developed and tested. The findings indicate that there is a significant positive effect of the ease of use of SNS on perceived usefulness. Both variables have a direct effect on the intention to use the SNS and an indirect effect on the attitude towards the applications. Moreover the study has shown that intention to use SNS has a direct and positive effect on the degree of final use of SNS. Results demonstrate empirically that the TAM can explain and predict the acceptance of SNS by users as new communication system connecting them to peers and businesses.

This study presents an overview of the SNS user behavior and underlines the importance of using these applications as new communication technology.

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Constantinides, E., Lorenzo-Romero, C., Alarcón-del-Amo, MdC. (2013). Social Networking Sites as Business Tool: A Study of User Behavior. In: Glykas, M. (eds) Business Process Management. Studies in Computational Intelligence, vol 444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28409-0_9

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