Determinants of Online Social Business Network Usage Behavior—Applying the Technology Acceptance Model and Its Extensions

Guido Moeser, Heiko Moryson, Gero Schwenk

Department of Cultural and Social Sciences, University of Giessen, Giessen, Germany.
Research Department, Masem Research Institute, Wiesbaden, Germany.

DOI: 10.4236/psych.2013.44061                

Abstract

Usage of online social business networks like LinkedIn and XING have become commonplace in today’s workplace. This research addresses the question of what factors drive the intention to use online social business networks. Theoretical frame of the study is the Technology Acceptance Model (TAM) and its extensions, most importantly the TAM2 model. Data has been collected via a Web Survey among users of LinkedIn and XING from January to April 2010. Of 541 initial responders 321 finished the questionnaire. Operationalization was tested using confirmatory factor analyses and causal hypotheses were evaluated by means of structural equation modeling. Core result is that the TAM2 model generally holds in the case of online social business network usage behavior, explaining 73% of the observed usage intention. This intention is most importantly driven by perceived usefulness, attitude towards usage and social norm, with the latter effecting both directly and indirectly over perceived usefulness. However, perceived ease of use has—contrary to hypothesis—no direct effect on the attitude towards usage of online social business networks. Social norm has a strong indirect influence via perceived usefulness on attitude and intention, creating a network effect for peer users. The results of this research provide implications for online social business network design and marketing. Customers seem to evaluate ease of use as an integral part of the usefulness of such a service which leads to a situation where it cannot be dealt with separately by a service provider. Furthermore, the strong direct impact of social norm implies application of viral and peer-to-peer marketing techniques while it’s also strong indirect effect implies the presence of a network effect which stabilizes the ecosystem of online social business service vendors.

Keywords

Technology Acceptance Model (TAM); Online Social Business Networks; Usage Intention; Structural Equation Modeling; Network Effect

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. doi:10.1016/0749-5978(91)90020-T
[2]	Ajzen, I. (2005). Attitudes, personality, and behavior (2nd ed.). Milton-Keynes: Open University Press/McGraw-Hill.
[3]	Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
[4]	Benbasat, I., & Barki, H. (2007). Quo Vadis TAM? Journal of AIS, 8, 211-218.
[5]	Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13, 210-230. doi:10.1111/j.1083-6101.2007.00393.x
[6]	Castaneda, J., Munoz-Leiva, F., & Luque T. (2007). Web Acceptance Model (WAM): Moderating effects of user experience. Information & Management, 44, 384-396. doi:10.1016/j.im.2007.02.003
[7]	Davis, F. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. Management Information Systems Quarterly, 13, 319-340. doi:10.2307/249008
[8]	Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003. doi:10.1287/mnsc.35.8.982
[9]	Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
[10]	Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach. New York: Psychology Press/Taylor & Francis.
[11]	Gentry, L., & Calantone, R. (2002). A comparison of three models to explain shop-bot-use on the web. Psychology & Marketing, 19, 945-956. doi:10.1002/mar.10045
[12]	Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1-55. doi:10.1080/10705519909540118
[13]	Kim, B. G., Park, S. C., & Lee, K. J. (2007). A structural equation modeling of the Internet acceptance in Korea. Electronic Commerce Research and Applications, 6, 425-432. doi:10.1016/j.elerap.2006.08.005
[14]	King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43, 740-755. doi:10.1016/j.im.2006.05.003
[15]	Lee, Y., Kozar, K., & Larsen, K. (2003). The technology acceptance model: Past, present and future. Communications of the Association for Information Systems, 12.
[16]	LinkedIn Audience (2013). LinkedIn marketing solutions. http://marketing.linkedin.com/audience
[17]	Ma, Q., & Liu, L. (2004). The technology acceptance model: A meta-analysis of empirical findings. Journal of Organizational and End User Computing, 16, 59-72. doi:10.4018/joeuc.2004010104
[18]	Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14, 224-235. doi:10.1108/10662240410542652
[19]	Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44, 90-103. doi:10.1016/j.im.2006.10.007
[20]	Straub Jr., D. W., & Burton-Jones, A. (2007). Veni, Vidi, Vici: Breaking the TAM logjam. Journal of the Association for Information Systems, 8, 223-229.
[21]	Sun, H., & Zhang, P. (2006). Causal relationships between perceived enjoyment and perceived ease of use: An alternative approach. Journal of the Association for Information Systems, 7, 618-645.
[22]	Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6, 144-176. doi:10.1287/isre.6.2.144
[23]	Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 186-204. doi:10.1287/mnsc.46.2.186.11926
[24]	Venkatesh, V., Morris, M. G., & Davis, F. D. (2003). User acceptance of information management: Toward a unified view. MIS Quartely, 27, 425-478.
[25]	XING Mediadaten (2010). Deutschland Osterreich Schweiz (DACH). http://de.slideshare.net/Jotschkins/XING-mediadaten-4716467
[26]	Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007). Technology acceptance: A meta-analysis of the TAM: Part 1. Journal of Modelling in Management, 2, 251-280. doi:10.1108/17465660710834453