Social media enablers and inhibitors: Understanding their relationships in a social networking site context |
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| Institution: | 1. Hankamer School of Business, Baylor University, Waco, TX, USA;2. Information Technology and Decision Science Department, College of Business, University of North Texas, Denton, TX, USA;1. Department of Hospital and Health Care Administration, Chia Nan University of Pharmacy & Science, No. 60, Section 1, Erren Rd., Rende Dist., Tainan 71710, Taiwan, ROC;2. Department of Hospital and Health Care Administration, Chia Nan University of Pharmacy & Science, No. 60, Section 1, Erren Rd., Rende Dist., Tainan 71710, Taiwan, ROC;1. Dept of Computer Science, Chungbuk National University, Cheongju, Republic of Korea;2. School of Information and Communication Engineering, Chungbuk National University, Cheongju, Republic of Korea;3. Department of Computer Science, Hanyang University, Seoul, Republic of Korea;4. Department of Computer Science and Engineering, Sungkyunkwan University, Suwon, Republic of Korea;5. School of Computer Science and Engineering, Soongsil University, Seoul, Republic of Korea;1. Operations & Information Systems Department, Manning School of Business (MSB), PTB Center, 74 University Avenue, Lowell, MA 01854, USA;2. King Fahd University of Petroleum & Minerals, KFUPM Business School, Dhahran 31261, Saudi Arabia;3. Department of Management Information Systems, College of Business and Management, University of Illinois Springfield, One University Plaza, UHB 4030, Springfield, IL 62703, USA;1. Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, Romania;2. Department of Management Studies and Quantitative Methods, Parthenope University, Italy;3. Department of Informatics, University of Salerno, Italy;1. Department of Information Systems, Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt;2. Department of Decision Support, Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt;1. Research Institute for Shenzhen, University of International Business and Economics, China;2. Anderson School of Management, University of New Mexico, USA;3. McLane College of Business, University of Mary Hardin-Baylor, USA |
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| Abstract: | This study extends and tests the dual factor model of technology usage (Cenfetelli, 2004, Cenfetelli and Schwarz, 2011), which recognizes enablers and inhibitors as two distinct constructs in the context of social media. We test the effect of two enablers: perceived usefulness and perceived enjoyment on perceived communication quality and social media continuance intention. We advance the understanding of the conceptualization of inhibitors from object-based, social-based, behavioral-based, and affective-based perspectives. We investigate the moderating effects of affective-based inhibitors (i.e., perceived social media distress and perceived social media anxiety) and the direct effects of object-based inhibitor (rapid change), social-based inhibitor (i.e., distorted reputation), and behavioral-based inhibitor (perceived complexity) on communication quality and continuance intention. To test the hypotheses, we collected data using an Online Crowdsourcing Markets (OCMs) technique. Using a sample of 268 Facebook users, our findings suggest perceived enjoyment is the main enabler, whereas perceived complexity is the main inhibitor of social media continuance intention. The findings also suggest that perceived social media anxiety moderates the relationships between (1) perceived complexity and perceived enjoyment, (2) perceived complexity and perceived usefulness, and (3) perceived complexity and perceived communication quality. We also find distorted reputation has a positive effect on perceived complexity but rapid change does not have a significant effect on perceived complexity. Perceived communication quality also significantly influences social media continuance intention. Our study confirms the dual factor model of technology usage and advances social media research by demonstrating that inhibitors are distinct from enablers. |
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| Keywords: | Social media Social networking sites Enablers Inhibitors Dual factor model |
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