Browse > Article
http://dx.doi.org/10.14400/JDC.2020.18.5.187

The Effect of Communication Media Richness on Continuous Intention to Use: The Moderating Effect of User Experience  

Choi, Ju-Choel (Department for Future Innovation, Kyung Hee University)
Kim, Te-Gyun (Department for Extension, Kyung Hee University)
Publication Information
Journal of Digital Convergence / v.18, no.5, 2020 , pp. 187-195 More about this Journal
Abstract
Although multimedia messaging services (MMS) are becoming increasingly popular, and companies are maximizing the use of their content, few systematic studies on MMSs exist. This study examined technology acceptance factors for MMS in 398 young people aged 10 to 39 to identify MMS users' continuous intention to use (CITU) via a questionnaire and SPSS21 and PLS-Graph 3.0. The results showed that perceived media richness (PMR) had a positive effect on perceived usefulness, perceived ease of use, and most importantly, CITU. Furthermore, PMR had a positive effect on perceived ease of use as a moderating effect on experience. To increase use efficiency in MMSs based on these results, media richness, perceived ease of use, perceived usefulness, and user experience may serve as important variables affecting users' CITU and provide a basic reference and development direction for MMS users. Future studies should include more variables and examine additional factors when analyzing the structural model.
Keywords
MMS; TAM; Media richness; intention; experience;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Q. Q. Chen & H. J. Park. (2018). Consumer Study on the Acceptance of VR Headsets based on the Extended TAM. Journal of Digital Convergence, 16(6), 117-126.   DOI
2 H. P. Shih. (2004). Extended technology acceptance model of Internet utilization behavior. Information & Management, 41(6), 719-729. DOI : 10.1016/j.im.2003.08.009   DOI
3 E. Karahanna & Limayem. (2000). E-Mail and V-Mail Usage: Generalizing Across Technologies. Journal of Organizational Computing and Electronic Commerce. 10(1), 49-66. DOI : 10.1207/s15327744joce100103   DOI
4 F. D. Davis, R. D. Bagozzi & P. R. Warshaw. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. MIS Journal of Applied Social Psychology, 22(14), 1111-1132.   DOI
5 M. Igbaria, T. Guimaraes & G. Davis. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of Management Information Systems, 11(4), 87-114. DOI : 10.1080/07421222.1995.11518061   DOI
6 J. R. Carlson & R. W. Zmud. (1999). Channel Expansion Theory and the Experiential Nature of Media Richness Perceptions. Academy of Management Journal, 42(2), 153-170. DOI : 10.2307/257090   DOI
7 V. Venkatesh & F. D. Davis. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204. DOI : 10.1287/mnsc.46.2.186.11926   DOI
8 K. Mathieson. (1991). Predicting user intention: comparing the technology acceptance model with theory of planned behavior. Information Systems Research, 2(3), 173-191. DOI : 10.1287/isre.2.3.173   DOI
9 A. Bhattacjerjee. (2001). Understanding information systems continuance: An expectation - confirmation model. MIS Quarterly, 25(3), 351-370. DOI : 10.2307/3250921   DOI
10 W. W. Chin & P. A. Todd. (1995). On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution. MIS Quarterly, 19(2), 237-246. DOI : 10.2307/249690   DOI
11 I. Ajzen & M. Fishbein. (1980). Understanding attitudes and predicting social behaviour. NewJersey: Prentice-Hall.
12 J. C. Nunnally. (1978). Psychometric theory (2nd ed.). New York, NY: McGraw-Hill.
13 R. P. Bagozzi & Y. Yi. (1998). On the Evaluation of Structural Equation Models. Journal of Academy of Marketing Science, 16(1), 74-94.   DOI
14 J. C. Anderson & D. W. Gerbing. (1998). Structural equation modeling in practice: A review and recommended two-step approach. Psychology Bulletin, 103(3), 411-423. DOI : 10.1037/0033-2909.103.3.411
15 S. Y. Yang & Y. S. Park. (2005). A prediction model of cellular phone tendency among adolescents. The Korean Home Economics Association, 43(4), 1-16.
16 G. Jiang & W. Deng. (2011). An empirical analysis of factors influencing the adoption of mobile instant messaging in china. International Journal of Mobile Communications, 9(6), 563-584. DOI : 10.1504/ijmc.2011.042777   DOI
17 B. K. Lee & B. S. Kim. (2012). A study on customers' impulsive buying in social commerce environment: the role of flow and emotion. The Korean Journal of Information Systems, 21(3), 117-136. DOI : 10.5859/kais.2012.21.3.117
18 F. D. Davis. (1989). Perceived usefulness, perceived ease of use, and user acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. DOI : 10.2307/249008   DOI
19 M. Igbaria, N. Zinatelli, P. Cragg & A. L. M. Cavaye. (1997). Personal computing acceptance factors in small firms: a structural equation model. MIS Quarterly, 21(30), 279-305. DOI : 10.2307/249498   DOI
20 C. Fornell & D. F. Larcker. (1981). Evaluating Structure Equation Models with Unobservable variables and Measurement Error. Journal of Marketing Science, 18(1), 39-50. DOI : 10.2307/3151335
21 M. K. Ahuja & J. B. Thatcher. (2005). Moving Be-yond Intentions and Toward the Theory of Trying: Effects of Work Environment and Gender on Post-Adoption Information Technology Use. MIS Quarterly, 29(3), 427-459. DOI : 10.2307/25148691   DOI
22 S. H. Oh & S. H. Kim. (2006). Structural Relationships among Factors Affecting Usage of Internet Banking: Focusing on extended technology acceptance model. Korean Journal of Marketing, 21(1), 1-27.
23 R. L. Daft & R. H. Lengel. (1986). Organizational Information Requirement, Media richness and structural design. Management Science, 32(5), 554-571. DOI : 10.1287/mnsc.32.5.554   DOI
24 S. J. Choi, K. J. Kang & I. S. Ko. (2007). The impacts of media richness, media usefulness, and media experience on the leaner's satisfaction with e-learning systems. Journal of Information Technology Applications & Management, 14(2), 27-47.
25 D. Adam, R. Nelson & P. Todd. (1992). Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication. MIS Quarterly, 16(2), 227-248. DOI : 10.2307/249577   DOI
26 W. H. DeLone & E. R. McLean. (1992). Information Systems Success : The Quest for the Dependent Variable. Information Systems Research, 3(1), 60-95. DOI : 10.1287/isre.3.1.60   DOI
27 P. B. Seddon. (1997). A Respecification and Extension of the DeLone and McLean Model of IS Success. Information Systems Research, 8(3), 240-253. DOI : 10.1287/isre.8.3.240   DOI
28 K. O. Matthew, C. Lee, M. K. Cheung & Z. H. Chen. (2007). Understanding user acceptance of multimedia messaging services: An empirical study. Journal of the American Society for Information Science and Technology, 58(13), 2066-2077. DOI : 10.1002/asi.20670   DOI
29 J. Fulk & D. Ryu. (1990). Perceiving Electronic Mail Systems : A Partial Test of the Social Information Processing Model. Paper presented to a meeting of the International Communication Association, Dublin.
30 H. D. Moon & J. W. Kim. (2009). A Study on the TAM (Technology Acceptance Model) in Involuntary IT Usage Environment. Journal of Digital Convergence, 7(3), 13-24.
31 P. Legris, J. Ingham & P. Collerette. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204. DOI : 10.1016/s0378-7206(01)00143-4   DOI
32 L. Y. Zhu & J. K. Park. (2018). A Effects of Travel Agents' Application Quality on the Intention to Reuse: Applying a Technical Acceptance Model. Journal of Tourism Management Research, 22(1), 259-279. DOI : 10.18604/tmro.2018.22.1.12   DOI
33 K. M. Kim & N. J. Kim. (2019). Analysis of food consumers of usage attitude and usage intention towards technology-based self-service(TBSS) : Focused on TRAM(integrated technology readiness and acceptance model). Journal of Tourism and Leisure Research, 31(3), 237-257. DOI : 10.31336/JTLR.2019.3.31.3.237   DOI
34 V. Venkatesh, M. G. Morris, G. B. Davis & F. D. Davis. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3). 425-478. DOI : 10.2307/30036540   DOI
35 Y. H. Kim. (2003). Social network analysis. Seoul : Pakyoungsa.
36 R. Scherer, F. Siddiq & J. Tondeur. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education. Computers & Education, 128, 13-35. DOI : 10.1016/j.compedu.2018.09.009   DOI
37 J. Y. Jeong & T. W. Roh. (2017). The Intention of Using Wearable Devices: Based on Modified Technology Acceptance Model. Journal of Digital Convergence, 15(4), 205-212.   DOI