What influences Internet-based learning?

Main Article Content

Pei-Wen Liao
Jun-Yi Hsieh
Cite this article:  Liao, P., & Hsieh, J. (2011). What influences Internet-based learning?. Social Behavior and Personality: An international journal, 39(7), 887-896.


Abstract
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The aim in this study was to determine whether Internet-based learning is affected by perceived playfulness, satisfaction, and the performance expectancy effect. The performance expectancy of students based on experience may also be generated directly by the target that caused the emotions. Thus, we used perceived playfulness to explore the impact of performance expectancy. Data collected from 600 university students in Taiwan were tested against the research model using structural equation modeling. The results strongly support the proposed model in predicting performance expectancy when using Internet-based learning. Several implications for social influence, perceived playfulness, satisfaction, and performance expectancy research in relation to Internet-based learning are discussed.

Because it is asynchronous, self-paced, and not confined to a physical classroom situation (Angiello, 2010; Kilkelly, 2010), Internet-based learning has changed the way that students learn. The main purpose in this study was to understand the basis of Internet-based learning in terms of whether perceived playfulness, satisfaction, and the performance expectancy effect could generate both a direct and an indirect (Preacher & Hayes, 2004, 2008) effect on the target leading on to follow-up emotions of perceptions of ease of use and increased learning productivity. Thus, the purpose of investigating perceived playfulness in this study was to explore the impact of the performance expectancy.

Research Model and Hypotheses

Performance Expectancy

Expectancies are beliefs about the future (Olson, Roese, & Zanna, 1996) and people’s beliefs and behaviors depend on their expectancies (Reinhard & Dickhäuser, 2009). In this study we focus on the formation of performance expectancy, which can be defined as subjective ratings of how well one will perform an achievement-related task (Eccles, 1983; Jussim, 1990; Marshall & Brown, 2004). Information science researchers have examined how different factors contribute to the use of computers. Venkatesh, Morris, Davis, and Davis (2003) used a unified model to explain that performance expectancy, effort expectancy, social influence, and facilitating conditions are the four significant factors that determine technology acceptance and use.

Social Influence

Social influence has frequently been measured as a subjective norm in technology adoption research (Lee, Lee, & Lee, 2006). Technology (non-)adoption is strongly related to the definition of social influence as a change of mind in behaviors, thoughts or feelings from an individual’s perspective as revealed by interaction with another individual or a group. Another term for social influence is peer group pressure, or the pressure on a person to conform to a distinct group resulting in a specific behavior. Eckhardt, Laumer, and Weitzel (2009) define social influence as not only an individuals perception of the opinion of important others within his or her environment but also as their actual recommendations and behaviors. The social influence in technology (non-)adoption is strongly related to social comparison theory (Festinger, 1954) and to peer group pressure (Asch, 1951; Sherif, 1935). Eckhardt and colleagues (2009) found that people tend to act in conformity with a distinct group. On the one hand, they continually compare their behavior to that of important others, while on the other hand, they feel pressured to act in a way that will not make them unpopular. Burns and Stalker (1961) integrated social influence in the organizational context into their work on innovation. Venkatesh et al. (2003) define social influence as the extent to which a person perceives that important others believe that he or she should use a new information system. Prior researchers have suggested that social influence is significant in shaping an individual’s intention to use new technology (Moore & Benbasat, 1991; Venkatesh & Davis, 2000). Thus, the following hypothesis was tested:
Hypothesis 1: Social influence will have a positive effect on performance expectancy when using Internet-based learning.

Perceived Playfulness

Playfulness can be defined as the extent to which an individual believes that using a product or service is as enjoyable in its own right, apart from any performance consequence that may be anticipated (Davis, Bagozzi, & Warshaw, 1992). As the Internet is often used not only for work but also for entertainment, many have argued that to understand behavioral intentions the entertaining features need to be addressed in the technology acceptance model (TAM). Playfulness has been found to have a significant role in developing the intention to use and attitudes toward Internet-based learning systems (Agarwal & Karahanna, 2000; Childers, Carr, Peck, & Carson, 2001; Moon & Kim, 2001; Pihlström & Brush, 2008; Teo, Lim, & Lai, 1999; Zhang & Mao, 2008). Past researchers have suggested that the use of information technology (IT) is influenced by constructs related to perceived playfulness (Agarwal & Karahanna, 2000; Chung & Tan, 2004; Davis et al., 1992; Igbaria, Schiffman, & Wieckowski, 1994). Perceived playfulness is defined as a state of mind that includes three dimensions: the extent to which the individual (1) perceives that his or her attention is focused on the interaction with mobile-learning (concentration); (2) is curious during the interaction (curiosity); and (3) finds the interaction intrinsically enjoyable or interesting (enjoyment) (Moon & Kim, 2001). Previous researchers have found that people with obsessive passion often engage in behaviors associated with negative outcomes, such as problematic gambling (Mageau, Vallerand, Rousseau, Ratelle, & Provencher, 2005; Ratelle, Vallerand, Mageau, Rousseau, & Provencher, 2004) and online game addiction (Wang & Chu, 2007). Vallerand et al. (2003) found that people with an obsessive passion for a particular activity are unable to stop participating in that activity. This obsessive passion may lead to rigid persistence. Van der Heijden (2004) contends that for hedonic systems, perceived enjoyment (a dimension of perceived playfulness) is a stronger predictor of behavioral intention to use than is perceived usefulness. As an individual’s intention to use Internet-based learning will be influenced by his or her perceptions of the playfulness of the systems, the following hypotheses were tested:
Hypothesis 2: Social influence will have a positive effect on perceived playfulness when using Internet-based learning.
Hypothesis 3: Perceived playfulness will have a positive effect on performance expectancy when using Internet-based learning.

Satisfaction

User information satisfaction (UIS) instruments are examples of user satisfaction measures (Bailey & Pearson, 1983; Doll & Torkzadeh, 1998; Doll, Raghunathan, Lim, & Gupta, 1995; Ives, Olson, & Baroudi, 1983; McHaney, Hightower, & Pearson, 2002; McHaney, Hightower, & White, 1999). However, measures of user satisfaction (US) and students’ evaluation of teaching effectiveness (SETE) developed for the organizational information systems (IS) or classroom teaching context may no longer be appropriate for the electronic learning (e-learning) context, because the role of an e-learner is different from that of a traditional end user or student. US instruments focus on teaching quality or user information satisfaction rather than on learner satisfaction with regard to asynchronous e-learning systems. Bitner (1990) identified customer satisfaction as a transaction-specific judgment. Therefore, we developed the following hypotheses:
Hypothesis 4: Social influence will have a positive effect on satisfaction when using Internet-based learning.
Hypothesis 5: Perceived playfulness will have a positive effect on satisfaction to when using Internet-based learning.
Hypothesis 6: Satisfaction will have a positive effect on performance expectancy when using Internet-based learning.

Research Model

In the initial phase of this study we developed a conceptual foundation to understand the interaction of social influence, perceived playfulness, satisfaction, and performance expectancy. Social influence is expected to be influenced by perceived playfulness, satisfaction, and performance expectancy. Consequently, the perceived playfulness and satisfaction should have both a direct and a mediator effect. Furthermore, member loyalty and intention to use should be positively influenced by member satisfaction. Figure 1 depicts the research model of this study.

Table/Figure

Figure 1. Research model

Method

Sample

The total sample consisted of 600 students. Approximately one-third (34.5%) were male (n = 207), and 65.5% were female (n = 393), with 11.4% aged over 25 years (n = 69), 42.7% aged 21-24 years (n = 256), and 45.8% aged under 20 years (n = 275).

Measures

Our questionnaire was based on those used in the studies by Wang (2003) and Wang, Wu, and Wang (2009) (see Table 1). To ensure the content validity of the scales, the items selected must represent the concept about which generalizations are to be made. Therefore, the items were modified to make them relevant to the Internet context. Pretesting of the measures was conducted in which both users and experts familiar with the Internet tested the selected items for validity and reliability. As a result, the items were adjusted to make their wording more precise. We used 7-point Likert scales with anchors ranging from 1 = strongly disagree to 7 = strongly agree for all construct items. The original items used in this study are listed in Table 1 below.

Table 1. The Questionnaire Design

Table/Figure

Results

Measurement Model

A confirmatory factor analysis using LISREL version 8.51 was conducted to test the measurement model. Six common model fit measures were used to assess the model’s overall goodness-of-fit: the ratio of chi-square (χ2) to degrees of freedom (df), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), normalized fit index (NFI), comparative fit index (CFI), and root mean square residual (RMSR). Reliability and convergent validity of the factors were estimated using composite reliability and average variance extracted (see Table 2). The interpretation of the composite reliability is similar to that of Cronbach’s alpha, except that it also takes into account the actual factor loadings rather than assuming that each item is equally weighted in the composite load determination.

Table 2. Reliability, Average Variance Extracted, and Discriminant Validity

Table/Figure

Note. Diagonal elements are the average variance extracted. Off-diagonal elements are the shared variance.

Composite reliability for all the factors in our measurement model was above .90. The average extracted variances were all above the recommended .50 level (Hair, Anderson, Tatham, & Black, 1992), and accounted for more than one-half of the variance. Convergent validity can also be evaluated by examining the factor loadings and squared multiple correlations from the confirmatory factor analysis (see Table 3). Following the recommendation made by Hair et al., a factor loading greater than .50 was considered to be significant.

Table 3. Factor Loadings, T Values, and Error Terms

Table/Figure

Note. χ2 = 92.88, df = 21, GFI = 0.97, CFI = 0.95, NNFI = 0.92, RMSEA = 0.076.

Structural Model

A similar set of fit indices was used to examine the structural model (χ2 = 92.88, df = 21, RMSEA = 0.076, GFI = 0.97, CFI = 0.95, NNFI = 0.92). A comparison of all fit indices with their corresponding recommended values provided evidence of a good model fit. Thus, we could examine the path coefficients of the structural model. Properties of the causal paths, including standardized path coefficients, t values, and variance explained, for each equation in the hypothesized model, are presented in Figure 2. Hypotheses H1, H2, H4, H5, and H6 were supported in that social influence, perceived playfulness, satisfaction, and self-efficacy all had a significant effect on performance expectancy.

Table/Figure

Figure 2. Results of hypotheses testing.
* p < .05.

Social influence was found to have a significant influence on performance expectancy (β = .17). Hypotheses H2 and H5 were also supported. Social influence was found to have a significant influence on perceived playfulness (β = .65) and perceived playfulness had a positive effect on satisfaction (β = .26). Hypotheses H4 and H6 were supported in that social influence was found to have a significant influence on satisfaction (β = .37), which had a positive effect on performance expectancy (β = .79). However, as the effect of perceived playfulness on performance expectancy was not significant, with a t value of .59, H3 was not supported.

Discussion

We found that social influence had a positive direct effect on perceived playfulness, satisfaction, and performance expectancy. Satisfaction with social influence plays an important mediating role, in that social influence through satisfaction on performance expectancy (.37*.79 = .29) had a greater impact than the direct relationship between social influence and performance expectancy (.17). Although perceived playfulness had no direct effect on performance expectancy, through satisfaction on performance expectancy a value of .21 (.26*.79 = .2054) was gained, indicating that there is a mediating relationship. Internet-based learning should increase students’ satisfaction with learning, and since satisfaction has a very important mediating role, students’ performance expectancy would be expected to increase.

References

Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694.

Angiello, R. (2010). Study looks at online learning vs. traditional instruction. The Hispanic Outlook in Higher Education, 20(14), 18-20.

Asch, S. E. (1951). Effects of group pressure upon the modification and distortion of judgments. In H. Guetzkow (Ed.), Groups, leadership, and men: Research in human relations (pp. 177-190). Pittsburgh, PA: Carnegie Press.

Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530-545.

Bitner, M. J. (1990). Evaluating service encounters: The effects of physical surroundings and employee responses. Journal of Marketing, 54(2), 69-82.

Burns, T., & Stalker, G. M. (1961). The management of innovation. London: Tavistock.

Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivation for online retail shopping behavior. Journal of Retailing, 77(4), 511-535.

Chung, J., & Tan, F. B. (2004). Antecedents of perceived playfulness: An exploratory study on user acceptance of general information-searching websites. Information & Management, 41(7), 869- 881.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132.

Doll, W. J., & Torkzadeh, G. (1998). The measurement of end-user computing satisfaction. MIS Quarterly, 12(2), 259-274.

Doll, W. J., Raghunathan, T. S., Lim, J. S., & Gupta, Y. P. (1995). A confirmatory factor analysis of the user information satisfaction instrument. Information Systems Research, 6(2), 177-188.

Eccles, J. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motives (pp. 75-146). San Francisco, CA: Freeman.

Eckhardt, A., Laumer, S., & Weitzel, T. (2009). Who influences whom? Analyzing workplace referents’ social influence on IT adoption and non-adoption. Journal of Information Technology, 24(1), 11-24.

Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7(2), 117-140.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1992). Multivariate data analysis with readings (3rd ed.). New York: Macmillan.

Igbaria, M., Schiffman, S. J., & Wieckowski, T. J. (1994). The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology. Behaviour and Information Technology, 13(6), 349-361.

Ives, B., Olson, M. H., & Baroudi, J. J. (1983). The measurement of user information satisfaction. Communications of the ACM, 26(10), 785-793.

Jussim, L. (1990). Expectancies and social issues: Introduction. Journal of Social Issues, 46, 1-8.

Kilkelly, E. (2010). Best practice: Project management meets e-learning. Training and Development in Australia, 37(4), 34-36.

Lee, Y., Lee, J., & Lee, Z. (2006). Social influence on technology acceptance behavior: Self-identity theory perspective. Database for Advances in Information Systems, 37(2/3), 60-75.

McHaney, R., Hightower, R., & White, D. (1999). EUCS test-retest reliability in representational model decision support systems. Information & Management, 36(2), 109-119.

McHaney, R., Hightower, R., & Pearson, J. (2002). A validation of the end-user computing satisfaction instrument in Taiwan. Information & Management, 39(6), 503-511.

Mageau, G. A., Vallerand, R. J., Rousseau, F. L., Ratelle, C. F., & Provencher, P. J. (2005). Passion and gambling: Investigating the divergent affective and cognitive consequences of gambling. Journal of Applied Social Psychology, 35(1), 100-118.

Marshall, M. A., & Brown, J. D. (2004). Expectations and realizations: The role of expectancies in achievement settings. Motivation and Emotion, 28(4), 347-361.

Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230.

Moore, G., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2, 192-223.

Olson, J. M., Roese, N. J., & Zanna, M. P. (1996). Expectancies. In E. T. Higgins & A. W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp. 211-238). New York: Guilford.

Pihlström, M., & Brush, G. J. (2008). Comparing the perceived value of information and entertainment mobile services. Psychology and Marketing, 25(8), 732-755.

Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, and Computers, 36(4), 717-731.

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879-891.

Ratelle, C. F., Vallerand, R. J., Mageau, G. A., Rousseau, F. L., & Provencher, P. (2004). When passion leads to problematic outcomes: A look at gambling. Journal of Gambling Studies, 20(2), 105-119.

Reinhard, M. A., & Dickhäuser, O. (2009). Need for cognition, task difficulty, and the formation of performance expectancies. Journal of Personality and Social Psychology, 96(5), 1062-1076.

Sherif, M. (1935). A study of some social factors in perception. Archives of Psychology, 27(187), 1-60.

Teo, T. S. H., Lim, V. K. G., & Lai, R. Y. C. (1999). Intrinsic and extrinsic motivation in Internet usage. Omega, 27(1), 25-37.

Vallerand, R. J., Blanchard, C., Mageau, G. A., Koestner, R., Ratelle, C., Leonard, M., ... Marsolais, J. (2003). Les passions de l’âme: On obsessive and harmonious passion. Journal of Personality and Social Psychology, 85(4), 756-767.

Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.

Wang, C. C., & Chu, Y. S. (2007). Harmonious passion and obsessive passion in playing online games. Social Behavior and Personality: An international journal, 35(7), 997-1006.

Wang, Y. S. (2003). Assessment of learner satisfaction with asynchronous electronic learning systems. Information & Management, 41(1), 75-86.

Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92-118.

Zhang, J., & Mao, E. (2008). Understanding the acceptance of mobile SMS advertising among young Chinese consumers. Psychology & Marketing, 25(8), 787-805.

Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694.

Angiello, R. (2010). Study looks at online learning vs. traditional instruction. The Hispanic Outlook in Higher Education, 20(14), 18-20.

Asch, S. E. (1951). Effects of group pressure upon the modification and distortion of judgments. In H. Guetzkow (Ed.), Groups, leadership, and men: Research in human relations (pp. 177-190). Pittsburgh, PA: Carnegie Press.

Bailey, J. E., & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530-545.

Bitner, M. J. (1990). Evaluating service encounters: The effects of physical surroundings and employee responses. Journal of Marketing, 54(2), 69-82.

Burns, T., & Stalker, G. M. (1961). The management of innovation. London: Tavistock.

Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivation for online retail shopping behavior. Journal of Retailing, 77(4), 511-535.

Chung, J., & Tan, F. B. (2004). Antecedents of perceived playfulness: An exploratory study on user acceptance of general information-searching websites. Information & Management, 41(7), 869- 881.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132.

Doll, W. J., & Torkzadeh, G. (1998). The measurement of end-user computing satisfaction. MIS Quarterly, 12(2), 259-274.

Doll, W. J., Raghunathan, T. S., Lim, J. S., & Gupta, Y. P. (1995). A confirmatory factor analysis of the user information satisfaction instrument. Information Systems Research, 6(2), 177-188.

Eccles, J. (1983). Expectancies, values, and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motives (pp. 75-146). San Francisco, CA: Freeman.

Eckhardt, A., Laumer, S., & Weitzel, T. (2009). Who influences whom? Analyzing workplace referents’ social influence on IT adoption and non-adoption. Journal of Information Technology, 24(1), 11-24.

Festinger, L. (1954). A theory of social comparison processes. Human Relations, 7(2), 117-140.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1992). Multivariate data analysis with readings (3rd ed.). New York: Macmillan.

Igbaria, M., Schiffman, S. J., & Wieckowski, T. J. (1994). The respective roles of perceived usefulness and perceived fun in the acceptance of microcomputer technology. Behaviour and Information Technology, 13(6), 349-361.

Ives, B., Olson, M. H., & Baroudi, J. J. (1983). The measurement of user information satisfaction. Communications of the ACM, 26(10), 785-793.

Jussim, L. (1990). Expectancies and social issues: Introduction. Journal of Social Issues, 46, 1-8.

Kilkelly, E. (2010). Best practice: Project management meets e-learning. Training and Development in Australia, 37(4), 34-36.

Lee, Y., Lee, J., & Lee, Z. (2006). Social influence on technology acceptance behavior: Self-identity theory perspective. Database for Advances in Information Systems, 37(2/3), 60-75.

McHaney, R., Hightower, R., & White, D. (1999). EUCS test-retest reliability in representational model decision support systems. Information & Management, 36(2), 109-119.

McHaney, R., Hightower, R., & Pearson, J. (2002). A validation of the end-user computing satisfaction instrument in Taiwan. Information & Management, 39(6), 503-511.

Mageau, G. A., Vallerand, R. J., Rousseau, F. L., Ratelle, C. F., & Provencher, P. J. (2005). Passion and gambling: Investigating the divergent affective and cognitive consequences of gambling. Journal of Applied Social Psychology, 35(1), 100-118.

Marshall, M. A., & Brown, J. D. (2004). Expectations and realizations: The role of expectancies in achievement settings. Motivation and Emotion, 28(4), 347-361.

Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230.

Moore, G., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2, 192-223.

Olson, J. M., Roese, N. J., & Zanna, M. P. (1996). Expectancies. In E. T. Higgins & A. W. Kruglanski (Eds.), Social psychology: Handbook of basic principles (pp. 211-238). New York: Guilford.

Pihlström, M., & Brush, G. J. (2008). Comparing the perceived value of information and entertainment mobile services. Psychology and Marketing, 25(8), 732-755.

Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, and Computers, 36(4), 717-731.

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879-891.

Ratelle, C. F., Vallerand, R. J., Mageau, G. A., Rousseau, F. L., & Provencher, P. (2004). When passion leads to problematic outcomes: A look at gambling. Journal of Gambling Studies, 20(2), 105-119.

Reinhard, M. A., & Dickhäuser, O. (2009). Need for cognition, task difficulty, and the formation of performance expectancies. Journal of Personality and Social Psychology, 96(5), 1062-1076.

Sherif, M. (1935). A study of some social factors in perception. Archives of Psychology, 27(187), 1-60.

Teo, T. S. H., Lim, V. K. G., & Lai, R. Y. C. (1999). Intrinsic and extrinsic motivation in Internet usage. Omega, 27(1), 25-37.

Vallerand, R. J., Blanchard, C., Mageau, G. A., Koestner, R., Ratelle, C., Leonard, M., ... Marsolais, J. (2003). Les passions de l’âme: On obsessive and harmonious passion. Journal of Personality and Social Psychology, 85(4), 756-767.

Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS Quarterly, 28(4), 695-704.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.

Wang, C. C., & Chu, Y. S. (2007). Harmonious passion and obsessive passion in playing online games. Social Behavior and Personality: An international journal, 35(7), 997-1006.

Wang, Y. S. (2003). Assessment of learner satisfaction with asynchronous electronic learning systems. Information & Management, 41(1), 75-86.

Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92-118.

Zhang, J., & Mao, E. (2008). Understanding the acceptance of mobile SMS advertising among young Chinese consumers. Psychology & Marketing, 25(8), 787-805.

Table/Figure

Figure 1. Research model


Table 1. The Questionnaire Design

Table/Figure

Table 2. Reliability, Average Variance Extracted, and Discriminant Validity

Table/Figure

Note. Diagonal elements are the average variance extracted. Off-diagonal elements are the shared variance.


Table 3. Factor Loadings, T Values, and Error Terms

Table/Figure

Note. χ2 = 92.88, df = 21, GFI = 0.97, CFI = 0.95, NNFI = 0.92, RMSEA = 0.076.


Table/Figure

Figure 2. Results of hypotheses testing.
* p < .05.


Appreciation is due to reviewers including

Ana Jiminez-Zarco

Open University of Catalonia

Barcelona

Spain

Email

[email protected]

Pei-Wen Liao, Department of Applied Technology and Human Resource Development, National Taiwan Normal University, No. 162, Sec. 1, Ho-Ping E. Road, Taipei, Taiwan, ROC. Email: [email protected]

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