STUDENT S ATTITUDE TOWARD WEBCAST LECTURE: AN ONLINE SURVEY RESULT. Paulus Insap Santosa 1)

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1 STUDENT S ATTITUDE TOWARD WEBCAST LECTURE: AN ONLINE SURVEY RESULT Paulus Insap Santosa 1) Abstract This paper reports the result of an online survey that has been conducted to observe students attitude toward webcast lecture. The research model was based on Technology Acceptance Model, in which attitude is a function of perceived ease of use as well as perceived usefulness. It was hypothesized that perceived ease of use and perceived usefulness affect positively with the overall user satisfaction about webcast lecture, which in turn affect attitude toward webcast lecture. The results were consistent with the TAM. However, perceived ease of use shows no effect on user satisfaction. This paper also discusses motivating factors of webcast lecture usage as well as its inhibiting factors. 1. INTRODUCTION With the proliferation and wide spread availability of the Internet, more activities, including education, are being conducted online. With more lectures conducted online, more students can attend these lectures virtually that they can earn their degree without physically present at where live lectures are conducted. With so much easier ways to prepare Web-based learning materials, Clark and Lyons [9] have predicted that Web-based learning will be the future of all types of distance learning. One mean of delivering course online is through broadcasting the course materials with the help of the Internet. Here is the term of webcast appears. In general, webcast is a real-time broadcast of information (usually in audio and/or video format) over the Internet (World Wide Web) ( With webcast, students are not only able to attend the lecture from the comfort of their homes, see the course materials broadcasted from a distance, but they also are able to see who are actually delivering the lecture as well as to see the lecturer s face. The usefulness of webcast, however, may be dampened by several factors. Since webcast is practically sending audio and video data over the Internet channel, students need to have a sufficient Internet connection in order for them to receive a good quality broadcast. This situation may have impact on students attitude toward webcast lecture. This is exactly the purpose of this survey. Another purpose of this survey is to identify motivating factors as well as the inhibiting factors that may influence students attitude toward webcast lecture. This study adopted a well known, and much discussed, information system theory better known as Technology Acceptance Model or TAM [10], [11]. 1) Department of Information Systems, National University of Singapore. santosa@comp.nus.edu.sg

2 2. TECHNOLOGY ACCEPTANCE MODEL Technology Acceptance Model (TAM) postulates that two particular beliefs, perceived usefulness and perceived ease of use, are of primary relevance for computer acceptance behaviors [10], [11] Davis [10] defined perceived ease of use as the degree to which a person believes that using a particular system would be free of effort (p. 320). Perceived usefulness refers to the degree to which a person believes that using a particular system would enhance his or her job performance [10] (p. 320). In his framework, perceived usefulness is a function of perceived ease of use. Technology Acceptance Model, which is an extension of the Theory of Reasoned Action (TRA) [3], has gain popularity since it was first proposed. Several replications have been made to test the robustness and validity of the questionnaire instrument used by Davis [10] at the first place, e.g. [1], [21], and [29]. In order to explain perceived usefulness and usage intentions in a social setting and cognitive instrumental processes, Venkatesh and Davis [31] have extended the original TAM. Dishaw and Strong [12] extended the original model of TAM by combining technology fit construct into the model that provide more explanatory power than either model alone. Several empirical studies have shown how TAM positively predicts acceptance of intention toward behaviors and the usage of technologies. Selim [28] uses perceived usefulness and perceived ease of use constructs to assess university students' acceptance of course websites as an effective learning tool. Course website usefulness and ease of use proved to be key determinants of the acceptance and usage of course website as an effective and efficient learning technology. Hong et al. [22] used TAM on their investigation to determine factors that determine users' adoption of digital libraries. Their results strongly support the utilization of TAM in predicting users' intention to adopt digital libraries, and demonstrate the effects of critical external variables on behavior intention through perceived ease of use and perceived usefulness. Lin and Lu [25] used TAM to investigate factors why users accept or reject a Website. They showed that TAM fully mediated a Website usage behavior. Lu et al. [25] have also used TAM to predict wireless Internet usage based on several factors including individual differences, technology complexity, facilitating conditions, social influences, and wireless trust environment. 3. SATISFACTION ISO [23] defines satisfaction as the user s comfort with and positive attitude towards the use of the system. Satisfied users may spend a longer time at a Website, revisit it, and may recommend it to others. Hence, user satisfaction with a Website is a highly desirable Web design goal [33]. Al-Gahtani and King [4] have shown that perceived ease of use and perceived usefulness are valuable tools for predicting attitudes, satisfaction, and usage. Bhattacherjee [7] also showed that perceived usefulness of information systems use positively associated with user satisfaction. Mahmood et al. [27] observed that perceived usefulness relates very significantly to end user IT satisfaction. However, perceived ease of use was the least significant toward user satisfaction. 4. ATTITUDE Theory of Reasoned Action, or TRA, [14], [3], which then expanded to become Theory of Planned Behavior, or TPB, [2], showed that individuals will intend to perform a behavior when they evaluate it positively and when they believe that important others think they should perform it. The strong relation between attitude and intention is showed by the fact that the more favorable a

3 person s attitude toward some object, the more he will intend to perform positive behavior (and the less he will intend to perform negative behaviors) which respect to that object, [14] (p. 288). As stated, TAM was actually developed from TRA Many research using Technology Acceptance Model [10], which is based on Theory of Planned Behavior, have confirmed the above claim, e.g. [3]. 5. HYPOTHESES A Webcast lecture involves quite a few technologies including the World Wide Web technology with all necessary means, multimedia technology as well as communication technology. These technologies are combined to make the webcast possible. As such, it is argued that TAM will be very useful model to predict students attitude toward webcast lecture. The research model is depicted in Figure 1. Figure 1. Research model. Figure 1 also shows the hypotheses that this study is trying to prove. The hypotheses are as follow: H1a: Perceived ease of use of a webcast lecture will have a positive effect on its perceived usefulness. H1b: Perceived ease of use of a webcast lecture will have a positive effect on user satisfaction. H1c: Perceived ease of use of a webcast lecture will have a positive effect on attitude toward webcast lecture. H2a: Perceived usefulness of a webcast lecture will have a positive effect on user satisfaction. H2b: Perceived usefulness of a webcast lecture will have a positive effect on attitude toward webcast lecture. H3: Overall user satisfaction with webcast lecture will affect attitude toward webcast lecture positively.

4 6. METHODOLOGY 6.1 Operationalization Figure 1 shows the relationships among four constructs used in this study, i.e. perceived ease of use, perceived usefulness, user satisfaction, and attitude toward webcast lecture. For the purpose of this study, the above constructs are defined as follow: Following Davis [10], perceived ease of use (PEU) is defined as the degree to which students believe that using a webcast lecture would be free of effort. Perceived usefulness (PU) is defined as the degree to which students believe that using a webcast lecture would enhance their study performance. Perceived ease of use was measured using four 7-point Likert scale items and perceived usefulness was measured using five 7-point Likert scale items, in which 1 means strongly disagree and 7 means strongly agree. Items for measuring perceived usefulness and perceived ease of use were adopted and modified from [32] and [22]. User satisfaction (USAT) is defined as the overall user satisfaction with the webcast quality, user comfort with and positive attitude toward the use of a webcast lecture. This construct was also measured using four 7-point Likert scale items, in which 1 means strongly disagree and 7 means strongly agree. Items were adopted and modified from [24] and [30]. Attitude toward webcast lecture (ATWL) is defined as a complex mental state involving beliefs, feelings, values, and dispositions to use webcast lecture ( Attitude was measured using seven 7-point semantic differential items, in which 1 and 7 mean the extreme different side of the measurements, e.g. extremely foolish and extremely wise, respectively. Items were adopted from [4], [6], and [30]. 6.2 Online Survey An online survey was conducted with the target audiences were the undergraduate students from six different faculties who have been using the webcast before. They were sent an to participate on this online survey. At the end, about 109 students participated voluntarily on this online survey. Besides answering questionnaire items pertinent to the above constructs, students were also asked to write what the most motivating factors to use, and inhibiting factors from using, webcast. Questionnaire items are presented in Appendix A. 7. RESULT Hypotheses testing were conducted using partial least square (PLS). Three reasons why hypotheses testing were conducted using PLS. First, PLS allows users to combine both formative indicators and reflective indicators into a single model [8]. Second, PLS does not require data to follow a strict normal distribution [5] and [15]. Third, it can handle a small sample size [16]. In this study, the sample size of 109 is considered a small size. As such, PLS was chosen for data analysis. Due to page limitation, this paper does not explain how to conduct a data analysis using PLS in detail. However, the author has provided the readers with a simple tutorial about a data analysis using PLS in the following link: Barclay et al. [5] suggested a guideline for data analysis using PLS that comprises two steps: (1) assessment of its measurement model describing the relationship between indicators and their corresponding latent constructs, and (2) assessment of its structural model describing the relationships among latent constructs in the whole model. Assessment of the measurement model is

5 based on indicators loadings. For reflective indicators, Chin [8] indicates that, loadings should be inspected for determining the appropriateness of the indicators (p. 306). Loading represents the correlation between indicators and its corresponding latent variable. 7.1 Assessment of The Measurement Model Assessment of the measurement model concerns with construct validity or the extent to which the manifest indicators reflect their underlying constructs [19]. Construct validity includes an assessment of convergent validity and discriminant validity. These assessments are to make sure that the measurement instrument is valid Convergent Validity Convergent validity consists of individual item reliability and its internal consistency (or construct validity). Item reliability can be assessed by examining the manifests (indicators) loadings to their corresponding latent constructs. Fornell et al. [16] suggested that item reliability is judged to be adequate if the item s loading to its latent construct is equal or greater than (l 0.707). Item reliability is calculated directly by PLS. Table 1 shows that all items have their loadings greater than As such, all items are deemed reliable. Table 1. Convergent Validity of The Measurement Model. Latent Variable PEU (x 1 ) PU (h 1 ) USAT (h 2 ) ATWL (h 3 ) * For comparison purpose only. Manifest Variable Item Reliability (l) PEU PEU PEU PEU PU PU PU PU PU USAT USAT USAT USAT ATWL ATWL ATWL ATWL ATWL ATWL ATWL Internal Consistency (r h ) *Cronbach Alpha Internal consistency (r x for exogenous latent variables or r h for endogenous latent variables), or construct reliability, is the second reliability measure to evaluate the measurement model. It can be calculated by using Equation 1 [17]. Internal consistency = (Sl i ) 2 ((Sl i ) 2 + S(1 - l i 2 )) 1)

6 where l i is an individual indicator variable loading to its corresponding latent variable. Table 1 shows that internal consistency for every latent variable is very high. Thus, every latent variable is deemed reliable. As a comparison purpose, Cronbach s alpha scores for every construct also shown in Table Discriminant Validity Discriminant validity is also conducted for both indicator and construct level. For indicator level, Barclay et al. [5] suggested that no manifest variable should load higher on other constructs than on the construct it intends to measure. Table 2 shows that all manifest variables load higher on their respective corresponding latent variable compared to other latent variables. Thus, discriminant validity at the indicator level is adequate. Table 2. Loading and cross-loading matrix. Latent Construct Construct Item PEU (x 1 ) PU (h 1 ) USAT (h 2 ) ATWL (h 3 ) Perceived Ease of Use PEU (x 1 ) Perceived Usefulness (h 1 ) User Satisfaction (h 2 ) Attitude toward Webcast Lecture (h 3 ) PEU * PEU * PEU * PEU * PU * PU * PU * PU * PU * USAT * USAT * USAT * USAT * ATWL * ATWL * ATWL * ATWL * ATWL * ATWL * ATWL * * Significant at the 0.01 level. At the construct level, discriminant validity can be assessed by comparing a square root of Average Variance Extracted (AVE) with the correlation of that construct with the rest of the constructs. AVE is the amount of variance captured by the construct in relation to the amount of variance attributable to measurement error. PLS does not calculate AVE automatically. It can be calculated by using Equation 2 [17]. AVE = Sl i 2 (Sl i 2 + S(1- l i 2 )) 2) Table 3 shows that square rooted AVE, written as diagonal items, for every latent variable is greater than the correlation between that latent variable with the rest of the latent variables. Therefore every latent variable is deemed adequate on its convergent validity. As such, the model exhibits acceptable discriminant validity [5].

7 Table 3. Square rooted AVE and Correlation Among Constructs. 7.2 Assessment of Structural Model Construct PEU PU USAT ATWL PEU PU USAT ATWL The structural model comprises the hypothesized relationship between latent constructs in the research model. By using Bootstrap or Jackknife sampling, we can obtain path coefficient and its corresponding t-value. With these values, we can assess statistical significance of the model by testing the null hypothesis for each relationship path. Table 4 shows the coefficient of each hypothesized path (see Figure 1) and its corresponding t-value obtained from 100-sample Bootstrap procedure in PLS. It can be seen from this table that four path coefficients are significant at a = 0.05, providing support for hypotheses H1a, H1c, H2a, and H3. Hypotheses H1b is not supported by the data, and hypotheses H2b is marginally supported by the data [18]. Hypothesis From Table 4. Path Coefficients and Their T-values. Path To Path Coefficient (b) t-value Significance (2-tailed) Supported H1a PEU PU p < Yes H1b PEU USAT ns* No H1c PEU ATWL p < 0.01 Yes H2a PU USAT p < Yes H2b PU ATWL p < 0.1 Msup** H3 USAT ATWL p < 0.05 Yes *ns: not significant **Msup: Marginally supported The explanatory power of the estimated model, or nomological validity, can be assessed by observing the R 2 of endogenous constructs. Table 5 shows the R 2 for perceived usefulness, user satisfaction and attitude toward webcast lecture that is 0.152, 0.347, and 0.339, respectively. Falk and Miller [13] recommended that R 2 should be at least 0.10 in order for the latent construct to be judged adequate. Table 5 shows that all of the R 2 scores satisfy this recommendation. As such, nomological validity is satisfactory. Figure 2 shows that the model explains about 34% percent of total variability of attitude toward webcast lecture. To wrap up the above analysis, Figure 2 depicts the PLS estimation of the proposed research model. It shows the path coefficients and their corresponding t-values written inside parenthesis. Perceived Usefulness (h 1 ) Table 5. R 2 for Endogenous Constructs. User Satisfaction (h 2 ) Attitude toward Webcast Lecture (h 3 ) R

8 8. DISCUSION AND CONCLUSSION This study has been conducted to observe students attitude toward webcast lecture. The result shows that perceived ease of use affects perceived usefulness significantly positively (b = 0.390, t- value = 3.859, p < 0.001), perceived ease of use affects attitude toward webcast lecture significantly positively as well (b = 0.271, t-value = 3.059, p < 0.01). However, perceived usefulness is only marginally significantly affects attitude toward webcast lecture (b = 0.222, t-value = 1.822, p < 0.1) [18]. These results are consistent with the original model of TAM [10] and several other empirical studies, although the last result needs further explanations that will be given below. As hypothesized, perceived usefulness is also significantly predicts user satisfaction with the webcast lecture (b = 0.590, t-value = 9.077, p < 0.001), which in turn affects an attitude toward webcast lecture positively (b = 0.259, t-value = 2.156, p < 0.05). However, perceived ease of use shows no effect on user satisfaction (b = , t-value = ). This finding is actually consistent with [27]. Figure 2. PLS estimations of the research model. At the end of the survey, students were asked to name four different factors that motivate them to use webcast lecture, and four different factors that may hinder them to use a webcast lecture. Of those factors on why students want to use a webcast lecture, students were mostly stated that convenience, saving resources (money, time, transportation), accessibility, flexibility to view/review the material, better understanding of the course material, may skip parts which is not important, better concentration, and it is free of charge, were prominent motivating factors. Of those inhibiting factors, students mostly mentioned about quality of the webcast (picture, sound, speed), screen size, buffering, Internet speed, Internet connection availability, reliability, fast forward speed, and synchronization. The other inhibiting factors that the students also mentioned were tiresome especially for the eyes, lecturer position, no immediate feedback, and there is neither direct interaction with classmates nor with the lecturer. It was mentioned that perceived usefulness has marginally significant impact on attitude toward webcast lecture, with sticker measure this relationship may even be considered as insignificant. From the inhibiting factors mentioned, we can understand that the lack of technical adequacy may

9 hinder the usefulness of a particular technology, in this case webcast lecture, which in turn may have negative effect on their attitude toward using such technology. The fact that there are also several inhibiting factors not related to the technology demands further studies to access the usefulness of webcast lecture. 9. REFERENCES [1] ADAMS, D.A., NELSON, R.R. and TODD, P.A., Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication, MIS Quarterly, Vol. 16, 1992, pp [2] AJZEN, I. (1991), The Theory of Planned Behavior, Organizational Behavior and Human Decision Processes, Vol. 50, No. 2, 1991, pp [3] AJZEN, I. and FISHBEIN, M., Understanding Attitudes and Predicting Social Behavior, Prentice Hall, Inc. Englewood Cliffs, N.J., [4] AL-GAHTANI, S.S. and KING, M., Attitudes, Satisfaction and Usage: Factors Contributing to Each in the Acceptance of Information Technology, Behaviour & Information Technology Vol. 18, 1999, pp [5] BARCLAY, D., HIGGINS, C., and THOMPSON, R., The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration, Technology Studies, Vol. 2, No. 2, 1995, pp [6] BARKI, H. and HARTWICK, J., Measuring User Participation, User Involvement, and User Attitude, MIS Quarterly, March 1994, pp [7] BHATTACHERJEE, A., Understanding Information Systems Continuance: An Expectation-Confirmation Model, MIS Quarterly, Vol. 25 No. 3, 2001, pp [8] CHIN, W.W., The Partial Least Squares Approach to Structural Equation Modeling. In Marcoulides, G.A. (Ed). Modern Methods for Business Research. Lawrence Erlbaum Associates, Publisher, [9] CLARK, R. and LYONS, C., Using Web-based training wisely, Training, Vol. 36, pp [10] DAVIS, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology, MIS Quarterly, Vol. 13, No. 3, 1989, pp [11] DAVIS, F. D., BAGOZZI, R. P. and WARSHAW, P. R., User Acceptance of Computer Technology: A Comparison of Two Theoretical Models, Management Science, Vol. 35, 1989, pp [12] DISHAW, M.T. and STRONG, D.M., Extending The Technology Acceptance Model with Task Technology Fit Constructs, Information & Management, Vol. 36, 1999, pp [13] FALK, R.F. and MILLER, N.B. A Primer Soft Modeling, Akron, Ohio: University of Akron Press, [14] FISHBEIN, M. and AJZEN, I., Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley Publishing Company, [15] FORNELL, C. and CHA, J., Partial Least Square. In Bagozzi, R.P. (Ed.) Advanced Methods in Marketing Research, Basil Blackwell Ltd., [16] FORNELL, C., TELLIS, G.J. and ZINKHAN, G.M., Validity Assessment: A Structural Equations Approach Using Partial Least Squares. An assessment of Marketing Thought and Practice, Proceedings of American Marketing Association Educator s Conference 48, 1982, pp [17] GEFEN, D., STRAUB, D.W. and BOUDREAU, M-C., Structural Equation Modeling and Regression: Guidelines for Research Practice, Communication of the Association for Information Systems, Vol. 4, No. 7, 2000, pp [18] GEORGE, D. and MALLERY, P. SPPS for Windows Step by Step: A Simple Guide and Reference 11.0 Update, Fourth Edition, Pearson Education, Inc., [19] HANLON, D. Vision and Support in New Venture Start-ups, Frontiers for Entrepreneurship Research, 2001, XIB.htm, accessed on 5 March [20] HEIJDEN, H.V.D., VERHAGEN, T., and CREEMERS, M., Understanding Online Purchase Intentions: Contributions from Technology and Trust Perspectives, European Journal of Information Systems, Vol. 12, 2003, pp [21] HENDRICKSON, A.R., MASSEY, P.D., and CRONAN, T.P. On the Test-Retest Reliability of Perceived Usefulness and Perceived Ease of Use Scales. MIS Quarterly, Vol. 17, 1993, pp [22] HONG, W., THONG, J.Y.L., WONG, W-M., and TAM, K.Y. ( ), Determinants of User Acceptance of Digital Libraries: An Empirical Examination of Individual Differences and System Characteristics, Journal of Management Information Systems, Vol. 18, No. 3, 2001, pp [23] ISO , Ergonomic requirements for office work with visual display terminal (VDTs) Part 11: guidance on usability, 1988.

10 [24] LEE, J-N., PI, S-M., KWOK, R.C. and HUYNH, M.H., The Contribution of Commitment Value in Internet Commerce: An Empirical Investigation, Journal of the Association for Information Systems, Vol. 4, 2003, pp [25] LIN, J.C.C. and LU, H., Towards an Understanding of the Behavioral Intention to use a Website, International Journal of Information Management, Vol. 20, 2000, pp [26] LU, J., YU, C-S., LIU, C. and YAO, J.E., Technology Acceptance Model for Wireless Internet, Internet Research: Electronic Networking Applications and Policy, Vol. 13, No. 3, 2003, pp [27] MAHMOOD, M.A., BURN, J.M., GEMOETS, L.A. and JACQUEZ, C., Variables Affecting Information Technology End-User Satisfaction: A Meta-Analysis of the Empirical Literature, International Journal of Human-Computer Studies, Vol. 52, 2000, pp [28] SELIM, H.M., An Empirical Investigation of Student Acceptance of Course Websites, Computers & Education, Vol. 40, 2003, pp [29] SZAJNA, B. Software Evaluation and Choice: Predictive Evaluation of the Technology Acceptance Instrument. MIS Quarterly, Vol. 18, No. 3, 1994, pp [30] TEO, H.H., OH, L.B., LIU, C. and WEI, K.K., An Empirical Study of The Effects of Interactivity on Web User Attitude, International Journal of Human-Computer Studies, Vol. 58, 2003, pp [31] VENKATESH, V. and DAVIS, F.D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, Vol. 46, No. 2, 2000, pp [32] VENKATESH, V. and MORRIS, M.G., Why Don't Men Ever Stop to Ask for Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior, MIS Quarterly, Vol. 24, No. 1, 2000, pp [33] ZHANG, P. and DRAN, G.M.V., Satisfiers and Dissatisfiers: A Two-Factor Model for Website Design and Evaluation, Journal of the American Society for Information Science, Vol. 51, 2000, pp APPENDIX A. QUESTIONNAIRE ITEMS Perceived Ease of Use (7-point Likert scale, 1 means strongly disagree and 7 means strongly agree) PEU1 I believe that webcast is cumbersome to use PEU2 Using webcast was often frustrating PEU3 My using of the webcast required a lot of mental effort PEU4 Overall, webcast is not easy to use Perceived Usefulness (7-point Likert scale, 1 means strongly disagree and 7 means strongly agree) PU1 PU2 PU3 PU4 PU5 Webcast improve the effectiveness of my study Webcast increases my productivity in understanding the lecture Webcast makes me easier to attend the lecture without physically presence at lecture hall Webcast improves my study performance By using webcast I can save my resources (e.g. time, money) User satisfaction (7-point Likert scale, 1 means strongly disagree and 7 means strongly agree) USAT1 I felt a strong involvement with the webcast USAT2 I feel satisfied with the webcast quality USAT3 I feel satisfied with my using of the webcast USAT4 I feel pleased with my using of the webcast Attitude toward Webcast Lecture (7-point semantic differential scale) ATWL1 ATWL2 ATWL3 ATWL4 ATWL5 ATWL6 ATWL7 Extremely foolish / Extremely wise Extremely harmful / Extremely Beneficial Extremely negative / Extremely positive Extremely unfavorable / Extremely favorable Extremely dislike / Extremely like Extremely bad idea / Extremely good idea Extremely terrible / Extremely terrific