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Factors Influencing Faculty to Adopt Web Applications in their Teaching A dissertation presented to the faculty of The Patton College of Education of Ohio University In partial fulfillment of the requirements for the degree Doctor of Philosophy Elham A Alsadoon August 2013 © 2013 Elham A Alsadoon All Rights Reserved This dissertation titled Factors Influencing Faculty to Adopt Web Applications in their Teaching by ELHAM A ALSADOON has been approved for the Department of Educational Studies and The Patton College of Education by Teresa J Franklin Professor of Educational Studies Renée A Middleton Dean, The Patton College of Education Abstract ALSADOON, ELHAM A., Ph.D., August 2013, Instructional Technology Factors Influencing Faculty to Adopt Web Applications in their Teaching Director of Dissertation: Teresa J Franklin The social nature of Web applications can empower education if used properly (Light 2011) These applications provide a learning environment in which students can construct their learning, collaborate with others, generate ideas, edit and distribute their material, and more The better way to seed Web applications into the learning environment and to make them effective educational tools is to implement them in the pre-service teachers programs This research study aimed to investigate the influence of knowledge and experience of Web applications, perceived ease-of use, perceived usefulness, perceived pedagogical support, perceived risk, and colleagues’ influence on the faculty’s decision to adopt Web applications in their teaching within the pre-service teacher programs Two hundred forty-nine faculty participated in this study by filling an online questionnaire that was self-designed and was distributed to a random proportional stratified sample of the faculty who teach at the colleges of education in American universities The findings of this research study reflect that the faculty currently teaching in these programs are knowledgeable of and have experience in using Web applications and even intend to implement them more in their teaching in the future The findings showed that faculty knowledge and experience of Web applications and faculty perception of the usefulness of such applications were significant predictors of faculty intention to adopt Web applications in teaching This, in turn, is a strong predictor of their actual use Implementation of the study was provided, along with recommendations for further research Acknowledgments First, praise be to God until the Praise run its course, who facilitated the completion of this work I would like thank to my committee members A special appreciation goes to my advisor and committee chair, Dr Teresa Franklin, for her help, flexibility and kindness in guiding me during my educational journey in theUnited States I also would like to express my appreciation to Dr George Johanson for his encouragement and availability, and for all his unforgettable support and help Further thanks go to Dr Greg Kessler for his valuable assitance and support Thanks to Dr Gordon Brooks for his thoughtful advice and help I would like to thank my husband, Hamadah, for his love, caring and support Thanks to my children, Yousef, Jori, and Sarah, for their sacrifice and patience Great gratitude goes to my parents for their love, prayers and for being always there for me Thanks to all my sisters, brothers and friends for thier love and caring Table of Contents Page Abstract 3! Acknowledgments 5! List of Tables 10! List of Figures 11! Chapter 1: Introduction 12! The Generations of Web; Web 1.0, Web 2.0 and Web 3.0 12! Using Web Applications in Pre-service Education Teacher Programs 15! Age and Gender 17! Statement of Problem 18! The Purpose of the Study 18! The Significance of the Study 19! Research Questions 21! Limitations and Delimitation of the Study 22! Definition of Terms 23! Organization of the Study 24! Chapter 2: Literature Review 25! Introduction 25! Web Applications and Their Use in Higher Education 25 Wiki 25 Blog 27 Multimedia Sharing 30 YouTube 30 Podcast 31 Social Networking 33 Facebook 33 Twitter 34 The Need to implement Web Application in Pre-service Teachers Programs 36 Teachers Preparation and the Use of Technology in the United States 37 Web Applications and Digital Students 38 The Advantages of Web Applications 39 The Risk of Web Applications 40 Theoretical Framework 44 Adopting technology theories 44 Diffusion of Innovation 44 Theory of Reasoned Action 45 Theory of Planned Behavior 47 The Technology Acceptances Model 48 Factors Influencing the Adoption of Web Applications .51 Adopters' Characteristics 55 Age .55 Gender 56 Knowledge and Experience 57 Attributes of the Technology 57 Perceived Usefulness 57 Perceived Ease of Use 59 Perceived Risk .59 Perceived Pedagogical Support 62 Social Environment 64 Colleagues’ Influence 64 Summary 65! Chapter 3: Methodology 67! Research Design 67! Research Questions 67! The Research Hypotheses 68! Defining the Variables Operationally 68! Instrumentation 70! Pilot Study 75! The Population 76! The Sample 76 Sample Size .76 Sampling 77 Data Collection Procedure 78 Data Analysis Procedures 79! Summary 80 Chapter 4: Results 81 Demographic Findings 82 Statistical Analysis .85 Preliminary Data Screening .85 Reliability of Instrument 85 Descriptive statistics .86 Multiple Regression Analysis 110 Diagnosis of Multivariate Outliers 111 Examining other Assumption Violations 112 Results for Hierarchical Regression 114 Other analysis 118 Open-ended Question 120 Summary 124 Chapter 5: Discussions, Conclusions, and Recommendations 125 Discussion 125 Web Applications 125 Attitude toward the Use of Web applications 128 Subjective Norms 129 Factors of the Intention to Use Web applications 130 Comments regarding the use of Web applications in teaching .134 Conclusion 135! Recommendations 136! References 139! Appendix A: Permission Request (TPB) 157! Appendix B: Permission Request (TAM) 158! Appendix C: IRB Approval 159! Appendix D: Colleges of Education at the American Universities 161! Appendix E: Multiple Regression Using a Hierarchical Method (Pilot Study) 165! Appendix F: Consent Email 166! Appendix G: The Questionnaire 167! Appendix H: Literature Support the Survey’s Items 173! Appendix I: Chapter Concept Map 176! Appendix J: The Online Version of The Questionnaire 177! Appendix K: Histograms of the DV and IDs 184! Appendix L: Scatter Plot of the DV with each of the IVs 185! Appendix M: Correlations among Predictors 186! Appendix N: Hierarchical Multiple Regression 187! Appendix O: Bivariate Regression (IU and USE) 189! Appendix P: One-Way ANOVA (Teaching Philosophy Groups) 190! Appendix Q: T-test Employment Status (Tenure-track vs Nontenure) 191! Appendix R: T-test Employment Status (Full-time vs Part-time) 192! Appendix S: Factor Analysis 193! Appendix T: One-Way ANOVA (Content Area) 194! Appendix U: One-way MANOVA (Gender Differences ) 196 Appendix V: Final Model 197 Appendix W: One-way ANOVA (Rank) .198 10 List of Tables Page Table 2.1 Theories and Models of Accepting Technology 50 Table 2.2 Studies which Investigated Adoption of Web Applications 53 Table 3.1 Items Measuring the Perceived Usefulness of Web applications…….… .72 Table 3.2 Items Measuring the Perceived Ease of Use………………………… … 73 Table 3.3 Items Measuring Perceived Risk .73 Table 3.4 Items Measuring Perceived Pedagogical Support 74 Table 3.5 Items Measuring Colleagues’ Influence 74 Table 3.6 Cronbach's Alpha for Each Construct of the Questionnaire (Pilot) 75 Table 3.7 Proportional Stratified Sampling .78 Table 4.1 Participants’ Demographic Data .83 Table 4.2 Participants’ Race 83 Table 4.3 Participants’ Content Area .84 Table 4.4 Cronbach's Alpha for Each Construct of the Questionnaire 86 Table 4.5 Participants’ Knowledge and Personal Experience (KPE) .90 Table 4.6 Participants’ Use of Web Applications in Teaching (USE) .94 Table 4.7 Participants’ Intention to Use Web Applications in Teaching (IU) .99 Table 4.8 Participants’ Perceived Usefulness of Web Applications (PU) .101 Table 4.9 Participants’ Perceived Ease of Use of Web Applications (PE) 103 Table 4.10 Participants’ Perceived Risk of Using Web Applications (PR) 105 Table 4.11 Participants’ Perceived Pedagogical Support (PSS) 107 Table 4.12 Participants’ Colleagues Support to Use Web Applications (CI) 109 Table 4.13 Casewise Diagnostics 111 Table 4.14 Residuals Statistics .112 Table 4.15 Mahalanobis Distance for the violated cases 112 Table 4.16 Multiple Regression : Model Summary 115 Table 4.17 Multiple Regression : Coefficients .115 185 Appendix L: Scatter Plot of the DV with each of the IVs ! ! ! ! ! ! ! ! 186 Appendix M: Correlations among Predictors Age in KPE IU PU CI PPS PR PE -.017 023 105 088 118 -.039 -.019 786 716 100 166 063 539 760 249 249 249 249 249 249 249 249 -.017 years Age in years Pearson Correlation Sig (2-tailed) N Pearson KPE Correlation Sig (2-tailed) 786 N 249 Pearson IU Correlation PPS PR PE 249 ** 749 604 ** ** ** 616 ** 249 249 249 249 ** 673 247 ** 884 ** 249 249 249 ** ** -.013 000 842 000 249 249 249 249 ** 384 884 ** 324 324 063 000 000 000 000 N 249 249 249 249 249 -.039 -.160 ** -.013 Sig (2-tailed) 539 012 007 002 842 000 N 249 249 249 249 249 249 ** ** 249 Sig (2-tailed) 530 602 249 249 ** ** 000 249 -.169 -.199 002 249 ** ** 000 249 616 384 000 N ** ** 249 000 570 530 249 000 -.019 ** 000 000 * -.169 007 166 ** ** 000 Sig (2-tailed) 586 247 000 249 250 673 000 249 Correlation ** 249 249 Pearson 570 249 N Correlation * 249 000 Pearson -.160 249 000 118 ** 249 100 Correlation 586 249 Sig (2-tailed) Pearson ** 000 249 088 250 012 249 Correlation ** 000 N 105 604 000 000 Correlation ** 000 716 Pearson CI 023 749 000 Sig (2-tailed) Pearson PU -.199 602 ** 345 ** -.254 ** 345 547 ** ** 000 000 249 249 249 ** -.254 547 ** -.334 ** 000 249 249 ** -.334 Sig (2-tailed) 760 000 000 000 000 000 000 N 249 249 249 249 249 249 249 ** Correlation is significant at the 0.01 level (2-tailed) * Correlation is significant at the 0.05 level (2-tailed) 249 187 Appendix N: Hierarchical Multiple Regression Variables Entered/Removeda Variables Entered Variables Removed b Age in years, Gender KPE, PR, CI, PPS, PE, PUb Model Method Enter Enter a Dependent Variable: IU b All requested variables entered Mo del R 044a 799b Model Summaryc R Adjuste Std Change Statistics Squa dR Error of R F df1 df2 Sig F re Square the Square Change Change Estimat Change e 002 -.006 90553 002 235 246 790 639 627 55149 637 70.540 240 000 a Predictors: (Constant), Age in years, Gender b Predictors: (Constant), Age in years, Gender, KPE, PR, CI, PPS, PE, PU c Dependent Variable: IU Model Regression Residual Total Regression Residual Total Sum of Squares 386 201.716 202.103 129.109 72.993 202.103 ANOVAa df 246 248 240 248 Mean Square 193 820 a Dependent Variable: IU b Predictors: (Constant), Age in years, Gender c Predictors: (Constant), Age in years, Gender, KPE, PR, CI, PPS, PE, PU 16.139 304 F Sig .235 790b 53.064 000c 188 Model Coefficientsa Unstandardized Standar t Sig Coefficients dized Coefficie nts B Std Beta Error 2.720 350 7.771 000 073 126 037 582 561 (Constant) Gender Age in 002 years (Constant) -.393 Gender 025 Age in 000 years KPE 571 PU 615 CI -.063 PPS -.062 PR -.018 PE 055 a Dependent Variable: IU Model KPE PU CI PPS PR PE 005 Correlations Zeroorder Parti al Part 035 037 037 026 401 689 023 026 026 441 078 013 -.891 374 318 751 035 021 012 003 001 028 978 023 002 001 056 146 095 137 086 104 Beta In t 749b 679b 247b 625b -.167b 534b 17.731 14.291 3.975 12.307 -2.638 9.878 533 10.210 000 376 4.201 000 -.028 -.661 509 -.039 -.454 650 -.009 -.209 835 029 527 599 749 673 247 616 -.169 530 550 262 -.043 -.029 -.013 034 396 163 -.026 -.018 -.008 020 Excluded Variablesa Sig Partial Collinearity Statistics Correlation Toleran VIF Minimum ce Tolerance 000 750 1.000 1.000 995 000 674 984 1.017 984 000 246 992 1.008 988 000 618 976 1.025 976 009 -.166 992 1.008 989 000 534 998 1.002 994 a Dependent Variable: IU b Predictors in the Model: (Constant), Age in years, Gender 189 Appendix O: Bivariate Regression (IU and USE) Model Variables Entered/Removeda Variables Variables Method Entered Removed b IU Enter a Dependent Variable: USE b All requested variables entered Model Model Summary R Square Adjusted R Square R 874a 764 763 Std Error of the Estimate 44930 a Predictors: (Constant), IU Model Regression Residual Total Sum of Squares 161.128 49.862 210.990 ANOVAa df 247 248 Mean Square 161.128 202 F 798.185 Sig .000b a Dependent Variable: USE b Predictors: (Constant), IU Model (Constant) IU a Dependent Variable: USE Coefficientsa Unstandardized Standardized Coefficients Coefficients B Std Error Beta -.115 097 893 032 874 t -1.180 28.252 Sig .239 000 190 Appendix P: One-Way ANOVA (Teaching Philosophy Groups) Descriptives IU Group1 Group2 Group3 Total N Mean Std Deviati on Std Error 112 91 46 249 3.0670 3.0041 2.5462 2.9478 87748 91524 84260 90273 08291 09594 12423 05721 95% Confidence Minim Maxim Interval for Mean um um Lower Upper Bound Bound 2.9027 3.2313 1.00 5.00 2.8135 3.1947 1.00 4.88 2.2960 2.7964 1.00 4.38 2.8351 3.0605 1.00 5.00 ANOVA IU Sum of Squares Between Groups Within Groups Total df Mean Square 9.298 4.649 192.804 202.103 246 248 784 F 5.932 Sig .003 191 Appendix Q: T-test Employment Status (Tenure-track vs Nontenure) IU Group Statistics N Mean Are you tenuretrack? Yes No 180 69 Std Deviation 87783 94185 2.8708 3.1486 Std Error Mean 06543 11339 Independent Samples Test Levene's t-test for Equality of Means Test for Equality of Variances F Sig t df Sig Mean Std 95% Confidence (2- Differenc Error Interval of the tailed e Differe Difference ) nce Lower Upper Equal variances IU 451 503 -2.189 247 030 -.27772 12685 -.5275 -.0278 -2.121 115.943 036 -.27772 13091 -.5370 -.0184 assumed Equal variances not assumed 192 Appendix R: T-test Employment Status (Full-time vs Part-time) Are you full-time? IU Yes No Equal variances assumed IU Equal variances not assumed Group Statistics N Mean Std Deviation 224 25 2.9141 3.2500 89476 93611 Std Error Mean 05978 18722 Independent Samples Test Levene's t-test for Equality of Means Test for Equality of Variances F Sig t df Sig Mean Std 95% (2Differ Error Confidence tailed) ence Differ Interval of the ence Difference Lower Upper 1895 0373 257 613 -1.772 247 078 3359 7092 29 -1.709 111 1965 0659 098 3359 7378 193 Appendix S: Factor Analysis Total Variance Explained Compone nt Initial Eigenvalues Total Extraction Sums of Squared Loadings % of Cumulative Variance % Total % of Cumulative Variance % 11.216 36.181 36.181 11.216 36.181 36.181 2.861 9.228 45.409 2.861 9.228 45.409 2.314 7.465 52.874 2.314 7.465 52.874 1.576 5.084 57.959 1.576 5.084 57.959 1.171 3.778 61.737 1.171 3.778 61.737 983 3.172 64.909 899 2.899 67.808 812 2.619 70.426 802 2.586 73.012 10 696 2.244 75.256 11 641 2.068 77.324 12 618 1.993 79.317 13 610 1.967 81.285 14 606 1.956 83.241 15 531 1.713 84.954 16 485 1.565 86.519 17 473 1.526 88.045 18 402 1.298 89.344 19 389 1.255 90.598 20 372 1.200 91.798 21 339 1.095 92.893 22 331 1.069 93.962 23 294 950 94.912 24 261 841 95.753 25 246 793 96.546 26 223 721 97.267 27 220 710 97.977 28 184 593 98.570 29 178 574 99.144 30 167 538 99.682 31 099 318 100.000 194 PU1 PU2 PU3 PU4 PU5 CI6 PE1 PE2 PE3 PE4 PE5 PE6 PR1 PR2 PU6 PR5 PR3 CI1 CI2 CI3 CI4 CI5 PR4 CI6 PPS1 PPS2 PPS3 PPS4 PPS5 PPS6 PPS7 Component Matrixa Component 849 -.047 115 710 -.047 212 867 -.072 086 768 -.051 108 832 -.002 148 446 493 -.125 687 -.061 -.225 493 207 -.098 739 041 -.061 -.060 -.173 -.553 193 -.333 -.489 673 -.090 -.198 -.072 267 427 -.172 349 536 846 -.082 086 -.252 441 333 -.178 250 493 340 542 -.284 307 496 -.315 223 575 -.168 393 612 -.264 419 485 -.116 -.264 271 471 261 519 -.251 809 -.006 187 836 -.175 124 793 -.116 074 791 -.151 148 771 -.144 093 731 -.168 194 833 -.110 143 Extraction Method: Principal Component Analysis a components extracted 025 -.111 011 026 -.035 -.228 410 116 062 326 442 344 454 354 123 172 361 -.232 112 -.249 -.256 198 -.065 321 -.056 -.065 015 -.103 -.169 -.036 -.099 079 058 007 039 042 138 013 -.434 150 467 042 120 166 313 037 -.139 065 205 -.426 396 141 -.101 -.013 -.329 037 031 018 -.074 -.072 -.097 -.048 195 Appendix T: One-Way ANOVA (Content Area) Descriptive IU N Ed., General Ed., Other Elementary and/or Early Ed Science Ed Math Ed Special Ed Secondary Ed and/or adolescent Other Total Mean Std Deviatio n 95% Confidence Mini Maxi Interval for Mean mum mum Lower Upper Bound Bound 20 2.9563 1.00760 22531 2.4847 3.4278 1.00 4.75 40 2.8344 1.00694 15921 2.5123 3.1564 1.00 4.75 38 2.9901 Std Error 76802 12459 2.7377 3.2426 1.50 4.25 12 2.8958 95916 27688 2.7917 81729 27243 22 3.0057 1.02388 21829 2.2864 2.1634 2.5517 3.5053 3.4199 3.4596 1.75 1.00 1.00 4.50 3.50 4.63 18 3.3125 96277 22693 2.8337 3.7913 1.25 4.63 81 2.8627 24 2.9318 80927 08992 2.6837 3.0416 1.50 4.88 89189 05757 2.8184 3.0452 1.00 4.88 Test of Homogeneity of Variances IU Levene Statistic 974 df1 df2 Sig .451 232 ANOVA IU Sum of Squares Between Groups Within Groups Total df Mean Square 3.829 547 186.288 190.117 232 239 803 F 681 Sig .688 196 Appendix U: One-way MANOVA (Gender Differences ) Between-Subjects Factors Value Label N Male 74 Gender Female 175 Effect Multivariate Testsa Value F Hypothesis Error df df b 990 3089.664 8.000 240.000 b 010 3089.664 8.000 240.000 Pillai's Trace Wilks' Lambda Hotelling's Intercept 102.989 3089.664b Trace Roy's Largest 102.989 3089.664b Root Pillai's Trace 043 1.361b Wilks' Lambda 957 1.361b Hotelling's Gender 045 1.361b Trace Roy's Largest 045 1.361b Root a Design: Intercept + Gender b Exact statistic Sig .000 000 8.000 240.000 000 8.000 240.000 000 8.000 240.000 8.000 240.000 214 214 8.000 240.000 214 8.000 240.000 214 197 Appendix V: Final Model Model Variables Entered/Removeda Variables Variables Method Entered Removed b PU, KPE Enter a Dependent Variable: IU b All requested variables entered Model R 798a Model Summary R Square Adjusted R Square 637 634 Std Error of the Estimate 54587 a Predictors: (Constant), PU, KPE Model Sum of Squares Regressio n Residual Total ANOVAa df Mean Square 128.800 64.400 73.303 202.103 246 248 298 F 216.123 Sig .000b a Dependent Variable: IU b Predictors: (Constant), PU, KPE Model (Constant) KPE PU a Dependent Variable: IU Coefficientsa Unstandardized Standardized Coefficients Coefficients B Std Error Beta -.464 201 576 052 538 569 079 348 t -2.314 11.173 7.227 Sig .022 000 000 198 Appendix W: One-way ANOVA (Rank) Descriptives IU N Mean Std Deviatio n Std Error 95% Minim Maxi Confidence um mum Interval for Mean Lower Upper Bound Bound P Associate 46 2.6957 99918 14732 2.3989 2.9924 1.00 4.63 P Assistant 78 2.9215 83187 09419 2.7339 3.1090 1.25 4.75 Professor 78 3.0801 81483 09226 2.8964 3.2638 1.00 4.50 Instructor 27 3.0185 1.08070 20798 2.5910 3.4460 1.00 5.00 3.4720 3.0605 1.38 1.00 4.88 5.00 Other Total 20 3.0188 249 2.9478 96848 21656 2.5655 90273 05721 2.8351 ANOVA IU Sum of Squares Between Groups Within Groups Total df Mean Square 4.580 1.145 197.522 202.103 244 248 810 F 1.414 Sig .230 Thesis and Dissertation Services ... of Education Abstract ALSADOON, ELHAM A., Ph.D., August 2013, Instructional Technology Factors Influencing Faculty to Adopt Web Applications in their Teaching Director of Dissertation: Teresa...2 This dissertation titled Factors Influencing Faculty to Adopt Web Applications in their Teaching by ELHAM A ALSADOON has been approved for the Department... not prefer wikis or they were frustrated when their attempts to post their contributions were not accepted since others were working at the same time Wikis, furthermore, involve the risk of inaccurate
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