跳到主要內容

臺灣博碩士論文加值系統

(44.200.86.95) 您好!臺灣時間:2024/05/30 01:43
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:秦如玉
研究生(外文):Nuntanut Sae-Ching
論文名稱:Extending the TAM Model based on the Website of Social Network – Dual Perspective of Incremental /Disruptive Switching
論文名稱(外文):Extending the TAM Model based on the Website of Social Network – Dual Perspective of Incremental /Disruptive Switching
指導教授:郭幸萍博士李國瑋博士
指導教授(外文):Dr. Kuo, Hsing-PingDr. Lee, Kuo-Wei
學位類別:碩士
校院名稱:南台科技大學
系所名稱:企業管理系
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:101
畢業學年度:100
語文別:英文
論文頁數:92
中文關鍵詞:Technology Acceptance ModelSocial Network ServiceFacebook
外文關鍵詞:Technology Acceptance Model, Social Network Service, Facebook
相關次數:
  • 被引用被引用:0
  • 點閱點閱:258
  • 評分評分:
  • 下載下載:46
  • 收藏至我的研究室書目清單書目收藏:0
ABSTRACT
Nowadays, internet becomes part of our daily life. Many people communicate and exchange information via internet instead of traditional way. Social network service is one of the popular ways to communicate in this era. The huge membership of social network websites can prove the popularity of social network services. Therefore, we would like to examine why people accept this technology by using Technology Acceptance Model (TAM) which is widely used in many studies.
Since the social network services gain popularity, there’re many social network websites emerge. To consider why people shift from one social network website to another is also interesting. Then we also propose Technology Switching Model (TSM) to figure out the reason that people change from one website to another and use Facebook to examine the case. We also extended TSM by included pattern of switching as a moderator. Incremental switching is a group of people who gradually switch from one social network website to Facebook. Disruptive switching is a group of people who permanently or completely switched from one social network website to Facebook.
To examine this topic, we conducted a survey by using questionnaire 470 sets to collect data from social network website users in Bangkok, Thailand. Statistic analysis included descriptive statistics, factor analysis, and the Structural Equation Modeling (SEM) techniques.
The results revealed that people who have no experience with any social network websites (TAM group), perceived enjoyment is the most important factor followed by subjective norm, and perceived ease of use respectively. But for people who switch from one social network website to Facebook, the most important factor is perceived usefulness followed by perceived enjoyment.
Pattern of switching has a moderating effect on attitude toward switch and behavioral intention to switch. The effects of perceived usefulness and perceived enjoyment on attitude toward switch are stronger in disruptive group than incremental group. Subjective norm has negative effect on behavioral intention to switch for disruptive group while there’s no effect with incremental group.
ABSTRACT
Nowadays, internet becomes part of our daily life. Many people communicate and exchange information via internet instead of traditional way. Social network service is one of the popular ways to communicate in this era. The huge membership of social network websites can prove the popularity of social network services. Therefore, we would like to examine why people accept this technology by using Technology Acceptance Model (TAM) which is widely used in many studies.
Since the social network services gain popularity, there’re many social network websites emerge. To consider why people shift from one social network website to another is also interesting. Then we also propose Technology Switching Model (TSM) to figure out the reason that people change from one website to another and use Facebook to examine the case. We also extended TSM by included pattern of switching as a moderator. Incremental switching is a group of people who gradually switch from one social network website to Facebook. Disruptive switching is a group of people who permanently or completely switched from one social network website to Facebook.
To examine this topic, we conducted a survey by using questionnaire 470 sets to collect data from social network website users in Bangkok, Thailand. Statistic analysis included descriptive statistics, factor analysis, and the Structural Equation Modeling (SEM) techniques.
The results revealed that people who have no experience with any social network websites (TAM group), perceived enjoyment is the most important factor followed by subjective norm, and perceived ease of use respectively. But for people who switch from one social network website to Facebook, the most important factor is perceived usefulness followed by perceived enjoyment.
Pattern of switching has a moderating effect on attitude toward switch and behavioral intention to switch. The effects of perceived usefulness and perceived enjoyment on attitude toward switch are stronger in disruptive group than incremental group. Subjective norm has negative effect on behavioral intention to switch for disruptive group while there’s no effect with incremental group.
TABLE OF CONTENTS
Page
ABSTRACT I
ACKNOWLEDGEMENTS II
TABLE OF CONTENTS III
LIST OF TABLES VI
LIST OF FIGURES VII
CHAPTER I INTRODUCTION 1
1.1 Research background and motivations 1
1.1.1 Social Network Services in Thailand 2
1.2 Theoretical background 4
1.3 The purpose of study 6
1.4 The Scope of the study 6
CHAPTER II LITERATURE REVIEW 7
2.1 Social network service 7
2.1.1 Hi5 7
2.1.2 Facebook 8
2.2 Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB) 9
2.2.1 Theory of Reasoned Action (TRA) 9
2.2.1.1 Attitude toward Act or Behavior 10
2.2.1.2 Subjective norm 10
2.2.1.3 Behavioral Intention 11
2.2.1.4 Behavior 11
2.2.2 Theory of Planned Behavior (TPB) 11
2.2.2.1 Behavioral Beliefs & Attitude toward Behavior 12
2.2.2.2 Normative beliefs and Subjective norm 12
2.2.2.3 Control Beliefs & Perceived Behavioral Control 13
2.3 The Technology Acceptance Model (TAM) and Technology Switching Model (TSM) 13
2.3.1 The Technology Acceptance Model (TAM) 13
2.3.1.1 Perceived Usefulness 14
2.3.1.2 Perceived Ease of use 14
2.3.2 Technology Switching Model (TSM) 14
2.3.2.1 Perceived Enjoyment 16
2.3.2.2 Attitude toward switch (ATS) 16
2.3.2.3 Behavioral Intention to Switch (BS) 16
2.3.2.4 Actual System Switch (AS) 16
2.4 Moderating Variables 17
2.4.1 Incremental Switching 17
2.4.2 Disruptive Switching 17
2.5 Hypothesis Development 18
CHAPTER III RESEARCH DESIGN AND METHODOLOGY 24
3.1 Conceptual Framework 24
3.1.1 Technology Acceptance Model (TAM) 24
3.1.2 Technology Switching Model (TSM) 24
3.1.3 Extended Technology Switching Model (TSM) –Incremental / Disruptive switching perspective 25
3.2 Hypotheses 26
3.3 Measurement Development 28
3.3.1 Constructs included in TAM 29
3.3.1.1 Perceived Usefulness (PU) 29
3.3.1.2 Perceived Ease of Use (PEOU) 29
3.3.1.3 Perceived Enjoyment (PE) 29
3.3.1.4 Subjective Norm (SN) 30
3.3.1.5 Attitude towards Use (ATU) 30
3.3.1.6 Behavioral Intention to Use (BU) 30
3.3.1.7 Actual System Use (AU) 31
3.3.2 Constructs included in TSM 31
3.3.2.1 Attitude towards Switch (ATS) 31
3.3.2.2 Behavioral Intention to Switch (BS) 32
3.3.2.3 Actual System Switch (AS) 32
3.3.3 General Information of Respondents 35
3.4 Research Design 36
3.5 Sample plan 36
3.6 Data Analysis 37
3.6.1 Descriptive Statistic Analysis 37
3.6.2 Structural Equation Modeling 37
CHAPTER IV DATA ANALYSIS 39
4.1 Descriptive Analysis Sample Characteristic 39
4.1.1 Demographic of the respondents 39
4.1.2 Users behavior 40
4.2 Descriptive Analysis of Questionnaire Items 42
4.3 Validity and reliability analysis 48
4.3.1 Exploratory Factor Analysis (EFA) 48
4.3.2 Confirmatory Factor Analysis (CFA) 50
4.3.2.1 Goodness of-Fit Statistics 50
4.3.2.2 Convergent Validity 52
4.3.2.3 Discriminant Validity 53
4.3.2.4 Evaluation of Reliability 55
4.4 The result of hypothesis testing 57
4.4.1 Analysis of the Structural Model 57
4.5 Test of moderating variables 61
4.6 Verify the Hypotheses 64
CHAPTER V CONCLUSION AND SUGGESTION 67
5.1 Research Conclusion 67
5.2 Discussion 68
5.2.1 The concept of TSM 68
5.2.2 Comparing between TAM and TSM 68
5.2.3 Comparing between Disruptive and Incremental group 70
5.3 Theoretical Contributions 71
5.4 Management Implications 72
5.5 Limitations and Further Research 73
References 75
APPENDIX: SURVEY QUESTIONNAIRE 86
References
Ajzen, I. and Fishbein, M. (1980). Understanding Attitudes and Predicting Social
Behavior. NJ: Prentice-Hall.
Ajzen, I. (1985). From intentions to actions. In J.Kuhl,&J.Beckman (Eds.), Action
Control from Cognition to Behavior (pp. 11−39). New York: Springer Verlag.
Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behaviour: Attitudes,
intentions and perceived behavioural control. Journal of Experimental
Psychology, 29, 71–90.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human
Decision Process, 50, 179–211.
Ajzen, I., & Fishbein, M. (2000). Attitudes and the attitude–behavior relation: Reasoned
and automatic processes. In W. Stroebe & M. Hewstone (Eds.), European Review of Social Psychology (pp. 1–28). John Wiley & Sons.
Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology, 52,
27-58.
Ajzen, I. (2002). Perceived behavior control, self-efficacy, locus of control, and the
theory of planned behavior. Journal of Applied Social Psychology, 32, 1–20.
Alexa. (2011). Top Sites in Thailand. Retrieved October 4, 2011,
from http://www.alexa.com/topsites/countries/TH
Amjad, N. & Wood, A.M. (2009). Identifying and Changing the Normative Beliefs
About Aggression Which Lead Young Muslim Adults to Join Extremist Anti-
Semitic Groups in Pakistan. Aggressive Behavior, 35, 514–519.
Baker, R. K., & White, K.M. (2010). Predicting adolescents’ use of social networking
sites from an extended Theory of planned behaviour perspective. Computers in Human Behavior, 26, 1591–1597.
Barclay, D., Thompson, R., & Higgines, C. (1995). The partial least squares (PLS)
approach to causal modeling: Personal computer adoption and use an illustration. Technology Studies , 2 (2), 285-309.
Barker, V. (2009). Older adolescents’ motivations for social network site use.
Cyber Psychology and Behavior, 10(3), 478–481.
Barrigar, J. (2009). Social Network Site Privacy A comparative analysis of six sites.
Ottawa, Ontario: Office of the Privacy Commissioner of Canada.
Bearden, W.O., Netemeyer, R.G., Teel, J.E. (1989). Measurement of consumer
susceptibility to interpersonal influence. Journal of Consumer Research, 15(4), 473–481.
Benner, M. J., & Tushman, M. L. (2003). Exploitation, exploration, and process
management: The productivity dilemma revisited. Academy of Management Review, 28(2), 238—256.
Bentler, P.M., & Bonett, D.G. (1989). Significance tests and goodness of fit in the
analysis of covariance structures, Psychological Bulletin, 88, 588–606.
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An
Expectation Confirmation Model. MIS Quarterly, 25(3), 351-370.
Boyd, D. M., & Ellison, N. B. (2008). Social network sites: definition, history, and
scholarship. Journal of Computer-Mediated Communication, 13, 210–230.
Brown, T.A. (2006). Confirmatory Factor Analysis for Applied Research. New York,
The Guilford Press.
Bryne, B. (2001). Structural equation modeling with AMOS: Basic concepts,
application and programming. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
Campbell, D., & Fiske, D. (1959). Convergent and discriminant validation by multitrait-
multimethod matrix. Psychological Bulletin , 56, 81-105.
Chan, S.C., & Lu, M.T. (2004). Understanding internet banking adoption and use
behavior: a Hong Kong perspective. Journal of Global Information Management, 12, 21–43.
Chandy, R. K., & Tellis, G. J. (1998). Organizing for radical product innovation: The
overlooked role of willingness to cannibalize. Journal of Marketing Research, 35(4), 474—487.
Chang, M. K. (1998). Predicting unethical behavior: a comparison of the theory of
reasoned action and the theory of planned behavior. Journal of Business Ethics, 17, 1825–1834.
Chang, Y.P., Zhu, D.H. (2011). Understanding social networking sites adoption in
China: A comparison of pre-adoption and post-adoption. Computers in Human Behavior, 27, 1840–1848.
Cheung, C.M.K., Chiu, P.Y., & Lee, M.K.O. (2011). Online social networks: Why do
students use facebook? Computers in Human Behavior, 27, 1337–1343.
Choi, D. H., Kim, J., & Kim, S. H. (2007). ERP training with a webbased electronic
learning system: The flow theory perspective. International Journal of Human-Computer Studies, 65(3), 223-243.
Chow, W.S., & Chan, L.S. (2008). Social network, social trust and shared goals in
organizational knowledge sharing. Information & Management, 45, 458–465.
Christensen, C. (1997). The innovator’s dilemma: when new technologies cause great
firms to fail. Boston: Harvard Business School Press.
ComScore. (2011a). Unique Visitor Trend to Social Networking Category & Facebook.
Retrieved October 12, 2011, from http://www.comscoredatamine.com/2011/06/
unique-visitor-trend-to-social-networking-category-facebook/
ComScore. (2011b). Growth of Facebook.com Across Global Regions.
Retrieved October 12, 2011, from http://www.comscoredatamine.com/2011/05/
growth-of-facebook-com-across-global-regions/
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests.
Psychometrika, 16(3), 297-334.
Dabholkar, P.A. & Bagozzi, R. (2002). An attitudinal model of technology-based self-
service: moderating effects of consumer traits and situational factors. Journal of Academy of Marketing Science, 30 (3), 184–201.
Davis, F. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information systems: Theory and Results. Doctoral dissertation, Sloan School of Management, Massachusetts Institute of Technology.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of
information technology. MIS Quarterly, 13, 319−340.
Davis, F.D., Bagozzi, R.P., Warshaw, P.R. (1989). User acceptance of computer
technology: a comparison of two theoretical models. Management Science, 35, 982–1002.
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, 1111-1132.
Davis, L. E., Ajzen, I., Saunders, J., & Williams, T. (2002). The decision of African
American students to complete high school: An application of the theory of
planned behavior. Journal of Educational Psychology, 94, 810–819.
Dillon, A., & Morris, M. G. (1997). User acceptance of information technology:
Theories and models. Annual Review of Information Science and Technology (ARIST), 01(31), 3−33.
Dishaw, M.T. & Strong D.M. (1999). Extending the technology acceptance model with
task-technology fit constructs. Information and Management, 36, 9-21.
Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook ‘‘friends:”
Social capital and college students’ use of online social network sites. Journal of
Computer-Mediated Communication, 12(4), 1143–1168.
Facebook. (n.d.). In Wikipedia, the free encyclopedia. Retrieved October 12, 2011, from
http://en.wikipedia.org/wiki/Facebook
Facebook. (2011). Timeline. Retrieved October 2, 2011,
from https://www.facebook.com/press/info.php?timeline
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An
introduction to theory and research. Reading, MA: Addison-Wesley.
Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with
unobservable variables and measurement errors. Journal of Marketing Research , 18, 39-50.
Garcia, R., & Calantone, R. (2002). A critical look at the technological innovation
typology and innovativeness terminology: A literature review. The Journal of Product Innovation Management, 19(2), 110—132.
Garton, L., Haythornthwaite, C., & Wellman, B. (1997). Studying online social
networks, Journal of Computer-Mediated Communication, 3(1). Available from
www.ascusc.org/jcmc/vol3/issue1/garton.html.
George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide
reference.11.0 update (4th ed.). Biston: Allyn & Bacon.
Google Trends. (n.d.). Retrieved November 6, 2011, from
http://trends.google.com/websites?q=facebook.com%2C+hi5.com%2C+twitter.com%2C+youtube.com%2Clinkedin.com&geo=TH&date=all&sort=0
Gorsuch, R. L. (1983), Factor Analysis (2nd. Ed). Hillsdale, NJ: Erlbaum.
Grandon, E.E. & Peter P. Mykytyn, J. (2004). Theory-Based Instrumentation to Measure
The Intention to Use Electronic Commerce in Small and Medium Sized Businesses, The Journal of Computer Information Systems, 44 (3), 44-57.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate Data
Analysis. New Jersey: Prentice-Hall.
Harris, B. (n.d.). What is Brand Switching? Retrieved October 21, 2011, from
http://www.wisegeek.com/what-is-brand-switching.htm
Hernandez, B., Jimenez, J., & Martin, M.J. (2009). The impact of self-efficacy, ease of
use and usefulness on e-purchasing: An analysis of experienced e-shoppers. Interacting with Computers, 21, 146–156.
Hosein, N.Z. (2009). Internet Banking: An Empirical Study Of Adoption Rates Among
Midwest Community Banks. Journal of Business & Economics Research, 7(11), 51-72.
Hsieh, Y.C., Hsieh, J.K., & Feng Y.C. (2011). Switching between social media: The role
of motivation and cost. International Proceedings of Economics Development and Research, 22, 92-96.
Hsu, C.L., & Lu, H.P. (2004). Why do people play on-line games? An extended TAM
with social influences and flow experience. Information and Management, 41 (7), 853–868.
Hsu, C.-L.,& Lin, J.-C. (2008). Acceptance of blog usage: The roles of technology
acceptance, social influence and knowledge sharing motivation. Information &
Management, 45, 65–74.
Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A motivational model of
microcomputer usage. Journal of Management Information Systems, 13(1), 127-143.
Im, I., Kim Y.B., & Han H.J. (2008). The effects of perceived risk and technology type
on users’ acceptance of technologies. Information & Management, 45, 1-9.
Kang, Y.S., & Lee, H. (2010). Understanding the role of an IT artifact in online service
continuance: An extended perspective of user satisfaction. Computers in Human Behavior, 26, 353–364.
Karahanna, E., Straub, D.W., & Chervany, (1999). N.L. Information technology
adoption across time: a cross-sectional comparison of pre-adoption and post –adoption beliefs. MIS Quaterly, 23 (2), 183-213.
Kempe, D., Kleinberg, J., & Tardos, E. (2003). Research track: Maximizing the spread
of influence through a social network. In Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 137–146). New York, NY: ACM Press
K.G. Jo¨eskog, & D. So¨bom. (1993). LISREL8 User’s Reference Guide, Science
Software, Chicago.
Kim, G., Shin, B., & Lee, H.G. (2006). A study of factors that affect user intentions
toward email service switching. Information & Management, 43, 884–893.
Kline, R.B. (2005). Principles and practice of structural equation modeling (2nd ed.),
New York: The Guilford Press.
Kostoff, R.N., Boylan, R., & Simons, G.R. (2004). Disruptive technology roadmaps.
Technological Forecasting & Social Change, 71, 141–159.
Kotelnikov, V. (n.d.). Customer Retention Driving Profits Through Giving Customers
Lots of Reasons to Stay. Retrieved October 22, 2011, from http://www.1000ventures.com/business_guide/crosscutttings/customer_retention.html
Kwon, O., & Wen, Y. (2010). An empirical study of the factors affecting social network
service use. Computers in Human Behavior, 26, 254–263.
Lai, V.S., & Li, H. (2005). Technology acceptance model for internet banking: an
invariance analysis. Information & Management, 42, 373–386
Lee, E.-J., & Park, J.K. (2009). Online service personalization for apparel shopping.
Journal of Retailing and Consumer Services, 16, 83–91.
Lee, J., Cho, H., Gay, G., Davison, B., & Ingraffea, T. (2003). Technology acceptance
and social networking in distance learning. Educational Technology & Society, 6(2), 50−61.
Lee, M.-C. (2009). Factors influencing the adoption of internet banking: An integration
of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8, 130–141.
Lee, Y.-H., Hsieh, Y.-C., & Ma, C.-Y. (2011). A model of organizational employees’ e-
learning systems acceptance. Knowledge-Based Systems, 24, 355–366.
Li, D., Browne, G. J., & Wetherbe, J. C. (2007). Online Consumers' Switching
Behavior: A Buyer-Seller Relationship Perspective. Journal of Electronic Commerce in Organizations, 5(1), 30-42.
Li, Y., Qi, J., & Shu, H. (2008). Review of Relationships Among Variables in TAM.
TSINGHUA SCIENCE AND TECHNOLOGY, 13(3), 273-278.
Lin, C. P., & Bhattacherjee, A. (2008). Elucidating individual intention to use
interactive information technologies: The role of network externalities. International Journal of Electronic Commerce, 13, 85–108.
Lin, K.-Y., & Lu, H.-P. (2011). Why people use social networking sites: An empirical
study integrating network externalities and motivation theory. Computers in Human Behavior, 27, 1152–1161.
Liu, S.H., Liao, H.L., & Pratt, J.A. (2009). Impact of media richness and flow on e-
learning technology acceptance. Computers & Education, 52, 599–607.
Lu, Y., Zhou, T., & Wang, B. (2009). Exploring Chinese users’ acceptance of instant
messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Computers in Human Behavior, 25, 29–39.
MacCallum, R. C., Widaman, K. F., Zhang, S. & Hong, S., (1999), Sample size in
factor analysis, Psychological Methods, 4, 84-99.
MacMillan, D. (2010). Social Network Hi5 Gets Its Game On. Retrieved November 6,
2011, from http://www.businessweek.com/technology/content/mar2010/tc20100312_481
808.htm
Malone, T. W. (1981). Toward a theory of intrinsically motivating instruction.
Cognitive Science, 5(4): 333-369.
Marler, J.H., & Dulebohn J.H. (2005). A model of employee self-service technology
Acceptance. Research in Personnel and Human Resources Management, 24, 137–180.
McKenna,KatelynY. A., Green, A. S.,&Glenson,Marci E. J. (2002).Relationship
formation on the Internet:What’s the big attraction? Journal of Social Issues, 58(1), 9–31.
Meyers, L.S., Gamst, G. & Guarino, A.J. (2006). Applied Multivariate Research:
Design and Interpretation. Thousand Oaks, CA: Sage Publications, Inc.
Miller, K. (2005). Communications theories: perspectives, processes, and contexts. New
York: McGraw-Hill.
Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a world-wide-web context.
Information & Management, 38(4), 217–230.
Murray, K. E., & Waller, R. (2007). Social networking goes abroad. International
Educator, 16(3), 56–59.
National Electronics and Computer Technology Center. (2008). Internet User Profile of
Thailand 2008. Pathumthani, Thailand: SE-EDUCATION Public Company Limited.
Neuman, W.L. (2006). Social Research Methods: Qualitative and Quantitative
Approaches, (6th ed.), Boston: Pearson Education, Inc.
Nielsen (2010). Led by Facebook, Twitter, Global Time Spent on Social Media Sites up
82% Year over Year. Retrieved October3, 2011, from http://blog.nielsen.com/nielsenwire/global/led-by-facebook-twitter-global-time-spent-on-social-media-sites-up-82-year-over-year/
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed., pp. 264–265).
New York: McGraw-Hill.
O’Murchu, I., Breslin, J.G., & Decker, S. (2004). Online social and business
networking communities, DERI Technical Report 2004-08-11, SIGKDD’03. Washington, DC.
Oyeniyi, O. J., & Abiodun A. J. (2010). Switching Cost and Customers Loyalty in the
Mobile Phone Market: The Nigerian Experience. Business Intelligence Journal, 3(1), 111-121.
Pavlou, P.A. (2003). Consumer Acceptance of Electronic Commerce: Integrating Trust
and Risk with the Technology Acceptance Model. International Journal of Electronic Commerce, 7(3), 101-134.
Perugini, M. & Bagozzi, R. (2001). The role of desires and anticipated emotions in goal
directed behaviours: Broadening and deepening the theory of planned behavior.
British Journal of Social Psychology, 40(1), 79-99.
Peterson, R.J. (1994). A meta-analysis of Cronbach’s coefficient alpha, Journal of
Consumer Research 21, 381–391.
Pfeil, U., Arjan, R., & Zaphiris, P. (2009). Age differences in online social networking:
A study of user profiles and the social capital divide among teenagers and older
users in MySpace. Computers in Human Behavior, 25, 643–654.
Phillips, J. (2008). Incremental and Disruptive Innovation. Retrieved October 18, 2011,
from http://innovateonpurpose.blogspot.com/2008/03/incremental-and-disruptive-innovation.html
Pookulangara, S., & Koesler, K. (2011). Cultural influence on consumers’ usage of
social networks and its’ impact on online purchase intentions. Journal of Retailing and Consumer Services, 18, 348–354.
Powell, J. (2009). 33 Million people in the room: How to create, influence, and run a
successful business with social networking. NJ: FT Press.
Ramayah, T., Rouibah, K., Gopi, M., & Rangel, G.J. (2009). A decomposed theory of
reasoned action to explain intention to use Internet stock trading among Malaysian investors. Computers in Human Behavior, 25, 1222–1230.
Rogers, E.M. (2003). Diffusion of Innovations (5th ed.). New York, NY: The Free Press.
Schepers, J., & Wetzels M. (2007). A meta-analysis of the technology acceptance
model: Investigating subjective norm and moderation effects. Information & Management, 44, 90–103.
Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action:
a meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15, 325–343.
Shin, D.-H., & Kim, W.-Y. (2008). Forecasting customer switching intention in mobile
service: An exploratory study of predictive factors in mobile number portability. Technological Forecasting & Social Change, 75, 854–874.
Shin, D.-H. (2010). The effects of trust, security and privacy in social networking: A
security-based approach to understand the pattern of adoption. Interacting with
Computers, 22, 428–438.
Shin, D.-H., & Shin, Y.-J. (2011). Why do people play social network games?
Computers in Human Behavior, 27, 852–861.
Sledgianowski, D., & Kulviwat, S. (2009). Using social network sites: The effects of
playfulness, critical mass and trust in a hedonic context. Journal of Computer
Information Systems, 49, 74–83.
Social Media Influence. (2011). The train wreck that is Murdoch’s social network.
Retrieved October 5, 2011, from http://socialmediainfluence.com/2011/02/24/the-train-wreck-that-is-murdochs-social-network/
Steenkamp, J.E., Hofstede, F., & Wedel M. (1999). A cross-national investigation into
the individual and national cultural antecedents of consumer innovativeness. Journal of Marketing, 63, 55-69.
Sun, H., & Zhang P. (2006). The role of moderating factors in user technology
acceptance. International Journal of Human-Computer Studies, 64, 53–78.
Swanson, E. B. (1988). Information System Implementation: Bridging the Gap Between
Design and Utilization. Homewood, IL: Irwin.
Szajna, B. (1996). Empirical-evaluation of the revised technology acceptance model.
Management Science 42 (1), 85–92.
Sze, K. (2011). Thailand Social Network Trends in 2011. Retrieved November 6, 2011
from http://www.searchblog.asia/thailand-social-network-trends-in-2011
Tabachnick, B.G. & Fidell, L.S. (2001). Using multivariate statistics (4th ed.). Boston,
MA: Allyn & Bacon.
Tao, D. (2008). Using Theory of Reasoned Action (TRA) in Understanding Selection
and Use of Information Resources: An Information Resource Selection and Use Model. The Faculty of Graduate School, University of Missouri-Columbia.
Tapscott, D. (2008). Grown up digital: How the next generation is changing your
world. New York: McGraw-Hill.
Taylor, S., & Todd, P. (1995a). Assessing IT usage: The role of prior experience. MIS
Quarterly, 19(4), 561.
Taylor, S., & Todd, P. (1995b). Understanding the information technology usage: A
test of competing models. Information Systems Research, 6(2), 144–176.
Teo, T.S.H., Lim, V.K.G., & Lai, R.Y.C. (1999). Intrinsic and extrinsic motivation in
Internet usage, OMEGA International Journal of Management Science, 27 (1), 25–37.
Triandis, H.C. (1971), Attitude and Attitude Change. NewYork: Wiley.
Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS
Quarterly, 28, 695–704.
Venkataram, M.P., & Price, L.L. (1990). Differentiating between cognitive and sensory
innovativeness: Concepts, measurement, and implications. Journal of Business Research, 20, 293-315.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control,
intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365.
Venkatesh, V., & Davis, F.D. (2000). A theoretical extension of the technology
acceptance model: four longitudinal field studies. Manage 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.
Venkatesh V, Speier C, Morris M G. (2002). User acceptance enablers in individual
decision making about technology: Toward an integrated model. Decision Sciences, 33(2), 297-316.
Vijayasarathy, L.R. (2004). Predicting consumer intentions to use on-line shopping:
the case for an augmented technology acceptance model. Information &
Management, 41, 747–762.
Werner, P. (2004). Reasoned Action and Planned Behavior. In S.J. Peterson & T.S.
Bredow (eds), Middle range Theories: Application to Nursing Research (pp. 125-147). Philadelphia: Lippincott Williams & Wilkins.
Wertsm, C., Linn, R., & Joreskog, K. (1974). Interclass reliability estimates: Testing
structural assumptions. Education and Psychological Measurement , 34, 25-33.
Wu, I.-L., & Chen, J.-L. (2005). An extension of Trust and TAM model with TPB in the
initial adoption of on-line tax: An empirical study. International Journal of Human-Computer Studies, 62, 784–808.
Yang, Z., & Perterson, R. T. (2004). Perceived Value, Satisfaction, and Loyalty: The
Role of Switching Costs. Psychology & Marketing, 21, 799-822.
Zengyan, C., & Lim, J. (2009). Cyber Migration: An Empirical Investigation on Factors
that Affect Usersʼ Switch Intentions in Social Networking Sites.SciencesNew York, 0, 1-11. IEEE Computer Society. Retrieved from http://www.computer.org/portal/web/csdl/doi?doc=doi/10.1109/HICSS.2009.653
Zhang, K.Z.K., Lee, M.K.O., Cheung, C.M.K., & Chen, H. (2009). Understanding the
role of gender in bloggers' switching behavior. Decision Support Systems, 47(4), 540-546.
Zhang, S., Zhao, J., & Tan, W. (2008). Extending TAM for Online Learning Systems:
An Intrinsic Motivation Perspective. Tsinghua Science and Technology, 13(3), 312-317.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top