1. Advisor: 謝焸君 教授 Student: 7099026007 賴千惠
Predicting consumer intentions to shop online : An empirical test of competing theories
Advisor: 謝焸君 教授
Student: 賴千惠

2. Online Introduction Objective Theoretical models Research methodology
Results
Findings
Comparison and selection of models
Implication for research and practice
Limitations and further research

3. Introduction (1/4)
B2C e-commerce provides an effective method for online retailers and their consumers to perform online transactions through commercial Web sites.
Benefits
Saving time
Convenience
Competitive pricing
Broader selection
Greater access to information

4. Introduction (2/4)
Web interface
Face-to-face X
找面對面和面對電腦和信任感的圖

5. Introduction (3/4)
In order to develop an effective Web site to facilitate online transactions and services, it is important to understand consumer decisions regarding the use of online shopping.
Three models have used to understand the determinants of consumer intentions to use Internet services.
TAM, TPB, decomposed TPB
因決定因素容易因為web site而變動，因此能夠提早發現問題，那麼線上零售商就可以考慮實施web site。

6. Introduction (4/4)
Online shopping is similar to general Internet-based IS that have a significant impact on individual decision-making behaviors and Internet marketing strategies.
This study suggests that simultaneously testing the three competing models (TAM, TPB, and decomposed TPB model) can help understand consumer intentions to shop online.
線上購物對於個人決策行為和網路經營策略有顯著的影響。
因此可用三種模型去了解消費者的線上購物意圖。

7. Objective
The main purpose of this study is to examine and compare which intention-based model is best for predicting consumer intentions to shop online.

8. Theoretical models Technology acceptance model (TAM)
Theory of planned behavior (TPB)
Decomposed theory of planned behavior
(Decomposed TPB)

9. Technology acceptance model (TAM)
The TAM was conceived to explain and predict individual acceptance of IT .
TAM is an adaptation of the TRA (Theory of reasoned action), which specifies two beliefs, perceived usefulness (PU) and perceived ease of use (PEU), as determinants of attitude towards behavioral intentions and IT usage.
In the TAM, behavioral intention (BI) to use leads to actual IT usage (AU).

10. Technology acceptance model (TAM)
Attitude PU
Behavioral intention
行為意圖是由態度和知覺有用性共同決定的。而知覺有用性會影響態度，而知覺易用性會直接影響態度和知覺有用性。
PEU

11. Technology acceptance model (TAM)
Perceived usefulness (PU) :
The extent to which a person believes that using a particular system would enhance his or her job performance.
Three key items
Facilitating comparison-shopping
Providing access to useful information
Reducing shopping time

12. Technology acceptance model (TAM)
Perceived ease of use (PEU) :
The degree to which a Web site is perceived to be easy to understand, learn or operate.

13. Theory of planned behavior (TPB)
The TPB extends the TRA , to account for conditions where individuals do not have complete control over their behavior.
Behavioral intention
Perceived behavioral control
(PBC)
Actual usage
(AU)

14. Decomposed theory of planned behavior
(TPB)
The decomposed TPB model specified that, based on the diffusion of innovation theory , the attitudinal belief has three innovation characteristics that influence behavioral intentions are relative advantage, complexity and compatibility.

15. Decomposed theory of planned behavior
(TPB)
Relative advantage refers to the degree to which an innovation providers benefits which supersede those of its precursor and may incorporate factors such as economic benefits.
The relative advantage is often considered to be the ‘‘perceived usefulness’’ in TAM. The complexity construct is extremely similar to ‘‘perceived ease of use’’ concept in TAM.

16. Decomposed theory of planned behavior
(TPB)
Compatibility is the degree to which the innovation fits with the potential adopter’s existing values, previous experiences and current needs.
Decomposing attitude into three components: Perceived usefulness, Perceived ease of use, and Compatibility

17. Decomposed theory of planned behavior
(TPB)
Subjective norms as including two influences: Interpersonal influence and External influence
Extended TPB using the decomposition PBC component into two dimensions:
Self-efficacy and Facilitating conditions.

18. Research methodology (1/3)
Target :
Students taking the course on Electronic Commerce at a large university located in the north Taiwan.
Subjects selected reasons :
About 40% of Internet users are college students
Online consumers generally are younger and better educated
The use of students as subjects in this study can decrease the effect of variance in web-based literacy.

19. Research methodology (2/3)
All items were measured using a seven-point Likert-type scale (ranging from 1 = strongly disagree to 7 = strongly agree).
With the establishment of content validity, the questionnaire was refined through rigorous pre-testing.
The appendix lists was generated.

20. Research methodology (3/3)
The hypothesized models are empirically tested using the structural equation modeling (SEM) approach.
The measurement model was estimated using confirmatory factor analysis (CFA) to test reliability and validity of the measurement model.

21. Measure reliability and validity
Use confirmatory factor analysis (CFA) to examine the reliability and validity.

22. Structural model results

23. Findings (1/4)
behavioral intention is the primary direct determinant of actual usage. The TPB and decomposed TPB model add perceived behavioral control as an additional direct determinant of behavior.
The decomposition of beliefs provide some additional insight into behavioral intention.

24. Findings (2/4)
There is good evidence that innovation characteristics (such as consumer perceptions of usefulness, ease of use and compatibility of online shopping) provide a more efficient approach for assessing consumer attitudes towards online shopping

25. Findings (3/4)
The crucial to develop a Web site with valuable functions, such as designing simple ordering providing customizable personal websites.
Regarding the subjective norms associated with online shopping, two TPB models found the influence of subjective norms on behavioral intentions to be insignificant.

26. Findings (4/4)
The results showed that only self-efficacy is positively associated with perceived behavioral control. This result is consistent with previous research on B2C e-commerce.
The facilitating conditions for online hopping did not significantly influence perceptions of behavioral control.

27. Comparison and selection of models
The decomposed TPB model helps improve understanding of perceived behavioral control as a determinant of behavioral intention.
If the key objective is to predict actual usage, TAM can be considered preferable.
However, the decomposed TPB model provides a fuller understanding of the determinants of behavioral intentions.

28. Implication for research and practice
Managerial perspective : This study indicates that using innovation characteristics (such as consumer perceptions of usefulness, ease of use and compatibility of online shopping) for decomposition provides useful, actionable information which online retailer managers can use to plan appropriate online shopping services.

29. Limitations and further research
The IS literature suggests that determinants of intended behavioral change are based on user level of experience. (Included high- and low-experience consumers.)
Apply the three models to examine other types of online retailers.
The three competing models should be tested further using samples from other countries, and further testing would provide a more robust test of the three competing models.