Social%20Media%20Marketing%20Analytics%20????????

1
Social Media Marketing Analytics????????
Tamkang University
??????? (Confirmatory Factor Analysis)
1032SMMA07 TLMXJ1A (MIS EMBA)Fri 12,13,14
(1920-2210) D326
Min-Yuh Day ??? Assistant Professor ?????? Dept.
of Information Management, Tamkang
University ???? ?????? http//mail.
tku.edu.tw/myday/ 2015-05-15
2
???? (Syllabus)

• ?? (Week) ?? (Date) ?? (Subject/Topics)
• 1 2015/02/27 ???????(????)
• 2 2015/03/06 ????????????
  (Course Orientation for Social Media
  Marketing Analytics)
• 3 2015/03/13 ???????? (Social Media
  Marketing Analytics)
• 4 2015/03/20 ???????? (Social Media
  Marketing Research)
• 5 2015/03/27 ???? (Measuring the Construct)
• 6 2015/04/03 ?????(????)
• 7 2015/04/10 ?????????? I
  (Case Study on Social Media Marketing I)
• 8 2015/04/17 ????? (Measurement and
  Scaling)
• 9 2015/04/24 ??????? (Exploratory Factor
  Analysis)

3
???? (Syllabus)

• ?? (Week) ?? (Date) ?? (Subject/Topics)
• 10 2015/05/01 ?????????? (Social Computing
  and Big Data Analytics)
  Invited Speaker Irene Chen,
  Consultant, Teradata
• 11 2015/05/08 ???? (Midterm Presentation)
• 12 2015/05/15 ??????? (Confirmatory Factor
  Analysis)
• 13 2015/05/22 ?????? (Social Network
  Analysis)
• 14 2015/05/29 ?????????? II
  (Case Study on Social Media
  Marketing II)
• 15 2015/06/05 ???????? (Sentiment Analysis
  on Social Media)
• 16 2015/06/12 ???? I (Term Project
  Presentation I)
• 17 2015/06/19 ????? (????)
• 18 2015/06/26 ???? II (Term Project
  Presentation II)

4
Outline

• Confirmatory Factor Analysis (CFA)
• Structured Equation Modeling (SEM)
• Partial-least-squares (PLS) based SEM (PLS-SEM)
• PLS, PLS-Graph, Smart-PLS
• Covariance based SEM (CB-SEM)
• LISREL, EQS, AMOS

5
Joseph F. Hair, G. Tomas M. Hult, Christian M.
Ringle, Marko Sarstedt, A Primer on Partial
Least Squares Structural Equation Modeling
(PLS-SEM), SAGE, 2013
Source http//www.amazon.com/Partial-Squares-Stru
ctural-Equation-Modeling/dp/1452217440/
6
???, ?????????SPSS???PLS-SEM (SmartPLS), ????,
2014
Source http//24h.pchome.com.tw/books/prod/DJAV0S
-A82328045
7
Second generation Data Analysis Techniques
Confirmatory Factor Analysis (CFA)
Structural Equation Modeling (SEM)
Partial-least-squares-based SEM (PLS-SEM)
Covariance-based SEM (CB-SEM)
LISREL EQS AMOS
PLS PLS-Graph Smart-PLS
8
Types of Factor Analysis

• Exploratory Factor Analysis (EFA)
• is used to discover the factor structure of a
  construct and examine its reliability. It is
  data driven.
• Confirmatory Factor Analysis (CFA)
• is used to confirm the fit of the hypothesized
  factor structure to the observed (sample) data.
  It is theory driven.

9
Structural Equation Modeling (SEM)

• Structural Equation Modeling (SEM) techniques
  such as LISREL and Partial Least Squares (PLS)
  are second generation data analysis techniques

10
Data Analysis Techniques

• Second generation data analysis techniques
• SEM
• PLS, LISREL
• statistical conclusion validity
• First generation statistical tools
• Regression models
• linear regression, LOGIT, ANOVA, and MANOVA

11
SEM models in the IT literature

• Partial-least-squares-based SEM (PLS-SEM)
• PLS, PLS-Graph, Smart-PLS
• Covariance-based SEM (CB-SEM)
• LISREL, EQS, AMOS

12
The TAM Model
13
Structured Equation Modeling (SEM)

• Structural model
• the assumed causation among a set of dependent
  and independent constructs
• Measurement model
• loadings of observed items (measurements)on
  their expected latent variables (constructs).

14
Structured Equation Modeling (SEM)

• The combined analysis of the measurement and the
  structural model enables
• measurement errors of the observed variables to
  be analyzed as an integral part of the model
• factor analysis to be combined in one operation
  with the hypotheses testing
• SEM
• factor analysis and hypotheses are tested in the
  same analysis

15
Structure Model
16
Structured Equation Modeling (SEM) Path Model
(Causal Model)
X
Y
Satisfaction
Loyalty
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
17
Structured Equation Modeling (SEM) Path Model and
Constructs
Satisfaction
Reputation
Loyalty
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
18
Mediating Effect (Mediator)
Satisfaction
Loyalty
Reputation
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
19
Continuous Moderating Effect (Moderator)
Income
Loyalty
Reputation
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
20
Categorical Moderation Effect (Moderator)
Loyalty
Reputation
Females
Significant Difference?
Loyalty
Reputation
Males
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
21
Hierarchical Component Model First Order
Construct vs. Second Order Construct
First (Lower)Order Components
Second (Higher)Order Components
Price
Service Quality
Satisfaction
Personnel
Service-scape
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
22
Measurement Model
23
Measuring Loyalty5 Variables (Items)
(51)(Zeithaml, Berry Parasuraman, 1996)
Say positive things about XYZ to other people.
Loyalty
Recommend XYZ to someone who seeks your advice.
Encourage friends and relatives to do business
with XYZ.
Consider XYZ your first choice to buy services.
Do more business with XYZ in the next few years.
Source Valarie A. Zeithaml, Leonard L. Berry and
A. Parasuraman, The Behavioral Consequences of
Service Quality, Journal of Marketing, Vol. 60,
No. 2 (Apr., 1996), pp. 31-46
24
Measurement Model
Loy_1
Loyalty
Loy_2
Loy_3
Loy_4
Loy_5
25
Example of a Path Model With Three Constructs
CSOR Corporate Social Responsibility ATTR
Attractiveness COMP Competence
csor_1
csor_2
CSOR
csor_3
csor_4
comp_1
COMP
csor_5
comp_2
comp_3
ATTR
attr_1
attr_2
attr_3
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
26
Difference Between Reflective and Formative
Measures
Construct domain
Construct domain
Reflective Measurement Model
Formative Measurement Model
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
27
Satisfaction as a Reflective Construct
I appreciate this hotel
SAT
I am looking forward to staying in this hotel
I recommend this hotel to others
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
28
Satisfaction as a Formative Construct
Formative Construct
The service is good
SAT
The personnel is friendly
The rooms are clean
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
29
Satisfaction as a Reflective and Formative
Construct
Reflective Measurement Model
Formative Measurement Model
I appreciate this hotel
The service is good
SAT
SAT
I am looking forward to staying in this hotel
The personnel is friendly
I recommend this hotel to others
The rooms are clean
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
30
Reflective Construct ?Formative Construct ?
1
Causal priority between the indicator and the
construct From the construct to the indicators
reflective From the indicators to the construct
formative Diamantopoulos and Winklhofer (2001)
Reflective Measurement Model
Formative Measurement Model
Indicator 1
Indicator 1
Construct
Construct
Indicator 2
Indicator 2
Indicator 3
Indicator 3
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
31
Reflective Construct ?Formative Construct ?
2
Is the construct a trait explaining the
indicators or rather a combination of the
indicator? If trait reflective If combination
formative Fornell and Bookstein (1982)
Reflective Measurement Model
Formative Measurement Model
Indicator 1
Indicator 1
Construct
Construct
Indicator 2
Indicator 2
Indicator 3
Indicator 3
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
32
Reflective Construct ?Formative Construct ?
3
Do the indicators represent consequences or
causes of the construct? If consequences
reflective If causes formative Rossieter (2002)
Reflective Measurement Model
Formative Measurement Model
Indicator 1
Indicator 1
Construct
Construct
Indicator 2
Indicator 2
Indicator 3
Indicator 3
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
33
Reflective Construct ?Formative Construct ?
4
Are the items mutually interchangeable? If yes
reflective If no formative Jarvis, MacKenzie,
and Podsakoff (2003)
Reflective Measurement Model
Formative Measurement Model
Indicator 1
Indicator 1
Construct
Construct
Indicator 2
Indicator 2
Indicator 3
Indicator 3
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
34
Structured Equation Modeling (SEM)
Source Nils Urbach and Frederik Ahlemann (2010)
"Structural equation modeling in information
systems research using partial least squares, "
Journal of Information Technology Theory and
Application, 11(2), 5-40.
35
Structured Equation Modeling (SEM) with Partial
Least Squares (PLS)
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
36
Framework for Applying PLS in Structural Equation
Modeling
Source Nils Urbach and Frederik Ahlemann (2010)
"Structural equation modeling in information
systems research using partial least squares, "
Journal of Information Technology Theory and
Application, 11(2), 5-40.
37
CB-SEM vs. PLS-SEM
CB-SEM
PLS-SEM
Theory Testing
Prediction (Theory Development)
Source Nils Urbach and Frederik Ahlemann (2010)
"Structural equation modeling in information
systems research using partial least squares, "
Journal of Information Technology Theory and
Application, 11(2), 5-40.
38
Source Joseph F. Hair, G. Tomas M. Hult,
Christian M. Ringle, Marko Sarstedt (2013), A
Primer on Partial Least Squares Structural
Equation Modeling (PLS-SEM), SAGE
39
Use of Structural Equation Modeling Tools
1994-1997
40
Comparative Analysis between Techniques
41
Capabilities by Research Approach
42
TAM Model and Hypothesis
43
TAM Causal Path Findings via Linear Regression
Analysis
44
Factor Analysis and Reliabilities for Example
Dataset
45
TAM Standardized Causal Path Findings via LISREL
Analysis
46
Standardized Loadings and Reliabilities in LISREL
Analysis
47
TAM Causal Path Findings via PLS Analysis
48
Loadings in PLS Analysis
49
AVE and Correlation Among Constructs in PLS
Analysis
50
Generic Theoretical Network with Constructs and
Measures
51
Number of Covariance-based SEM Articles Reporting
SEM Statistics in IS Research
52
Number of PLS Studies Reporting PLS Statistics in
IS Research(Rows in gray should receive special
attention when reporting results)
53
Structure Model
54
Structure Model
55
Measurement Model
56
SEM

• The holistic analysis that SEM is capable of
  performing is carried out via one of two distinct
  statistical techniques
• 1. covariance analysis employed in LISREL, EQS
  and AMOS
• 2. partial least squares employed in PLS and
  PLS-Graph

57
Comparative Analysis Based on Statistics Provided
by SEM
58
Comparative Analysis Based on Capabilities
59
Comparative Analysis Based on Capabilities
60
Heuristics for Statistical Conclusion Validity
(Part 1)
61
Heuristics for Statistical Conclusion Validity
(Part 2)
62
(No Transcript)
63
(No Transcript)
64
A Practical Guide To Factorial Validity Using
PLS-Graph

• Gefen, David and Straub, Detmar (2005) "A
  Practical Guide To Factorial Validity Using
  PLS-Graph Tutorial And Annotated Example,"
  Communications of the Association for Information
  Systems Vol. 16, Article 5.Available at
  http//aisel.aisnet.org/cais/vol16/iss1/5

65
PLS-Graph Model
66
Extracting PLS-Graph Model
67
Displaying the PLS-Graph Model
68
PCA with a Varimax Rotation of the Same Data
69
Correlations in the lst file as compared with the
Square Root of the AVE
70
Explaining Information Technology Usage A Test
of Competing Models
Source Premkumar, G., and Anol Bhattacherjee
(2008), "Explaining information technology usage
A test of competing models," Omega 36(1), 64-75.
71
Explaining Information Technology Usage A Test
of Competing Models
Source Premkumar, G., and Anol Bhattacherjee
(2008), "Explaining information technology usage
A test of competing models," Omega 36(1), 64-75.
72
Explaining Information Technology Usage A Test
of Competing Models
Source Premkumar, G., and Anol Bhattacherjee
(2008), "Explaining information technology usage
A test of competing models," Omega 36(1), 64-75.
73
Explaining Information Technology Usage A Test
of Competing Models
Source Premkumar, G., and Anol Bhattacherjee
(2008), "Explaining information technology usage
A test of competing models," Omega 36(1), 64-75.
74
Explaining Information Technology Usage A Test
of Competing Models
Source Premkumar, G., and Anol Bhattacherjee
(2008), "Explaining information technology usage
A test of competing models," Omega 36(1), 64-75.
75
Explaining Information Technology Usage A Test
of Competing Models
Source Premkumar, G., and Anol Bhattacherjee
(2008), "Explaining information technology usage
A test of competing models," Omega 36(1), 64-75.
76
Summary

• Confirmatory Factor Analysis (CFA)
• Structured Equation Modeling (SEM)
• Partial-least-squares (PLS) based SEM (PLS-SEM)
• PLS
• Covariance based SEM (CB-SEM)
• LISREL

77
References

• Joseph F. Hair, William C. Black, Barry J. Babin,
  Rolph E. Anderson (2009), Multivariate Data
  Analysis, 7th Edition, Prentice Hall
• Joseph F. Hair, G. Tomas M. Hult, Christian M.
  Ringle, Marko Sarstedt (2013), A Primer on
  Partial Least Squares Structural Equation
  Modeling (PLS-SEM), SAGE
• Gefen, David Straub, Detmar and Boudreau,
  Marie-Claude (2000) "Structural Equation Modeling
  and Regression Guidelines for Research
  Practice," Communications of the Association for
  Information Systems Vol. 4, Article 7.Available
  at http//aisel.aisnet.org/cais/vol4/iss1/7
• Straub, Detmar Boudreau, Marie-Claude and
  Gefen, David (2004) "Validation Guidelines for IS
  Positivist Research," Communications of the
  Association for Information Systems Vol. 13,
  Article 24. Available at http//aisel.aisnet.org
  /cais/vol13/iss1/24
• Gefen, David and Straub, Detmar (2005) "A
  Practical Guide To Factorial Validity Using
  PLS-Graph Tutorial And Annotated Example,"
  Communications of the Association for Information
  Systems Vol. 16, Article 5.Available at
  http//aisel.aisnet.org/cais/vol16/iss1/5
• Urbach, Nils, and Frederik Ahlemann (2010)
  "Structural equation modeling in information
  systems research using partial least squares, "
  Journal of Information Technology Theory and
  Application, 11(2), 5-40. Available at
  http//aisel.aisnet.org/cgi/viewcontent.cgi?articl
  e1247contextjitta
• Premkumar, G., and Anol Bhattacherjee (2008),
  "Explaining information technology usage A test
  of competing models," Omega 36(1), 64-75.
• ??? (2014), ?????????SPSS???PLS-SEM (SmartPLS),
  ????