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Analytics and Data Science - Advances in Research and Pedagogy
of: Amit V. Deokar, Ashish Gupta, Lakshmi S. Iyer, Mary C. Jones
Springer-Verlag, 2017
ISBN: 9783319580975 , 299 Pages
Format: PDF, Read online
Copy protection: DRM
Contents
5
About the Authors
7
Chapter 1: Exploring the Analytics Frontiers Through Research and Pedagogy
9
Chapter 2: Introduction: Research and Research-in-Progress
14
2.1 Introduction
14
2.2 Organizational Use and Impact of Business Intelligence and Analytics
15
2.3 Social Media Analytics
16
2.4 Individual, Organizational and Societal Implications of Big Data
17
2.5 Conclusion
18
References
19
Chapter 3: Business Intelligence Capabilities
21
3.1 Introduction
22
3.2 What is BI?
23
3.3 Classification of BI Capabilities
24
3.3.1 BI Innovation Infrastructure Capability
25
3.3.2 BI Process Capabilities
27
3.3.3 BI Integration Capability
28
3.4 Using the Taxonomy
29
References
31
Chapter 4: Big Data Capabilities: An Organizational Information Processing Perspective
34
4.1 Introduction
34
4.2 Literature Review and Research Model
36
4.3 Methodology
39
4.3.1 Research Model Fine-Tuning
39
4.3.2 Research Design and Measures
40
4.3.2.1 The Conceptualization and Measurement of ‘Fit’
40
4.3.2.2 Measures
41
4.3.3 Pilot Testing
42
4.3.4 Data Collection
42
4.4 Current State of the Research and Preliminary Findings
42
4.5 Conclusion
43
References
44
Chapter 5: Business Analytics Capabilities and Use: A Value Chain Perspective
46
5.1 Introduction
47
5.2 Background and Related Literature
48
5.2.1 Porter’s Value Chain
49
5.2.1.1 Analytics Capabilities of Organization
49
5.3 Methodology
49
5.4 Preliminary Analysis and Results
50
5.4.1 Discussion of Results
54
5.5 Conclusion and Future Research
54
References
57
Chapter 6: Critical Value Factors in Business Intelligence Systems Implementations
60
6.1 Introduction
61
6.2 Theoretical Background
62
6.2.1 Value Theory
62
6.2.1.1 IS and BI Success Theory
63
6.3 Methodology
69
6.3.1 Phase I: Expert Panel and Open-Ended Questionnaire
70
6.3.2 Phase II: Instrument, Data Collection, and Exploratory Factor Analysis (EFA)
71
6.3.3 Phase III: Confirmatory Factor Analysis (CFA)
72
6.4 Data Analysis and Results
72
6.4.1 SQ: Exploratory Factor Analysis—PCA
72
6.4.2 IQ: Exploratory Factor Analysis—PCA
73
6.4.3 Confirmatory Factor Analysis (CFA)
75
6.5 Findings
76
6.6 Discussion
77
6.7 Contributions of the Study
79
6.8 Limitations and Suggestions for Future Research
79
6.9 Conclusion
80
References
81
Chapter 7: Business Intelligence System Use in Chinese Organizations
84
7.1 Introduction
84
7.2 Theoretical Background
85
7.2.1 IS and BI Research in China
85
7.2.2 Guanxi and Other Chinese Cultural Norms
86
7.2.3 Research Constructs and Concepts
86
7.3 Research Method and Design
89
7.3.1 Case Study Sites
89
7.3.2 Data Collection and Analysis Method
90
7.4 Preliminary Results and Discussion
90
7.4.1 Changes to the Research Construct Set
90
7.4.2 Propositions about Chinese BI Systems Use
91
7.5 Working Conclusion
96
References
97
Chapter 8: The Impact of Customer Reviews on Product Innovation: Empirical Evidence in Mobile Apps
100
8.1 Introduction
100
8.2 A Persuasion Theory—Elaboration Likelihood Model
103
8.3 Research Hypotheses
104
8.3.1 The Amount of Information
105
8.3.2 Review Readability
105
8.3.3 Review Sentiment
106
8.4 Research Methodology
107
8.4.1 The Stratified Cox Proportional Hazard Model
107
8.4.2 Data
108
8.4.3 Variables
108
8.4.4 Results
109
8.4.4.1 Descriptive Statistics
109
8.4.4.2 Hypotheses Testing Results
110
8.5 Discussion and Conclusions
111
References
113
Chapter 9: Whispering on Social Media
116
9.1 Introduction
116
9.2 Literature Review
117
9.3 Research Questions
118
9.4 Data Description
119
9.5 Empirical Results
121
9.6 Conclusion
123
References
123
Chapter 10: Does Social Media Reflect Metropolitan Attractiveness? Behavioral Information from Twitter Activity in Urban Areas
124
10.1 Introduction
124
10.2 Related Work
126
10.2.1 Definition and Measurement of Geo-spatial Attractiveness
127
10.2.2 Location-Based Recommendation Systems
128
10.2.3 Recognition of Events from Social Media Streams
129
10.2.4 Research Gap
129
10.3 Identifying Areas of Social Attractiveness
130
10.3.1 Twitter Data Characteristics
132
10.3.2 Social Attractiveness
133
10.4 Regression Analysis
138
10.4.1 Assessing Explanatory Value of Twitter Measures
141
10.4.2 Findings
143
10.5 Concluding Remarks
143
References
146
Chapter 11: The Competitive Landscape of Mobile Communications Industry in Canada: Predictive Analytic Modeling with Google Trends and Twitter
148
11.1 Introduction
148
11.2 Literature Review
150
11.2.1 Consumer Related Research Involving Google Trends Data
150
11.2.2 Use of Social Media and Twitter in Predictive Models
152
11.3 Predictive Modeling
153
11.3.1 Market Data
154
11.3.2 Competitor Effects
157
11.3.3 Effects of Sentiments and Twitter Data
157
11.4 Results
159
11.5 Discussion
163
11.6 Conclusions
164
References
167
Chapter 12: Scale Development Using Twitter Data: Applying Contemporary Natural Language Processing Methods in IS Research
168
12.1 Background
168
12.2 The State of Scale Development
170
12.2.1 Extracting Meaning from Social Media Data
171
12.3 Natural Language Processing (NLP) Methods
172
12.3.1 The NLP Approach: Syntax-Aware Phrase Extraction
172
12.3.2 The Need for a Technology Delights and Hassles Scale
173
12.4 Analysis and Preliminary Results
174
12.5 Analysis and Results
175
12.5.1 Collection of Tweets
175
12.5.2 Pre-filtering and POS Tagging
175
12.5.3 Syntax-Aware n-Gram Selection
176
12.5.4 Generating Themes from Tri-gram Lists
176
12.5.5 Cross-Validation of Themes from Twitter Data
177
12.6 Discussion and Next Steps
179
12.7 Conclusion and Future Directions
179
References
181
Chapter 13: Information Privacy on Online Social Networks: Illusion-in-Progress in the Age of Big Data?
184
13.1 Introduction
184
13.2 Literature Review
187
13.3 Theoretical Framework and Hypotheses
188
13.3.1 Prospect Theory
188
13.3.2 Rational Apathy Theory
188
13.4 Hypotheses Testing
190
13.5 Hypotheses Testing
192
13.6 Conclusion
196
13.6.1 Study Summarization
196
13.6.2 Key Findings
196
13.6.3 Contribution and Implications
196
13.6.4 Limitations of this Study
197
References
198
Chapter 14: Online Information Processing of Scent-Related Words and Implications for Decision Making
202
14.1 Introduction
202
14.2 Study 1: Individual Differences in Affective Responses to Scent-Related Words
204
14.2.1 Literature Review and Hypotheses
204
14.2.2 Methods and Procedures
207
14.2.3 Electrophysiological Recordings
208
14.2.4 Results
208
14.2.5 Discussion
210
14.3 Study 2: Evaluations and Behavioral Intentions to Scented Brand Names
211
14.3.1 Literature Review and Hypotheses
211
14.3.2 Method and Procedures
212
14.3.3 Results
214
14.3.4 Discussion
216
14.4 General Conclusion and Discussion
217
References
220
Chapter 15: Say It Right: IS Prototype to Enable Evidence-Based Communication Using Big Data
222
15.1 Introduction
223
15.2 IS Prototype Architecture
223
15.2.1 Building Block 1: Backend Architecture with Big Data Analytics
224
15.2.2 Building Block 2: User Interface
224
15.3 Conclusion
225
References
226
Chapter 16: Introduction: Pedagogy in Analytics and Data Science
227
16.1 Introduction
227
16.2 The Papers in the Teaching Track
228
References
230
Chapter 17: Tools for Academic Business Intelligence and Analytics Teaching: Results of an Evaluation
231
17.1 Introduction
231
17.2 Theoretical Foundations
232
17.2.1 The Value of Hands-on Lessons
233
17.2.2 The BI&A Framework
233
17.3 Methodology
235
17.3.1 University-Specific Requirements
236
17.4 Tool Evaluations and Recommendations
238
17.4.1 Sub-domain “(Big) Data Analytics”
238
17.4.2 Sub-domain “Text Analytics”
241
17.4.3 Sub-domain “Web Analytics”
244
17.4.4 Sub-domain “Network Analytics”
246
17.4.5 Sub-domain “Mobile Analytics”
249
17.5 Conclusion, Limitations, and Further Work
251
References
253
Chapter 18: Neural Net Tutorial
255
18.1 Introduction
255
18.2 Overview of Neural Nets
256
18.2.1 Structure of a Neurode
256
18.2.2 Layout of a Neural Net
257
18.2.3 Training a Neural Net
258
18.2.4 Advantages and Disadvantages of Neural Nets
260
18.3 Example Implementation of a Neural Net
260
18.3.1 Download the Neural Network Software
260
18.3.2 Download a Copy of the Data File
260
18.3.3 Create the Neural Network
263
18.3.3.1 Start the Application
263
18.3.3.2 Define Input and Output
263
18.3.3.3 Growing and Training the Network
264
18.3.3.4 Results of Training
265
18.3.3.5 Using the Neural Network to Make a Prediction
266
18.4 Conclusion
266
References
267
Chapter 19: An Examination of ERP Learning Outcomes: A Text Mining Approach
268
19.1 Introduction
268
19.1.1 ERP Course Overview
270
19.2 Background and Theory
270
19.2.1 ERP Simulation and Learning
270
19.2.2 Situational Learning Theory
271
19.2.3 Importance of ERP Learning
272
19.2.4 Role Adaptions in ERPSIM
272
19.3 Research Methodology
272
19.3.1 Background/Classroom Setting
272
19.3.2 Situated Learning Adaption
274
19.3.3 ERP Role Play Strategy
274
19.4 Results
275
19.4.1 Qualitative Analysis of Student Role Responses
275
19.4.2 Quantitate Content Analysis of Student Role Responses
275
19.5 Conclusions and Limitations
279
References
280
Chapter 20: Data Science for All: A University-Wide Course in Data Literacy
283
20.1 Introduction
283
20.2 The Environment
284
20.3 Course Goals
285
20.4 Course Structure
287
20.4.1 Overview of Module 1: Data in Our Daily Lives
288
20.4.2 Overview of Module 2: Telling Stories with Data
289
20.4.3 Overview of Module 3: Working with Data in the Real World
290
20.4.4 Overview of Module 4: Analyzing Data
292
20.5 Final Project
292
20.6 Conclusions
294
Appendix: Abbreviated Course Syllabus for Data Science
295
Course Description
295
Course Objectives
295
Assignments
295
Schedule and Reading List (Current Configuration Is for Two 80-min Sessions per Week)
296
References
299
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