Analytics and Data Science (eBook)

Amit V. Deokar is an Assistant Professor of Management Information Systems in the Robert J. Manning School of Business at the University of Massachusetts Lowell. Dr. Deokar received his PhD in Management Information Systems from the University of Arizona. He also earned a MS in Industrial Engineering from the University of Arizona and a BE in Mechanical Engineering from VJTI, University of Mumbai. His research interests include data analytics, enterprise data management, business intelligence, business process management, and collaboration processes. His work has been published in journals such as Journal of Management Information Systems, Decision Support Systems (DSS), The DATA BASE for Advances in Information Systems, Information Systems Frontiers, Business Process Management Journal (BPMJ) and IEEE Transactions. He is currently a member of the editorial board of DSS and BPMJ journals. He has been serving as the Decision Support and Analytics Track Chair at the international AMCIS 2014-17 conferences, and is currently the Chair-Elect of the AIS Special Interest Group on Decision Support and Analytics (SIGDSA). He was recognized with the 2014 IBM Faculty Award for his research and teaching in the areas of analytics and big data.Ashish Gupta is an Associate Professor of Analytics in Raymond J. Harbert College of Business at the Auburn University. Prior to this, he served as the (founding) director of Analytics Research Center and an Associate Professor of Analytics & IS in the College of Business at the University of Tennessee Chattanooga. He has been a Visiting Research Scientist at the Mayo Clinic Rochester, Visiting Associate Professor in Biomedical Informatics at the Arizona State University and research affiliate with University of Tennessee Health Science Center in Memphis. He has a PhD in MSIS from Spears School of Business at Oklahoma State University. Dr. Gupta’s research interests are in the areas of data analytics, healthcare informatics, sports analytics, organizational and individual performance. His recent articles have appeared in journals such as MIT Sloan Management Review, Journal of Biomedical Informatics, IEEE Transactions, Information Systems Journal, European Journal of Information Systems, Decision Support Systems, Information Systems Frontiers, and Communications of the Association for Information Systems. His research has been funded by several agencies and private enterprises. He has published 4 edited books.Lakshmi Iyer is Professor and Director of the Master’s in Applied Data Analytics Graduate Programs at the Walker College of Business, Appalachian State University. Her research interests are in the area of business analytics, knowledge management, emerging technologies & its impact on organizations and users, and social inclusion in computing. Her research work has been published in or forthcoming in Communications of the AIS, Journal of Association for Information Systems, European Journal of Information Systems, Communications of the ACM, Decision Support Systems, eService Journal, Journal of Electronic Commerce Research, International Journal of Business Intelligence Research, Information Systems Management, Journal of Global Information Technology and Management, and others. She is a Board member of Teradata University Network, recent past-chair of the Special Interest Group in Decision Support and Analytics (SIGDSA, formerly SIGDSS). She has served as a Guest Editor for Communications of the ACM, and the Journal of Electronic Commerce Research. She is also co-editor of Annals of Information Systems Special Issue on “Reshaping Society through Analytics, Collaboration, and Decision Support: Role of BI and Social Media,” from the 2013 pre-ICIS workshop in Milan, Italy.Mary C. Jones is Professor of information systems and Chair of the Information Technology and Decision Sciences Department at the University of North Texas. She received her doctorate from the University of Oklahoma in 1990. Her work appears in numerous journals including MIS Quarterly, European Journal of Information Systems, Behavioral Science, Decision Support Systems, System Dynamics Review, and Information and Management. Her research interests are primarily in the impact on organizations of large scale, organizational spanning information systems such as ERP or business intelligence systems. She teaches a variety of courses including Enterprise Applications of Business Intelligence, IT Project Management, and a doctoral seminar in General Systems Theory.

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
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