Deokar / Gupta / Iyer Analytics and Data Science

Advances in Research and Pedagogy
1. Auflage 2018
ISBN: 978-3-319-58097-5
Verlag: Springer Nature Switzerland
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 299 Seiten

Reihe: Annals of Information Systems

This book explores emerging research and pedagogy in analytics and data science that have become core to many businesses as they work to derive value from data. The chapters examine the role of analytics and data science to create, spread, develop and utilize analytics applications for practice. Selected chapters provide a good balance between discussing research advances and pedagogical tools in key topic areas in analytics and data science in a systematic manner. This book also focuses on several business applications of these emerging technologies in decision making, i.e., business analytics. The chapters in Analytics and Data Science: Advances in Research and Pedagogy are written by leading academics and practitioners that participated at the Business Analytics Congress 2015. Applications of analytics and data science technologies in various domains are still evolving. For instance, the explosive growth in big data and social media analytics requires examination of the impact of these technologies and applications on business and society. As organizations in various sectors formulate their IT strategies and investments, it is imperative to understand how various analytics and data science approaches contribute to the improvements in organizational information processing and decision making. Recent advances in computational capacities coupled by improvements in areas such as data warehousing, big data, analytics, semantics, predictive and descriptive analytics, visualization, and real-time analytics have particularly strong implications on the growth of analytics and data science.

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.

Autoren/Hrsg.

Deokar, Amit V.

Gupta, Ashish

Iyer, Lakshmi S.

Jones, Mary C.

Inhaltsverzeichnis

1;Contents;5
2;About the Authors;7
3;Chapter 1: Exploring the Analytics Frontiers Through Research and Pedagogy;9
4;Chapter 2: Introduction: Research and Research-in-Progress;14
4.1;2.1 Introduction;14
4.2;2.2 Organizational Use and Impact of Business Intelligence and Analytics;15
4.3;2.3 Social Media Analytics;16
4.4;2.4 Individual, Organizational and Societal Implications of Big Data;17
4.5;2.5 Conclusion;18
4.6;References;19
5;Chapter 3: Business Intelligence Capabilities;21
5.1;3.1 Introduction;22
5.2;3.2 What is BI?;23
5.3;3.3 Classification of BI Capabilities;24
5.3.1;3.3.1 BI Innovation Infrastructure Capability;25
5.3.2;3.3.2 BI Process Capabilities;27
5.3.3;3.3.3 BI Integration Capability;28
5.4;3.4 Using the Taxonomy;29
5.5;References;31
6;Chapter 4: Big Data Capabilities: An Organizational Information Processing Perspective;34
6.1;4.1 Introduction;34
6.2;4.2 Literature Review and Research Model;36
6.3;4.3 Methodology;39
6.3.1;4.3.1 Research Model Fine-Tuning;39
6.3.2;4.3.2 Research Design and Measures;40
6.3.2.1;4.3.2.1 The Conceptualization and Measurement of ‘Fit’;40
6.3.2.2;4.3.2.2 Measures;41
6.3.3;4.3.3 Pilot Testing;42
6.3.4;4.3.4 Data Collection;42
6.4;4.4 Current State of the Research and Preliminary Findings;42
6.5;4.5 Conclusion;43
6.6;References;44
7;Chapter 5: Business Analytics Capabilities and Use: A Value Chain Perspective;46
7.1;5.1 Introduction;47
7.2;5.2 Background and Related Literature;48
7.2.1;5.2.1 Porter’s Value Chain;49
7.2.1.1;5.2.1.1 Analytics Capabilities of Organization;49
7.3;5.3 Methodology;49
7.4;5.4 Preliminary Analysis and Results;50
7.4.1;5.4.1 Discussion of Results;54
7.5;5.5 Conclusion and Future Research;54
7.6;References;57
8;Chapter 6: Critical Value Factors in Business Intelligence Systems Implementations;60
8.1;6.1 Introduction;61
8.2;6.2 Theoretical Background;62
8.2.1;6.2.1 Value Theory;62
8.2.1.1;6.2.1.1 IS and BI Success Theory;63
8.3;6.3 Methodology;69
8.3.1;6.3.1 Phase I: Expert Panel and Open-Ended Questionnaire;70
8.3.2;6.3.2 Phase II: Instrument, Data Collection, and Exploratory Factor Analysis (EFA);71
8.3.3;6.3.3 Phase III: Confirmatory Factor Analysis (CFA);72
8.4;6.4 Data Analysis and Results;72
8.4.1;6.4.1 SQ: Exploratory Factor Analysis—PCA;72
8.4.2;6.4.2 IQ: Exploratory Factor Analysis—PCA;73
8.4.3;6.4.3 Confirmatory Factor Analysis (CFA);75
8.5;6.5 Findings;76
8.6;6.6 Discussion;77
8.7;6.7 Contributions of the Study;79
8.8;6.8 Limitations and Suggestions for Future Research;79
8.9;6.9 Conclusion;80
8.10;References;81
9;Chapter 7: Business Intelligence System Use in Chinese Organizations;84
9.1;7.1 Introduction;84
9.2;7.2 Theoretical Background;85
9.2.1;7.2.1 IS and BI Research in China;85
9.2.2;7.2.2 Guanxi and Other Chinese Cultural Norms;86
9.2.3;7.2.3 Research Constructs and Concepts;86
9.3;7.3 Research Method and Design;89
9.3.1;7.3.1 Case Study Sites;89
9.3.2;7.3.2 Data Collection and Analysis Method;90
9.4;7.4 Preliminary Results and Discussion;90
9.4.1;7.4.1 Changes to the Research Construct Set;90
9.4.2;7.4.2 Propositions about Chinese BI Systems Use;91
9.5;7.5 Working Conclusion;96
9.6;References;97
10;Chapter 8: The Impact of Customer Reviews on Product Innovation: Empirical Evidence in Mobile Apps;100
10.1;8.1 Introduction;100
10.2;8.2 A Persuasion Theory—Elaboration Likelihood Model;103
10.3;8.3 Research Hypotheses;104
10.3.1;8.3.1 The Amount of Information;105
10.3.2;8.3.2 Review Readability;105
10.3.3;8.3.3 Review Sentiment;106
10.4;8.4 Research Methodology;107
10.4.1;8.4.1 The Stratified Cox Proportional Hazard Model;107
10.4.2;8.4.2 Data;108
10.4.3;8.4.3 Variables;108
10.4.4;8.4.4 Results;109
10.4.4.1;8.4.4.1 Descriptive Statistics;109
10.4.4.2;8.4.4.2 Hypotheses Testing Results;110
10.5;8.5 Discussion and Conclusions;111
10.6;References;113
11;Chapter 9: Whispering on Social Media;116
11.1;9.1 Introduction;116
11.2;9.2 Literature Review;117
11.3;9.3 Research Questions;118
11.4;9.4 Data Description;119
11.5;9.5 Empirical Results;121
11.6;9.6 Conclusion;123
11.7;References;123
12;Chapter 10: Does Social Media Reflect Metropolitan Attractiveness? Behavioral Information from Twitter Activity in Urban Areas;124
12.1;10.1 Introduction;124
12.2;10.2 Related Work;126
12.2.1;10.2.1 Definition and Measurement of Geo-spatial Attractiveness;127
12.2.2;10.2.2 Location-Based Recommendation Systems;128
12.2.3;10.2.3 Recognition of Events from Social Media Streams;129
12.2.4;10.2.4 Research Gap;129
12.3;10.3 Identifying Areas of Social Attractiveness;130
12.3.1;10.3.1 Twitter Data Characteristics;132
12.3.2;10.3.2 Social Attractiveness;133
12.4;10.4 Regression Analysis;138
12.4.1;10.4.1 Assessing Explanatory Value of Twitter Measures;141
12.4.2;10.4.2 Findings;143
12.5;10.5 Concluding Remarks;143
12.6;References;146
13;Chapter 11: The Competitive Landscape of Mobile Communications Industry in Canada: Predictive Analytic Modeling with Google Trends and Twitter;148
13.1;11.1 Introduction;148
13.2;11.2 Literature Review;150
13.2.1;11.2.1 Consumer Related Research Involving Google Trends Data;150
13.2.2;11.2.2 Use of Social Media and Twitter in Predictive Models;152
13.3;11.3 Predictive Modeling;153
13.3.1;11.3.1 Market Data;154
13.3.2;11.3.2 Competitor Effects;157
13.3.3;11.3.3 Effects of Sentiments and Twitter Data;157
13.4;11.4 Results;159
13.5;11.5 Discussion;163
13.6;11.6 Conclusions;164
13.7;References;167
14;Chapter 12: Scale Development Using Twitter Data: Applying Contemporary Natural Language Processing Methods in IS Research;168
14.1;12.1 Background;168
14.2;12.2 The State of Scale Development;170
14.2.1;12.2.1 Extracting Meaning from Social Media Data;171
14.3;12.3 Natural Language Processing (NLP) Methods;172
14.3.1;12.3.1 The NLP Approach: Syntax-Aware Phrase Extraction;172
14.3.2;12.3.2 The Need for a Technology Delights and Hassles Scale;173
14.4;12.4 Analysis and Preliminary Results;174
14.5;12.5 Analysis and Results;175
14.5.1;12.5.1 Collection of Tweets;175
14.5.2;12.5.2 Pre-filtering and POS Tagging;175
14.5.3;12.5.3 Syntax-Aware n-Gram Selection;176
14.5.4;12.5.4 Generating Themes from Tri-gram Lists;176
14.5.5;12.5.5 Cross-Validation of Themes from Twitter Data;177
14.6;12.6 Discussion and Next Steps;179
14.7;12.7 Conclusion and Future Directions;179
14.8;References;181
15;Chapter 13: Information Privacy on Online Social Networks: Illusion-in-Progress in the Age of Big Data?;184
15.1;13.1 Introduction;184
15.2;13.2 Literature Review;187
15.3;13.3 Theoretical Framework and Hypotheses;188
15.3.1;13.3.1 Prospect Theory;188
15.3.2;13.3.2 Rational Apathy Theory;188
15.4;13.4 Hypotheses Testing;190
15.5;13.5 Hypotheses Testing;192
15.6;13.6 Conclusion;196
15.6.1;13.6.1 Study Summarization;196
15.6.2;13.6.2 Key Findings;196
15.6.3;13.6.3 Contribution and Implications;196
15.6.4;13.6.4 Limitations of this Study;197
15.7;References;198
16;Chapter 14: Online Information Processing of Scent-­Related Words and Implications for Decision Making;202
16.1;14.1 Introduction;202
16.2;14.2 Study 1: Individual Differences in Affective Responses to Scent-Related Words;204
16.2.1;14.2.1 Literature Review and Hypotheses;204
16.2.2;14.2.2 Methods and Procedures;207
16.2.3;14.2.3 Electrophysiological Recordings;208
16.2.4;14.2.4 Results;208
16.2.5;14.2.5 Discussion;210
16.3;14.3 Study 2: Evaluations and Behavioral Intentions to Scented Brand Names;211
16.3.1;14.3.1 Literature Review and Hypotheses;211
16.3.2;14.3.2 Method and Procedures;212
16.3.3;14.3.3 Results;214
16.3.4;14.3.4 Discussion;216
16.4;14.4 General Conclusion and Discussion;217
16.5;References;220
17;Chapter 15: Say It Right: IS Prototype to Enable Evidence-­Based Communication Using Big Data;222
17.1;15.1 Introduction;223
17.2;15.2 IS Prototype Architecture;223
17.2.1;15.2.1 Building Block 1: Backend Architecture with Big Data Analytics;224
17.2.2;15.2.2 Building Block 2: User Interface;224
17.3;15.3 Conclusion;225
17.4;References;226
18;Chapter 16: Introduction: Pedagogy in Analytics and Data Science;227
18.1;16.1 Introduction;227
18.2;16.2 The Papers in the Teaching Track;228
18.3;References;230
19;Chapter 17: Tools for Academic Business Intelligence and Analytics Teaching: Results of an Evaluation;231
19.1;17.1 Introduction;231
19.2;17.2 Theoretical Foundations;232
19.2.1;17.2.1 The Value of Hands-on Lessons;233
19.2.2;17.2.2 The BI&A Framework;233
19.3;17.3 Methodology;235
19.3.1;17.3.1 University-Specific Requirements;236
19.4;17.4 Tool Evaluations and Recommendations;238
19.4.1;17.4.1 Sub-domain “(Big) Data Analytics”;238
19.4.2;17.4.2 Sub-domain “Text Analytics”;241
19.4.3;17.4.3 Sub-domain “Web Analytics”;244
19.4.4;17.4.4 Sub-domain “Network Analytics”;246
19.4.5;17.4.5 Sub-domain “Mobile Analytics”;249
19.5;17.5 Conclusion, Limitations, and Further Work;251
19.6;References;253
20;Chapter 18: Neural Net Tutorial;255
20.1;18.1 Introduction;255
20.2;18.2 Overview of Neural Nets;256
20.2.1;18.2.1 Structure of a Neurode;256
20.2.2;18.2.2 Layout of a Neural Net;257
20.2.3;18.2.3 Training a Neural Net;258
20.2.4;18.2.4 Advantages and Disadvantages of Neural Nets;260
20.3;18.3 Example Implementation of a Neural Net;260
20.3.1;18.3.1 Download the Neural Network Software;260
20.3.2;18.3.2 Download a Copy of the Data File;260
20.3.3;18.3.3 Create the Neural Network;263
20.3.3.1;18.3.3.1 Start the Application;263
20.3.3.2;18.3.3.2 Define Input and Output;263
20.3.3.3;18.3.3.3 Growing and Training the Network;264
20.3.3.4;18.3.3.4 Results of Training;265
20.3.3.5;18.3.3.5 Using the Neural Network to Make a Prediction;266
20.4;18.4 Conclusion;266
20.5;References;267
21;Chapter 19: An Examination of ERP Learning Outcomes: A Text Mining Approach;268
21.1;19.1 Introduction;268
21.1.1;19.1.1 ERP Course Overview;270
21.2;19.2 Background and Theory;270
21.2.1;19.2.1 ERP Simulation and Learning;270
21.2.2;19.2.2 Situational Learning Theory;271
21.2.3;19.2.3 Importance of ERP Learning;272
21.2.4;19.2.4 Role Adaptions in ERPSIM;272
21.3;19.3 Research Methodology;272
21.3.1;19.3.1 Background/Classroom Setting;272
21.3.2;19.3.2 Situated Learning Adaption;274
21.3.3;19.3.3 ERP Role Play Strategy;274
21.4;19.4 Results;275
21.4.1;19.4.1 Qualitative Analysis of Student Role Responses;275
21.4.2;19.4.2 Quantitate Content Analysis of Student Role Responses;275
21.5;19.5 Conclusions and Limitations;279
21.6;References;280
22;Chapter 20: Data Science for All: A University-Wide Course in Data Literacy;283
22.1;20.1 Introduction;283
22.2;20.2 The Environment;284
22.3;20.3 Course Goals;285
22.4;20.4 Course Structure;287
22.4.1;20.4.1 Overview of Module 1: Data in Our Daily Lives;288
22.4.2;20.4.2 Overview of Module 2: Telling Stories with Data;289
22.4.3;20.4.3 Overview of Module 3: Working with Data in the Real World;290
22.4.4;20.4.4 Overview of Module 4: Analyzing Data;292
22.5;20.5 Final Project;292
22.6;20.6 Conclusions;294
22.7; Appendix: Abbreviated Course Syllabus for Data Science;295
22.7.1; Course Description;295
22.7.2; Course Objectives;295
22.7.3; Assignments;295
22.7.4; Schedule and Reading List (Current Configuration Is for Two 80-min Sessions per Week);296
22.8;References;299