Analytics Enabled Decision Making 9811996571, 9789811996573

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Analytics Enabled Decision Making Edited by Vinod Sharma Chandan Maheshkar Jeanne Poulose

Analytics Enabled Decision Making “Analytics has become critically important in all aspects of the business. If you want to familiarize yourself with cutting-edge thinking on this topic, do read ‘Analytics Enabled Decision Making ’. It provides an excellent introduction to analytics in key areas of decision-making.” —Professor Jochen Wirtz, Vice Dean MBA Programmes, National University of Singapore

Vinod Sharma · Chandan Maheshkar · Jeanne Poulose Editors

Analytics Enabled Decision Making

Editors Vinod Sharma Symbiosis Centre for Management and Human Resource Development (SCMHRD) Symbiosis International (Deemed University) Pune, Maharashtra, India

Chandan Maheshkar East Nimar Education Society Indore, Madhya Pradesh, India

Jeanne Poulose CHRIST (Deemed to be University) Ghaziabad, Uttar Pradesh, India

ISBN 978-981-19-9657-3 ISBN 978-981-19-9658-0 (eBook) https://doi.org/10.1007/978-981-19-9658-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Palgrave Macmillan imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Foreword

The use of analytics can be found as far back as the nineteenth century when Frederick Winslow Taylor initiated time management exercises. Another example is when Henry Ford measured the speed of assembly lines. In the late 1960s, analytics began receiving more attention as computers became decision-making support systems. Today, Data, undoubtedly, is what we get to see almost anywhere and everywhere. Needless to say, the Data is enormous and does not stop there; it is growing at a pace beyond imagination! Data is not just about numbers; it is more than just digits! It is no longer just a tool for analysing what has already happened. It is used to inform decisions and helps us understand what may happen in the future. An enterprise needs to have a data-driven culture so that it can make better decisions with confidence. This book provides a valuable window on information assurance and covers the necessary components of how today, owing to the amalgamation of an increasingly complicated world, the vast proliferation of data and the pressing desire to stay at the forefront of competition has prompted organizations to focus on using analytics for driving strategic business decisions. The chapters provide an in-depth view of analytics, with its far-reaching use cases and diverse applications now emerging as the keystone of strategic business decision making. From enabling businesses to make consumer-oriented marketing decisions to helping them address key operational inefficiencies, analytics is radically changing the perception of the importance of data. Advanced statistical models are v

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furthering this cause by providing valuable insights from unconventional data sets and enabling companies to explore new business territories. In this volatile environment of data-driven disruption, organizations must look through two lenses simultaneously. Firstly, they must identify high-risk and rewarding opportunities, such as entering new markets and changing existing business models. Secondly, they must focus on including analytics in their core business decision-making process. By embedding data analytics into their core strategy, organizations can streamline internal business processes, identify unfolding consumer trends, interpret and monitor emerging risks, and build mechanisms for constant feedback and improvement. Driving analytical transformations will thereby enable organizations to gain a competitive edge and stay at the forefront of digital disruption. Having seen how “analytics” has evolved over the years, from manually getting the tasks done to inventing sophisticated platforms and algorithms, it would not be surprising to see what it has in store for the future and how technologically advanced the world will become. Gearing up for better and more advanced technologies ahead! Dr. Anish Agarwal Director Analytics, Dr. Reddy’s Laboratories, Hyderabad, India

Preface

Nimbleness, resilience and transparency are the new norms in decision making, given the volume, variety and velocity of the data being generated and managed every minute. Business analytics comes to the aid of decision-makers in this context by combining data, mathematical and statistical models, and information technology to produce insightful alternatives. It provides systematic mechanisms to explore a range of contexts, factors and relationships to gain insights and drive business in competitive settings. In simple words, analytics equips practitioners with business intelligence and the predictive capabilities needed to navigate the volatile and ever-changing work environment. Globalization and increasing technological interventions have created new challenges for organizations that compel them to look for strategies to ensure their competence and control operational sustainability. The increased attention that the analytics domain is garnering can be ascertained by the explosion in the number and variety of data science and analytics-related job openings in almost every domain, be it marketing, finance, HR, sports or medicine. The demand for data analysts with critical thinking and interpersonal skills has risen enormously. A report by Imarticus Learning suggests that in India alone, the analytics and data science jobs have seen a jump of 30% in April 2022 as compared to April 2021. Along with the number of jobs, the compensation packages have exploded, with the annual data science package going up by almost 25.4%. Data analytics professionals are witnessing growth in median salaries to 35%. It indicates that organizations increasingly acknowledge the importance of data literacy among their employees. They have also commenced vii

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investing in the data training of their employees, having understood the tangible benefits of better decision-making capability among employees and lower employee turnover. The scope of analytics is thus continuously expanding, leading to an increased demand for trained individuals. This people demand is only set to increase given the digital transformation that almost every sector, including retail, banking, healthcare, etc, is witnessing. This rapid growth has stimulated the interest of students and working professionals worldwide to acquire knowledge in this field. Scholars and practitioners are also trying to explore and add further the existing body of knowledge. While a reasonable number of books talk about business analytics, their emphasis is largely conceptual. This book, Analytics Enabled Decision Making, is also a sincere effort to fill the existing gap by addressing the need for content on applying analytics in decision making in the real world. It provides critical insights into decision making and enables its readers to consider analytics tools in different cases and contexts critically. This book would empower practitioners and scholars with an advanced understanding of analytical methodologies for decision making. Since this book will be an edited volume, it offers different methodological perspectives and eliminates gaps between theory and practice by eliminating dubious assumptions regarding which decision-making practices have been considered. The book’s coverage has considered the relationship between analytical/statistical theory and practice for explaining different concepts, the character of variables, and possible relationships among these variables and influencers in the runtime business environment. All the chapters are attributed that ‘relevance’ and ‘being critical’ are qualities that fill the gaps between theory and practice in the knowledge economy, which would make this book universally acceptable. Chapter “Analytics Enabled Decision Making “Tracing the Journey from Data to Decisions ”,” by the editors of this book, introduces analytics and analytics-enabled decision making and enlightens the potential of analytics to drive decision making. This chapter presents a decisionmaking framework exhibiting how the decision-making functions. The authors have used different contexts and cases to establish the relevance of each step of this decision-making framework. Chapter “Algorithms as Decision-Makers,” by Rauno Rusko, SannaAnnika Koivisto and Sara Jestilä, presents algorithms as decision-makers. It showcases the role of DMA and DSA in algorithm-based decision

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making. This literature review-based chapter uses the case of Wolt Enterprise for its algorithm-based business model. Chapter “Influence of Big Data Analytics on Business Intelligence,” by Sudhanshu Kumar Guru, highlights how the advancement of Internet technology and Cloud computing, Big Data became popular for big and mid-level businesses. The chapter provides a fundamental understanding of data warehousing and BI to compel progressive transformation of business processes with Big Data analytics. It explains how Big Data analytics influences BI processes. This chapter lets the readers conceptualize the power of Big Data analytics, which can handle high volumes of data. Chapter “Determining the Degree of Dominance of Factors Deriving the Comparative Choice Hierarchy: An Operational Generalization of Latent Choice Models,” by Salman Cheema, Tanveer Kifayat, Irene L. Hudson, Asif Mehmood, Kalim Ullah, and Abdur R. Rahman, determines the dominance of factors responsible for comparative choice hierarchy. It presents an operational generalization of latent choice models. The chapter demonstrates the application of a well-cherished exponential family of distributions. Authors have used the varying extent of worth parameters describing the preference order, different sample sizes and distinguished stochastic formations to utilize the historical data to describe choice behaviours. Chapter “Baseball Informatics—From MiLB to MLB Debut,” by Chung-Hao Lee and Woei-jyh Lee, concentrates on Baseball Informatics. Authors have presented analytics to estimate players’ likelihood of being a part of the Major League. They performed exploratory data analysis to filter non-baseball data and baseball performance variables. They have used machine learning techniques to analyse and rank stats and data variables. The chapter compared four sets of variable selections to train and validate models to predict the likelihood of a drafted player reaching the Major Leagues. Chapter “Efficacy of Artificial Neural Networks (ANN) as a Tool for Predictive Analytics,” by Deepti Sinha, Pradeepta Kumar Sarangi and Sachin Sinha, is focused on defining predictive analytics and the tools used in predictive analytics, with a particular orientation on artificial neural networks. The chapter establishes ANN as an effective technique for making appropriate predictions, thereby contributing to decision making in various spheres using the outcomes from various research. Chapter “The Role of Financial Analytics in Decision-Making for Better Firm Performance,” by Sangeetha Rangasamy, Kavitha

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Rajamohan, Anju Kalluvelil Janardhanan, and K. S. Manu, focuses on the role of financial analytics in decision making concerning a firm’s compelling performance. Thus, the chapter discusses the evolution of big data analytics, the theoretical underpinnings of financial analytics, and SWOC analysis. Chapter “Using Analytics to Manage and Predict Employee Performance,” by James E. Phelan, provides an overview of several human behaviour/psychological analytics that can be used to assess current performances and predict future performance. Further, the chapter presents case illustrations for using analytics to attain meaningful data that improve corporate performance. The author presented ways to control uncertainty and enhance organizations’ performance through analytics. Chapter “Using Analytics to Manage Employee Behavioural Traits and Predict Employee Performance,” by Namita Mangal, explores the importance of HR analytics for performance management and the metrics used by organizations for measuring employee engagement and performance management. The chapter explains how predictive analytics can identify the factors influencing individual or team performance. Chapter “Platform Business Model for Intelligent Supply Chain Operations,” by Manikandan M. K. Manicka, highlights platform business as a new-age business model. According to the author, this business format greatly impacts how business operations are executed. The chapter describes that the platform business model makes the entire business operations more transparent. Real-time data transfer is leading to efficiency across the entire supply chain operations. Chapter “The Role of Consumption in the Identity Formation of Conservative Women: A Web Analytics and Netnographic Exploration,” by Altan Kar, Rifat Kamasak and Baris Yalcinkaya, explains the web analytics and netnography approaches. The chapter presents an empirical investigation of the consumption patterns of traditional and modern women in Turkey using the Search Engine Results Page (SERP) and thematic analyses. Chapter “Using Analytics to Measure the Impact of Pollution Parameters in Major Cities of India,” by Manohar Kapse, N. Elangovan, Abhishek Kumar, and Joseph Durai Selvam, measures the impact of pollution parameters in major cities of India. This chapter relates the air pollution levels with the spread of Covid-19 in the major cities of India during the second phase of the pandemic. The results showcased in this chapter state that some pollutants positively and negatively affect the level of infection.

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Analytics empowers practitioners with an epistemology to assess, evaluate and implement decisions to achieve intended objectives. It helps to generate better insights into decision making, analyse the impacts of decisions upon their implementations and make corrective measures. It also enables practitioners to appreciate the dynamics of their organization, predict shifts in their areas of operations, and manage risks. The book tries to capture these aspects through the contributions of various authors. The chapters sufficiently communicate the intended objectives. This book is helpful to stakeholders involved in decision-making practices with its concepts and cases, which enable them to embrace the potential of analytics to discover and interpret opportunities and challenges via the systematic study of data. This book can serve as a reference for practitioners, academicians and scholars. The impetus for the editors to present this book volume is to encourage the use of analytics in decision making so that organizations can maintain pace with competition and competence in a dynamic socio-economic environment. Vinod Sharma Symbiosis Centre for Management and Human Resource Development Symbiosis International University Pune, India Chandan Maheshkar Centre of Internal Quality Assurance Madhya Pradesh Bhoj (Open) University Bhopal, India Jeanne Poulose School of Business and Management CHRIST (Deemed to be University) Ghaziabad, India

Acknowledgements

In the Name of God, Most Gracious, Most Merciful This book ‘Analytics Enabled Decision Making ’ is a collection of plentiful research works by authors and researchers from different professional backgrounds and countries. We want to thank all the academicians, researchers, reviewers and individuals whose sincere efforts have helped us complete this book in the best possible manner. We are extremely thankful to Professor Jochen Wirtz, Vice Dean of MBA Programmes, National University of Singapore, for providing an endorsement for the book. Their invaluable words of appreciation will help every one of us to understand the real worth of our hard work. We are highly thankful to Dr. Anish Agarwal, Director of Analytics, Dr. Reddy’s Laboratories, for sparing their valuable time and penning the book’s foreword. We consider ourselves truly fortunate for the encouragement we received from him. Special thanks to all the reviewers who gave their precious time and made sincere efforts to review all the manuscripts. Their honest suggestions and advice helped us enrich the quality of the chapters of the book. We are grateful to all the authors who have contributed their work to this book. Also, we thank the people who permitted them to execute their

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research and develop chapters through current descriptions to make this book a noteworthy contribution in the area of analytics. The contribution of our families can never be undermined for bearing with us while we pretended to work. Their love, sacrifice and support helped us focus and continue in this direction. We are also grateful for the support of our colleagues, both past and present. Above all, we are thankful to the almighty for his continued blessings upon us. We wish to pray to the Almighty for his kindness and eternal grace on us at all times to help us accomplish our goals.

Contents

Analytics Enabled Decision Making “Tracing the Journey from Data to Decisions ” Vinod Sharma, Jeanne Poulose, and Chandan Maheshkar

1

Algorithms as Decision-Makers Rauno Rusko, Sanna-Annika Koivisto, and Sara Jestilä

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Influence of Big Data Analytics on Business Intelligence Sudhanshu Kumar Guru

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Determining the Degree of Dominance of Factors Deriving the Comparative Choice Hierarchy: An Operational Generalization of Latent Choice Models Salman A. Cheema, Tanveer Kifayat, Irene L. Hudson, Asif Mehmood, Kalim Ullah, and Abdur R. Rahman Baseball Informatics—From MiLB to MLB Debut Chung-Hao Lee and Woei-jyh Lee Efficacy of Artificial Neural Networks (ANN) as a Tool for Predictive Analytics Deepti Sinha, Pradeepta Kumar Sarangi, and Sachin Sinha

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The Role of Financial Analytics in Decision-Making for Better Firm Performance Sangeetha Rangasamy, Kavitha Rajamohan, Anju Kalluvelil Janardhanan, and K. S. Manu

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Using Analytics to Manage and Predict Employee Performance James E. Phelan

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Using Analytics to Manage Employee Behavioural Traits and Predict Employee Performance Namita Mangal

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Platform Business Model for Intelligent Supply Chain Operations Manikandan M. K. Manicka

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The Role of Consumption in the Identity Formation of Conservative Women: A Web Analytics and Netnographic Exploration Altan Kar, Rifat Kamasak, and Baris Yalcinkaya Using Analytics to Measure the Impact of Pollution Parameters in Major Cities of India Manohar Kapse, N. Elangovan, Abhishek Kumar, and Joseph Durai Selvam Index

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Notes on Contributors

Salman A. Cheema is currently serving National Textile University Faisalabad, Pakistan in the role of Assistant Professor of Statistics. Dr. Cheema gained his Ph.D. Degree from University of Newcastle, Australia. He completed his graduation from Virginia Tech, USA. His research interests include, data masking, privacy protection in the self-reported data, analysis of choice behaviours and utility determinants, negotiation strategies and health surveillance. His collaborative research has been published at forums such as Sociological Research and Methods, Communication in Statistics, Stat and Optik. He is a regular speaker at Modelling and Simulation Society of Australia and New Zealand. N. Elangovan is Associate Professor in the School of Business and Management at Christ (Deemed to be University), Bangalore, India. He also coordinates the Ph.D. Programme at the School. He was earlier the Director of the National Institute of Fashion Technology (NIFT), Kannur Campus. He comes to academics after a long experience in running a textile business. He earned a Ph.D. in Management Science from Anna University, Chennai, India. He holds an MBA in Marketing and an M.Sc. in Psychology. He also holds a BE in Mechanical from Bharathiar University and a B.A. in psychology from Madras University. He has published in journals including the International Journal of enterprise resource planning, MethodsX, Journal of International Technology and Information Management and International Journal of Innovation and Technology

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Management. His research interest is in the areas of Strategic Information Systems, Entrepreneurship, Consumer behaviour studies, Design and innovation. His book chapters have appeared in “Research into Design for a Connected World, Smart Innovation, Systems and Technologies” published by Springer and “Handbook of Research on Remote Work and Worker Well-Being in the Post-COVID-19 Era” published by IGI Global. Sudhanshu Kumar Guru is currently working as Solution Architect in Micron a world-leading organization in memory chip manufacturing, in India. He has 15 years of IT experience with depth in Data Technologies, Cloud and Business Intelligence. He loves to solve complex data problems with the help of modern data platforms and tools like Cloud computing, Big Data, Machine Learning etc. Sudhanshu has completed M.Tech. from BITS Pilani with specialization in Data Analytics. He loves mentoring college graduates and professionals in the field of Information Technology. Prior to working with Micron, He has worked with Accenture and EPAM systems in the USA and India. He lives in Hyderabad. Irene L. Hudson is a professor of Statistics and Data Analytics, Royal Melbourne Institute of Technology (RMIT), Australia and Conjoint Professor, the University of Newcastle Australia. Prof. Hudson is elected fellow of Royal Statistical Society. She has co-authored two books, numerous book chapters, journal articles and conference proceedings. Her collaborative research interests include health surveillance, gender studies, drug discovery, informatics, data visualization, causal inference and climate change analytics. Over the years, her research has been appreciated by a wide range of academic research circles. Anju Kalluvelil Janardhanan is a higher education academic with a paramount vision to achieve successful outcomes for stakeholders. She has extensive experience in teaching graduate and postgraduate learners from varied nationalities in the areas of Accounting, Finance and General Management. She has successfully embedded activities and resources into her professional practice which support and enhance the student learning experience both in Australia and overseas. Currently, she is working as a Lecturer with Crown Institute of Higher Education, North Sydney, NSW, Australia. Her average teaching feedback is 4.4/5 in the last two years. Her research articles have received several best paper awards and are published in academic journals of international repute. Her qualifications include Ph.D. (Commerce), M.Phil. (Commerce), MBA (General

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Management), M.Com. (Financial Management) and B.Com. (Taxation). She is an IBM Certified Business Analyst, Member—Institute of Public Accountants Australia, Associate—Institute of Financial Accountants UK, Certified Microsoft Innovative Educator, MIE Trainer, Certified Peer Reviewer, UGC NET qualified Assistant Professor in Commerce and ACCP Advanced Diploma in Software Engineering. Sara Jestilä is a project designer at the University of Lapland. She is specialized in organizational and business development and digitalization. She has worked on projects regarding these specialized areas as a project designer and project manager. Rifat Kamasak is Professor of Management and Strategy at Yeditepe University, Istanbul, Turkey. He also holds board membership positions in several companies listed in Istanbul Stock Exchange (Borsa Istanbul, BIST—100). He worked in the food, confectionery, carpet, textile, aluminum, metal, retailing, trading and consulting industries for nearly twenty years. He has done research, consultancy and training at a large number of organizations and runs his family’s traditional hand-made carpet business. Having completed his bachelor’s degree in economics and postgraduate diploma in international management in the University of Istanbul, he received his M.A. in marketing from Middlesex University London, M.A. in management from Durham University, and M.Sc. in applied linguistics from the University of Oxford and Ph.D. in management studies from the University of Exeter (2014). His primary interest areas are strategic management, knowledge and innovation and diversity management. Manohar Kapse has more than 15 years of teaching experience at UG and PG levels in various programs. He has taught Machine Learning, Statistics, Multivariate Analysis, R, SPSS, Research Methodology, and Quantitative Techniques. He had also conducted many workshops on SPSS, R, and Machine Learning as Resource Person in many renowned organizations. Other than teaching, he is also involved in statistical consultancy to researchers working in different domains. He had published more than 20 research papers, many cases, and a book. His research interest lies in the application of Machine Learning using R to problems in various disciplines. Presently he is associated with Symbiosis International University, Pune, SCMHRD as Assistant Professor in Business Analytics. He had been awarded a Ph.D. in Management from the

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Institute of Management Studies, Devi Ahilya Vishwavidyalaya Indore, M.Phil. in Statistics, Master’s Degree in Statistics, and presently pursuing an MBA (Data Science and Analytics). Altan Kar is Associate Professor of Sociology at Yeditepe University, Istanbul Turkey and she acts as the head of E-business department. She has thirty years of experience in the higher education, training and consulting industries. Altan Kar has conducted research in consumer behaviour, cross-cultural studies, sociology of communication, people’s body and identity formation and fashion, and popular culture related consumption patterns of social media users. She is an activist and engaged scholar, driven by values of equality for all in work and society. Her recent project which included more than three hundred 65+ aged individuals aimed to increase the e-literacy awareness of the participants has been completed with great success. She has a bachelor’s degree in social anthropology and holds master’s and Ph.D. degrees in sociology. Tanveer Kifayat earned her doctoral degree in Statistics. Her research advances the methodological tools for the exploration of latent choice phenomena through paired comparison models. Her collaborative research has been appreciated at reputed forums such as, Communication in Statistics, Computers, Materials & Continua and Applied Sciences. Sanna-Annika Koivisto is an M.A. in education and a project manager at the University of Lapland. She is specialized in, e.g., leadership phenomenon, organizational development, digitalization and education. Abhishek Kumar is working in TCS as a Business analyst for the past 10 years. He has a rich experience in the analysis of customer requirements and providing on-time delivery. He has worked in domains like Retail, Healthcare and Banking. For the past few years, banking is his major area of specialization. Chung-Hao Lee was a research assistant at the University of Maryland, College Park (UMD). He holds a bachelor’s degree from the National Tsing Hua University and a master’s degrees from UMD. His works focus on data analytics, machine learning, and supply chain analytics. As an avid baseball fan, he has great domain knowledge of all aspects of baseball. That’s why he chose baseball as his first analytics topic. He received recognition from the UMD as an outstanding graduate student award nomination and case competitions prize winner.

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Woei-jyh Lee received BSE degree from the National Taiwan University, M.S. degree from the Courant Institute at New York University and Ph.D. degree from the University of Maryland at College Park (UMD). Chandan Maheshkar is a Senior Consultant at the Centre for Internal Quality Assurance (CIQA), Madhya Pradesh Bhoj (Open) University, Bhopal, India. He has served several management institutes in central India including the University of Indore, India, in various academic roles. Also, he is one of the founders of the East Nimar Society for Education (2019) dedicated to quality improvement in higher education and the development of educator competencies. Dr. Maheshkar earned his MBA and Ph.D. from the University of Indore, India. In 2014, the University of Indore awarded him Golden Jubilee Research Scholarship on the occasion of the completion of its successful 50 years. Business education, HRD, Cross-Culture Business, and organizational behaviour are his core areas of research interest. Namita Mangal is working as an assistant professor at SGT University. She has done her Ph.D. from FAM, University of Delhi. Her research is in the field of Cross-cultural management and organizational development. She has expertise in statistical tools like SPSS, AMOS, R and Tableau. She has taught HR Analytics at several reputed institutes. She has a passion for research and has several publications on her name. She has around four years of academic and industry experience. Manikandan M. K. Manicka is a seasoned teacher of marketing subjects. He has eighteen years of teaching experience. Dr. Manikandan has done his Ph.D. from Anna University Chennai, in the area of Private Label Brands. He has published 14 research articles and has presented papers at international and national conferences. He is currently associated with CHRIST (Deemed to be University), Bengaluru as Assistant Professor. Dr. Manikandan strongly feels that the activities of Marketing are in transition due to the disruption caused by technology. Marketing will embrace more concepts and techniques that are specific to operations management for better value creation for the customers. Dr. Manikandan MK Manicka has handled courses related to operations like Supply Chain Management, Material Management, and Operations Management. He is keen to explore the opportunity that is provided by Services Operations. There is a big scope to create more value for both the company and customers through better design of services operations.

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K. S. Manu is presently working as an Assistant Professor in the School of Business and Management, CHRIST (Deemed to be) University, Bengaluru. He completed Mechanical engineering from VTU, Belgaum and Completed MBA from University of Bangalore. He received his Doctor of Philosophy (Ph.D.) from University of Mysore. He possesses more than twelve years of teaching, research and consultancy experience. He teaches finance and analytics subjects such as Financial Econometrics, Artificial Intelligence and Python Programming. His area of interest includes Business Analytics, Financial Econometrics, Capital and Derivatives Markets, and other Financial Markets. He has published various research articles in refereed journals. He has attended and presented papers in various National and International Conferences. He has been a Financial Econometrics Resource person trainer for few Faculty Development Programs. Asif Mehmood is serving in the Department of Mathematics, at Air University Islamabad, Pakistan. His primary research interests include advances in fluid mechanics, intelligent algorithm development and regularities in complex phenomena. James E. Phelan, LCSW, MBA, Psy.D received a Master degree in Social Work from Marywood University, a Master in Business Administration from Franklin University, and Doctorate from California Southern University. He is presently a program coordinator for the Veterans Health Administration, Columbus Ohio and field practicum instructor for The Ohio State University. He also serves as an online faculty professor for Liberty University, Grand Canyon University, and Indiana Wesleyan University. Jeanne Poulose is an Assistant Professor of Management with the School of Business and Management, CHRIST (Deemed to be University), Delhi NCR, India. She has a Ph.D. and M. Phil. in Management besides an MBA with a specialization in Finance and Human Resources She has around 20 years of Industry-Academia experience in the Retail, Banking and Educational sectors through leadership and teaching roles in organizations like ICICI Bank, GlobalNxt University, St. Joseph’s Degree & PG College, etc. She teaches Organizational Behaviour, Human Resource Management, Workforce Planning and Employee Selection and Performance Management.

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Abdur R. Rahman recently earned his MPhil degree in Statistics from Quaid-e-Azam University Islamabad. His collaborative research includes machine learning models, Statistics in Physics and child malnutrition. He has published at research forums such as Computers, Materials & Continua, Digest Journal of Nanomaterials and Biostructures and MODSIM conference. Kavitha Rajamohan is Associate Professor of Computer Science with Christ University since 2009. She has had nearly eighteen years of teaching experience with 11 years research experience. In 2017, she has received a Best Ph.D. project award from Anna University, Chennai, Tamil Nadu. Dr. Kavitha has presented many research papers at National and International conferences and received Best Paper awards. She also served as a session chair for International conferences. She has published many research works in double-blind peer-reviewed journals and SCOPUS-indexed journals. Dr. Kavitha is an Empaneled guide for Ph.D. and M.Phil. for CHRIST University. Her research interests include Data Analytics, IOT, Smart Home Wireless Sensor Networks, Mobile Technology and Blockchain. Sangeetha Rangasamy is currently working as an associate professor of Management, in CHRIST (Deemed to be University), Bengaluru, India. Research interests and publications are in the fields of Banking, Stock market, Big Data Analytics and Technology based Education. Has done a major research project on, “Financial Literacy and Investment Behavior of Middle-Class Families in Karnataka” which is funded by CHRIST Deemed to be University. Acted as resource person in national level workshops and FDP titled Business Analytics and Data Visualization Tool—TABLEAU. Have published research papers in National and International peer-reviewed journals. Also acted as reviewers in Ushus Journal of Management, International Journal of Finance and Banking Research, Global Business Review and Finance Research Letters. Rauno Rusko is a lecturer at the University of Lapland. His research activities focus on cooperation, coopetition, strategic management, supply chain management and entrepreneurship mainly in the branches of information communication technology, forest industry and tourism. He has several published chapters in the scientific books of several publishers. His articles appeared in the European Management Journal, Forest Policy

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and Economics, International Journal of Business Environment, Industrial Marketing Management, International Journal of Innovation in the Digital Economy and International Journal of Tourism Research, among others. Pradeepta Kumar Sarangi holds a Doctorate degree in Computer Science and Engineering, Master of Technology degree in Computer Science and Engineering, Master Degree in Computer Application and Master Degree in Business Administration. Dr. Sarangi is having 22 years of teaching and administrative experiences such as Head of the Department, Editor of the Journal, Controller of Examinations and IT in-charge, etc. He is having more than 20 research papers published in various national and international journals and conferences. His major subjects of interest are DBMS, Data Analytics and Machine Learning. Currently, Dr. Sarangi is working as a Professor and Dean in the Department of Computer Science and Engineering in Chitkara University, Punjab, India. Joseph Durai Selvam is an Associate Professor in Finance with a focus on Statistical Analysis of Financial Data. Joseph Durai Selvam has more than a decade of experience in Population Research and worked in Population Research Centre, supported by the Statistics Division of Ministry of Health and Family Welfare. He holds Master’s Degree in Statistics, Master’s Degree in Population Studies, Post Graduate Diploma in Computer Applications and Doctorate Degree in Applied Statistics. Joseph Durai Selvam has more than eight years of experience in Post Graduate Teaching to Management Students in the area related to Statistics, Research Methodology and Operations Research. Joseph Durai Selvam has conducted various externally Funded Research Projects and has more than 10 publications to his credit. Vinod Sharma holds a Doctorate in Marketing with over 21 years of industry-academia experience. He has been associated with multiple management institutes handling Marketing and Analytics courses at the PG level for around 13 years. Specifically, his teaching and research interests include Principles of Marketing, Consumer Behaviour, Marketing Research, Marketing Analytics, Business Research Methodology, and Business Analytics. He has authored over 50 articles in national and international journals, published a book titled Handbook of Research on Cross-Cultural Business Education (2018). He has also completed in 3 International projects with Yale University, USA on Climate Change and

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Business Strategy. He has been involved in many consultation researches projects, conducted various research workshops and also conducted training program in association with MSME and FIEO on various subjects of management. Before joining academia, he was also connected for almost 8 years with the pharmaceutical and energy sector in various capacities in MNCs like Macleod Pharmaceuticals Ltd, Universal Medicare Pvt Ltd., and SSE Plc, UK in the area of sales management. Deepti Sinha has an overall experience of 20 years and is presently associated with Christ (Deemed to be University), Delhi NCR. Her specialization is in Human Resource Management and she has carried out her doctoral work in the area of Quality of Work Life. She is presently on the editorial and review board of a few journals and has published about 24+ research papers in various national and international journals and one book. She is also on the panel of evaluators of NMIMS Global Access School of Continuing Education, Mumbai and is certified as Accredited Management Teacher in the area of Organizational Behaviour by All India Management Association, New Delhi. Sachin Sinha holds a career spanning 25 years till now. He has taught at different business schools and also worked in the field of corporate communications. His doctoral research is on the role of psychographics in consumer behaviour. His areas of academic interest include consumer psychology and marketing communications. Dr. Sinha has been engaged in prestigious consultancy assignments, including projects commissioned by the Ministry of Defence and Ministry of Rural Development of the Government of India, and also the leading advertising agency Mudra Communications. He is empanelled as Visiting Faculty with the National Institute for Micro, Small and Medium Enterprises (ni-msme), Hyderabad, an organisation of the Ministry of MSMEs, Government of India. He has authored and presented research papers at eminent institutions like IIM Ahmedabad, IIM Indore, IIT Delhi and MDI Gurgaon. He has close to 30 publications in international and national journals of repute. He is also a literature and cinema enthusiast, and, in addition to his academic publications, he also has two literary books to his credit. Kalim Ullah is serving one of the leading health facilities in the role of a statistical officer. His research interests encompass health surveillance, optimal sample selection strategies and survival analysis. He has been

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published at forums such as Complexity, Concurrency and Computation: Practice and Experience and MODSIM conference. Baris Yalcinkaya is a senior consultant with fifteen years of experience working alongside executive teams of different businesses in different industries. Baris’s tenure in consulting, including the largest travel & tourism companies and retail services in B2B and B2C, grounded him with a foundation of best methodologies, leading practices, and outstanding client experience. It was these experiences that inspired and compelled him to found a management consulting organization serving the travel, education, manufacturing services, pharmaceutical organizations, and retail industries. He specializes in branding, search engine optimization, creating sales funnels and building traffic for websites and social networks. His responsibilities include but are not limited to monitoring competition for businesses and monitoring behaviours of buyer personas. Baris is also responsible for educating other employees on using digitally transformed sales and marketing systems, including digital marketing tools, Google marketing procedures and organizational marketing apps. Baris Yalcinkaya has been an instructor of digital marketing and e-commerce-related classes for over six years. Areas of teaching include e-commerce, digital marketing, search engine optimization, digital innovations, networking principles and networking security.

List of Figures

Analytics Enabled Decision Making “Tracing the Journey from Data to Decisions ” Fig. 1

Decision-making framework (Source Authors’ own)

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Algorithms as Decision-Makers Fig. 1 Fig. 2

Supply chain of decision-making algorithms and the emphasis of the systematic review The interaction and power of human being vs. algorithm in decision making

35 37

Influence of Big Data Analytics on Business Intelligence Fig. Fig. Fig. Fig. Fig.

1 2 3 4 5

Typical DW/BI flow ETL process ETL vs. ETL process 3Vs—Volume, velocity and variety Cloud computing + Big Data = More data power

47 50 51 52 55

Determining the Degree of Dominance of Factors Deriving the Comparative Choice Hierarchy: An Operational Generalization of Latent Choice Models Fig. 1

(a–n): MPDs under both priors, for both data sets and with respect to all considered sub-cases

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LIST OF FIGURES

Baseball Informatics—From MiLB to MLB Debut Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12

Fig. 13

Fig. 14 Fig. 15 Fig. 16

Work flow in predicting MLB debut Correlation matrix among 31 numerical variables Number of players and MLB debut percentage per Age at Draft group Number of players and MLB debut percentage hitting Bats position Number of players and MLB debut percentage per Height group Number of players and MLB debut percentage per Weight group Number of players and MLB debut percentage per MiLB fielder position Number of players and MLB debut percentage per draft round Number of players and MLB debut percentage per MLB team Number of players and MLB debut percentage per draft year Four baseball stats in each year from 2001 to 2010. a AVG in years. b OBP in years. c SLG in years. d OPS in years Number of players and MLB debut percentage per AVG, OBP, SLG, OPS, and ISO groups. a AVG groups. b OBP groups. c SLG groups. d OPS groups. e ISO groups Intercepts and coefficients in Lasso regressions on All and LCH variable selection. a λ = 4.0e−4 on All variable selection. b λ = 3.8e−4 on LCH variable selection Receiver operating characteristics (ROC) curves on four variable selections across four ML models MLB debut versus OPS on four variable selections across four ML models MLB Debut status on Three True Outcomes (TTO) rate (HR + BB + SO)/G per year from 2001 to 2010

95 96 100 100 101 101 102 103 103 104 105

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107 110 113 119

Efficacy of Artificial Neural Networks (ANN) as a Tool for Predictive Analytics Fig. Fig. Fig. Fig. Fig. Fig.

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The process of predictive analytics A simple ANN architecture Sample network architecture Training of the network Actual vs. predicted graph Actual vs. predicted graph (training)

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LIST OF FIGURES

Fig. Fig. Fig. Fig.

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Residual graph (training) Forecasting the future values using trained network Forecasted values Actual and forecasted graph

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The Role of Financial Analytics in Decision-Making for Better Firm Performance Fig. 1

Hierarchical view of financial technology architecture in organisational decision-making

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Using Analytics to Manage and Predict Employee Performance Fig. 1

Foundations of high-performance teams

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Using Analytics to Manage Employee Behavioural Traits and Predict Employee Performance Fig. Fig. Fig. Fig. Fig. Fig.

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Heatmap for correlation Decision tree for Objective 1 Detailed decision tree rules for Objective 1 Heatmap for Objective 2 Decision tree for Objective 2 Output of decision tree

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The Role of Consumption in the Identity Formation of Conservative Women: A Web Analytics and Netnographic Exploration Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5

The research design of the study (Source Authors’ Own) The average visitor numbers and visit durations of the websites (Source Authors’ Own) The web traffic keywords searched (Source Authors’ Own) The word clouds of the websites analysed (Source Authors’ Own) The overlapping word cloud (Source Authors’ Own)

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List of Tables

Algorithms as Decision-Makers Table 1

The orientation of studies between DSA and DMA

29

Influence of Big Data Analytics on Business Intelligence Table 1

The below table describes more on data warehouse vs. OLTP system

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Determining the Degree of Dominance of Factors Deriving the Comparative Choice Hierarchy: An Operational Generalization of Latent Choice Models Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9

Choice matrix of one decision-maker and k possible strategies, Y = yes and N = No The members of proposed generalization Artificial data sets generated under above-documented specifications Elicit values of Hyper-parameters Estimates of worth parameters and associated absolute errors The estimates of preference probabilities Posterior probabilities of hypotheses and associated Bayes factor Smokers’ choice data Elicited Hyper-parameter for smokers’ choice data

61 65 69 72 76 78 80 82 83 xxxi

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LIST OF TABLES

Table 10 Table 11 Table 12

Estimated values of worth parameters for smokers’ choice data Estimated preference probabilities for smokers’ choice data Posterior probabilities of hypotheses and associated Bayes factor for smokers’ choice data

84 85 86

Baseball Informatics—From MiLB to MLB Debut Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10

Table 11

Draft statistics in five major professional sports in the US Python code to download full names and non-baseball data of MLB drafted players Create bins on variables with a wide range of continuous numeric values Tuned parameters in eXtreme Gradient Boosting (XGB) to build prediction application Tuned parameters in Random Forest (RF) to build prediction application Tuned parameters in Decision Tree (DT) to build prediction application Tuned parameters in Support Vector Machine (SVM) to build prediction application Performance measurements from validation across four ML models on four variable selections Top 10 variables in the variable importance plots (VIP) across four ML models on four variable selections Example prediction application in R built on the tuned XGB model on the Lasso4lch variable selection to predict MLB debut on the MiLB players drafted in 2013 Performance measurements on the application to predict 2013 drafted players across four ML models on four variable selections

92 93 96 98 98 98 99 108 111

114

115

Using Analytics to Manage Employee Behavioural Traits and Predict Employee Performance Table Table Table Table Table Table

1 2 3 4 5 6

Correlation of variables with Per1 or Performance Ratings Regression results of SPSS with Per1 as dependent variables Regression results with Coefficients and t-statistics Correlation between variables for Objective 2 Regression Results from SPSS for Objective 2 Regression results from SPSS with coefficients for Objective 2

212 215 216 219 220 221

LIST OF TABLES

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Using Analytics to Measure the Impact of Pollution Parameters in Major Cities of India Table 1 Table 2

Rt could have the possible values Coefficients of pollutants using elastic net

271 272

Analytics Enabled Decision Making “Tracing the Journey from Data to Decisions ” Vinod Sharma , Jeanne Poulose , and Chandan Maheshkar

1

Introduction

Each day, 2.5 quintillion bytes of data is being created with each click, swipe, or press of a button across the globe (Akter et al., 2019; Marr, 2018). From outer space to the drawing room, data is getting generated continuously, be it through the Mars orbiters relaying data back to Earth at a staggering speed of 160 bits per second (NASA, 2022) or through the 24 million e-commerce websites generating a huge amount of data every second when a customer clicks or scrolls (Gennaro, 2022). Data is undoubtedly the new ammunition that can both win and prevent wars in

V. Sharma (B) Symbiosis Centre for Management and Human Resource Development (SCMHRD), Symbiosis International (Deemed University), Pune, India e-mail: [email protected] J. Poulose School of Business and Management, CHRIST (Deemed to Be University), Delhi NCR, India e-mail: [email protected] C. Maheshkar East Nimar Society for Education, Khandwa, India

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. Sharma et al. (eds.), Analytics Enabled Decision Making, https://doi.org/10.1007/978-981-19-9658-0_1

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the boardroom, battlefield, or a football ground. But data in itself is not the key to success, it is the ability of individuals, teams, and organizations to leverage this data to arrive at insightful and informed decisions, that will provide a clear edge. The strength of this weapon is such that it has galvanized organizations to invest substantially to strengthen their data analysis capabilities (Muhammad et al., 2022). Analytics has emerged as a game changer considering its ability to assist organizations to not only take accurate and timely decisions but also to augment the effectiveness of the traditional decision-making process. Managers can now successfully foresee and forestall problems and capitalize on opportunities. This has led to a significant increase in the demand for analytics-related roles. The Monster annual trends report revealed that big data analytics related roles in organizations is going to be the most in-demand in 2022 (ET Bureau, 2022). The dataset which is big in size, having high velocity and variety, known as big data and the application used to create value of the data known as BDA (Akter et al., 2019) is the buzz word today. Big data is both an opportunity and a challenge. Opportunity, because IoT and cloud access ensures that size doesn’t hinder recording of the data, challenge because the size does pose problems when it comes to reviewing this data. For instance, when an organization has 2 billion active customers worldwide and is able to record every single move on their website, it is indeed great news from the bottom-line perspective. But how does it decide where to focus and what to essentially make of this humongous data? To be effective it has to monitor the activities of these billions of customers, pre-empt their needs and fulfill them. Instagram is doing it and doing it quite well! How does the world’s largest social media network customize content for each user like the famous flashback videos to all its customers so effortlessly? Facebook found the answer yet again in data analytics. Google’s famed project oxygen helped it identify the best practices of its top-performing managers and used the same to train their low-scoring managers. To someone looking at these two categories of managers, there would upfront appear no major difference given the quality of hires at Google but analytics revealed that even the most minor differences could have a deep impact on teams and ultimately the organization (Garvin, 2013). Thus, extracting meaningful information from big data is at the core of data analytics (Yichuan & Terry, 2017). Data analysts possess the ability to see through the data collected from multiple sources and extract the insights for delivering faster and better results (Janssen

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et al., 2017), be it for a product launch, pricing strategy, launching a satellite, fighting a pandemic, training for the Olympics, or even tracing a criminal. While the pervasiveness of data analytics is undoubtedly true, in the context of businesses it will not be an exaggeration to claim that analyticsenabled decision making is the only ‘mantra’ to success. Therefore, organizations are constantly looking for methods to harness the power of analytics to improve their decision making. Data-enabled organizations are able to pre-empt challenging scenarios and prepare in advance, making them strategically competent, therefore, in no time, Big Data Analytics (BDA) has become the mainstream activity of the organization (Yichuan & Terry, 2017). Traditionally, there were three physical factors of production that were responsible for the growth of the organization such as land, capital, labour and now data has transpired as a virtual factor of production which is extensively used by organizations. Studies accentuate that ‘data factor’ has begun to promote industrial and economic growth (Brynjolfsson & McElheran, 2016). The five most successful organizations of the present times also referred to as ‘FAANG’ (Facebook, Amazon, Apple, Netflix, and Google), are all data-driven organizations (Verhoef et al., 2021)and they have attained remarkable advantages over others in collecting, integrating, processing, and utilizing the data to make informed choices (Janssen et al., 2017). The value of data thus can be seen to be often measured by its ability to enhance the quality of decisions which is solely not dependent on the data, but also on the process through which the data has been collected, integrated, and utilized (Janssen & Kuk, 2016). Analytics often requires bringing all the departments of the organization together to examine the underexplored relationships from the data. This can help in making decisions based on the collected data to improve response to a future occurrence (Akter et al., 2019). For example, big data driven recommendation engine has facilitated Amazon to upsurge its sales revenue by 30 percent, Capital One increased its customer retention rate by 87 percent (Akter et al., 2019) and Progressive boosted its market capitalization of over $19 billion by using real-time information, products, and rate comparisons (Davenport & Harris, 2017). Learning from these large companies, small and medium business houses have also started investing heavily in BDA. However, these investments will generate revenues only when BDA is integrated into the decision-making process.

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Despite analytics potential to add value throughout the value chain of a business, there is relatively scant attention given in the literature of BDA and its contribution to the decision-making process (Awan et al., 2021). According to Ransbotham et al. (2016, p. 4) firms often fail to capitalize upon the investment made in integrating analytics for attaining competitive advantage. Therefore, this study attempts to present analytics-enabled decision-making framework and discuss using examples from the real world how the use of analytics helped the decision-making process across sectors and domains. To do this, the current study uses the decisionmaking framework as a base to explore how organizations have used BDA real-time in each step leading to the decision implementation. Understanding the descriptive, diagnostic, predictive, and prescriptive levels of BDA is essential to gain better understanding of its usage in the decision-making process (Delen, 2014). As an organization ups its level of analytics it places itself in a more advantageous position vis-à-vis its competitors as suggested by Gartner’s analytics ascendancy model that suggests that analytics evolves from being descriptive to being prescriptive (Eriksson et al., 2020). 1.1

Descriptive Analytics

Known as the simplest form of analysis it captures all the information there is to capture and presents the varied features of each attribute in either metric forms or at times visually through graphs and charts. For instance, in the healthcare context, the average number of female patients who reported positive during the pandemic is descriptive analytics (Ahmed et al., 2021). It merely describes an attribute but doesn’t explain the why and the how? It simply states what is. Getting the descriptive analytics right is extremely crucial to get diagnostic analytics which is at the next level right (Houtmeyers et al., 2021). In short, this is data mining to understand what may have happened in the past. 1.2

Diagnostic Analytics

While descriptive analytics helps answer the question, ‘what’, diagnostic takes it to the next level of maturity by asking the question, ‘Why’. Data mining in this stage is aimed at understanding why something happened, i.e., diagnosing the root cause. This while based on hindsight data is

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aimed at providing insight to the manager. And it is this insight that further leads to foresight to be able to predict and prescribe. 1.3

Predictive Analytics

This is aimed at trying to forecast and state what is likely to happen in the future given the past and the current data context. The process here also involves data mining but with the objective of predicting and not just describing, rapidly analyzing the data to offer detailed insights. Thus, it is not analysis that is cut off and happening in an independent manner but in a specific direction and with a specific purpose (Gartner). 1.4

Prescriptive Analytics

The focus of prescriptive analytics is as the name itself suggests, to prescribe the most appropriate course of action to be implemented based on the prediction made using data. At this stage, the organization is said to have evolved to the highest level of analytics ability wherein it is able to move ahead of predicting as to what is likely to happen and move towards prescribing what an organization could do to make it happen (Eriksson et al., 2020).

2

Decision-Making Framework

The human mind does not have the ability to comprehend completely a complex problem and take precise decisions within a limited time using inadequate resources (Simon, 1972). Usually, decision making is approached in the manner documented below (Fig. 1): 2.1

Step 1: Problem Identification

The success of any analytics project majorly relies on defining a problem clearly and having the capability to ask the right questions. The literature is overtly flooded with the content on problem identification but there is barely any study where the usages of analytics has been discussed to identify a business problem (Akter et al., 2019). As stated by Davenport and Kim (2013) and Akbarov et al. (2008) in their articles, the business problem can be addressed only if it is pinpointed with thorough detailing.

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Problem Identification

Strategic Implementation

Reveiw of Past Data

Model Building

Data Collection & Processing Fig. 1 Decision-making framework (Source Authors’ own)

What goes around freely in most managerial circles when it comes to problem identification is opinions. When a university sees higher thannormal levels of attrition, the top management may opine that leadership failure is the cause. This belief may result in the decision to change the leadership. But is leadership failure truly the real cause of higher levels of attrition or did the management miss the bus? In a study conducted in a substantially large software development firm, it was noted that there were not many creative ideas forthcoming from the teams. Usually, the creative flow of ideas is perceived to be linked with the experience, educational qualification, or general creativity of an individual. The most natural opinion to hold here would be that people are not just motivated enough to be creative. But data analytics helped identify the real problem here, which was that employees interacted in smaller groups among themselves and rarely with other teams and this resulted in fewer managers spanning the networks and integrating the inputs across groups. This was what stifled their creativity. Data analysis provided the insight that individuals

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who were moving across teams and maintaining relationships with all were able to leverage an initiative of one department and apply it in another. The problem thus was not a lack of creativity but the lack of interaction and networking that is much needed for triggering the creative thought process (Leonardi, 2018). The cross-movement of individuals between teams was found to be an important predictor of creativity among the individuals. Delving into the case of big basket should help establish this point beyond doubt. The online grocery market was small but had a huge potential to grow in India. It was estimated by EY that the Indian online grocery market would reach $350 billion mark by 2015. This sector was growing by 35 percent and had a penetration of 2.3 percent. This potential led to the establishment of Bigbasket.com by a group of entrepreneurs Hari Menon, Vipul Parekh, V. S. Ramesh, V. S. Sudhakar, and Abhinay Choudhary in 2011. Initially, there were no leaders in this sector. A small number of big players like bigbasket.com, Amazon, Flipkart, Grofer, EkStop, and LocalBanya, etc. was serving the market. Gradually Bigbasket.com attained a market share of 35 percent and became the largest online grocery store in India. It now processes around 20,000 orders a day (Department, 2022). Bigbasket.com was the first online store in India. It initiated its operations in tier one cities such as Bengaluru, Delhi, Mumbai, Pune, etc. where traveling time is high and therefore, the unique selling proposition of Bigbasket.com, to begin with, was to offer convenience to its customers. As stated by Mr. Hari Menon, CEO and Co-founder of Bigbasket.com, ‘We need to continuously improve the shopping experience of our customers . With more and more customers choosing mobile handsets to place orders, browsing the entire merchandise is challenging, we need innovative ideas to make Bigbasket customer friendly’ (Customer Analytics at Bigbasket—Product Recommendations, 2016). Subsequently, Bigbasket.com came up with additional interesting features like ‘did you forget ’. Many customers have the tendency to forget items they intended to buy. It was found in one of the studies conducted by Fernandes et al. (2016) that more than 30 percent of customers forget to include items while shopping online. This forgetfulness may translate into financial loss to these online grocery stores. Customers may proceed to buy these forgotten items either from another online store or from physical store situated in nearby location. If they opted to buy online, they would have to bear a higher logistic cost considering the smaller

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order size and this cost would have to be borne by either the customers or e-tailers. Customers would be discouraged to shop if they had to bear the brunt and may opt to shop these forgotten items at a nearby physical store. When these customers shop at the physical store, they generally end up buying more items which would result in a reduction in the next order from the online stores. Thus, Bigbasket.com came up with an algorithm with the ability to predict the items that a customer may have forgotten to order and introduce ‘did you forget ’ feature in the application (Customer Analytics at Bigbasket—Product Recommendations, 2016). Bigbasket.com is a great example to manifest the importance of asking the right question and that identifying the right business problem is the key to success in making in-time and accurate decisions. Data with the right set of questions is thus the first and foremost skill requirement of data scientist. The problem identified rightly is half solved right away and data analytics aids in improving the efficiency of this process. The clarity in problem statement is essential to produce accurate results. Bigbasket had large amount of unstructured data, which it used to comprehend the relationship between various data attributes. It used descriptive analytics to understand the data better and with tools like Tableau and Power BI it was able to gain further insights by simplifying the data (Rangaiah, 2020). 2.2

Step 2: Review Past Data

Reviewing the past findings and data helps in contextualizing the problem statement better. Studies confirmed that formulating the right set of questions not only requires identifying the problem but also reconnoitering the past data (Davenport & Harris, 2017; Salehan & Kim, 2016). The pandemic brought the spotlight on health care data that is constantly being generated through test reports, patient indexes, apps collecting patient information, government bodies seeking health information and so on. The review of this past data helped identify patterns, improve patient care being offered, increase efficiency all around, and by and large predict and prevent in certain cases possible health concerns. Reviewing past data helped researchers identify the virus, provided vital information as to how it could be contained, offered insights to practitioners on the best approaches to fight the virus, track, and trace infected patients, monitor the existing and predict the future hospitalization requirements and so much more (Health Informatics, 2021).

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Steve Jobs may have been right about intuitive decision making in a certain context when he said, ‘Have the courage to follow your heart and intuition; they somehow already know what you want to become’ (Page, 2021) but managers are better advised to back their intuition and passion with analysis of past data to fill the informational gaps prior to taking any major decisions. Intuitive decisions of the most experienced, confident and skilled managers are to be tested in the real world in the backdrop of data. Inappropriate decisions taken purely on instinct without any data backup may cost the organisation and the individual alike. A case in point would be Toyota’s decision based on historical data analysis, to pay more attention to safety features that would make it difficult for someone to hit the accelerator even if they wished to. The right decision at the right time helped the organization improve its efficiency and save lives. A detailed analysis of historical data in Japan pertaining to road accidents revealed that around 15 percent of the fatal accidents involved people who were 75 years and above at the wheel. The analysis of this data further revealed that the elderly were mostly involved in these accidents as they hit the accelerator accidentally assuming it to be the brake. This understanding gained from past data helped automaker Toyota to invest heavily in safety features aimed at reducing the risks involved in vehicles driven by the elderly and the policymakers to come up with stringent driving norms and roll out policy initiatives to discourage the elderly from driving (Reuters, 2020). Here descriptive analytics paved the way for diagnosing the problem and prescribing the solution to the same. Yet again we can refer to the growth story of big basket to understand the significance of analytics at this stage. The company identified that Amazon and Flipkart, the two major giants in the online business, feature several items, as many as 100, on the screen. It takes a considerably long time to search for all the items and place an order and thus, it discouraged customers to shop online. Specifically, it becomes miserably difficult when they are using smartphones to shop. Customers buy grocery online for two major reasons, ease of use and time saving, therefore, Bigbasket.com created a ‘smart basket ’ which is an Artificial Intelligence (AI) based recommended basket, consisting of items that a customer is likely to buy. This feature works on two types of recommendations, content based (use historical data and recommend similar items purchase earlier by matching the common features ) and knowledge based (use knowledge of the users, items, and their relationships ), which help in reducing a good deal of time needed to place the order (Customer Analytics at Bigbasket—Product

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Recommendations, 2016). Studying the historical data not only helped understand the customer better but along with it came an opportunity to enhance the shopping experience of the customer. Sitting on data piles will not help an organizations grow, reviewing them to identify problems and opportunities is what will help. Otherwise, as someone stated, ‘fool with the tool is still a fool ’. Analytics is not only about technology but also about the curious brain to become the artist and bring meaningful information from the collected data by asking the right sets of questions. Big Basket, Amazon, and such other e-commerce sites are using descriptive analytics alongside predictive and prescriptive analytics to sift through past historical shopping data, identify patterns, predict consumer buying behaviour and prescribes that the order that has not yet been placed to be moved to the warehouse thereby reducing the delivery time of the product once the order is actually placed (Sachdeva, 2014). 2.3

Step 3: Data Collection and Data Processing

After stating the problem statement, data collection begins. The ERP system can be extremely useful to have access to relevant data (Dinesh, 2017, p. 23). The organizations need to ensure that they get hold of the large volumes of data, which is originating through an online search, online transactions, and online streaming, etc. (Akter et al., 2019). It is evident from past studies that data preparation and data processing are the two most time-consuming processes (Janssen & Kuk, 2016). This includes cleaning the data, merging the data set so that they can talk to each other, data imputation, and creating of additional variables such as a dummy, so that test can be performed (Muhammad et al., 2022). It is not the data but having important tools to exploit the data that are also essential. The advanced predictive and prescriptive analytical methods simplify the process of identifying relationships among variables (Awan et al., 2021). Data analysis helps solve problems not only in the corporate world but also aids the decision-making process of policymakers of a nation. For instance, in 2016, Ministry of Petroleum & Natural Gas came up with a scheme named ‘Pradhan Mantri Ujjwala Yojana’ where women below the poverty line were offered a free LPG connection. Under this scheme, 8 Crore LPG connections were to be released to the deprived households by March 2020.

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To facilitate these LPG connections government of India decided to open 10,000 more distribution centers. Sounds simple but in a country like India, this was a herculean task. This basically involved identifying locations where these LPG distribution centers can be started while keeping in mind other important factors like ease of access to the people living in villages. The government realized soon that if this task is to be completed manually then they would require at-least two–three years and several project leaders to manage the work. And the entire purpose of the scheme would be defeated due to the delay. Understanding the nitty– gritty of the task, the Ministry of Petroleum & Natural Gas partnered with atlan.com to accomplish this arduous task. Atlan.com is a data intelligence company that was founded in 2016. It brings the entire decision-making process to one place—from collecting primary data and accessing secondary data to merging internal data and visualizing data via easy-to-use dashboards. They work with over 150 organizations in 7+ countries, including the Gates Foundation, Tata Trusts, Unilever, and Frost & Sullivan. But even for an organization with such expertise it was not an easy journey. They had to consider the interest of both distributors and beneficiaries before proposing any decision. The requirements that had to be met included: 1. Keep the interest of every distributer in mind as they are run by a small entrepreneur, and they would be demotivated if there is no profit for them to serve. 2. Distribution centers should be in a reach to 120 million people living in these villages within 10 kilometers from their home, otherwise the objectivity of the entire scheme would be defeated. Atlan started collecting data from the oil and gas companies but realized quickly that it was not very useful. This data was typically sales data through which taking a decision to reduce the distance traveled by the villages was not feasible. To address this issue, they bought in all the data from different sources and mapped all the 604,000 villages on the map. Collecting and merging this data was not an easy task as it was captured in the local regional languages and mostly old data is captured traditionally in the physical registers. So, to manage it, the business intelligence algorithm was used. This helped in merging this unstructured data and permitted the data to talk to each other. Another challenge was to identify

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the location of the current 17,000 LPG distributors so they circulated an app that would fetch their exact location coordinates. After collaborating on all the data, they were ready to make the decision but ideally, this decision also had various constraints, for instance, it should be a place that is centrally accessible, it should preferably be a market that villagers are already traveling to, it should have a good access road and other facilities (like electricity, bank facility, etc.). The algorithm incorporated all these requirements and helped to identify a location where these LPG distribution centers could be opened. This helped the Ministry of Petroleum & Natural Gas to open 10,000 new LPG distribution centers at just a 4-kilometer distance to the beneficiaries and circulated over 1 crore LPG connections to the highly deprived households. The success of this superhuman project is an example of how big data analytics can help organization to take the right decisions if the right data is collected, and reviewed and if they have clarity in their data deliberations. The Ministry of Petroleum and Natural Gas was able to use Predictive analytics to ensure that the distribution centers were located such that neither the customer nor the distributor had to suffer. 2.4

Step 4: Model Building

A model building is an abridged illustration of a specific problem (Sivarajah et al., 2017). It is an iterative process in the field of analytics which aims at the best model to predict the results (Dinesh, 2017). There are several analytics tools are used to find the best model. The process involves creating data to train and validate outcomes (Akbarov et al., 2008). The primary role of analytics is to support organizations in the decision-making process by delving into the implied information in the data. It means the data scientist should be able to communicate a story based on the findings in a manner that can be understood by all stakeholders (Sharma et al., 2014). A study conducted by a team of Stanford University and University of Cambridge confirmed that a machine learning algorithm was able to predict the personality of Facebook users by seeing their liking pattern. The study was conducted on 86,000 people and this algorithm successfully identified the ‘big five’ personality traits of the users. The algorithm was also able to predict the skin color of users with 95 percent and their gender with 93 percent accuracy by seeing their pattern of Facebook likes (Youyou et al., 2015).

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Nielsen developed an algorithm that helps them to deliver effective and focused advertising. This algorithm was designed to analyze brainwave activities, eye tracking, and electrical conductance of skin when a person was exposed to an advertisement (Akter et al., 2019). According to Saboo (2016) organizations revenue can be amplified by as much as by 17 percent if they reallocate their marketing budget in decision making through data. Amazon developed an anticipatory shipping model which helps in predicting consumer decision-making process. It initiates the carriage even before the order is actually placed by the consumers. This model helped Amazon to deliver the goods faster (Lee, 2017). Organizations use a rating system to predict the sentiments of their consumers. This is quite useful in identifying the potential threat and opportunities (Salehan & Kim, 2016). But then again analytics is not limited to serving corporates. As was seen in the case of the welfare measures of the state through the Pradhan Mantri Ujjwala Yojana’, the government was able to predict the most ideal location for setting up the distribution centers using BDA. Data analytics has become such an expansive field that it is now being used in almost every domain, be it space exploration, nature conservation, or even sports. Flight Caster, for example, is able to predict whether there will be a flight delay 6 hours before the airline’s alerts (Siegel, 2013). On July 14, 2014, Germany stunned the world by winning International Federation of Association Football (FIFA) World Cup finals against Argentina. This was not the first time Germany was winning the FIFA world cup, they already had lifted this title three times before (1954, 1974 and 1990). So, what made this win so different? In 2012, the German Football Association collaborated with German software giant SAP AG to create a custom match analysis tool called ‘Match Insights’ (SAP News, 2014). This was a special software which would measure and analyze individual and team performance. The software was helpful in offering individual player wise recommendation with the help of simulation. It is a common practice among teams to have a dedicated video and performance analyst in their team. One of the major objectives of implementing this technology was to help players to improve their speed, flexibility, and accuracy for the 2014 world cup. The player performance was analyzed using eight cameras that surround the pitch. The pitch itself is transformed into a grid, and each player is assigned a unique identifier, allowing their movements to be tracked digitally. This data was then used to measure key performance indicators, such as the

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number of touches, average possession time, distance traveled, movement speeds, and directional changes. ‘Match Insights ’ enabled the Team Germany to analyze statistics about average possession time and reduce it from 3.4 seconds in 2012 to about 1.1 seconds in 2014 (Norton, 2014). Oliver Bierhoff, manager of the German national football team said: SAP’s involvement has transformed the football experience for coaches, players, fans, and the media. Imagine this: In just 10 minutes, 10 players with three balls can produce over seven million data points. With SAP, our team can analyze this huge amount of data to customize training and prepare for the next match (Curtis, 2014). Right after every game, ‘Match Insights ’ used to send short clips of analysis to each player where several visual examples of doing things well and poorly were captured and shared. The data captured by the Match Insight was converted into simulations and graphs that could be viewed on a tablet or smartphone, enabling trainers, coaches and players to identify and assess key situations of each match. These insights were then used during pre-match preparations to improve player and team performance. Match Insights also helped coaches and players to identify opponents’ strengths and weaknesses and develop appropriate defensive tactics (Choudhury, 2016). On the match day all these scientific inputs shared through match insights helped the team put up a stunning display of talent. The team was able to pass the ball 759 times (around 50 percent more than Argentina) with 85 percent pass accuracy and had 5 shots on target whereas Argentina stood at zero only (McNulty, 2014). Germany had the ball in their possession 64 percent of the time (ESPN Staff, 2014). Finally, with increased speed, flexibility, and teamwork Team Germany won the match by 1 goal. While in the earlier wins, Germany may have played better, in this win they were able to widen the performance gap with their opponent substantially. In fact, the stunning win of the team is an ode to the entire decision-making process, identifying problems and opportunities accurately, collecting appropriate data, reviewing the same, and using it to build predictive models aimed at increasing efficiency. As stated by Perter F. Drucker, ‘you cannot manage if you cannot measure’. In order to comprehend clearly, data scientists need to measure accurately, decode what they read, make a connection with what they read and what they know from past, and think deeply about what they have just right now.

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While Descriptive analytics helped identify the key performance indicators, Diagnostic analytics helped answer the question of why a player was better off or worse off than others, predictive analytics helped correlate and forecast what would be the outcome if the players continued with their game in the same manner and what may help change the outcome. Combining all this information prescriptive analytics helped suggest the best play alternatives for each player such that the team’s competitive advantage improved. 2.5

Step 5: Strategic Implementation

Strategic implementation is the process that involves close monitoring of the activities and constant evaluation to ensure course correction is initiated if necessary. The fundamental principal of implementing analytics is to develop an actionable strategy that can be used to gain a competitive edge. The implementation of the strategy totally relies on the top management. It is in their hand to use a model in developing a new product line such as a recommender engine developed by Netflix to what customer wants. This recommendation engine predicts with 75 percent accuracy (Dinesh, 2017). Similarly, Hewlett Packard (HP) developed a flight risk score for their employees to predict their attrition behavior. All of this is great but the implementation stage of the data-driven decision-making process is crucial in itself because organizations need to have the right skill set to be able to interpret accurately and interpret in the right context before taking action, based on data insights. For instance, the decision to control the intake process by limiting the entry of students belonging to a certain race into schools, if your data analysis had revealed that they were contributing largely to the declining grades may not be the best decision. This may in fact widen the gap between the privileged and the marginalized further leading to social inequity (Dodman et al., 2021). Strategy implementation failure can lie anywhere between 7 and 90 percent. This failure could be attributed to a number of causes including a lack of clear communication, isolated and fragmented actions, lack of commitment from those that matter, ambiguous goals, and such others (Jeroen, 2019). A single obstacle can create other obstacles leading to a series of blocking the implementation of a data-driven strategy (Cândido & Santos, 2018). Amazon and automation have become almost synonymous. In 2014 the company began to invest in developing a hiring tool that would swiftly

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skim through several applications and shortlist the best among them. It was a solution meant to drive down hiring costs and increase the quality of hires by removing human subjectivity. But the whole solution had to be relooked at when the company realized that the AI-backed tool did not like women! The tool was built to develop a model based on the historical data and this was how hiring was done historically by mostly men in the male-dominated tech industry. Here implementation failed because machine language here turned out to be masculine language pointing to the difficulty in judging if an algorithm built as a solution to a given problem is actually fair (Dastin, 2018). This indicates that during the implementation stage, the think tank needs to constantly monitor and see if the model has factored in the right constructs and if it is truly reflective of the real world. Yet another corporate giant GE that had repositioned itself as a digital industrial company failed to make waves with its digital initiative Predix that was started with great fanfare and the objective to make software, data, and analytics their key differentiator from competitors. Predix was successful in parts, for instance, it was through this that GE was able to identify the reason why its jet aircraft engines were requiring more unplanned maintenance. The data that was collected from numerous sensors helped pinpoint the problem, which was that extreme climates like hot regions caused the engines to clog and heat up reducing their efficiency. The solution was to simply wash them more which resulted in direct benefits in costs due to reduced use of jet fuel and indirect benefit to the environment. But over the years GE’s digital arm began to fail. Research indicates that almost 84 percent of organisations digital transformations initiated tends to fail as somewhere down the line the exercise fails to factor in the most important component—the needs of the customer (Powell, 2019). When a public school in Greater Atlanta, Gwinnett County public school was faced with the challenge of declining student performance and reduced graduate pass-out rates especially among the at-risk and underprivileged category of students they turned to data analytics to find solutions. The data crunching helped the school management understand that a student’s performance in algebra in the 9th and 10th standard was a clear indicator of the future pass-out possibility of that student. The solution was simple to ensure that students’ performance in algebra is enhanced. But the challenge was in the implementation given the fact

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most students had always feared math and done poorly in the qualifying exams too. The school could have taken a decision to probably only enroll students with good number crunching skills but then this would not have solved the problem but created a much larger one. To strengthen the implementation, they turned to analytics again to ask what could help enhance algebra skills among students. Surprisingly the answer was to improve the creative writing skills of students in the eighth grade. Thus, the school began to invest heavily in activities aimed at improving the creative writing skills of eighth graders which soon translated into better grades in algebra. In 2010 the school went on to win the muchcoveted Broad prize that is given to schools that demonstrate the highest student achievement and marked improvement in reducing the gap in the performance of the underprivileged groups (Forbes, 2011). Healthcare is yet another area that is witnessing the increased use of predictive and prescriptive analytics. The current global healthcare analytics market that is poised at $14 billion is expected to almost triple and reach around $51 billion by 2025 (Joy, 2019). Oncologists are increasingly using predictive and prescriptive analytical tools to augment their decision making related to patient treatment. For instance, predictive analytics may suggest that a patient is unlikely to respond to chemotherapy given his race, ethnicity, or the presence of a certain element in his blood. The doctor may then use this to prescribe an alternative treatment method that would help reduce the costs incurred by the patient, the trauma of a failed treatment, and the associated discomfort of a treatment like chemotherapy. Thus, analytics is even coming to the aid of healthcare providers to arrive at the appropriate strategy to be implemented and monitor its progress closely. It has helped the health care managers to improve the overall patient experience by being able to reduce patient wait time, and increase the number of patients that are treated at any given time without compromising any aspect of quality (Joy, 2019). Thus, cancer which is considered an almost fatal and extremely complex disease to treat is now more manageable due to the technical support that doctors are receiving to pinpoint the problem areas and reduce the associated risks (Kent, 2020).

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3

Conclusion

Collecting and analyzing data is not optional for any organization anymore, it needs to become a habit. Managers may rely on their instinct but must never exclusively on the same. They must back their intuition with verified data and data analysis before taking any major decision in an organization. Adopting a data-driven decision-making approach will make an organization become more proactive in identifying both business opportunities and challenges. Reduction of costs, mitigating risks, preempting problems, foreseeing opportunities, benchmarking performances and an overall increase in efficiency, effectiveness and thereby the organizational outcome be it productivity, reduced turnover, increased market share, etc. are all likely benefits of adopting an analytics-enabled decisionmaking process. Through the varied maturing levels of data analytics beginning with descriptive and ending with prescriptive organizations can train themselves to ask the right questions using data and find the critical answers using the same data. It may not be wishful thinking to assume that with BDA we will be better equipped to face epidemics and pandemics, floods and famines, accidents and diseases like cancer, global warming, and climate change or still better be able to reverse dangerous trends and prevent others from recurring.

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Algorithms as Decision-Makers Rauno Rusko, Sanna-Annika Koivisto, and Sara Jestilä

1

Introduction

Contemporary society and economy are based on algorithms. Algorithms are possible to understand several ways and forms. Historically, algorithms were used in mathematics in the forms of arithmetical operations, linear equations, differential equations and interpolation, among others (Bullynck, 2015). Gradually, term ‘algorithm’ has been used more and more in the context of computers and digital platforms (Aboueid et al., 2019; Bullynck, 2015). Thus, the role of algorithms has changed towards practical applications and decision making. Algorithms will help and enhance business and decision-making processes. At the same time, there seems to be a need for transparency of algorithms in decision making. Decision making, provided by algorithms, has some ethical problems. Mittelstadt and colleagues (2016) have found the following six ethical problems of algorithms:

R. Rusko (B) · S.-A. Koivisto · S. Jestilä University of Lapland, Rovaniemi, Finland e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. Sharma et al. (eds.), Analytics Enabled Decision Making, https://doi.org/10.1007/978-981-19-9658-0_2

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1. Inconclusive evidence (Algorithms are used as approximators in the situations, where other technologies are not plausible or are too costly) 2. Inscrutable evidence (Lacking exact background of data). 3. Misguided evidence 4. Unfair outcomes (The threat of discrimination in spite of the wellfounded evidence) 5. Transformative effects (Algorithm profiles the world in new ways) 6. Traceability (Algorithm causes nuisance, but it is difficult to find offender). Therefore, algorithm in the role of decision-maker is an important research theme. There are two essential types of algorithms as the decision-maker: decision support algorithms (DSA) and decision-making algorithms (DMA) (Nof, 1983; cf. Oxholm et al., 2022). That is with simplification: in DMA the decision-maker is an algorithm, but in DSA algorithm provides information for decision making by human beings (cf. Nof, 1983). At first, this chapter emphasizes this traditional dichotomy, which is distinct and comprehensible, but nearly forgotten in the contemporary literature of algorithm-based decision making. Then, this chapter suggests a new initiative, where decision-making framework—instead of partly ambiguous dichotomy DMA vs. DSA—is based on a step-less scale. The role of algorithms in decision making needs more scientific and systematic effort in the future. Though algorithms as decision-makers potentially provide several advantages to the processes of the organizations the disadvantages might be also possible: according to Todolí-Signes (2021), due to decision-making algorithms, own decision making, work management and self-organization of workers may gradually decrease, which might have effects on the well-being at work. Thus, it is important for well-being and safety at work to understand the relationships between humans and algorithm in decision making. This chapter participates in these discussions. The aim of this study is to map the literature and prevailing discussions about algorithms and their role in decision-making. Especially the juxtaposition between DSA and DMA, and generally the role of ethical questions in algorithm-based decision making (see, e.g. van Leeuwen et al., 2021) are the main perspectives of this chapter. Another aim of this study is to define the focal point of the literature along the supply chain

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of algorithm-based products: whether it is on the upstream, midstream or downstream part of the supply chain. The content of the study is based on literature reviews: complementary general literature review and systematic literature review focused on algorithm-based decision-making completed case introduction to algorithms using Wolt company as an example. Thus, the method of this study resembles the case study strategy, where research exploits several methods and sources in order to achieve holistic vision, rather than tunnel vision (Verschuren, 2003). Especially important in this study is the connection between algorithm-aided decision making and the activities and decisions of human beings. Especially Wolt-case illustrates this important practical connection, which is topical in several technology-assisted supplemental works. However, this chapter will not provide the overall solution or perspective for relationships between algorithms and humans in decision making, but participates in the discussions, which will finally lead larger and multifaceted solutions and perspectives in this research area.

2 2.1

Literature Review

The Role of Algorithms in Decision Making

The role of algorithms has changed gradually. At first, algorithms were linked with mathematical theoretical operations, but tardily their importance and involvement in the practical business models and business concepts have increased (Aboueid et al., 2019; Bullynck, 2015) from transaction platforms, such as Uber and AirBNB, to the more confined use of data, such as managing risks and pricing products at banks and insurance companies (Schildt, 2020). To the everyday business and consumer activities, algorithms appear especially in the forms of recommendations and other decision-support tools. The practical benefits of algorithms are multifaceted. In decision making, algorithms mostly provide suggestions for managers, consumers or for the other human beings, who are in the role of decision-maker (Rusko, 2019). However, the role of human beings as the decision-maker might be even missing due to holistic and all-encompassing algorithms in decision making (cf. Chen & Tokuda, 2010). Furthermore, machine learning has been associated with holistic decision-making algorithms, especially in the branch of the face and image identification (Chengeta & Viriri, 2019; Rajabizadeh & Rezghi, 2021).

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In algorithm-based decision making the exceptions and serendipity cause challenging situations, where the equity in the decisions might be threatened (van Leeuwen et al., 2021). More generally, the role of algorithms as the holistic decision-maker has caused criticism and ethical conversations in the several branches and sectors of business and society. These problems have been noticed in the Human Resource Management (HRM) and hiring, activities of medicine and healthcare, social services, education, and criminal justice (Fazelpour & Danks, 2021). HRM algorithms have an exponential interest of practitioners, but the development of interest among scholars is not similar, that is there is a research-practitioner divide (Cheng & Hackett, 2021). Both algorithm-based HR decision making to monitor workers (Leicht-Deobald et al., 2019) and HRM and algorithm-based HR recruitment (Köchling & Wehner, 2020) might cause problems. The former might cut employees’ personal integrity and emphasize their compliance (Leicht-Deobald et al., 2019). The latter might save costs, increase efficiency and objectivity, but at the same time might lead to the implicit discrimination of certain groups of people and unfairness (Köchling & Wehner, 2020). Cheng and Hackett (2021) see HRM-related algorithms to be mainly heuristics without exact accuracy. In healthcare the ethical problems of algorithm-based decision making have been widely noticed (Biller-Andorno & Biller, 2019; Lerzynski, 2021). Algorithms help to alleviate illnesses, but at the same time, they trigger fears and insecurities in patients (Lerzynski, 2021). Especially important is to find the balance between the opportunities and the challenges of digital algorithms, and especially the profound understanding of digital algorithms (Lerzynski, 2021). 2.2

Two Types of Decision-Based Algorithms

There are two essential types of algorithms: decision support algorithms (DSA) and decision-making algorithms (DMA). An early definition by Nof (1983) expresses that decision-making algorithms are used for the structured decisions which are assigned completely to the management operating system (MOS) by management for automatic resolution whereas decision support algorithms perform data reduction, computation and evaluation of performance measures that are useful. In the literature of healthcare, for instance, this distinction has been noticed. Oxholm et al. (2022) divide clinical algorithms into two types: CDS and

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CDM algorithms, that is clinical decision support algorithms and clinical decision-making algorithms. CDS algorithms give only recommendations and instructions for medication and treatment and conduct preventive screening for particular diseases, whereas CDM algorithms make clinical decisions on their own (Oxholm et al., 2022). Some studies do not distinguish these two concepts, decision support algorithms and decision-making algorithms, but understand these two terms linked with each other. Atkins (2008), for example, sees in his thesis about On-line decision support for take-off runaway scheduling at London Heathrow airport that in this case the decision-making algorithms are decision-support tools to help a runway controller. That is, decision-making algorithm is the tool for the decisions, but it is not the decision-maker. The distinction between algorithms, which support decisions vs. which make decisions is especially important in the branch of medicine (Engelbrecht, 2009; Meireles et al., 2014). Furthermore, these two different types of algorithms, DSA and DMA, have been noticed in the branches of the Internet of Things (IoT) for sustainable manufacturing (Kovacova & Lewis, 2021), the general manufacturing control and automatic manufacturing systems (Nof, 1983; Nof et al., 1980). However, this distinction (decision support algorithm vs. decisionmaking algorithm) is not downright. In the literature of medicine, for example, clinical decision support (CDS) algorithm is achieved established meaning, but especially abbreviation CDM (e.g. clinical decision-making algorithm) have several alternative meanings, such as Common Data Model and Cognitive Decision Making (Naghshvarianjahromi et al., 2019; Richesson et al., 2016). In addition, the situations, where the algorithm makes decisions alone are not typical in the branch of medicine, but topical in the near future (Oxholm et al., 2022). Furthermore, to the question whether algorithm will make decision alone or will support decision making by human beings is sometimes difficult to answer: it is possible that the features of algorithm will suit some situations in both of these categories. Often literature of algorithm-based decision making do not use this distinction at all. This chapter emphasize, however, the importance to define the power of algorithm in decision making compared to power of human being as decision-maker. The use of dichotomy DSA vs. DMA will express this important perspective, but it is not all-inclusive. This chapter uses

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this dichotomy, and provides some suggestions to diversify further this perspective, but also challenges scholars to more wide and more sophisticated further research in this field.

3 3.1

Methodology Research Design

The research design involves in the systematic literature review to identify the role of decision making among the latest algorithm-based articles. The articles were theoretical, empirical and practical. Furthermore, analysis was completed with a case example of an algorithm-based company, Wolt. Thus, this study is an exploratory type of qualitative research, an embedded case study with multiple units of analysis (Claude et al., 2019; Yin, 2009). The method of this study follows the case study strategy, where research is often based on several methods and sources in order to achieve triangulation or holistic vision, rather than tunnel vision (Kohlbacher, 2006; Verschuren, 2003). The systemic literature review, of this study was collected by doing a Google Scholar search on December 12, 2021 (10 a.m., UCT+2) with keywords: Algorithm decision making. The search terms provided 2020 or later publications. The search returned 33,700 results of which the first 30 were selected as empirical material for this study. Data analysis was focused on details, how abstracts deal with algorithms, decision making, and human-algorithm interaction. Table 1 was created for the compilation and classification of findings. The results of the literature review in Table 1 focused on two research questions among these 30 articles: (1) The attitude of the article to human decision making DMA or DSA and (2) The position of algorithm along the supply chain in the article. 3.2

Case Study: Wolt

To illustrate the use of DSA and DMA concepts in order to examine algorithms and human roles and interactions in decision making we view Wolt, a Finnish technology company based on algorithm optimization. Basic information about the company and its operations are available in presented sources Wolt’s online publications and news coverage. Wolt is founded in 2014. It delivers mostly food as a home delivery on its own

Wang et al. (2020)

Burton et al. (2020) Khorsand and Ramezanpour (2020) Wang and Garg (2021)

Zhang and Li (2020) Yang, Nazier, et al. (2020) Fu et al. (2020) Saha et al. (2021)

Mohamadi et al. (2021) Wu et al. (2021) Yang, Ouyang et al. (2020)

Aranizadeh et al. (2020)

Garg et al. (2021)

1.

2. 3. 4.

5. 6. 7. 8.

9. 10. 11.

12.

13.

DMA DMA DMA Both DSA and DMA The algorithm offers the best options to choose from DMA DMA Both DSA and DMA (algorithm as a translator/interpreter) Both DSA and DMA Human as a decision-maker but the role of algorithm in decision making is also highlighted DMA (The role of algorithm in decision making is highlighted, and the role of human in the decision-making process is not discussed)

Mainly DSA, partly DMA Assume that algorithms make decisions but take into account the human interpretation of the decisions DSA DMA DMA

Orientation of studies DSAa or DMAb

The orientation of studies between DSA and DMA

Studies

Table 1

(continued)

1 (Proposing mathematical, logical and technical ideas to plan algorithms)

2 (Comparative analyses) 2 and 3 3 (Proposing an online shopping support model) 3 (Proposing certain optimization method as potential solution provider)

1 and 5 4 4 (Designs an algorithm to solve the multiple attribute decision-making issues.) 2 (Testing algorithms) 4 2 2 and 4 (Presents H-MCDM algorithm and sensitivity analysis, Real-life test case)

2 and 5 (Online experiment and user feedback)

Position of algorithm in supply-chain*

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(continued)

Alexandrino et al. (2020)

Grote and Berens (2020)

Suresh et al. (2020)

Wan and Dong (2020)

Wei et al. (2020)

Riaz et al. (2020)

14.

15.

16.

17.

18.

19.

Studies

Table 1

DMA (The role of algorithm in decision making is highlighted, and the role of human in the decision-making process is not discussed) DSA (Questioning algorithms as decision-makers) DMA (Algorithms are presented more accurate than humans) DMA (The role of algorithm in decision making is highlighted, and the role of human in the decision-making process is not discussed) DMA (The role of algorithm in decision making is highlighted) DMA (The role of algorithm in decision making is highlighted, and the role of human in the decision-making process is not discussed)

Orientation of studies DSAa or DMAb

2 (presented optimization method is investigated and illustrated through a practical application 1, 2 (Mathematical ideas and concepts are presented and slightly illustrated through a few practical examples)

5 (Examining and considering the ethical problems of the practical application of algorithms) 2, 3 (Presenting and experimenting certain algorithm in forecasting misclassified malignant cancers) 1, 2 (Mathematical, logical and technical ideas to plan algorithms are given and illustrated through a practical application)

2, 3 (Presenting and experimenting certain optimization method as a tool for structural health monitoring)

Position of algorithm in supply-chain*

30 R. RUSKO ET AL.

Elmachtoub et al. (2020) Hu (2021) Sahoo et al. (2020)

Welch (2021) Araujo et al. (2020) Liu and You (2020) Eshankulov (2020)

Kumar (2020) Peng and Huang (2020) Pourhomayoun and Shakibi (2020)

21. 22. 23.

24. 25. 26. 27.

28. 29. 30.

DSA DSA DSA

DMA DMA (apps) and DSA (attitude) DSA DSA (Algorithms for decision making)

DMA (The role of algorithm in decision making is highlighted, and the role of human in the decision-making process is not discussed) DSA (Technical paper without apps) DSA DSA(DMA) Final decision by human-being

Orientation of studies DSAa or DMAb

1 (technical idea paper) 3, 4 (product modeling) 3, 4 (Compares different machine learning methods in the prediction of diseases) 4 (Considers algorithms in final products) 5 (focused on perspectives of final users) 1, 2 (idea paper or experiment) 1, 2 and 3 (Technical paper for the modules in the creation of a software package) 2 (Technical proposition or experiment) 5 3 Machine Learning and AI

1, 2 (Mathematical, logical and technical ideas are presented to plan algorithms. Some experiment is presented)

Position of algorithm in supply-chain*

*Please refer 1—idea/plan, 2—experiment (prototype), 3—semi-product, 4—algorithm in the use of product; 5—experiences of user, aftercare, ethics a DMA: The human perspective is ignored and the role of algorithms is emphasized b DSA: The human role is emphasized

Yang et al. (2021)

20.

Studies

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31

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online platform. Wolt has more than 55,000 restaurants and retail partners and over 125,000 courier partners in 23 countries covering more than 210 cities (Wolt A.) The turnover in 2020 was 164,340,000 euros. The ownership of Wolt has changed: the company is acquired by Doordash in the United States, which operates in the same food ordering and delivery service industry as Wolt. The companies will continue as independent operators until the transaction is completed. Wolt will continue to operate under its current name and product and Wolt’s CEO Miki Kuusi will continue to lead Wolt and will join Doordash’s management team when the transaction is completed (Heikkilä, 2021). In the competition for the food supply market, Wolt’s advantage is in technology. Wolt’s operations are based on an application that uses optimization algorithms. Application guides the activities of couriers and customers. With real-time logistics optimization, customer orders are combined with available couriers and restaurants so that the order arrives fast to the customer and the courier receives as many transports as possible (Mäntylä, 2021). Wolt has an optimization algorithm, which decides which one of the courier partners does which tasks and in what order. The decision is based on various variables, such as time windows, minimizing the delivery distance for courier partners and travel times based on historical traffic patterns. Furthermore, important is the special requirements of that specific delivery and how long pickups and dropoffs take (Wolt B). Wolt is constantly developing the application for example by utilizing existing human data, to improve its operations, create something new and make it more personal (Wolt C). Wolt tests how changes in the route optimizer perform in Wolt-city sim before they are published to the live environment (Wolt B). Wolt recently added a feature that allows restaurants to tell if they prepared the meal early was added recently. This feature enables a courier to pick up the meal sooner which contributes to making deliveries faster (Vuori, 2021). Algorithms control the actions of the Wolt application users whether they are retail partners, courier partners or customers. Through the Wolt case, looking at the relationship between algorithm optimization and courier operation in a Wolt application, we illustrate the relationship and roles of human and algorithm optimization in decision making in a specific Wolt context by using DSA and DMA concepts. Since our focus here is on Wolt optimization algorithms related to courier operations, the customer and retail partner perspective is not addressed. For a courier to

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do the job requires the use of Wolt application. Through the application, the courier registers him/herself at work, gets gigs, driving directions and connects to support. Courier partners usually receive their reward based on the orders they carry (Silvander, 2020). 3.2.1

The Relationship of Algorithm Optimization and Courier Partner as DSA: Algorithm Optimization Supporting the Courier Partner in Decision Making The Wolt app offers gigs for couriers based on distances between restaurants or/and subscribers and couriers. Utilizing the information provided by the algorithm, the courier can make a decision to accept or reject the gig. The courier is given one minute to do this decision. The courier must be quick in his/her decision making as there may also be competition between couriers for the gigs. Wolt does not exercise algorithmic leadership or control over the couriers, and if the courier rejects a gig then there will be no punishment of any kind. Thus Wolt does not have any mutual rankings between the couriers (Muilu, 2021). At its best, algorithms help couriers to work efficiently and enable them to make money. Couriers themselves decide, for example, their working hours, workload and the area in which they work. Algorithms support couriers in making these decisions. Couriers can use the information provided by the algorithms in order to use their working hours as profitably as possible and earn as much as possible. 3.2.2

The Relationship of Algorithm Optimization and Courier Partner as DMA: The Algorithm Optimization Decides How the Courier Works Because the Wolt app offers gigs based on the locations of the couriers, the courier must be in the ‘right place’ to be offered a gig. Being in a less favorable location for a courier can mean losing a gig. The algorithm therefore decides, for example, based on the distances, to whom the gig is offered. There is a one-minute time limit for a courier to accept or reject a gig, so the application has to be constantly monitored (this might also make a security risk). Sometimes the gig might pass unintentionally if, for example, the courier’s attention is drawn elsewhere, such as to finish another gig. In these cases, the algorithm is the one making the decision, not the courier.

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Wolt application algorithms focus on certain transportation-related characteristics, such as distances and time estimates. These are the main things a courier is likely to pay attention to when choosing from available gigs. However, there may also be some other characteristics associated with the success of the gig that may not be taken into account by certain algorithms used and that may, however, significantly affect performing the driver’s work. An unexpected accident, problems with the operation of the restaurant, weather and road conditions could be these kinds of factors. Algorithms strongly lead couriers to consider certain factors when choosing gigs, potentially ignoring other factors. Nonetheless, Wolt is constantly developing algorithms based on observations of performance, and as a result, algorithms take more comprehensive account of the factors that affect the way work is done. Technical difficulties might restrict working. The application with its algorithms is essential for Wolt’s courier partners to do their job. Without the application and its algorithms work is not possible. Although couriers can, in principle, make many decisions about doing their job, if there are technical problems with the operation of the application and algorithms, this can prevent couriers from doing their job. 3.2.3 Case Summary An algorithm-based application controls the work of Wolt’s courier partners who earn money by doing home deliveries. Couriers’ work-related decision making can be viewed from DMA and DSA perspectives. As the examples above show, the Wolt application algorithms seem to both support couriers’ decision making and make decisions for couriers. At best, algorithms enable working successfully helping a courier to work efficiently. In some cases, algorithms play a quite dominant role in decision making and might restrict or even prevent working. Algorithms connect the activities of different parties into a functional entity benefiting the courier, the customer and the Wolt company. However, if there is an algorithm malfunction of some kind, the operation of the people involved may be disrupted or blocked. The case of Wolt shows that algorithms help and enhance business and decision-making processes in a way, which contains both DMA and DSA elements.

ALGORITHMS AS DECISION-MAKERS

4

35

Results

Many articles of the systematic review consider themes of algorithms, which are typical for basic research, that is mathematical, logical and technical ideas and experiments. One aim of this study is to define the focal point of the literature along the supply chain of algorithm-based of products: whether it is on upstream, midstream or downstream part of the supply chain. Figure 1 depicts the outcome of the analysis, which is based on the content of Table 1. Some of the articles focused on decision-making algorithms in the ready final products, and on the user experiences and ethics of these products and services. That is, articles focused on different elements of the supply chain for the products, which are algorithm intensive. Figure 1 describes the different phases of the supply chain for the products or services, which contain algorithm-based decision making. Typically, supply chain has three elements: upstream, midstream and downstream activities (Lima et al., 2016). Suggested mathematical, logical and technical ideas and experiments are upstream activities of supply chain, semi-product and ready products are midstream activities, and aftercare with user experiences, usefulness and ethics are placed in downstream activities (Fig. 1). This chapter exploits the supply chain perspective in the analysis of these algorithm-based decision-making articles. Table 1 classifies articles based on the main attitude or emphasis of these articles: whether they

Fig. 1 Supply chain of decision-making algorithms and the emphasis of the systematic review

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express tentative mathematical or logical ideas and experiences of algorithms in decision making, potential solutions to make ready products, algorithms in readily marketable products, or aftercare. Literature review above showed two types of decision-based algorithms: decision support algorithms (DSA) and decision-making algorithms (DMA). This chapter uses this DSA vs. DMA dichotomy in the analysis of the systematic literature review. Systematic literature review covers 30 articles and the analysis is based on the abstracts of these articles. Table 1 summarizes the orientation of these articles between DSA and DMA, and the position in the supply chain for the algorithm-based decision-making products. The analysis of the abstracts revealed some emphasis in the contemporary literature of algorithms for decision making. Though the popularity in the practical use of algorithms has increased the latest studies about algorithm and decision making focus on theoretical perspectives, not on the practical or ethical themes of decision-making algorithms. Thus, the focal point of the contemporary scientific algorithm-based decisionmaking discussions is not in the usefulness of the algorithm-based applications. All thirty (30) abstracts highlight the role of algorithms in decision making. Fifteen (15) abstracts highlight the role of algorithms in decision making and the role of human is not discussed at all (only DMA). Six (6) abstracts highlight the role of algorithms and the role of human in decision making is discussed (Both DMA and DSA). Nine (9) abstracts focus mainly on DSA. Questioning the domination of algorithms in decision making, focus on how human interprets algorithms or compares human and algorithms as decision-makers. We look at the placement of algorithms in the supply chain. The algorithms considered in the abstracts are placed in one or more stages of the supply chain. In eight (8) abstracts the algorithms are in the first stage of the supply chain and these abstracts deal with mathematical, logical, and technical ideas to plan algorithms. Fifteen (15) abstracts deal with mathematical, logical, and technical experiments to plan and produce algorithms for products, bringing the algorithms to the second stage of the supply chain. Nine (9) abstracts deal with semi-products (Potential solutions to make ready products for decision making) and algorithms are in the third stage of the supply chain. In seven (7) abstracts algorithms are in the fourth stage of the supply chain and algorithms are a ready product (marketable algorithms) for decision making. Five (5) abstracts deal with

ALGORITHMS AS DECISION-MAKERS

Human being makes decisions

50 %

DSA

Algorithm makes decisions

37

DMA

50 % Equal power in decision-making of humans and algorithms

Fig. 2 The interaction and power of human being vs. algorithm in decision making

user experiences (ethics, usefulness) and aftercare, bringing the algorithms to the fifth stage of the supply chain (see Fig. 2).

5

Discussions

Several articles consider technical and mathematical details in a way, where the solutions enable different possibilities for further development. They are, however, mainly some kind of abstract ideas, experiments or semiproducts for the final decision making or decision supporting algorithms. In other words, they are not focused on the ready decision making or decision supporting products and their user experiences with ethical and other qualitative questions. Figure 1 described the focal point of the articles in the systematic literature review. That is, Fig. 1 describes some kinds of supply chain for the algorithms, which focus on decision making. The articles of the systematic literature review are located mainly in the upstream parts of the supply chain. Midstream, and especially downstream parts, of the supply chain, are without great attention in the literature. The noticed emphasis will restrict the potential audience of the articles about the algorithm-based decision making. The research is mainly fundamental research, which is important and necessary. The connection to the final products in the market is narrow, however. The contemporary development in the branch of machine learning and AI, which are based on algorithms, is very fast and it enables large-scale business in several sectors of economy, consumption and society. The mainstream literature of algorithm-based decision-making does not reflect this development. This chapter emphasized the importance to define the power of algorithms in decision making compared to the power of human being as a

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decision-maker. We used the dichotomy DSA vs. DMA to express this important perspective. Analysis showed the difficulty to classify the role of algorithm only into the two categories. Thus, there is a need to have more than two categories to define the power of algorithm in decision making (Fig. 2). Especially DMA is problematic due to the possibility to understand DMA literally to be ‘decision-making algorithm’, where algorithm has total (100 percent) decision power. Practically, using DSA vs. DMA typology, the area of DMA has to be larger than depicted in Fig. 2. It might be necessary to have even numeric scale to measure the power of algorithms and humans in decision making. Though Fig. 2 is only illustrative tool to consider this decision power the criterions to indicate the role of algorithm in decision making are important to establish. This information might be added to the manual, instructions or other documents of the products, which contain algorithmic decision making.

6

Conclusions

This chapter showed the need to have more diverse research into the interaction between humans and algorithms. One possibility to have an exact measurement to measure the power of algorithm in decision making is to define a scale for decision power between algorithm and humans. This chapter showed an illustrative example to this direction. Wolt-case of the chapter showed the simultaneous DMA and DSA features in the use of Wolt platform. Wolt platform is the combination of the decisions of humans and algorithms. Thus, as proposed for further research, algorithms and decision-making phenomena could possibly be also more examined through the concept of power. As the article analysis shows, the role of algorithms in decision making is many times highlighted and, in some cases, algorithms can be seen as making decisions for humans, algorithms as power users come into important question. Another result of this chapter is the finding that many articles of algorithm-based decision making express ideas and experiments of algorithms, which are possible to interpret to be in the upstream parts of supply chain. Customer experiences, ethical questions and aftercare of the algorithm-based decision-making products, which are part of the downstream activities of supply chain, have a minor role in the literature. All in all, this chapter introduced several perspectives to study algorithm-based decision making. The possibility to apply supply chain

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framework for algorithm-based products was one of these perspectives. Another perspective was less used dichotomy between DSA and DMA. Furthermore, the tool to evaluate more exactly the decision power of human being vs. algorithm in decision making was introduced. However, there is a need in the future to enlarge and diversify these important perspectives due to the fact that algorithm-based decision making contains potential risks of safety and well-being for workers and customers, who are using them. One chapter has only a limited possibility to consider all these perspectives, but it provides, however, several themes for further studies.

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Influence of Big Data Analytics on Business Intelligence Sudhanshu Kumar Guru

1

Introduction

In today’s world, the biggest asset of any organization is data. Data gets collected by various means such as web applications, mobile apps, manual entries and system-generated data. Data can be used to get a deep insight into the business to take strategic decisions. Business Intelligence has been a key for almost two decades to look into business insights through the data collected by various means for any organization. It helps analyse current and historical data using OLAP tools, responsive reports, dashboards and fixed format reporting. However, in the past couple of years, there has been a paradigm shift in how data is used and collected for any organization. The advancement of technology and the omnipresence of the internet have changed how data is captured. Data is generated at a very high speed compared to the past, leading to a high volume of data accumulation. Data variety is also more; decisions are not made just with text data but

S. K. Guru (B) Solution Architect (Smart Manufacturing and Artificial Intelligence), Micron Technology, Hyderabad, India e-mail: [email protected]

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with other data formats like pictures, videos, audio files and geospatial data. Companies are also exploring possibilities to make business decisions in real time, like declining a fraudulent financial transaction or recommending a product to a customer based on his/her recent web searches. To help ever-growing business demands with this level of data volume and complexity, Big Data and Analytics are widely getting used to augment conventional business Intelligence.

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Business Intelligence at a Glance

Business intelligence (BI) is the ability of a company to make meaningful use of data it collects in the course of its day-to-day business operations (Kimble & Milolidakis, 2015). The BI could play an important role in improving organizational performance by identifying new opportunities, highlighting potential threats, revealing new business insights and enhancing decision-making processes, among many other benefits (Kowalczyk & Buxmann, 2014; Xia & Gong, 2014). BI has a history back to the 1960s when some specialized people used to gather data from different data sources and generate meaningful reports manually. Later in 1970, when Edgar Codd coined the idea of having data in relational form, this changed how data was stored and represented. To fulfil BI requirements, a separate DBMS was designed called Decision Support System (DSS), which can be termed as an initial foundation of Business Intelligence. Later many authors published different ideas to empower business intelligence through effective data storage structures like data warehouses, data marts, enterprise data warehouses, OLAP and the like. In the early 90s, Ralph Kimball presented the idea of having a different set of data models for a data warehouse or OLAP data storage called the Dimensional Data Model . It was more of de-normalized data, which became the gold standard for data warehousing projects, mostly using concepts of Facts and Dimensions. Bill Inmon, another computer scientist in the same timeframe, presented a different view of data warehouse and advocated normalized data called snowflake architecture. In Enterprises today, a combination of these two approaches is used for the data warehouse (Fig. 1).

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Fig. 1 Typical DW/BI flow

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Data Warehousing and ETL at Core

Let us dive deeper into the core concepts of data warehouse and the Extract Transform & Load (ETL) process. Organizations use a wide range of applications to execute their day-to-day operations. Every organization runs with various departments, requiring different applications suiting their needs. A different set of applications mostly runs on silos with different databases or file systems. This yields scattered data for any organization, making data analysis very difficult. For example, suppose for an Insurance company, the policy management application is based on Mainframes, and claim management is running on an Oracle-based application. Now, suppose the executives of the company are looking for a loss ratio which is calculated as (Total Claim amount + Claim Expenses)/Total Premium Collected for that period. It is possible if data is available from both the systems Premium and the claim in one place because we cannot write a query which can join Mainframes and Oracle. These requirements create the need for data unification in a single platform, generally referred to as Data Warehouse. A typical Data Warehouse collects data from multiple sources to help businesses look into unified business insights. The most critical problem a Data Warehouse solves is data unification from scattered sources (as the example given in the above paragraph) which is very important for any business to get a complete picture of the business from a single source. One can ask if the transactional system is unified, i.e. all the business units are using the same system for databases (which is rare), can

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people avoid having a Data Warehouse for analytics? The answer to this question throws another importance of having a Data Warehouse. The Transactional System or OLTP (OnLine Transaction Processing) System has different usage than the Data Analytics or OLAP (OnLine Analytical Processing) System. OLTP is Data-Write centric, and OLAP is DataRead centric. To make the writing faster, OLTP tables are made small and contain fewer data (no historical data), leading to more tables and requiring multiple joins to collect information. More tables mean more joins, and more joins mean slow reads. In contrast, the Data Warehouse or OLAP systems are designed for faster query performance, which leads to de-normalized data models (referred to as Dimensional Data Modelling). It is ok to spend hours getting the data loaded into a Data Warehouse, but while accessing the data, there should be no delay (online analytics). It is another reason to have a data warehouse (Table 1).

Table 1

The below table describes more on data warehouse vs. OLTP system

Category

Data warehouse

OLTP system

Business Requirement

It supports data analytics and ad hoc queries to get business insights It uses de-normalized data modelling called dimensional modelling to support faster read Generally data gets loaded in batch mode/ periodic updates like daily, monthly or hourly. Very few requires real time also called micro batch loads Generally complex queries and requires high volume data scan

It supports business transactions and day-to-day operations It uses normalized schema to ensure faster updates and reduced redundancy

Data Model

Data Load

Data Queries

Data Volume

Stores terabytes of data as it keeps historical data

Data gets updates as and when the transaction occurs means it has current data

Relatively simple and standardized queries which scans lesser records Stores less data generally few months or weeks of data. Older data gets archived into a different system

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Benefits of a Data Warehouse

Apart from giving a centralized view of enterprise data and being designed for OLAP, there are other characteristics a Data Warehouse possesses. Bill Inmon, generally referred as the Father of the Modern Data Warehouse, has provided four unique characteristics of a data warehouse. According to his definition, Data Warehouses are: . Subject-oriented: Data are subject-specific in a DW, like products, Finance, Sales etc., rather than ongoing operations. . Integrated: Data warehouses gather data from heterogeneous sources and give insights into the business. It also resolves data inconsistencies between disparate sources and holds a single version of Truth. . Non-volatile: Data doesn’t change over time in a DW. Over the period, more data gets accumulated from source systems, but older data are not removed (Based on the timelines defined by the business users). It gives a consistent view of the data. . Time-variant: Data in the DW are tagged with time which helps to analyse a business performance over the period. 3.2

ETL (Extract, Transform and Load)

To facilitate the data from heterogeneous sources into a data warehouse, there is a need to set up a process which will Extract the data from the designated sources, Process it as per the business needs and Load it into the specified schema of the Data Warehouse, referred as ETL Process. ETL process, also referred to as data pipeline, is very useful in Business Intelligence projects to provide high-quality data (Fig. 2). The ETL process starts with identifying the required data sources that have the data to be used for end users in BI reports. It is not an easy process; this requires strong domain knowledge and understanding of the Enterprise-wide applications that hold the organization’s transactional data. The next challenging task in the ETL process is the data modelling in the target data warehouse/data mart. It is done in accordance with the business user’s requirements, future scopes, existing data model and enterprise goals for Business Intelligence. Once the data model is composed, ETL development takes place using the selected ETL tools like Informatica Power Center, Abinitio, Microsoft’s SSIS etc. In the ETL

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Fig. 2 ETL process

process, a connection with the source system is made followed by required transformations applied for data cleansing and business rules, and finally, data is loaded into the destination tables. Most Financial companies like Banks, Insurance Companies, Capital management giants and tech-based companies heavily use the ETL process for enterprise data warehouse and support business intelligence. However, with the advent of high-speed internet, IoT, social media and more customer-centric business solutions started accumulating data, which had never happened before. Business users are no longer willing to wait for months to get insight from a huge heap of data, which was the major challenge with conventional ETL project executions. 3.3

Challenges of Conventional BI

The biggest challenge with conventional BI is time to market. Any analytics project took much time due to selecting attributes from the source applications required for the final product. Since data storage and processing used to be costly, immediate data availability was not easy. This was also causing ignorance of various datasets, which could have added substantial business values. With the advancement of Internet speed and a new set of applications like social media and IoT, data volume has increased rapidly. According

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Fig. 3 ETL vs. ETL process

to Forbes, 90% of the world’s comprehensive data has been created in the last two years. That means daily data generation is humongous, with around 2.5 quintillion bytes of data being created daily. At the same time, data complexity has increased. Previously, data was mainly structured, which fit into relational schema (row-column format). However, data is now semi-structured like log data, json or XML formatted data and unstructured data like voice messages, photos and videos etc. Businesses don’t want to waste data due to technical limitations and want to utilize it to a maximum potential to add value to the business. Conventional ETL and BI cannot cope with data volume, variety and business expectations. Here comes the concept of Extract Load and Transform (ELT), which means without any transformation get the data as it is in the source and load it in a common data storage generally referred to as Data Lake. As per the need, pull the data from Data Lake and transform it to help the business based on its requirement. The existing RDBMS and data storage processes were improper to support the ELT process. The power of a Big Data processing engine came into use to rescue this (Fig. 3).

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Evolution of Big Data

As the name suggests, big data generally refers to a very high volume of data but is not limited to just volume. Big data addresses other aspects of data processing apart from the volume like the Velocity in which data is generated and a variety of data commonly referenced as 3Vs of Big Data. When data processing using the file system and conventional RDBMS became challenging, people started exploring the power of distributed

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computing, which can be scaled greatly. To address these Vs (Volume, Velocity and Variety), Hadoop initially came as a big data powerhouse. Hadoop came into existence from two research papers by Google, one in 2003, Google file system (GFS), which could store huge datasets in a distributed computing environment and another paper in 2004, i.e. Map Reduce, a technique to process the data stored in GFS kind of file storage. Doug Cutting and Mike Cafarellawere working on a similar problem to index billions of pages for a search engine called the Nutch project. Later Yahoo funded their project, and in 2006 this project was named Hadoop (from Dough Cutting’s son’s elephant toy name). In 2007, Hadoop testing was successful on a 1 K node cluster. In 2008, Yahoo gave this project to Apache foundation as an open-source project. In 2011, Apache released Hadoop V1.0; currently, we are using Hadoop 3.0 (Fig. 4). Hadoop infra also went through various changes version by version and various advancements brought into the stack by Apache as well as other players in the market. From early 2012 through 2017, Hadoop got accepted by various data-powered companies. Due to its complexity and slowness, Hadoop’s MapReduce was not meeting the expectations of many companies. Later, Apache Spark was built by Apache Foundation,

Fig. 4 3Vs—Volume, velocity and variety

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an in-memory data processing engine decoupled from Hadoop infra, so it can also be used by other big data storage and resource management engines. In the last few years, major cloud players like AWS, Azure and Google Cloud platforms came up with Big Data processing capabilities that support hadoop, spark and their own services. Most organizations are now looking towards Big Data on Cloud; this is the organization-wide major enterprise transition. Let’s see how Big Data helped some of the leading tech-based industries in their business. 4.1

How Did Netflix Get Empowered with Big Data Capabilities?

We all know Netflix, the entertainment OTT video streaming giant that has been the market leader in its space since its inception in 2007. Prior to 2007, Netflix was providing content through DVDs through distribution deals where subscribers used to receive DVDs by mail and after watching, they used to return them through the mail. Then it was mostly seen as a DVD rental company. After the Internet TV model’s inception in 2007, the model changed completely. It was not just limited to a content provider or DVD renting. Now, Netflix has access to the user’s watching behavioural data and area of interest. It led to the possibility of creating and sharing content based on the user’s choice, which is highly likely to be a hit model (Mixson, 2021). Netflix captures the below attributes but is not limited to this list: . . . . . . . . . . .

Date and time when content was watched The device on which the content was watched User’s feedback How the nature of the content watched varied based on the device Searches made by the user on its platform Portions of content that got re-watched Whether content was paused if so what is the lapse time of the content User location data Time of the day and week in which content was watched and how it influences the kind of content watched Metadata from third parties like Nielsen Social media data from Facebook and Twitter.

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Having millions of users worldwide capturing data at this grain is not easy work. It leads to a storage challenge and requires a processing powerhouse to use the data for analytics. Here comes the power of Big Data to help Netflix. Initially, Netflix relied on an in-house data centre and Hadoop clusters to process the big data and support its business intelligence system. Later it was cumbersome for Netflix to manage the in-house infra, hence decided to move to the cloud and chose Amazon Web Services. It took around 7 years for Netflix to migrate to the cloud entirely. Netflix is using Elastic Map Reduce (EMR) to support big data processing. Netflix is using Big Data analytics to optimize the overall quality and user experience of the content delivery and prioritization. It also helps understand the mass interest of content type, which helps the production house invest money in the correct type of movies and series, ensuring success. This is only possible due to getting insights from the collected raw data powered by Big Data. Some say the level of intensive data analytics Netflix is performing makes it more of a tech company than an Entertainment company. Netflix is one of many examples which got benefitted due to Big Data. In recent years, companies like Uber, Ola, Swiggy, Zomato, Social Medias like Facebook, Whatsapp, Tik-Tok, LinkedIn and Instagram have empowered their business intelligence using Big Data and Cloud. Uber’s pricing strategy is based on multiple factors—timing, customer’s location, available driver’s location, route traffic conditions etc. and complex algorithms run on the fly to calculate the pricing. The amount of data Uber and its ecosystem generates and uses are not possible through the conventional RDBMS systems. Uber heavily relies on Big Data ecosystems like Hadoop and Hive for getting insights and executing its operational strategies. 4.2

How the Cloud Is Making Big Data Adoption Much Easier?

A few years back, adopting Big Data was an expensive and timeconsuming game because of the heavy investment in the infrastructure. It required procurement of machines and servers and then setting up the Hadoop ecosystem on a distributed computing cluster. Some organizations were not even convinced if they really needed big data and just for proof of concept adopting Big Data was a costly game (Fig. 5). In later years with cloud computing advancements, business organizations got the flexibility to create clusters in no time on the clouds

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Fig. 5 Cloud computing + Big Data = More data power

like Amazon Web Services, Microsoft’s Azure and Google Cloud Platform. With the required software, one can spin up a cluster in almost no time. It helps companies focus on the business problems rather than on underlying complex infrastructure and required security features. Clouds are not only helping in time-saving, but it also helps the companies to remove their stress to manage the infrastructure, leading to very little or almost zero operation cost in managing infrastructure and related platforms. These cloud vendors are also cost-competitive, i.e. pay-per-use and per-second billing. In case a company requires to process data monthly once, it can go for the on-demand cluster, which will have cost only while data is getting processed, whereas if a company requires real-time data processing or in daily mode, it can go for a reserved cluster which will be billed monthly and will be economical. Cloud guarantees high availability, which is more than 99% and can be scaled in no time. It takes off the burden from companies regarding future planning of infrastructure requirements. Hence, it can be said that Big Data got easier with advancements in cloud computing and heavily influenced business intelligence. Cloud computing also offers multiple platforms as service (PaaS) and software as a Service (SaaS) exclusively for Big Data and BI-related services, attracting more companies to go for Cloud offerings.

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4.3

Big Data Challenges

With all these advancements, various challenges are still associated with Big Data adoption for Business Intelligence projects for organizations. Many companies are moving towards big data due to peer pressures without evaluating their requirements. Later, they realize their problems are not fitting into big data problems. Therefore, we must study our requirements before adopting a Big Data solution for our problem. Another challenge is the noisy data. Companies are maintaining legacy data collected from various sources for a very long period and planning to encash that with the power of big data and Business Intelligence. However, the results are not fruitful due to insufficient data quality, leading to heavy investment and less returns. Due to rapid advancements in the Big Data tech space, finding resources is also very challenging. Engineers well trained in Big Data suits are too expensive, leading to higher costs and a longer time to find the solution. Any company must understand its own business, evaluate the suitable platforms, identify the outcomes and goals from the investment and plan the short-term and long-term strategy to execute the projects before adopting the Big Data BI. 4.4

Future of Big Data

There is no doubt that Big Data has already penetrated most tech-driven companies and is still making a good choice for most of them. The future looks bright for data-driven companies if they make the right decisions on adopting Big Data over conventional Business Intelligence at the right time. Data is the new fuel and can drive the company’s growth and operations smoothly if wisely used. Big Data ecosystems combined with machine learning will help the companies predict their business growth and challenges in advance and provide recommendations to be the leader in the market. Future looks even brighter with Big Data-driven solutions, which will impact humanity as a whole in different areas of life like medicine and drugs, self-driven automobiles, eco-friendly and smart living societies, precise weather forecasting, education and more. The omnipresence of Big Data saluting and will amaze us in the future.

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Discussion

Business Intelligence has been a powerful tool for a couple of decades in the business world where data is gathered from heterogeneous sources of the business functional areas like a customer, supply chain, product development, operations etc. With advancements in Internet technology and new era technologies like social media, IoT, mobile apps and other tools, data gathering is accelerated like never before. This data holds excellent business insights. The conventional ETL and BI tools cannot handle this humongous data, which is huge in size and verities. The conventional ETL and BI were time-consuming, and due to computational limitations, lots of time was spent on the selection of attributes which used to be of immediate business use for OLAP purposes. Data warehouse principles are the backbone of conventional ETL frameworks, and OLAP tools create business reporting and dashboards. Big Data technologies rescued the Business Intelligence word, which works on distributed computing concepts and provides highly scalable data computing infrastructure. Hadoop ecosystem evolved as a powerful Big Data computing engine to support complex and volumetric data processing systems, which later got equipped with a better and faster processing engine, i.e. Apache Spark. Data Lake became easy to cater for the required data for Machine Learning and Artificial Intelligence. Data Warehouses for BI reporting on top of the data lakes are now easy to build as all required data is ingested in the data lakes. This concept is termed ELT instead of ETL. The challenge persists in creating the big data infrastructure and maintaining the same. Cloud technology helped in that front. Cloud vendors like AWS, Azure and GCP provide a rich set of tools for customers looking for Big Data adoption. Cloud Computing is helping customers to create the cloud infrastructure in almost no time with any upfront cost. It attracted many customers to tap the power of big data to get their business intelligence.

6

Conclusion

With the advancement of Internet technology and Cloud computing, Big Data has become the most popular choice for big and mid-level businesses who want to tap the power of the omnipresent data within and outside their organizations. Conventional BI was expensive, at the same time,

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time-taking process to roll any new feature or business reports. Hadoop ecosystem and later spark big data engine along with cloud offerings has made the adoption of Big Data based BI very easy and with minimal or no upfront cost. Business users have started using the power of AI and ML due to faster and rich data available to take guided decisions and better predict their business trends. Modern tech stacks like IoT, blockchain, augmented and virtual reality, edutainment etc. generate massive data and also use a high volume of data which cannot be handled using conventional databases. Here comes the power of Big Data which is capable of handling a high volume of data and processing complex structured data like texts, images, audio and videos. Big data has influenced the Business Intelligence space in a big way. ETL has been replaced mainly by ELT, where data lakes are becoming big caterers of raw data for quick analysis. The future looks even brighter as big data evolves; companies no longer need to write complex big data codes because there are various big data processing tool offerings from cloud vendors and other thirdparty tech companies. Cloud migrations are also becoming easy due to the high range of compatible tools with legacy on-premise RDBMS and data warehousing systems.

References Kimble, C., & Milolidakis, G. (2015). Big Data and business intelligence: Debunking the myths. Global Business and Organizational Excellence, 35(1), 23–34. https://doi.org/10.1002/joe.21642 Kowalczyk, M., & Buxmann, P. (2014). Big Data and information processing in organizational decision processes—A multiple case study. Business & Information Systems Engineering, 6(5), 267–278. Mixson, E. (2021). Data science at Netflix: How advanced data & analytics helps Netflix generate billions. Retrieved August 2, 2022, from https:// www.aidataanalytics.network/data-science-ai/articles/data-science-at-netflixhow-advanced-data-analytics-helped-netflix-generate-billions Xia, S. B., & Gong, P. (2014). Review of business intelligence through data analysis. Benchmarking: An International Journal, 21(2), 300–311. https:// doi.org/10.1108/BIJ-08-2012-0050

Determining the Degree of Dominance of Factors Deriving the Comparative Choice Hierarchy: An Operational Generalization of Latent Choice Models Salman A. Cheema, Tanveer Kifayat, Irene L. Hudson, Asif Mehmood, Kalim Ullah, and Abdur R. Rahman

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Introduction 1.1

Motivation

Chester Barnard is commonly coined with the import of the term decisionmaking from the public administration spheres into business circles. By replacing relatively narrower descriptors such as resource management and policy design, the phrase introduced a more exciting ladder into a desire for conclusiveness (Elbanna et al., 2019). However, in the midst of action-driven crispness, decision-makers need to come to terms with constraints—both at the contextual level and psychological frontiers (Ceschi et al., 2017). Thus, considering virtuosity in manipulating

S. A. Cheema (B) National Textile University Faisalabad, Faisalabad, Pakistan e-mail: [email protected] T. Kifayat Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 V. Sharma et al. (eds.), Analytics Enabled Decision Making, https://doi.org/10.1007/978-981-19-9658-0_4

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numerous variables either generic or strategic through the realization of bounded rationality, it is pivotal to create optimal scenarios or at least acceptable ones. Consequently, composed decision-making demands formidable skill set assisting the assembly of valued information prioritizing the available options (Fischhoff & Broomell, 2020). Yet another frontier of competent decision-making is the realization of limitations of practical concerns (Dhami et al., 2015). By the extrapolation, the decision-making competency may arguably describe as the tendency to be self-regulated and use metacognitive process to rationally examine available choices. There is documented evidence in psychological literature and neuroimaging studies that various decision-making challenges lean onto unique latent stimuli—the ability to make optimal choices to maximize rewards in the wake of possible risks (Pacheco-Colón et al., 2019). The foundations of rational decision-making are then thought to be lying on essential components such as (i) utility: a latent phenomenon established through choice axiom (Huber et al., 2014) and (ii) consistency: the degree of robust judgment based on axiom and assessed through the launch of utility theory (Walters et al., 2017). Thus, relating the dynamics of utility of actions with determinants governing the contemporary options remains one of the prime objectives of better decision-making cookery.

I. L. Hudson Mathematical Sciences, School of Science, Royal Melbourne Institute of Technology (RMIT), Melbourne, Australia A. Mehmood Air University Islamabad, Islamabad, Pakistan K. Ullah Foundation University Medical College, Foundation University Islamabad, Islamabad, Pakistan A. R. Rahman Institute for Global Health and Development, The Aga Khan University, Karachi, Pakistan

DETERMINING THE DEGREE OF DOMINANCE OF FACTORS …

1.2

61

Prior Art

The research circles involve in methodological advancements ritualistically facilitate the attainment of above documented delicacies through the application of paired comparison (PC) models or choice models. For example, Cattelan et al. (2013), demonstrated the applicability of PC mechanism to assess the outcomes of sports events while allowing the time-varying ability (Beaudoin & Swartz, 2018). Similarly, Johnson et al. (2019) elucidated the applicability of PC models in public health administration while facilitating the arduous task of project prioritization. The PC models usually arise by considering a latent point-scoring process while conducting a pairwise comparison among a stream of objects, strategies or treatments (Sung & Wu, 2018). In its simplest form, a decisionmaker comparatively assesses, say k, strategies through a binary string in a straightforward fashion such as “strategy i is more optimal than strategy j.” Table 1 comprehends the hypothetical scenario reflecting the aforementioned decision-making initiation. It is noteworthy that, each cell of the table documents the comparative choice of decision-maker while comparing a pair of strategies capsulated by the row and column of the table. It is trivial to notify that the above-comprehended plan is extendable for n rival decision-makers deciding upon l different plausible sets of actions through the k available strategies. For a knowledgeable review of existing literature describing the applicability of PC models, one may consult to Annis and Craig (2005), Kingsley and Brown (2010), Stern (2011), Schauberger and Tutz (2017) and Cheema et al. (2019). Table 1 Choice matrix of one decision-maker and k possible strategies, Y = yes and N = No Strategies

1

2

3

4

5

---

k

1 2 3 4 5 --k

---

Y ---

Y N ---

Y N N ---

N N Y Y ---

-------------

Y N Y Y N -----

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Under the motivation of facilitating the above-documented ingredients of decision-making in an interconnected synchronized manner, we aim to propose a generalized framework covering a broad range of choice or comparative models in a single comprehensive expression. The legitimacy of the devised scheme is established by targeting four inter-related fronts; (i)—the diversity and flexibility of the devised generalization will be established by proving a large classification of existing models as a sub-category of the proposition, (ii)—we will exploit eminent luce’s choice axiom to stay consistent with the well-known notion of utility-based choice behaviors, (iii)—we will be studying the proposition in relation with Bayesian paradigm under the motives of using historic data or prior information in the pursuit of optimal decision-making and (iv)—we will be showing that the devised methods are rigorously treatable in the inferential domain in order to derive a statistically sound and mathematically workable line of action. The capability of the aimed generalization to encompass all aforementioned notions will be delineated through rigorous simulation investigations mimicking the wide range of real-life experimental states. Also, to advocate the applicability of the newly developed scheme, the smoking behaviors of 150 adults with respect to the nicotine level of their reported preferred cigarette brands are analyzed. This chapter is mainly divided into five parts. Section 2 delineates the foundational blocks of the devised generalized scheme. Section 3 reports simulation-based outcomes advocating the legitimacy of the devised scheme. Section 4 is dedicated to the empirical evaluation and lastly, Sect. 5 summarizes the main findings in a compact manner.

2

Mathematical Developments 2.1

General Scheme

Let us say that a pairwise comparison is persuaded among m strategies by n competitors, where the pair of stimuli preserves the continuous outlook of underlying discriminal phenomenon. The preferences between i’th strategy and strategy j are then marked to follow the exponential family of distributions over the consistent support in the population, such as; f (xi ; θi ) = a(θi )b(xi )e g(xi )h(θi ) ,

c < xi < d,

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63

and ) ( ) ( ) ( f x j ; θ j = a θ j b x j e g ( x j ) h (θ j ) ,

c < x j < d,

where, θi and θ j are worth parameters highlighting the utility associated with respective strategies. The focus remains intact in ) ( the quantification of the preference probabilities, such as pi j = P X i > X j where pi j denotes of preferring strategy i over strategy j and ) ( the probability p ji = P X j > X i represents the preference probability of j ’th scheme over i’th, as a function of estimated worth parameters. We proceed in accordance with Luce’s choice axiom by defining a general functional facilitating the estimation of preference probabilities with probability one given that the respective strategy has been preferred in the population. We approach by defining, ) d d ( ) ( P X i > X j = ∫ ∫ f x j ; θ j f (xi ; θi )d xi d x j c xj

(1)

) ( where, ∫dx j f (xi ; θi )d xi = F(d; θi ) − F x j ; θi By using this expression in Eq. (1), we obtained d ( ) d ( ) ) ( ) ( P X i > X j = ∫ f x j ; θ j F(d; θi )d x j − ∫ f x j ; θ j F x j ; θi d x j c

c

Let us denote, d ( ) A = ∫ f x j ; θ j F(d; θi )d x j , c

and d ( ) ( ) B = ∫ f x j ; θ j F x j ; θi d x j . c

In simplification, we get ) ( )| | ( A = F(d; θi ) F d; θ j − F c; θ j ) ) ( ( B = F(d; θi )F d; θ j − F(d; θi )F c; θ j − [F(d; θi ) − F(c; θi )] ) ( )| | ( F d; θ j − F c; θ j

(2)

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Equation (2) now becomes, ) ) ( )| ) ( | ( ( P X i > X j =F(d; θi ) F d; θ j − F c; θ j − F(d; θi )F d; θ j ) ( + F(d; θi )F c; θ j + [F(d; θi ) − F(c; θi )] ) ( )| | ( F d; θ j − F c; θ j , which on further simplification reduces to, ) ) ) ) ( ( ( ( P X i > X j =F(d; θi )F d; θ j − F(d; θi )F c; θ j − F(c; θi )F d; θ j ) ( + F(c; θi )F c; θ j ) | ( )| ( where, F(d; θi ) = [1 − F(c; θi )] and F d; θ j = 1 − F c; θ j Using these specifications, we finally achieve the general expression confirming the preference of strategy i over strategy j, as under ) ) ) ( ( ( (3) P X i > X j = 1 − 2F(c; θi ) − 2F c; θ j + 4F(c; θi )F c; θ j It remains verifiable that for any permissible value of the lower limit of the support, c, the above given functional reduces to 1, ensuring the ability of the general scheme in establishing the true preferences. The above given functional ) be derived ( for p ji) as in pairwise compar( can also ative environment P X j > X i = 1 − P X i > X j . It is noteworthy that Eq. (3) is a function of worth parameters and estimation of preference probabilities using Eq. (3) make it plausible to meet the first essential requirement of rational decision-making that is it must be governed by underlying utility. Thereby, the number of worth parameters stays equal to the number of competing strategies. The issue of non-identifiability and parametric estimability is resolved by imposing a constraint, such that E m i=1 θi = 1, when m competing schemes are under assessment. For a more interesting account of the non-identifiability problem and various possible constraints facilitating the issue, see Rayner and Best (2001). Table 2 comprehends the resultant seven simplifications of the above given generalizations. These advancements are specifically advantageous as they are based on the fundaments of the exponential family of distribution which governs the foundations of linear and generalized linear models available in the existing literature of analytics. Also, one may appreciate that, by doing so, we are in fact capable of entertaining utility behaviors governed by seven different probabilistic realizations through a single comprehensive expression.

Weibull

Rayleigh

Maxwell

Gamma

Exponential

Power

Beta

x

θ

x2

0