Big Data Research for Social Sciences and Social Impact Printed Edition of the Special Issue Published in Sustainability www.mdpi.com/journal/sustainability Miltiadis D. Lytras, Anna Visvizi and Kwok Tai Chui Edited by Big Data Research for Social Sciences and Social Impact Big Data Research for Social Sciences and Social Impact Special Issue Editors Miltiadis D. Lytras Anna Visvizi Kwok Tai Chui MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade Special Issue Editors Miltiadis D. Lytras The American College of Greece Greece Effat University Saudi Arabia Anna Visvizi The American College of Greece Greece Effat University Saudi Arabia Kwok Tai Chui The Open University of Hong Kong Hong Kong Editorial Office MDPI St. Alban-Anlage 66 4052 Basel, Switzerland This is a reprint of articles from the Special Issue published online in the open access journal Sustainability (ISSN 2071-1050) from 2018 to 2020 (available at: https://www.mdpi.com/journal/ sustainability/special issues/big data research). For citation purposes, cite each article independently as indicated on the article page online and as indicated below: LastName, A.A.; LastName, B.B.; LastName, C.C. Article Title. Journal Name Year , Article Number , Page Range. ISBN 978-3-03928-220-3 (Pbk) ISBN 978-3-03928-221-0 (PDF) c © 2020 by the authors. Articles in this book are Open Access and distributed under the Creative Commons Attribution (CC BY) license, which allows users to download, copy and build upon published articles, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. The book as a whole is distributed by MDPI under the terms and conditions of the Creative Commons license CC BY-NC-ND. Contents About the Special Issue Editors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Preface to ”Big Data Research for Social Sciences and Social Impact” . . . . . . . . . . . . . . . ix Miltiadis D. Lytras and Anna Visvizi Big Data Research for Social Science and Social Impact Reprinted from: Sustainability 2020 , 12 , 180, doi:10.3390/su12010180 . . . . . . . . . . . . . . . . . 1 Miltiades D. Lytras and Anna Visvizi Big Data and Their Social Impact: Preliminary Study Reprinted from: Sustainability 2019 , 11 , 5067, doi:10.3390/su11185067 . . . . . . . . . . . . . . . . 5 Michela Arnaboldi The Missing Variable in Big Data for Social Sciences: The Decision-Maker Reprinted from: Sustainability 2018 , 10 , 3415, doi:10.3390/su10103415 . . . . . . . . . . . . . . . . 23 Igor Calzada (Smart) Citizens from Data Providers to Decision-Makers? The Case Study of Barcelona Reprinted from: Sustainability 2018 , 10 , 3252, doi:10.3390/su10093252 . . . . . . . . . . . . . . . . 41 Jie Zhao, Jianfei Wang, Suping Fang and Peiquan Jin Towards Sustainable Development of Online Communities in the Big Data Era: A Study of the Causes and Possible Consequence of Voting on User Reviews Reprinted from: Sustainability 2018 , 10 , 3156, doi:10.3390/su10093156 . . . . . . . . . . . . . . . . 66 Berny Carrera and Jae-Yoon Jung SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks Reprinted from: Sustainability 2018 , 10 , 2731, doi:10.3390/su10082731 . . . . . . . . . . . . . . . . 84 Yung Yau and Wai Kin Lau Big Data Approach as an Institutional Innovation to Tackle Hong Kong’s Illegal Subdivided Unit Problem Reprinted from: Sustainability 2018 , 10 , 2709, doi:10.3390/su10082709 . . . . . . . . . . . . . . . . 100 Vasile-Daniel P ̆ av ̆ aloaia, Elena-M ̆ ad ̆ alina Teodor, Doina Fotache and Magdalena Danilet ̧ Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences Reprinted from: Sustainability 2019 , 11 , 4459, doi:10.3390/su11164459 . . . . . . . . . . . . . . . . 117 Yu Zhao, Guoqin Zhang, Tao Lin, Xiaofang Liu, Jiakun Liu, Meixia Lin, Hong Ye and Lingjie Kong Towards Sustainable Urban Communities: A Composite Spatial Accessibility Assessment for Residential Suitability Based on Network Big Data Reprinted from: Sustainability 2018 , 10 , 4767, doi:10.3390/su10124767 . . . . . . . . . . . . . . . . 138 Estela Marine-Roig Destination Image Analytics Through Traveller-Generated Content Reprinted from: Sustainability 2019 , 11 , 3392, doi:10.3390/su11123392 . . . . . . . . . . . . . . . . 156 v Rizwan Muhammad, Yaolong Zhao and Fan Liu Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China Reprinted from: Sustainability 2019 , 11 , 2822, doi:10.3390/su11102822 . . . . . . . . . . . . . . . . 179 Xiaozhong Lyu, Cuiqing Jiang, Yong Ding, Zhao Wang and Yao Liu Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions Reprinted from: Sustainability 2019 , 11 , 913, doi:10.3390/su11030913 . . . . . . . . . . . . . . . . . 209 Desamparados Blazquez, Josep Domenech and Jose-Maria Garcia-Alvarez-Coque Assessing Technology Platforms for Sustainability with Web Data Mining Techniques Reprinted from: Sustainability 2018 , 10 , 4497, doi:10.3390/su10124497 . . . . . . . . . . . . . . . . 227 Sung-Won Yoon and Sae Won Chung Semantic Network Analysis of Legacy News Media Perception in South Korea: The Case of PyeongChang 2018 Reprinted from: Sustainability 2018 , 10 , 4027, doi:10.3390/su10114027 . . . . . . . . . . . . . . . . 242 Monica Mihaela Maer-Matei, Cristina Mocanu, Ana-Maria Zamfir and Tiberiu Marian Georgescu Skill Needs for Early Career Researchers—A Text Mining Approach Reprinted from: Sustainability 2019 , 11 , 2789, doi:10.3390/su11102789 . . . . . . . . . . . . . . . . 266 Inchae Park and Byungun Yoon Identifying Promising Research Frontiers of Pattern Recognition through Bibliometric Analysis Reprinted from: Sustainability 2018 , 10 , 4055, doi:10.3390/su10114055 . . . . . . . . . . . . . . . . 283 Celina M. Olszak and Maria Mach-Kr ́ ol A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data Reprinted from: Sustainability 2018 , 10 , 3734, doi:10.3390/su10103734 . . . . . . . . . . . . . . . . 315 Diego Buena ̃ no-Fern ́ andez, David Gil and Sergio Luj ́ an-Mora Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study Reprinted from: Sustainability 2019 , 11 , 2833, doi:10.3390/su11102833 . . . . . . . . . . . . . . . . 346 En-Gir Kim and Se-Hak Chun Analyzing Online Car Reviews Using Text Mining Reprinted from: Sustainability 2019 , 11 , 1611, doi:10.3390/su11061611 . . . . . . . . . . . . . . . . 364 Jason Jihoon Ree, Cheolhyun Jeong, Hyunseok Park and Kwangsoo Kim Context–Problem Network and Quantitative Method of Patent Analysis: A Case Study of Wireless Energy Transmission Technology Reprinted from: Sustainability 2019 , 11 , 1484, doi:10.3390/su11051484 . . . . . . . . . . . . . . . . 386 vi About the Special Issue Editors Miltiadis D. Lytras Ph.D., is an expert in advanced computer science and management, and an editor, lecturer, and research consultant, with extensive experience in academia and the business sector in Europe and Asia. Dr. Lytras is Research Professor at Deree College—The American College of Greece—and a Distinguished Scientist at the King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia. Dr. Lytras is a world-class expert in the fields of cognitive computing, information systems, technology-enabled innovation, social networks, computers in human behavior, and knowledge management. In his work, Dr. Lytras seeks to bring together and exploit synergies among scholars and experts, and is committed to enhancing the quality of education for all. Anna Visvizi Ph.D., is a political scientist and economist, editor, and research consultant with extensive experience in academia and the think-tank sector in Europe and the US. As the author of several publications, Dr. Visvizi has presented her work across Europe and the US, including Capitol Hill. Dr. Visvizi’s expertise covers issues pertinent to the intersection of politics, economics, and ICT. This is translated in her research and advisory roles in the areas of AI and geopolitics, smart cities and smart villages, innovation promotion, global migration management, and economic integration, especially the EU and BRI. Currently, Dr. Visvizi serves as Associate Professor at Deree College—The American College of Greece. Until December 2018, Dr. Visvizi was Head of Research at the Institute of East-Central Europe (IESW), Poland. In her work, Dr. Visvizi places emphasis on engaging academia, the think-tank sector, and decision-makers in dialogue to ensure well-founded and evidence-driven policy-making. Kwok Tai Chui received the B.Eng. degree in electronic and communication engineering—Business Intelligence Minor and Ph.D. degree from City University of Hong Kong. He had industry experience as Senior Data Scientist in Internet of Things (IoT) company. He joined the Department of Technology, School of Science and Technology, at The Open University of Hong Kong as Research Assistant Professor. He was the recipient of 2nd Prize Award (Postgraduate Category) of 2014 IEEE Region 10 Student Paper Contest. Also, he received Best Paper Award in IEEE The International Conference on Consumer Electronics-China, in both 2014 and 2015. He has more than 45 research publications including edited books, book chapters, journal papers, and conference papers. He has served as various editorial position in SCI-listed journals including Managing Editor of International Journal on Semantic Web and Information Systems , Topic Editor of Sensors , Guest Editors of Sustainability , Sensors , Energies , Applied Sciences , Journal Future Generation Computer Systems and Journal of Internet Technology . His research interests include computational intelligence, data science, energy monitoring and management, intelligent transportation, smart metering, healthcare, machine learning algorithms and optimization. vii Preface to ”Big Data Research for Social Sciences and Social Impact” A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insights to social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of Big Data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. We are grateful to all the contributors to this edition for their intellectual work and their sound propositions and arguments. The relevant debate is aligned around three pillars: Section A. Big Data For Social Impact We emphasize an initial assessment of the social impact of Big Data. For this purpose, we communicate the main findings of a preliminary study. We then discuss the missing variable in big data for social sciences: the decision-maker. We analyze the role of (smart) citizens from data providers to decision-makers, using the city of Barcelona as a case study. The integration of sustainability in the context of Big Data research is also discussed though analysis of the impact of big data on the development of sustainable online communities, towards the sustainable development of online communities in the Big Data era: a study of the causes and possible consequence of voting on user reviews. More emphasis is paid to advanced social mining techniques, such as sentiment analysis and opinion mining, in the context of social networks. Finally, a research study on the impact of big data research on innovation is communicated. Selected topics in this section include: • Big Data and Their Social Impact: A Preliminary Study; • The Missing Variable in Big Data for Social Sciences: The Decision-Maker; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data Approach as an Institution. ix Section B. Techniques and Methods For Big Data-Driven Research on Social Sciences and Social Impact In this section, various, sophisticated research and case studies of Big Data-driven research on social impact are presented. Selected topics include: • Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences; • Towards Sustainable Urban Communities: A Composite Spatial Accessibility Assessment for Residential Suitability Based on Network Big Data; • Destination Image Analytics Through Traveller-Generated Content, Spatiotemporal Analysis to Observe Gender Based Check-In Behavior Using Social Media Big Data: A Case Study of Guangzhou, China; • Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions; • Assessing Technology Platforms for Sustainability with Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception in South Korea: The Case of PyeongChang. Section C. Big Data Research Strategies In the concluding section of this edition, the debate on the social impact of Big Data Research on sustainability is promoted, with an integrative discussion of complementary research strategies including: • Skill Needs for Early Career Researchers—A Text Mining Approach; • Identifying Promising Research Frontiers of Pattern Recognition through Bibliometric Analysis; • A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data; • Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study; • Analyzing Online Car Reviews Using Text Mining; • Context–Problem Network and Quantitative Method of Patent Analysis: A Case Study of Wireless Energy Transmission Technology. We want to thank the professional staff at MDPI for their qualitative work that made this edition possible. If you would like any further information on this edition, we are at your disposal, and we invite you to join us in our next editions, on the topics of Big Data Research , Social Sciences and Sustainability Miltiadis D. Lytras, Anna Visvizi, Kwok Tai Chui Special Issue Editors x sustainability Editorial Big Data Research for Social Science and Social Impact Miltiadis D. Lytras 1,2, * and Anna Visvizi 1,3 1 School of Business and Economics, Deree College—The American College of Greece, 153-42 Athens, Greece; avisvizi@acg.edu 2 E ff at College of Engineering, E ff at University, Jeddah P.O. Box 34689, Saudi Arabia 3 E ff at College of Business, E ff at University, Jeddah P.O. Box 34689, Saudi Arabia * Correspondence: mlytras@acg.edu; Tel.: + 30-210-600-9800 Received: 13 December 2019; Accepted: 14 December 2019; Published: 24 December 2019 Abstract: This Special Issue of Sustainability devoted to the topic of “Big Data Research for Social Sciences and Social Impact” attracted significant attention of scholars, practitioners, and policy-makers from all over the world. Locating themselves at the cross-section of advanced information systems and computer science research and insights from social science and engineering, all papers included in this Special Issue contribute to the debate on the use of big data in social sciences and big data social impact. By promoting a debate on the multifaceted challenges that our societies are exposed to today, this Special Issue o ff ers an in-depth, integrative, well-organized, comparative study into the most recent developments shaping the future directions of interdisciplinary research and policymaking. Keywords: big data research; social and humanistic computing; social sciences; social good; social impact; machine learning; knowledge management; web science; data science; social inclusive economic growth; sustainability; innovation; innovation networks 1. Introduction—Overview of the Edited Volume The evolution of big data research and social media and the contributions of individuals and organizations in social networking data ecosystems resulted in a new, sophisticated context for technology-facilitated social interactions [ 1 ]. In parallel, a number of social challenges and problems, and the critical need to enhance the capability of our society to deal with delicate social issues, sets new directions for research [ 2 ]. The focus of this edited volume is on the intersection of advanced information systems and social sciences research [ 3 , 4 ]. It is about analyzing the impact of data and their processing to the understanding and addressing of significant social problems. It is also about analyzing data as a social good that must be protected and be aligned with significant rules and ethical principles. Social good is generally an action or application that benefits society. In the past, governments and nonprofit organizations usually drove it. With the advancement of social media via computer-mediated technologies like WeChat, WhatsApp, Weibo, Twitter, Instagram, Facebook, and YouTube, billions of registered users utilize social interactions through social media. As a result, everyone can contribute to society easily and achieve social good. Tremendous growth of digital information (from granular data to aggregated data) is available for numerous social sciences and social impact applications, for instance, environmental protection, healthcare and education. Data analytics are ubiquitous and purpose-oriented in di ff erent forms: descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics. Typical challenges for adopting big data technologies for social sciences and social impact are data handling and storage, data quality, computational power of computers and algorithm customization for special Sustainability 2020 , 12 , 180; doi:10.3390 / su12010180 www.mdpi.com / journal / sustainability 1 Sustainability 2020 , 12 , 180 application and security. More importantly, it seems that our societies have an infinite need for utilizing the value of big data for social impact and thus applications on social good [5–7]. This edited volume aims to consolidate recent advances in big data for social good. Topics of interest for this Special Issue include (but are not limited to): • Innovative applications of data analytics to social sciences and social impact problems like energy, healthcare, education, food, poverty, injustice, and inequalities in society; • Machine learning algorithms for big data applications for social sciences and social impact; • Advanced techniques for handling unstructured, unlabeled and / or missing data; • Data quality control of big data for social good; • Big data research-driven policy-making for social impact; • Standardization for big data infrastructure and framework; • Big data implications for society; • Big-data-driven KPIs research for international benchmarking; • Socially inclusive economic development and growth through big data applications; • Human-centric big data research; • Ethical issues on social research of big data. The final selection of papers includes 19 research studies organized in three sections: 1.1. Section A: Big Data for Social Impact • Lytras, M.D.; Visvizi, A. Big Data and Their Social Impact: Preliminary Study. Sustainability 2019 , 11 , 5067. • Arnaboldi, M. The Missing Variable in Big Data for Social Sciences: The Decision-Maker. Sustainability 2018 , 10 , 3415. • Calzada, I. (Smart) Citizens from Data Providers to Decision-Makers? The Case Study of Barcelona. Sustainability 2018 , 10 , 3252. • Zhao, J.; Wang, J.; Fang, S.; Jin, P. Towards Sustainable Development of Online Communities in the Big Data Era: A Study of the Causes and Possible Consequence of Voting on User Reviews. Sustainability 2018 , 10 , 3156. • Carrera, B.; Jung, J.-Y. SentiFlow: An Information Di ff usion Process Discovery Based on Topic and Sentiment from Online Social Networks. Sustainability 2018 , 10 , 2731. • Yau, Y.; Lau, W.K. Big Data Approach as an Institutional Innovation to Tackle Hong Kong’s Illegal Subdivided Unit Problem. Sustainability 2018 , 10 , 2709. 1.2. Section B: Techniques and Methods for Big Data Driven Research for Social Sciences and Social Impact • P ă v ă loaia, V.-D.; Teodor, E.-M.; Fotache, D.; Danile ̧ t, M. Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences. Sustainability 2019 , 11 , 4459. • Zhao, Y.; Zhang, G.; Lin, T.; Liu, X.; Liu, J.; Lin, M.; Ye, H.; Kong, L. Towards Sustainable Urban Communities: A Composite Spatial Accessibility Assessment for Residential Suitability Based on Network Big Data. Sustainability 2018 , 10 , 4767. • Marine-Roig, E. Destination Image Analytics Through Traveller-Generated Content. Sustainability 2019 , 11 , 3392. • Muhammad, R.; Zhao, Y.; Liu, F. Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China. Sustainability 2019 , 11 , 2822. • Lyu, X.; Jiang, C.; Ding, Y.; Wang, Z.; Liu, Y. Sales Prediction by Integrating the Heat and Sentiments of Product Dimensions. Sustainability 2019 , 11 , 913. • Blazquez, D.; Domenech, J.; Garcia-Alvarez-Coque, J.-M. Assessing Technology Platforms for Sustainability with Web Data Mining Techniques. Sustainability 2018 , 10 , 4497. 2 Sustainability 2020 , 12 , 180 • Yoon, S.-W.; Chung, S.W. Semantic Network Analysis of Legacy News Media Perception in South Korea: The Case of PyeongChang 2018. Sustainability 2018 , 10 , 4027. 1.3. Section C: Big Data Research Strategies • Maer-Matei, M.M.; Mocanu, C.; Zamfir, A.-M.; Georgescu, T.M. Skill Needs for Early Career Researchers—A Text Mining Approach. Sustainability 2019 , 11 , 2789. • Park, I.; Yoon, B. Identifying Promising Research Frontiers of Pattern Recognition through Bibliometric Analysis. Sustainability 2018 , 10 , 4055. • Olszak, C.M.; Mach-Kr ó l, M. A Conceptual Framework for Assessing an Organization’s Readiness to Adopt Big Data. Sustainability 2018 , 10 , 3734. • Buenaño-Fern á ndez, D.; Gil, D.; Luj á n-Mora, S. Application of Machine Learning in Predicting Performance for Computer Engineering Students: A Case Study. Sustainability 2019 , 11 , 2833. • Kim, E.-G.; Chun, S.-H. Analyzing Online Car Reviews Using Text Mining. Sustainability 2019 , 11 , 1611. • Ree, J.J.; Jeong, C.; Park, H.; Kim, K. Context–Problem Network and Quantitative Method of Patent Analysis: A Case Study of Wireless Energy Transmission Technology. Sustainability 2019 , 11 , 1484. 2. Conclusions—The Value Added of this Special Issue This collection of papers provides an integrative discussion on key issues and challenges related to the adoption of big data research and their social impact. Below we provide a list of the key findings and ideas communicated in this Special Issue: • The key understanding of big data and their social impact requires sophisticated studies for the measurement of value and the associated perception from individuals and groups. It also requires a sophisticated approach for the linkage of social value to key social challenges and problems. In this context, various key performance indicators have to be justified. A number of individual concerns related to data privacy and anonymity are also important to be addressed. • The promotion of big data research for social impact still emphasizes the role of the missing variable in big data for social sciences: the decision-maker. The sophisticated analysis of social-sensitive data requires decision makers with the capability to analyze and to link these data with significant social problems. Without this human-centric approach in decision, making any e ff ort for social impact will be of limited contribution. • The evolution of big data research for social impact requires strategic e ff orts and initiatives towards sustainable development of online communities in the big data era. These communities will adopt rules and will promote the required culture for linking advanced research based on big data and analytics for addressing significant societal problems with actions. • Social evolution of big data research is also related to sophisticated data processing methods like analysis of sentiment from online social networks. • Big data research can justify also a new era for the evolution of social innovation. The exploitation of skills and capabilities beyond local boundaries will link social local capabilities to global social challenges. • Advanced data mining and analytics approaches are required for the revelation of hidden insights regarding big data linked to social problems. In this direction, many more things have to be done. In the current era, limited isolated approaches prove the capacity of these techniques to deal with social issues. At the other extreme, some rules and ethical norms have to applied in initiatives like social rating systems that violate privacy and personal human rights. • Sustainability concerns are significant in the context of the social impact of big data research. The perception of big data as social good that must promote social value is a basic axiomatic sentence, but there are many grey areas for the provision of this value as a transparent good in the benefit of the global society. 3 Sustainability 2020 , 12 , 180 We are pleased to be able to present this collection of papers to the research community. The promotion of socially sensitive research especially that that addresses topics related to the use of technology will be a trend in the years to come [ 8 , 9 ]. The understanding that social impact and social value are the key objective of any technology-driven innovation is the basic step towards sustainable and socially inclusive growth and development. Author Contributions: All authors contributed evenly to this Editorial. All authors have read and agreed to the published version of the manuscript. Conflicts of Interest: The authors declare no conflict of interest. References 1. Lytras, M.D.; Raghavan, V.; Damiani, E. Big data and data analytics research: From metaphors to value space for collective wisdom in human decision making and smart machines. Int. J. Semant. Web Inf. Syst. 2017 , 13 , 1–10. [CrossRef] 2. Lytras, M.D.; Mathkour, H.I.; Abdalla, H.; Al-Halabi, W.; Yanez-Marquez, C.; Siqueira, S.W.M. Enabling technologies and business infrastructures for next generation social media: Big data, cloud computing, internet of things and virtual reality. J. Univers. Comput. Sci. 2015 , 21 , 1379–1384. 3. Lytras, M.D.; Mathkour, H.I.; Abdalla, H.; Al-Halabi, W.; Yanez-Marquez, C.; Siqueira, S.W.M. An emerging— Social and emerging computing enabled philosophical paradigm for collaborative learning systems: Toward high e ff ective next generation learning systems for the knowledge society. Comput. Hum. Behav. 2015 , 5 , 557–561. [CrossRef] 4. Visvizi, A.; Lytras, M.D. Rescaling and refocusing smart cities research: From mega cities to smart villages. J. Sci. Technol. Policy Mak. 2018 . [CrossRef] 5. Lytras, M.D.; Aljohani, N.R.; Hussain, A.; Luo, J.; Zhang, X.Z. Cognitive Computing Track Chairs’ Welcome & Organization. In Proceedings of the Companion of the Web Conference, Lyon, France, 23–27 April 2018. 6. Visvizi, A.; Mazzucelli, C.; Lytras, M. Irregular migratory flows: Towards an ICT’ enabled integrated framework for resilient urban systems. J. Sci. Technol. Policy Manag. 2017 , 8 , 227–242. [CrossRef] 7. Crusoe, J.; Ahlin, K. Users’ activities for using open government data—A process framework. Transform. Gov. People Process Policy 2019 , 13 , 213–236. [CrossRef] 8. Visvizi, A.; Lytras, M.D. Politics and Technology in the Post-Truth Era ; Emerald Publishing: Bingley, UK, 2019; ISBN 9781787569843. 9. Visvizi, A.; Daniela, L. Technology-Enhanced Learning and the Pursuit of Sustainability. Sustainability 2019 , 11 , 4022. [CrossRef] © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http: // creativecommons.org / licenses / by / 4.0 / ). 4 sustainability Article Big Data and Their Social Impact: Preliminary Study Miltiades D. Lytras 1,2, * and Anna Visvizi 1,3 1 School of Business & Economics, Deree College, The American College of Greece, 153-42 Athens, Greece; avisvizi@acg.edu 2 E ff at College of Engineering, E ff at University, Jeddah P.O. Box 34689, Saudi Arabia 3 E ff at College of Business, E ff at University, Jeddah P.O. Box 34689, Saudi Arabia * Correspondence: mlytras@acg.edu; Tel.: + 30-210-600-9800 Received: 26 July 2019; Accepted: 10 September 2019; Published: 17 September 2019 Abstract: Big data is the buzz-word of today, and yet their specific impact on individuals and societies remains assumed rather than fully understood. Clearly, big data and their use have already given rise to a number of questions, including those of how data can be collected and used in ethical and socially sensitive ways. Building on these points, the objective of this study was to explore how precisely big data and big data based services influence individuals and societies. This paper elaborates on individuals’ perceptions of data, especially on how they perceive the actual sharing of their data. In this way, this paper defines a value space for the social impact of big data relevant to three factors, namely the intention to share personal data, individual’s concerns, and social impact of big data.The main contribution of this study consists of the insights into the still nascent area of research that unfolds at the cross-section of social science and computer science. We expect that in the next years this area of research will gain prominence. Keywords: social impact; big data research; information systems; analytics; decision making; social sciences 1. Introduction Recent developments in data-driven information systems, set big data research and business analytics at the core computer science and social science. In computer science research, there is a consensus that big data and data analytics research will foster a new generation of information systems capable of managing the collective wisdom in human decision making and smart machines [ 1 ]. Emerging research areas like cognitive computing [ 2 ] combined with artificial intelligence and machine learning, permit advanced and sophisticated methods for processing data, including sentiment analysis, image processing, natural speech recognition and text mining. In parallel emerging technologies, including cloud computing, internet of things and virtual reality, the value proposition of application and services that process data in di ff erent formats such as text, images, videos, microcontents in social media is further enhanced [ 3 , 4 ]. The development of a huge data ecosystem around the globe, in which providers and users of data promote business value in terms of data and decision making, is a key development of our times. In this context, users of applications and services worldwide participate consciously, or unintendedly, to an integrated data dissemination and aggregation process with critical trust and privacy issues. A great discussion on the real impact of big data research has been initiated. An interesting study [ 5 ] sets the significance of the human decision maker at the center of any type of big data information processing cycle. There is an agreement between di ff erent academics that big data can make a big impact [1,6]. Big data research is aligned with the evolution in emerging information technologies research. New information processing paradigms further promote the significance of big data, and have a great Sustainability 2019 , 11 , 5067; doi:10.3390 / su11185067 www.mdpi.com / journal / sustainability 5 Sustainability 2019 , 11 , 5067 impact on its volume and coverage. A number of application domains and industries already adopt big data research with significant success. Consider social networks research and the contribution of social networks to the big data ecosystem [ 2 , 3 ]. Other examples are artificial intelligence and machine learning applications in various domains, such as customers / clients of big data repositories for personalized and targeted services [3,4]. In the recent literature of big data research, an increasing section is dedicated to the capacity of big data to support social sciences research. There is the anticipation that big data is potentially a social good that must be secured and be used for the transparency of services, and for the evolution of a user-centric new culture for sustainable computing. In parallel, several concerns have been documented, mostly related to trust, privacy and the protection of personalities in the new technology-driven domain of services and applications. In Figure 1, below, we provide our initial framework for the investigation of the social impact of big data. Figure 1. An initial framework for understanding the social impact of big data research. Source: The authors. In our approach three critical factors need further investigation: • User concerns / a ff ordances: The first factor, namely user concerns or a ff ordances is related to all the psychological, social, personal or professional concerns of users in relevance to the use of applications and services that generate and share personal data or other kinds of data from individuals within the big data ecosystem • Intention to share data / informed consent: This aspect of our research problem is related to the conscious agreement or the intrinsic motivation of users to share their data for the purposes of big data application. In our research, we are interested in the connection of social challenges and social problems to the intention of users to share their data. Furthermore, we wanted to understand if in some scenarios, users of applications and services share their data without formal agreement due to their interest in exploiting the added value of the service for themselves or for the society. 6 Sustainability 2019 , 11 , 5067 • Social impact of big data: The third critical factor also determines the value space in Figure 1. The measurement of the social impact of big data seems to require interdisciplinary approaches and metrics, thus we must deploy heuristics for the attachment of value contribution to the perception of users for the impact of big data research to their lives and to our society. This is the ultimate objective of our research, nevertheless, the requirements and the various research strategies we deployed exceed the length and scope of this paper. The value space of big data is defined as an aggregation of three factors / forces. At the X -axis, the social impact of big data is presented in a spectrum of low to high value. Users of big data applications develop perceptions and have their own interpretation mechanisms for the impact of big data. On the Y -axis, concerns and fears of users of big data application develop an intrinsic motivation mechanism for the use of such application. They deploy di ff erent ways for the use of big data applications and they also express their concerns for various aspects of these applications. Furthermore, on the Z -axis in Figure 1, it is shown that users also execute a di ff erent degree of willingness to share their data, for the proper functioning of big data applications. Various studies in the literature mention these factors, and our previous research has tried to investigate these factors. The value space that is defined by these three axes, can be used as a model for discussing big data applications and services and for mapping such services in wider contexts e.g., smart cities research. From a practical point of view, this model can also be exploited by real users of big data applications for the customization of available services or the personalization for added value of such applications. Also, from a policy making view, such a model can guide public consultation and debate on how we protect the data and identity rights of citizens against big data applications without compromise of social value and impact. Figure 1 is used as a metaphor to communicate the overall idea of our research, that somehow users, with their perceptions and intention to use big data applications, define their personal value space and maybe also a societal value space. We understand that in our approach some key assumptions are integrated. We do, however, believe that it is worthy to investigate this research problem which has many psychological and social aspects. In the next section we provide a critical review of the relevant literature towards the justification of our research model that will be presented in Section 3 of this research study. 2. Literature Review—Understanding the Debate on Big Data and their Social Impact The agenda of big data research is quite wide and involved various multidisciplinary communities. From a computer science and information systems perspective issues related to standardization, data mining, aggregation of data, interoperability and recommendation systems are at the top of research priorities. From a social science perspective, data as a social construct a ff ecting issues related to identity management, personality, privacy and security are the focus of social research. Furthermore, the concept of the digital self, that combines personal, professional, social, and other features of individuals is gaining more interest [ 1 ]. In an evolving way, big data that refer to human entities and communities of people are established with convenient computational methods that permit social analysis and reference. The connection of big data research to social sciences as well as the big impact of data-intensive applications and processing methods to societal challenges provides a very interesting research challenge. From the one side we have the social actors, humans, decision makers that both provide and consume data available in diverse, interconnected information systems [ 5 ]. The quest for impact on big data platforms and big data [ 6 ] requires a detailed study of di ff erent factors and accordingly new metrics like analytics or KPIs (key performance indicators) [ 6 ]. Humans, from this point of view, realize a critical mental shift in their behavior. From data providers they are requested to perform a decision maker role, within the boundaries and across hi-tech socio-technical structures like smart cities [7]. From a di ff erent angle, the big data ecosystem requires distribution and aggregation of information in modes that were unforeseen in the past. The sophistication and the huge capacity of big data 7 Sustainability 2019 , 11 , 5067 services to process significant volumes of data, automatically, without human intervention, sets critical questions related to privacy, security and data protection [8]. Especially in the context of social networks and social media [ 9 ], the information di ff usion has exceeded any prediction. The ease of sharing information as well as the increased openness of such data warehouses permits advanced data processing that leads to critical insights about the data providers. In this situation, big data applications serve as intermediaries, matching the gap between the providers and the consumers of data, allowing several innovative business models to appear [ 10 ]. There is a connection that needs further investigation. The power of big data applications as intermediaries and as unique business models for adding value to raw data with data processing data, like sentiment analysis and opinion mining [ 11 ]. The capacity of new information processing methods to conclude about sentiments, attitudes or opinions is directly linked to some forms of social impact for such applications [12]. Within this complex big data ecosystem, individuals, organizations as well as governments need to develop frameworks to