Nội dung

International Journal of Mechanical Engineering and Technology (IJMET) Volume 10, Issue 03, March 2019, pp. 864-875. Article ID: IJMET_10_03_089 Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=3 ISSN Print: 0976-6340 and ISSN Online: 0976-6359 © IAEME Publication Scopus Indexed THE CRITICAL SUCCESS FACTORS FOR BIG DATA ADOPTION IN GOVERNMENT Novan Zulkarnain Computer Science Department, BINUS Graduate Program, Doctor of Computer Science Information System Department, School of Information System Bina Nusantara University, Jakarta, Indonesia 11480 Meyliana* Information System Department, School of Information System Bina Nusantara University, Jakarta, Indonesia 11480 Ahmad Nizar Hidayanto Faculty of Computer Science Universitas Indonesia, Depok, Indonesia 16424 Harjanto Prabowo Management Department, BINUS Business School Undergraduate Program Information System Department, School of Information System Bina Nusantara University, Jakarta, Indonesia 11480 ABSTRACT Over the past decade, governments around the world have been trying to take advantage of Big Data technology to improve public services with citizens. The adoption of Big Data has increased in most countries, but at the same time, the rate of successful adoption and management varies from one country to another. A systematic review of the literature (SLR) was carried out to identify the critical success factors (CSF) for the adoption of big data in the government. It includes the critical success factor of the adoption of Big Data in the government in the last 10 years. It presents the general trends that examine 183 journals and numerous literary works related to government operations, the provision of public services, citizen participation, decision making and policies, and governance reform. We selected 90 journals and found 11 classification factors that refer to the successions of a Big Data adoption in the government. Keywords: Critical success factors; CSFs; Big Data; E-Government; systematic literature review; SLR. http://www.iaeme.com/IJMET/index.asp 864 editor@iaeme.com The Critical Success Factors for Big Data Adoption in Government Cite this Article Novan Zulkarnain, Meyliana, Ahmad Nizar Hidayanto, Harjanto Prabowo The Critical Success Factors for Big Data Adoption in Government, International Journal of Mechanical Engineering and Technology, 10(3), 2019, pp. 864-875. http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=3 1. INTRODUCTION Amazingly, every day the world produces about 2.5 million bytes of data or 1 billion gigabytes [1]. These data include textual content (i.e. structured, semi-structured and unstructured), multimedia content (e.g. video, images, audio) on a variety of platforms (e.g., machine-to-machine communication, social networking sites, networks) of sensors, cybernetic systems and the Internet of Things [IoT]) [2]. A large amount of data may also come from other relevant areas, such as banks, education, military, medical research, public health, smart cities, security management, emergencies and disaster recovery [3]. The increase in data causes a problem about how to store and manage such heterogeneous datasets with moderate requirements in the hardware and software infrastructure [4]. Industry, education, and governments around the world are very aware and understand the importance of large amounts of data, commonly known as Big Data or Open Data [4]. Commonly, the industry uses big data for analysts of customer opinions, behavioral analysis, customer satisfaction, predictive support and fraud detection [5]. In the field of education, Big Data is used to analyze and extract useful trends, educational models, personalize learning, standardize the presentation of knowledge and use it to provide better education and also curriculum [6]. And the government uses Big Data to improve information and government service for its citizens [7], quickly addressing basic needs, providing quality education and reducing the unemployment rate [8]. It will create better health, better teachers, better education and better decision-making processes [9]. Large amounts of money are needed to build and implement large amounts of data. The government of the United States, in early 2006, the state legislator allocated $ 9.5 million to boost Big Data for the public sector [10]. In March 2012, the Obama administration announced a $ 200 million investment to launch the Big Data research and development plan in several government agencies [4]. In 2013, the Korean government decided to invest $ 500 million in the Big Data on Government 3.0 project over the next 5 years [11]. In addition, the lesson of the Chinese government has 731 million Internet users according to government statistics in January 2017. In addition, 695 million users use the Internet through mobile devices. And it has invested $ 14.4 billion to share data on the Internet of Thing (IoT) [12]. On this basis, it is clear that one of the critical success factors is the government budget. Many in developing countries have the same problem, the problem of costs and management. For funds to be used efficiently, funds must be properly managed. Good project management is the key to the adoption of big data in government. A top-down approach to managing and integrating big data is also needed [13]. Before Government Adopting Big data, they have to mature their infrastructure for supporting. One recommendation choice for adopting Big Data is using Cloud Computing Infrastructure. Its benefits include cost efficiency, unlimited storage, backup and recovery, automatic software integration, easy access to information, rapid deployment, a simpler scale of services and provision of new services [14]. Some paper also said parallel computing is one of the fundamental infrastructures for the management of big data activities. It is capable of performing simultaneously the activity of the algorithm in a group of machines or supercomputers [15]. http://www.iaeme.com/IJMET/index.asp 865 editor@iaeme.com Novan Zulkarnain, Meyliana, Ahmad Nizar Hidayanto, Harjanto Prabowo To build a sustainable Big Data infrastructure, data integration is the key [16]. Governments should try to rebuild large data control towers to integrate cumulative, structured or unstructured sets of departmental silos [13]. The basic requirements of the infrastructure level should be considered according to the types of government organizations, data use, and energy consumption with environmental impact. It is preferable to use your own data centers with a private cloud structure as a basic security measure. [17]. Furthermore, some types of research are underway in the adoption of big data in the government. They implemented it successfully. Some research claims that the adoption of large amounts of data in governments meant the same thing as the adoption of data in industry or academia [18]. Therefore, we use a review of the literature that has a relationship between Big Data and Industry or Business, Big Data and Education, and the last major focus on Big Data and Government. Therefore, this research tries to define a question: "What are the critical success factors of the adoption of big data in the government?" 2. METHODOLOGY This research conducted a comprehensive review of the study literature on the investigation of the adoption of large data on government. This process is divided into several parts, which are: determine the sources of research, define the keyword model for a research process, initiate the inclusion and exclusion criteria, extract data and analyze the result to answer a research question. 2.1. Search Process The first process is to define the source of literature to find an adequate article/journal. The sources selected for the systematic review of the literature are the following:  ACM Digital Library (dl.acm.org)  Elsevier (www.sciencedirect.com)  IEEE Xplore Digital Library (ieeexplore.ieee.org)  Igi Global (www.igi-global.com)  Sage (www.sage.com)  Springer (link.springer.com)  Taylor & Francis (taylorandfrancisgroup.com/journals/)  Wiley Online Library (onlinelibrary.wiley.com) To find the desired paper. We use several keywords and a combination of Boolean operators. The Boolean operators that we use in this paper, such as OR, and AND. The combination of keywords is as follows:  ((critical OR success OR factor) OR CSF) AND ((big AND data AND adoption) OR BD) AND (((electronic AND government) OR e-gov) OR ((public AND sector) OR nonprofit))  ((key OR success OR factor) OR CSF) AND ((big AND data AND project) OR BD) AND (((electronic AND government) OR e-gov) OR ((public AND sector) OR nonprofit))  (Success OR Adoption) AND (Big AND Data) AND (Government Or E-Government Or EGov)  (Big AND Data) And (Adoption OR Success) And (Government Or E-Government Or EGov) http://www.iaeme.com/IJMET/index.asp 866 editor@iaeme.com The Critical Success Factors for Big Data Adoption in Government In the meantime, to clarify the validity of the literature, the criteria for exclusion of research are defined in some procedures, which are:  The document based on its publication date between 2008 and 2018.  The structure of the complete document, which means that any identity (newspaper/conference, the identity of the author, etc.) is mentioned in the document.  Duplicate card from the same study in SLR is excluded. 2.2. Data Extractions The studied literature has been examined 572 documents as resources and criteria. Out of 572 articles examined, there are 183 articles that were to be candidate studies on the related and abstract title to the research question. After studying further, we look for a case that is relevant to the success of big data adoption in the government, and there are only 86 documents that could be used in this research. Table 1. Publisher and Number of Selected Papers Source ACM Elsevier IEEEXplorer IG Global SAGE Springer Taylor & Francis Wiley Total Found 242 149 107 8 5 23 21 16 572 Relevant 29 62 58 2 5 9 11 7 183 Selected 15 27 28 2 1 4 6 3 86 3. RESULT AND DISCUSSION This research aimed to investigate the critical success factor of the adoption of big data for a government. The use of social networks in a higher institution has emerged as a new opportunity and a challenge both for basic functional use and for specific academic use. On this basis, this study will identify the general component of e-learning to define the collaboration of social networks and e-learning [5]. Most authors expertise in" Big Data" was come from the USA, China, Netherland, United Kingdom, Indonesia, Canada, and India. For most Paper related to the topic’s was from the USA, China, United Kingdom, Netherland, India, and Canada. and Australia. Surprisingly, there no paper from Denmark, Germany, Iraq, Kuwait, Oman, Papua New Guinea, Portugal and Slovenia, even the author affiliate is coming from that country. Table 2. Number and Country of the Authors Country of the Author Australia Austria Brunei Canada China Czech Republic Denmark Finland France Germany http://www.iaeme.com/IJMET/index.asp Paper 6 6 3 11 23 2 1 4 4 1 867 % 3 3 2 6 12 1 1 2 2 1 Author 3 2 2 4 12 1 0 1 2 0 % 3 2 2 5 14 1 0 1 2 0 editor@iaeme.com Novan Zulkarnain, Meyliana, Ahmad Nizar Hidayanto, Harjanto Prabowo India Indonesia Iraq Italy Japan Kuwait Macao Malaysia Marocco Netherland Oman Papua New Guinea Portugal Republic of Korea Slovenia South Africa South Korea Sweden Switzerland UAE United Kingdom USA Total 10 11 1 3 1 1 2 9 5 16 1 1 1 2 1 4 5 1 1 1 15 43 196 5 6 1 2 1 1 1 5 3 8 1 1 1 1 1 2 3 1 1 1 8 22 100 5 2 0 2 1 0 1 3 2 6 0 0 0 1 0 2 2 1 1 1 6 23 86 6 2 0 2 1 0 1 3 2 7 0 0 0 1 0 2 2 1 1 1 7 27 100 Table 3. Critical Success Factor’s Critical Success Factor Cost Effective / Efficient Management Supporting Infrastructure Communication Skilled Team / Staff Source [2], [4], [28], [27], [25], [36], [37], [38] [39], [20] [28], [40], [25], [13], [41], [39], [20] [6], [42], [43], [25], [27], [44] [24], [45], [13], [25], [27], [44] [28], [27], [25], [46], [47], [48], [49], [20] [50] Political Stability [51], [32], [12], [10], [52], [53], [54], [55] Social & Culture [45], [56], [55], [57], [32], [58], [39], [4],[59], [35], [60], [61], [62], [63] Citizen Involvement [46], [11], [32], [54], [12], [53], [62], [64] [4], [65], [61], [21], [66], [67] Organization Maturity [21], [24], [53], [35], [4], [50], [48], [61] Privacy & Security [12], [54], [68], [39], [2], [69], [70], [38], [57], [64], [13] Realistic Plan / [12], [28], [39], [51], [4], [36], [21], [5] Objective [64], [71], [54] Table 3 shows the critical success factor that has been found in 86 selected literature. We found 11 CSF, that has a major issue which relevant on the topics. 3.1. Cost Effective / Efficient Cost is high for adopting Big Data System [19]. This will be a major problem and a challenge for developing country. Cost is mostly used for infrastructure, technology, and consultant for helping the government to integrate the big data system [12]. http://www.iaeme.com/IJMET/index.asp 868 editor@iaeme.com The Critical Success Factors for Big Data Adoption in Government 3.2. Management Supporting Management support means that senior managers are willing to allocate resources and encourage the initial adoption of future changes [20]. Management in Government often changes. Top Management will make a decision for every project. It will difficult to adopting Big Data if the decisions are not aligning from previous management [21]. 3.3. Infrastructure The most critical issue for adopting Big Data in Government is infrastructure [16]. To have Big Data Infrastructure, the government must integrate all the resources, especially datasets [22]. And also have a clear standardized for all department inside the organization [23]. 3.4. Communication The need for effective communication Effective communication is one of the key success factors for a Big Data project [24]. Active communication is very important to ensure that the information needed for the project is up-to-date, such as policies, procedures, and decisionmaking [25]. Resistance to change is the problem that is always the challenge, both internal and external, to manage it, good communication to gain security will help the project [26]. 3.5. Skilled Team / Staff One of the key success factors (CSF) of IT projects is a motivated and committed team. Therefore, it is important that the team is committed and motivated to achieve success [27]. It is very important that the government team possess the basic skills and experience to provide a highly skilled team for the project [24]. 3.6. Political Stability Another factor that must be considered for adopting Big Data in Government is political stability [28]. The sustain of a Big Data project is different in democracy country and in dictatorial regime country [29]. As mention before the change in government is often change. Therefore, all project must have top management that commits and can lead the project future success. 3.7. Social and Culture The moderating role of organizational culture has been considered a key influence factor in studies focused on the adoption of innovative information systems [30]. Culture is related to human behavior, the more viable the organizational culture, the greater the willingness of the staff to adopt the technology of big data. [31]. 3.8. Citizen Involvement The user or citizen involvement also consider as a factor for adopting big data [28]. Citizens are centric of all government services and need to be care [32]. Without citizen involvement and participation, the government cannot know what information have to discovered [22]. 3.9. Organization Maturity One of the indicators of readiness of Big Data Adoption is a maturity of the organization [21]. Maturity comes from the past experience of the government, with that they can have a deep understanding of how to adopt big data [33]. http://www.iaeme.com/IJMET/index.asp 869 editor@iaeme.com Novan Zulkarnain, Meyliana, Ahmad Nizar Hidayanto, Harjanto Prabowo 3.10. Privacy and Security The challenges in building trust between the public and the government are the concern for privacy and information security [34]. It should be considered very important in the context of big data while the proceedings are still in progress [35]. Citizens will not participate when privacy is not secure, so protecting the privacy of citizens should be a major concern for big data [13]. 3.11. Realistic Plan and Objective The success of adopting big data depend on the realistic plan and objective [28]. Learning from existing cases, saying that many of the big data have failed is the lack of preparation in planning [52]. Sometimes, government forces use technology that is not ready, do not plan according to existing conditions. Good planning will have an impact on the good performance of the project and, above all, on cost efficiency [12]. 4. IMPLICATION AND CONCLUSION This literatures on big data identified 11 factors related to the success of adopting big data on government. There are cost-effective/efficient, management supporting, infrastructure, communication, skilled team/staff, political stability, social and culture, citizen involvement, organization maturity, privacy and security, and realistic plan/objective. To classify these factors, a literature review was used. we selected 86 paper based on the topic that related to big data adoption in government. this study took several cases in various countries, as seen in table 3, with several authors who were experts in the field of big data. 5. LIMITATION AND FUTURE RESEARCH In future work, research should be conducted to investigate how much those factors have an impact on the success of big data adoption in government. This paper has a limitation on the number of databases. It has restricted access from reputable journal so few of them not included in this paper. Besides that, we will add some of the new search engines to find more journals and include with the only journal from the top publisher. The publication year should be in the last 20 years. For future research, 11 factors that have been found will be tested statistically. Then an information system model will be designed to monitor these factors for helping succession of big data adoption in government. REFERENCES [1] C. Dobre and F. Xhafa, “Intelligent services for big data science,” Future Generation Computer Systems, vol. 37, pp. 267–281, 2014. [2] U. Sivarajah, M. M. Kamal, Z. Irani, and V. Weerakkody, “Critical analysis of Big Data challenges and analytical methods,” Journal of Business Research, vol. 70, pp. 263–286, 2017. [Online]. Available: http://dx.doi.org/10.1016/j.jbusres.2016.08.001 [3] Z. Yan, “Big Data and Government Governance,” 2018 International Conference on Information Management and Processing (ICIMP), pp. 111–114, 2018. [4] M. Chen, S. Mao, and Y. Liu, “Big Data: A Survey,” Mobile Networks and Applications, vol. 19, no. 2, pp. 171–209, Apr 2014. [Online]. Available: http://link.springer.com/10.1007/s11036-013-0489-0 [5] D. W. Bates, A. Heitmueller, M. Kakad, and S. Saria, “Why policymakers should care about big data in healthcare,” Health Policy and Technology, vol. 7, no. 2, pp. 211–216, 2018. [Online]. Available: https://doi.org/10.1016/j.hlpt.2018.04.006 http://www.iaeme.com/IJMET/index.asp 870 editor@iaeme.com The Critical Success Factors for Big Data Adoption in Government [6] E. Al Nuaimi, H. Al Neyadi, N. Mohamed, and J. Al-Jaroodi, “Applications of big data to smart cities,” Journal of Internet Services and Applications, vol. 6, no. 1, pp. 1–15, 2015. [Online]. Available: http://dx.doi.org/10.1186/s13174-015-0041-5 [7] Y.-C. Chen and T.-C. Hsieh, “Big Data for Digital Government,” International Journal of Public Administration in the Digital Age, vol. 1, no. 1, pp. 1–14, 2014. [Online]. Available: http://services. igiglobal.com/resolvedoi/resolve.aspx?doi=10.4018/ijpada.2014010101 [8] N. Z. B. Zainal, H. Hussin, and M. N. M. Nazri, "Big data initiatives by governments Issues and challenges: A review," Proceedings - 6th International Conference on Information and Communication Technology for the Muslim World, ICT4M 2016, pp. 304–309, 2017. [9] D. J. Power, “Using Big Data’ for analytics and decision support, ”Journal of Decision Systems, vol. 23, no. 2, pp. 222–228, 2014. [10] H. Jarman, “Incentivizing health information exchange: collaborative governance, market failure, and the public interest,” Proceedings of the 15th Annual International Conference on Digital Government Research, pp. 227–235, 2014. [11] C. N. Jun and C. J. Chung, “Big data analysis of local government 3.0: Focusing on Gyeongsangbuk-do in Korea,” Technological Forecasting and Social Change, vol. 110, pp. 3–12, 2016. [Online]. Available: http://dx.doi.org/10.1016/j.techfore.2015.11.007 [12] M. Parasol, “The impact of China’s 2016 Cyber Security Law on foreign technology firms, and on China’s big data and Smart City dreams,” Computer Law and Security Review, vol. 34, no. 1, pp. 175–179, 2018. [Online]. Available: https://doi.org/10.1016/j.clsr.2017.05.022 [13] G.-H. Kim, S. Trim, and J.-H. Chung, “Big-data applications in the government sector,” Communications of the ACM, vol. 57, no. 3, pp. 78–85, 2014. [14] M. Al-Ruthe and E. Benkhelifa, “Analysis and Classification of Barriers and Critical Success Factors for Implementing a Cloud Data Governance Strategy,” Procedia Computer Science, vol. 113, pp. 223–232, 2017. [Online]. Available: http://dx.doi.org/10.1016/j.procs.2017.08.352 [15] V. S. Reinhardt, “a Statistical Model for Flicker Noise,” Le Journal de Physique Colloques, vol. 42, no. C8, pp. C8–211–C8–216, 1981. [Online]. Available: http://www.edpsciences.org/10.1051/jphyscol: 1981825 [16] P. B. Goes, "Editors Comments: Big Data and IS Research," MIS Quarterly, vol. 38, no. 3, pp. iii-viii, 2014. [17] P. P. Khine, “Big data in government organizations: A review Big Data in Government Organizations: A Review,” Big Data Analysis (ICBDA), 2017 IEEE 2nd International Conference, no. March 2017. [18] H. H. Huang and H. Liu, “Big data machine learning and graph analytics: Current state and future challenges,” Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014, pp. 16–17, 2015. [19] A. Chakravarthi, "The Etymology of Big Data on Government Processes,” Information Communication and Embedded Systems (ICICES), 2017 International Conference, no. Icicles, 2017. [20] S. Sun, C. G. Cegielski, L. Jia, and D. J. Hall, “Understanding the Factors Affecting the Organizational Adoption of Big Data,” Journal of Computer Information Systems, vol. 58, no. 3, pp. 193–203, 2018. http://www.iaeme.com/IJMET/index.asp 871 editor@iaeme.com Novan Zulkarnain, Meyliana, Ahmad Nizar Hidayanto, Harjanto Prabowo [21] B. Klievink, B. J. Romijn, S. Cunningham, and H. de Bruijn, “Big data in the public sector: Uncertainties and readiness,” Information Systems Frontiers, vol. 19, no. 2, pp. 267–283, 2017. [Online]. Available: http://dx.doi.org/10.1007/s10796-016-9686-2 [22] A. K. Bhadani and D. Jothimani, "Big Data: Challenges, Opportunities, and Realities," Effective Big Data Management and Opportunities for Implementation, pp. 1–24, 2016. [Online]. Available: https: //arxiv.org/ftp/arxiv/papers/1705/1705.04928.pdf [23] V. N. Gudivada, R. Baeza-Yates, Y. Labs, and V. V. Raghavan, “GUEST EDITORS’ INTRODUCTION Big Data: Promises and Problems,” Computer (2015), p. 21, 2015. [Online]. Available: http://www.cs.rug.nl/ ∼ roe/courses/isc/BigDataPromises.pdf [24] J. S. Saltz and I. Shamshurin, “Big data team process methodologies: A literature review and the identification of key factors for a project’s success,” Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016, pp. 2872–2879, 2016. [25] M. Guntur, B. Purwandari, T. Raharjo, I. Solichah, and L. Kumaralalita, "Critical Success Factors for Information Systems Development: A Case Study in e-Government," in Proceedings of the 2Nd International Conference on Business and Information Management, ser. ICBM '18. New York, NY, USA: ACM,2018, pp.29–33.[Online]. Available: http://doi.acm.org/10.1145/3278252.3278288 [26] M. Janssen, H. van der Voort, and A. Wahyudi, “Factors influencing big data decisionmaking quality”. Journal of Business Research, vol. 70, pp. 338–345, 2017. [Online]. Available: http://dx.doi.org/10. 1016/j.jbusres.2016.08.007 [27] G. Lee and Y. H. Kwak, “An Open Government Maturity Model for social media-based public engagement,” Government Information Quarterly, vol. 29, no. 4, pp. 492–503, 2012. [Online]. Available: http://dx.doi.org/10.1016/j.giq.2012.06.001 [28] J. Akoka, I. Comyn-Wattiau, and N. Laoufi, “Research on Big Data A systematic mapping study,” Computer Standards and Interfaces, vol. 54, pp. 105–115, 2017. [Online]. Available: http://dx.doi.org/10.1016/j.csi.2017.01.004 [29] F. Malomo and V. Sena, “Data Intelligence for Local Government? Assessing the Benefits and Barriers to Use of Big Data in the Public Sector,” Policy and Internet, vol. 9, no. 1, pp. 7–27, 2017. [30] N. Mehta and A. Pandit, “Concurrence of big data analytics and healthcare: A systematic review,” International Journal of Medical Informatics, vol. 114, no. January, pp. 57–65, 2018. [Online]. Available: https://doi.org/10.1016/j.ijmedinf.2018.03.013 [31] A. Oussous, F. Z. Benjelloun, A. Ait Lahcen, and S. Belfkih, “Big Data technologies: A survey,” Journal of King Saud University - Computer and Information Sciences, vol. 30, no. 4, pp. 431–448, 2017. [Online]. Available: https://doi.org/10.1016/j.jksuci.2017.06.001 [32] D. H. Shin, “Demystifying big data: Anatomy of big data developmental process,” Telecommunications Policy, vol. 40, no. 9, pp. 837–854, 2016. [Online]. Available: http://dx.doi.org/10.1016/j.telpol.2015.03.007 [33] Y. Gong and M. Janssen, “Enterprise Architectures for Supporting the Adoption of Big Data,” Proceedings of the 18th Annual International Conference on Digital Government Research dg.o ’17, pp. 505–510, 2017. [Online]. Available: http://dl.acm.org/citation.cfm?doid=3085228. 3085275 [34] G. J. Miller, “Comparative Analysis of Big Data and BI Projects,” 2018 Federated Conference on Computer Science and Information Systems (FedCSIS), vol. 15, pp. 701– 705, 2018. [Online]. Available: https://fedcsis.org/proceedings/2018/drp/125.html [35] Alexandros Labrinidis and H. V. Jagadish, “Challenges and Opportunities with Big Data," Proceedings of the VLDB Endowment, pp. 1–15, 2012. http://www.iaeme.com/IJMET/index.asp 872 editor@iaeme.com The Critical Success Factors for Big Data Adoption in Government [36] J. Chen, Y. Chen, X. Du, C. Li, J. Lu, S. Zhao, and X. Zhou, "Big data challenge: A data management perspective," Frontiers of Computer Science, vol. 7, no. 2, pp. 157–164, 2013. [37] N. Al-Qirim, A. Tarhini, and K. Rouibah, "Determinants of Big Data Adoption and Success," Proceedings of the International Conference on Algorithms, Computing and Systems ICACS 17, pp. 88–92 2017.[Online]. Available: http://dl.acm.org/citation.cfm?doid=3127942. [38] S. Joshi, S. Saxena, T. Godbole, and Shreya, “Developing Smart Cities: An Integrated Framework,” Procedia Computer Science, vol. 93, no. September, pp. 902–909, 2016. [Online]. Available: http://dx.doi.org/10.1016/j.procs.2016.07.258 [39] A. Y. Gheni, Y. Y. Jusoh, M. A. Jabar, and N. M. Ali, “The Critical Success Factors (CSFs) for IT Projects,” Journal of Theoretical Biology (2009), vol. 9, no. 3, pp. 13–17, 2015. [40] J. Fortune and D. White, “Framing of project critical success factors by a systems model,” International Journal of Project Management, vol. 24, no. 1, pp. 53–65, 2006. [41] S. Basu, “Government and developing countries: an overview,” International Review of Law, Computers & Technology, vol. 18, no. 1, pp. 109–132, 2004. [Online]. Available: http://www.tandfonline.com/ doi/abs/10.1080/13600860410001674779 [42] R. Dubey, A. Gunasekaran, S. J. Childe, T. Papadopoulos, Z. Luo, S. F. Wamba, and D. Roubaud, “Can big data and predictive analytics improve social and environmental sustainability?” Technological Forecasting and Social Change, no. June, pp. 0–1, 2017. [Online]. Available: http://dx.doi.org/10.1016/j.techfore.2017.06.020 [43] M. H. Alkharusi and A. H. Al-Badi, “IT personnel perspective of the slow adoption of cloud computing in public sector: Case study in Oman,” 2016 3rd MEC International Conference on Big Data and Smart City, ICBDSC 2016, pp. 179–186, 2016. [44] J. C. Bertot, P. T. Jaeger, and J. M. Grimes, "Using ICTs to create a culture of transparency: E-government and social media as openness and anti-corruption tools for societies," Government Information Quarterly, vol. 27, no. 3, pp. 264–271, 2010. [Online]. Available: http://dx.doi.org/10.1016/j.giq.2010.03.001 [45] E. Olshannikova, T. Olsson, J. Huhtamaki, and H. Karkkainen, “Cen- conceptualizing Big Social Data,” Journal of Big Data, vol. 4, no. 1, 2017. [46] E. W. Welch, M. K. Feeney, and C. H. Park, “Determinants of data sharing in U.S. city governments,” Government Information Quarterly, vol. 33, no. 3, pp. 393–403, 2016. [Online]. Available: http://dx.doi.org/10.1016/j.giq.2016.07.002 [47] Y. Zhan, K. H. Tan, Y. Li, and Y. K. Tse, “Unlocking the power of big data in new product development,” Annals of Operations Research, vol. 270, no. 1-2, pp. 577–595, 2018. [48] W. A. Gu¨nther, M. H. Rezazade Mehrizi, M. Huysman, and F. Feldberg, “Debating big data: A literature review on realizing value from big data,” Journal of Strategic Information Systems, vol. 26, no. 3, pp. 191–209, 2017. [49] F. Matsebula and E. Mnkandla, “Information Systems Innovation Adoption in Higher Education: Big Data and Analytics," Advances in Computing and Communication Engineering (ICAC), pp. 326–329, 2016. [50] S. Eybers and M. Hattingh, “Critical success factor categories for big data: A preliminary analysis of the current academic landscape,” 2017 IST-Africa Week Conference (ISTAfrica), no. June, pp. 1–11, 2017. [Online]. Available: http://ieeexplore.ieee.org/document/8102327/ http://www.iaeme.com/IJMET/index.asp 873 editor@iaeme.com