Social business intelligence: Review and research directions
DOI:
https://doi.org/10.37380/jisib.v8i2.320Keywords:
Βig data, business intelligence, review, social business intelligence, social mediaAbstract
Social business intelligence (SBI) is a rather novel discipline, emerged in the
academic and business literature as a result of the convergence of two distinct research
domains: business intelligence (BI) and social media. Traditional BI scientists and practitioners,
after an inevitable initial shock, are currently discovering and acknowledge the potential of user
generated content (UGD) published in social media as an invaluable and inexhaustible source
of information capable of supporting a wide range of business activities. The confluence of these
two emerging domains is already producing new added value organizational processes and
enhanced business capabilities utilized by companies all over the world to effectively harness
social media data and analyze them in order to produce added value information such as
customer profiles and demographics, search habits, and social behaviors. Currently the SBI
domain is largely uncharted, characterized by controversial definitions of terms and concepts,
fragmented and isolated research efforts, obstacles created by proprietary data, systems and
technologies that are not mature yet. This paper aspires to be one of the few -to our knowledge contemporary
efforts to explore the SBI scientific field, clarify definitions and concepts,
structure the documented research efforts in the area and finally formulate an agenda of future
research based on the identification of current research shortcomings and limitations.
References
Abrahams, A.S., Jiao, J., Wang, G.A. and Fan, W.
Vehicle defect discovery from social
media. Decision Support Systems, 54(1), 87-97.
Arora, D., Li, K.F. and Neville, S.W. 2015.
Consumers' sentiment analysis of popular
phone brands and operating system
preference using Twitter data: A feasibility
study. In: Proceedings of Advanced
Information Networking and Applications
(AINA) IEEE 29th International Conference,
pp. 680-686.
Bachmann, P. and Kantorová, K. 2016. From
customer orientation to social CRM. New
insights from Central Europe. Scientific
papers of the University of Pardubice, Series
D, Faculty of Economics and Administration,
/2016.
Banerjee, S. and Agarwal, N. 2012. Analyzing
collective behavior from blogs using swarm
intelligence. Knowledge and Information
Systems, 33(3), 523-547.
Basset, H., Stuart, D. and Silbe, D. 2012. From
Science 2.0 to Pharma 3.0 Semantic Search
and Social Media in the Pharmaceutical
Industry and Stm Publishing. A volume in
Chandos Publishing Social Media Series.
Baur, A., Lipenkova, J., Bühler, J. and Bick, M.
A Novel Design Science Approach for
Integrating Chinese User-Generated Content
in Non-Chinese Market Intelligence.
Baur, A.W. (016. Harnessing the social web to
enhance insights into people’s opinions in
business, government and public
administration. Information Systems
Frontiers, pp.1-21.
Beigi, G., Hu, X., Maciejewski, R. and Liu, H.
An overview of sentiment analysis in
social media and its applications in disaster
relief. Sentiment Analysis and Ontology
Engineering, pp. 313-340, Springer
International Publishing.
Bell, D. and Shirzad, S. R. 2013. Social media
business intelligence: A pharmaceutical
domain analysis study. International Journal
of Sociotechnology and Knowledge
Development (IJSKD), 5(3), pp. 51-73.
Bell, D. and Shirzad, S.R. 2013. Social Media
Domain Analysis (SoMeDoA)-A
Pharmaceutical Study. WEBIST, pp. 561-570.
Bendler, J., Ratku, A. and Neumann, D. 2014.
Crime mapping through geo-spatial social
media activity. In: Proceedings of 35th
International Conference on Information
Systems, Auckland 2014.
Berlanga, R., Aramburu, M.J., Llidó, D.M. and
García-Moya, L. 2014. Towards a semantic
data infrastructure for social business
intelligence. New Trends in Databases and
Information Systems, pp. 319-327, Springer
International Publishing.
Berlanga, R., García-Moya, L., Nebot, V.,
Aramburu, M.J., Sanz, I. and Llidó, D.M.
Slod-bi: An open data infrastructure for
enabling social business intelligence. Big
Data: Concepts, Methodologies, Tools, and
Applications, pp. 1784-1813, IGI Global.
Beverungen, D., Eggert, M., Voigt, M. and
Rosemann, M. 2014. Augmenting Analytical
CRM Strategies with Social BI. Digital Arts
and Entertainment: Concepts, Methodologies,
Tools, and Applications, pp. 558-576, IGI
Global.
Bjurstrom, S. and Plachkinova, M. 2015.
Sentiment Analysis Methodology for Social
Web Intelligence.
Bygstad, B. and Presthus, W. 2013. Social Media
as CRM? How two airline companies used
Facebook during the “Ash Crisis” in
Scandinavian Journal of Information
Systems, 25(1), 3.
Castellanos, M., Dayal, U., Hsu, M., Ghosh, R.,
Dekhil, M., Lu, Y., ... & Schreiman, M. 2011.
LCI: a social channel analysis platform for live
customer intelligence. In Proceedings of the
ACM SIGMOD International Conference
on Management of data (pp. 1049-1058). ACM.
Chan, H.K., Wang, X., Lacka, E. and Zhang, M.
A Mixed-Method Approach to Extracting
the Value of Social Media Data. Production
and Operations Management.
Chaudhuri, S., Dayal, U. and Narasayya, V. 2011.
An overview of business intelligence
technology. Communications of the ACM,
(8), 88-98.
Chen, H., Chiang, R.H. and Storey, V.C. 2012.
Business intelligence and analytics: From big
data to big impact. MIS quarterly, 36(4), 1165-
ISO 690
Chilhare, Y.R., Londhe, D.D. and Competiti, E.M.
Competitive Analytics Framework on
Bilingual Da Bilingual Dataset of Amazon
Food Product. IJCTA, 9(21), pp. 179-189.
Chung, W., Zeng, D. and O'Hanlon, N. 2014.
Identifying influential users in social media: A
study of US immigration reform. In:
Proceedings of the 20th Americas Conference on
Information Systems, Savannah, 2014.
Colombo, C., Grech, J.P. and Pace, G.J. 2015. A
controlled natural language for business
intelligence monitoring. Lecture Notes in
Computer Science (including subseries
Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics), 9103, pp.
-306.
Dey, L., Haque, S.M., Khurdiya, A. and Shroff, G.
Acquiring competitive intelligence from
social media. In: Proceedings of the 2011 joint
workshop on multilingual OCR and analytics
for noisy unstructured text data, p. 3. ACM.
Diamantopoulou, V., Charalabidis, Y., Loukis, E.,
Triantafillou, A., Sebou, G. Foley, P., Deluca,
A., Wiseman, I. and Koutzeris, T. 2010.
Categorization of Web 2.0 Social Media and
Stakeholder Characteristics. Nomad Project.
EU. pp.19. Available at:
http://www.padgets.eu/Downloads/Deliverabl
es/tabid/75/ctl/Versions/mid/623/Itemid/56/De
fault.aspx [Accessed 2 March 2017]
Dinter, B. and Lorenz, A. 2012. Social business
intelligence: a literature review and research
agenda. In: Proceedings of the 33rd
International Conference on Information
Systems, Orlando 2012.
Fan, S., Lau, R.Y. and Zhao, J.L. 2015.
Demystifying big data analytics for business
intelligence through the lens of marketing
mix. Big Data Research, 2(1), 28-32.
Ferrara, E., De Meo, P., Fiumara, G. and
Baumgartner, R. 2014. Web data extraction,
applications and techniques: A
survey. Knowledge-Based Systems, 70, 301-
Fourati-Jamoussi, F. 2015. E-reputation: A case
study of organic cosmetics in social media. In:
Proceedings of the Information Systems and
Economic Intelligence (SIIE) 6th International
Conference, pp. 125-132, IEEE.
Gallinucci, E., Golfarelli, M. and Rizzi, S. 2013.
Meta-stars: multidimensional modeling for
social business intelligence. In: Proceedings of
the 16th international workshop on Data
warehousing and OLAP, pp. 11-18, ACM.
Gallinucci, E., Golfarelli, M., & Rizzi, S. 2015.
Advanced topic modeling for social business
intelligence. Information Systems, 53, 87-106.
Golfarelli, M. 2014. Social business intelligence:
OLAP applied to user generated contents. In:
Proceedings of the e-Business (ICE-B) 11th
International Conference, pp. IS-11, IEEE.
Golfarelli, M. 2015. Design Issues in Social
Business Intelligence Projects. In European
Business Intelligence Summer School (pp. 62-
. Springer International Publishing.
Gronroos, C. 2008. Service logic revisited: Who
creates value? And who co-creates? European
Business Review, Vol. 20, No. 4, pp. 298–314.
Hart C. 1998. Doing a Literature Review. Sage
Publications, London
He, W., Tian, X., Chen, Y. and Chong, D. 2016.
Actionable social media competitive analytics
for understanding customer
experiences. Journal of Computer Information
Systems, 56(2), 145-155.
Heijnen, J., De Reuver, M., Bouwman, H.,
Warnier, M. and Horlings, H. 2013. Social
media data relevant for measuring key
performance indicators? A content analysis
approach. In: Proceedings of the International
Conference on Electronic Commerce, pp. 74-84,
Springer Berlin Heidelberg.
Jingjing, W., Changhong, T., Xiangwen, L. and
Guolong, C. 2013. Mining Social Influence in
Microblogging via Tensor Factorization
Approach. In: Proceedings of Cloud
Computing and Big Data (CloudCom-Asia),
December 2013 International Conference, pp.
-591, IEEE.
Kaplan, A. M. and Haenlein, M. 2010. Users of
the world, unite! The challenges and
opportunities of Social Media. Business
horizons, 53(1), 59-68.
Keele, S. 2007. Guidelines for performing
systematic literature reviews in software
engineering. In Technical report, Ver. 2.3
EBSE Technical Report. EBSE.
Kim, Y. and Jeong, S. R. 2015. Opinion-Mining
Methodology for Social Media
Analytics. TIIS, 9(1), 391-406.
Kucher, K., Kerren, A., Paradis, C. and Sahlgren,
M. 2014. Visual analysis of stance markers in
online social media. In: Proceedings of Visual
Analytics Science and Technology (VAST),
IEEE Conference, pp. 259-260, IEEE.
Kucher, K., Schamp-Bjerede, T., Kerren, A.,
Paradis, C. and Sahlgren, M. 2016. Visual
analysis of online social media to open up the
investigation of stance
phenomena. Information Visualization, 15(2),
-116.
Kulkarni, A. V., Joseph, S., Raman, R., Bharathi,
V., Goswami, A. and Kelkar, B. 2013. Blog
Content and User Engagement-An Insight
Using Statistical Analysis. International
Journal of Engineering and Technology, 5(3),
pp. 2719-2733.
Lee, C., Wu, C., Wen, W. and Yang, H. 2013.
Construction of an event ontology model using
a stream mining approach on social media. In:
Proceedings of the 28th International
Conference on Computers and Their
Applications, 2013, CATA 2013, pp.249-254.
Lin, Z. and Goh, K. Y. 2011. Measuring the
business value of online social media content
for marketers. In: Proceedings of the 32nd
International Conference on Information
Systems, Shanghai.
Liu, S., Wang, S. and Zhu, F. 2015. Structured
learning from heterogeneous behavior for
social identity linkage. IEEE Transactions on
Knowledge and Data Engineering, 27(7), 2005-
Liu, S., Wang, S., Zhu, F., Zhang, J. and
Krishnan, R. 2014. Hydra: Large-scale social
identity linkage via heterogeneous behavior
modeling. In: Proceedings of the 2014 ACM
SIGMOD international conference on
Management of data, pp. 51-62, ACM.
Liu, X. and Yang, J. 2012. Social buying met
network modeling and analysis. International
Journal of Services Technology and
Management, 18 (1- 2), 46-60.
Lotfy, A., El Tazi, N and El Gamal, N. 2016. SCIF:
Social-Corporate Data Integration
Framework. In: Proceedings of the 20th
International Database Engineering &
Applications Symposium, June 2016, pp. 328-
, ACM.
Lu, Y., Wang, F. and Maciejewski, R. 2014.
Business intelligence from social media: A
study from the vast box office challenge. IEEE
computer graphics and applications, 34(5), 58-
Luhn, H. P. 1958. A business intelligence system.
IBM Journal of Research and Development,
,14-31
Luo, J., Pan, X. and Zhu, X. 2015. Identifying
digital traces for business marketing through
topic probabilistic model. Technology Analysis
& Strategic Management, 27(10), 1176-1192.
Marine-Roig, E., & Clavé, S. A. 2015. Tourism
analytics with massive user-generated
content: A case study of Barcelona. Journal of
Destination Marketing & Management, 4(3),
-172.
McKinsey and Altagamma 2015. Digital inside:
Get wired for the ultimate luxury experience.
Available at:
https://www.mckinsey.de/files/dle-2015-
global-report.pdf [Accessed 5 March 2017]
Meredith, R. and O'Donnell, P. A. 2010. A
Functional Model of Social Media and its
Application to Business Intelligence. In:
Proceedings of the 2010 conference on Bridging
the Socio-technical Gap in Decision Support
Systems: Challenges for the Next Decade,
August 2010, pp. 129-140, IOS Press,
Netherlands.
Meredith, R. and O'Donnell, P. A. 2011. A
framework for understanding the role of social
media in business intelligence
systems. Journal of Decision Systems, 20(3),
-282.
Milolidakis, G., Akoumianakis, D. and Kimble, C.
Digital traces for business intelligence:
A case study of mobile telecoms service brands
in Greece. Journal of Enterprise Information
Management, 27(1), 66-98.
Moedeen, B. W. and Jeerooburkhan, A.S. 2016.
Evaluating the strategic role of Social Media
Analytics to gain business intelligence in
Higher Education Institutions. In:
Proceedings of Emerging Technologies and
Innovative Business Practices for the
Transformation of Societies (EmergiTech),
IEEE International Conference, pp. 303-308.
Ngo-Ye, T. L. and Sinha, A.P. 2012. Analyzing
online review helpfulness using a regressional
ReliefF-enhanced text mining method. ACM
Transactions on Management Information
Systems (TMIS), 3(2), 10.
Nithya, R. and Maheswari, D. 2016. Correlation
of feature score to overall sentiment score for
identifying the promising features. In:
Proceedings of Computer Communication and
Informatics (ICCCI) International Conference,
January 2016, pp. 1-5, IEEE.
O'Leary, D. E. 2015. Twitter Mining for
Discovery, Prediction and Causality:
Applications and Methodologies. Intelligent
Systems in Accounting, Finance and
Management, 22(3), 227-247.
Obradović, D., Baumann, S. and Dengel, A. 2013.
A social network analysis and mining
methodology for the monitoring of specific
domains in the blogosphere. Social Network
Analysis and Mining, 3(2), 221-232.
, C.M. 2016. Toward better understanding
and use of Business Intelligence in
organizations. Information Systems
Management, 33(2), 105-123.
Palacios-Marqués, D., Merigó, J. M. and Soto-
Acosta, P. 2015. Online social networks as an
enabler of innovation in
organizations. Management Decision, 53(9),
-1920.
Petychakis, M., Biliri, E., Arvanitakis, A.,
Michalitsi-Psarrou, A., Kokkinakos, P.,
Lampathaki, F. and Askounis, D. 2016.
Detecting Influencing Behaviour for Product-
Service Design through Big Data Intelligence
in Manufacturing. In: Proceedings of Working
Conference on Virtual Enterprises, pp. 361-
, Springer International Publishing.
Piccialli, F. and Jung, J. E. 2016. Understanding
Customer Experience Diffusion on Social
Networking Services by Big Data
Analytics. Mobile Networks and Applications,
-8.
Ponis, S. T., & Christou, I. T. 2013. Competitive
intelligence for SMEs: a web-based decision
support system. International Journal of
Business Information Systems, 12(3), 243-
Pu, J., Teng, Z., Gong, R., Wen, C. and Xu, Y.
Sci-Fin: Visual Mining Spatial and
Temporal Behavior Features from Social
Media. Sensors, 16(12), 2194.
Qazi, A., Raj, R.G., Tahir, M., Cambria, E. and
Syed, K.B.S. 2014. Enhancing business
intelligence by means of suggestive
reviews. The Scientific World Journal, 2014.
Ram, J., Zhang, C. and Koronios, A. 2016. The
Implications of Big Data Analytics on
Business Intelligence: A Qualitative Study in
China. Procedia Computer Science, 87, 221-
Ranjan, J. 2009. Business intelligence: Concepts,
components, techniques and benefits. Journal
of Theoretical and Applied Information
Technology, 9(1), 60-70.
Ranjan, R., Vyas, D. and Guntoju, D. P. 2014.
Balancing the trade-off between privacy and
profitability in Social Media using NMSANT.
In: Proceedings of Advance Computing
Conference (IACC), 2014 IEEE International,
pp. 477-483, IEEE.
Rosemann, M., Eggert, M., Voigt, M. and
Beverungen, D. 2012. Leveraging social
network data for analytical CRM strategies:
the introduction of social BI. In: Proceedings of
the 20th European Conference on Information
Systems (ECIS) 2012, AIS Electronic Library
(AISeL).
Ruhi, U. 2014. Social Media Analytics as a
business intelligence practice: current
landscape & future prospects. Journal of
Internet Social Networking & Virtual
Communities, 2014.
Rui, H., & Whinston, A. 2011. Designing a socialbroadcasting-
based business intelligence
system. ACM Transactions on Management
Information Systems (TMIS), 2(4), 22.
Sathyanarayana, P., Tran, P.N.K., Meredith, R.
and O'Donnell, P. A. 2012. Towards a Protocol
to Measure the Social Media Affordances of
Web Sites and Business Intelligence
Systems. DSS, pp. 317-322.
Seebach, C., Beck, R. and Denisova, O. 2012.
Sensing Social Media for Corporate
Reputation Management: a Business Agility
Perspective. ECIS, p. 140.
Shroff, G., Agarwal, P. and Dey, L. 2011.
Enterprise information fusion for real-time
business intelligence. In: Proceedings of the
th International Conference, Information
Fusion (FUSION), pp. 1-8, IEEE.
Sigman, B. P., Garr, W., Pongsajapan, R.,
Selvanadin, M., McWilliams, M. and Bolling,
K. 2016. Visualization of Twitter Data in the
Classroom. Decision Sciences Journal of
Innovative Education, 14(4), 362-381.
Sijtsma, B., Qvarfordt, P. and Chen, F. 2016.
Tweetviz: Visualizing Tweets for Business
Intelligence. In: Proceedings of the 39th
International ACM SIGIR conference on
Research and Development in Information
Retrieval, July 2016, pp. 1153-1156, ACM.
Sleem-Amer, M., Bigorgne, I., Brizard, S., Dos
Santos, L.D.P., El Bouhairi, Y., Goujon, B. and
Varga, L. 2012. Intelligent semantic search
engines for opinion and sentiment mining.
Next Generation Search Engines: Advanced
Models for Information Retrieval, pp. 191-215,
IGI Global.
Tayouri, D. 2015. The Human Factor in the Social
Media Security–Combining Education and
Technology to Reduce Social Engineering
Risks and Damages. Procedia
Manufacturing, 3, 1096-1100.
Tziralis, G., Vagenas, G., & Ponis, S. 2009.
Prediction markets, an emerging Web 2.0
business model: towards the competitive
intelligent enterprise. In Web 2.0 (pp. 1-21).
Springer, Boston, MA.
Wen, C., Teng, Z., Chen, J., Wu, Y., Gong, R. and
Pu, J. 2016. socialRadius: Visual Exploration
of User Check-in Behavior Based on Social
Media Data. In: Proceedings of
the International Conference on Cooperative
Design, October 2016, Visualization and
Engineering, pp. 300-308, Springer
International Publishing.
Wongthongtham, P., & Abu-Salih, B. 2015.
Ontology and trust based data warehouse in
new generation of business intelligence: Stateof-
the-art, challenges, and opportunities. In
Industrial Informatics (INDIN), 2015 IEEE
th International Conference on (pp. 476-
. IEEE.
Wu, Y., Liu, S., Yan, K., Liu, M. and Wu, F. 2014.
Opinionflow: Visual analysis of opinion
diffusion on social media. IEEE Transactions
on Visualization and Computer
Graphics, 20(12), 1763-1772.
Yang, C. S. and Shih, H. P. 2012. A Rule-Based
Approach for Effective Sentiment Analysis.
PACIS, p. 181).
Yang, C.S. and Chang, P.C. 2015. Mining Social
Media for Enhancing Personalized Document
Clustering. In: Proceedings of
the International Conference on HCI in
Business, pp. 185-196, Springer International
Publishing.
Yang, C.S. and Chen, L.C. 2014. Personalized
Recommendation in Social Media: a Profile
Expansion Approach. PACIS, p. 68.
Zeng, D., Chen, H., Lusch, R. and Li, S.H. 2010.
Social media analytics and intelligence. IEEE
Intelligent Systems, 25(6), 13-16.
Zhang, Z., Guo, C. and Goes, P. 2013. Product
comparison networks for competitive analysis
of online word-of-mouth. ACM Transactions
on Management Information Systems
(TMIS), 3(4), 20.
Zhang, Z., Li, X. and Chen, Y. 2012. Deciphering
word-of-mouth in social media: Text-based
metrics of consumer reviews. ACM
Transactions on Management Information
Systems (TMIS), 3(1), 5.
Zimmerman, C., & Vatrapu, R. 2015. The Social
Newsroom: Visual Analytics for Social
Business Intelligence. In: Proceedings of
the International Conference on Design Science
Research in Information Systems, pp. 386-390,
Springer International Publishing.
Zimmerman, C.J., Wessels, H.T. and Vatrapu, R.
Building a social newsroom: Visual
analytics for social business intelligence. In:
Proceedings of the IEEE 19th International
Conference, Enterprise Distributed Object
Computing Workshop (EDOCW), pp. 160-163,
IEEE.
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