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Harnessing the social web to enhance insights into people’s opinions in business, government and public administration

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Abstract

Transparency, participation, and collaboration are the core pillars of open government. For the systematic integration of citizens and other stakeholders into the policy and public value creation process, their opinions, wishes, and complaints first need to be received. In the future, including user-generated content from social media will become a main channel for the enrichment of this information base for public administrative bodies and commercial firms. However, the sheer speed of growth of this constantly updated data pool makes manual work infeasible. The automated gathering, combination, analysis, and visualization of user-generated content from various sources and multiple languages is therefore imperative.

In this study, we present a design science research approach to develop a general framework (‘MarketMiner’) to handle large amounts of foreign-language user-generated content. As a first empirical application, we implement the framework in the automotive industry by analyzing Chinese automotive forums for the benefit of English-speaking users. At the same time, the ideas, methods, and insights are transferred to the public sector context, especially in light of the current challenges of a high number of political refugees from Arabic countries entering into the European Union.

The results are promising in that MarketMiner can dramatically improve the utilization of multi-language, multi-source social media content. The modular set-up of the artifact allows an easy transfer to additional areas of application.

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Notes

  1. Good examples of related areas of applications include e-commerce (Zwass 2010; Doan et al. 2011) market intelligence (di Gangi et al. 2010), science and technology (Hand 2010), politics (Karpf 2009; Wattal et al. 2010), health care (Gao et al. 2010), and public safety (Dang et al. (2014).

  2. Formats include, i.a., semi-structured textual content (XML data from RSS feeds), unstructured textual content (text data from forums, online groups, or social networking sites), and unstructured visual content (photo and video images from social multimedia sites).

  3. As of November 30, 2015, the internet is used by about 873 million English-speaking and about 705 million Chinese-speaking users

  4. Text REtrieval [sic] Conference, http://trec.nist.gov/

  5. Conference and Labs of the Evaluation Forum, formerly known as Cross-Language Evaluation Forum, http://www.clef-initiative.eu/

  6. NII Testbeds and Community for Information access Research, http://research.nii.ac.jp/ntcir/index-en.html

  7. In their empirically derived taxonomy, Nicolai and Seidl distinguish three forms of practical relevance, i.e., instrumental relevance, conceptual relevance, and legitimative relevance. Instrumental relevance is comprised of schemes, technological rules/recipes and forecasts.

  8. Once the tool had been developed, these scenarios were evaluated by these social media analytics experts (see section 7).

  9. The system was also benchmarked quantitatively on a test set of 800 sentences that were annotated forfeature, evaluation, and emotion phrases and for relations between them. Details can be found in Lipenkova (2015).

  10. Hilgers and Ihl (2010) mention citizen ideation and innovation, collaborative administration and collaborative democracy as potential forms of open government.

  11. See http://seeclickfix.com for an example from the US, or https://www.fixmystreet.com/ for the UK.

References

  • Abbas, S., & Ojo, A. (2013). Towards a Linked Geospatial Data Infrastructure. In D. Hutchison, T. Kanade, J. Kittler, J. M. Kleinberg, F. Mattern, J. C. Mitchell, et al. (Eds.), .), Technology-Enabled Innovation for Democracy, Government and Governance (Vol. 8061, pp. 196–210, Lecture Notes in Computer Science). Berlin: Springer.

    Google Scholar 

  • Abney, S. P. (1991). Parsing by Chunks. In R. C. Berwick, S. P. Abney, & C. Tenny (Eds.), Principle-Based Parsing: Computation and Psycholinguistics (pp. 257–278, Studies in Linguistics and Philosophy, Vol. 44). Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Abraham, R., Aier, S., & Winter, R. (2014). Fail Early, Fail Often: Towards Coherent Feedback Loops in Design Science Research Evaluation. In Proceedings of the 35th International Conference on Information Systems. Auckland, New Zealand.

  • Abrahams, A. S., Jiao, J., Fan, W., Wang, G., Alan, & Zhang, Z. (2013). What's buzzing in the blizzard of buzz? Automotive component isolation in social media postings. Decision Support Systems, 55(4), 871–882.

    Article  Google Scholar 

  • Abusalah, M., Tait, J., & Oakes, M. (2005). Literature review of cross-language information retrieval. World Academy of Science, Engineering and Technology, 2005(4), 175–177.

  • Ackoff, R. (1989). From data to wisdom. Journal of Applied Systems Analysis, 16, 3–9.

    Google Scholar 

  • Ahmed, F., & Nurnberger, A. (2012). Literature Review of Interactive Cross Language Information Retrieval Tools. International Arab Journal of Information Technology, 9(5), 479–486.

    Google Scholar 

  • Alavi, M., & Leidner, D. E. (2001). Review: knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136.

    Article  Google Scholar 

  • Alter, S. (2004). A work system view of DSS in its fourth decade. Decision Support Systems, 38(3), 319–327.

    Article  Google Scholar 

  • Anderson-Lehman, R., Watson, H. J., Wixom, B. H., & Hoffer, J. A. (2004). Continental Airlines flies high with real-time business intelligence. MIS Quarterly Executive, 3(4), 1–30.

    Google Scholar 

  • Appleford, S., Bottum, J. R., & Thatcher, J. B. (2014). Understanding the social web. ACM SIGMIS Database, 45(1), 29–37. doi:10.1145/2591056.2591059.

    Article  Google Scholar 

  • Arnott, D. (2004). Decision Support Systems Evolution: Framework, Case Study and Research Agenda. European Journal of Information Systems, 13(4), 247–259. doi:10.1057/palgrave.ejis.3000509.

    Article  Google Scholar 

  • Arnott, D. (2010). Senior executive information behaviors and decision support. Journal of Decision Systems, 19(4), 465–480. doi:10.3166/jds.19.165-480.

    Article  Google Scholar 

  • Avrahami, T. T., Yau, L., Si, L., & Callan, J. (2006). The FedLemur project: federated search in the real world. Journal of the American Society for Information Science and Technology, 57(3), 347–358. doi:10.1002/asi.20283.

    Article  Google Scholar 

  • Barbosa, A. F., Pozzebon, M., & Diniz, E. H. (2013). Rethinking E-government performance assessment from a citizen perspective. Public Administration, 744–762. doi:10.1111/j.1467-9299.2012.02095.x.

  • Baur, A. W., Breitsprecher, M., & Bick, M. (2014a). Catching Fire: Start-Ups in the Text Analytics Software Industry. In Proceedings of the 20th Americas Conference on Information Systems. Savannah, Georgia.

  • Baur, A. W., Genova, A. C., Bühler, J., & Bick, M. (2014b). Customer is King? A Framework to Shift from Cost- to Value-Based Pricing in Software as a Service: The Case of Business Intelligence Software. In Proceedings of the 13th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society. Sanya.

  • Becker, H., Naaman, M., & Gravano, L. (2010). Learning similarity metrics for event identification in social media. In B. D. Davison (Ed.) (pp. 291–300). New York: ACM.

  • Bera, P., Burton-Jones, A., & Wand, Y. (2011). Guidelines for designing visual ontologies to support knowledge identification. MIS Quarterly, 35(4), 883.

    Google Scholar 

  • Berry, A. J., & Otley, D. (2004). Case-Based Research in Accounting. In C. Humphrey & B. Lee (Eds.), The real life guide to accounting research: A behind-the-scenes view of using qualitative research methods. (1st ed., pp. 231–256). Amsterdam: Elsevier.

    Google Scholar 

  • Berthon, P. R., Pitt, L. F., Plangger, K., & Shapiro, D. (2012). Marketing meets Web 2.0, social media, and creative consumers: Implications for international marketing strategy. Business Horizons, 55(3), 261–271. doi:10.1016/j.bushor.2012.01.007.

    Article  Google Scholar 

  • Bick, M., Hetmank, L., Kruse, P., Maier, R., Pawlowski, J., Peinl, R., et al. (2012). Manifesto for a Standard on Meaningful Representations of Knowledge in Social Knowledge Management Environments. In D. C. Mattfeld & S. Robra-Bissantz (Eds.), Tagungsband Multikonferenz Wirtschaftsinformatik (pp. 1–17). Braunschweig.

  • Bryman, A. (2012). Social Research Methods (4th ed.). Oxford: Oxford University Press.

    Google Scholar 

  • Campbell, D. A., Lambright, K. T., & Wells, C. J. (2014). Looking for friends, fans, and followers?: social media use in public and nonprofit human services. Public Administration Review, 74(5), 655–663. doi:10.1111/puar.12261.

    Article  Google Scholar 

  • Chambers, J. M. (1983). Graphical methods for data analysis. Belmont: Wadsworth Intern. Group.

    Google Scholar 

  • Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014a). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63(July), 67–80. doi:10.1016/j.dss.2013.08.008.

    Article  Google Scholar 

  • Chang, W.-L., Diaz, A. N., & Hung, P. C. K. (2014b). Estimating trust value: a social network perspective. Information Systems Frontiers, 1–20. doi:10.1007/s10796-014-9519-0.

  • Charalabidis, Y., Janssen, M., & Krcmar, H. (2015). Introduction to the Big, Open, and Linked Data (BOLD), Analytics, and Interoperability Infrastructures in Government Minitrack. In Proceedings of the 48th Hawaii International Conference on System Sciences (p. 2074). Kauai, HI.

  • Chen, J., & Bao, Y. (2009). Cross–language search: The case of Google Language Tools. First Monday, 14(3). doi:10.5210/fm.v14i3.2335

  • Chen, Y.-C., & Chu, P.-Y. (2012). Electronic governance and cross-boundary collaboration: Innovations and advancing tools. Hershey: IGI Global.

    Book  Google Scholar 

  • Chen, M., Ebert, D., Hagen, H., Laramee, R. S., van Liere, R., Ma, K.-L., et al. (2009). Data, information, and knowledge in visualization. IEEE Computer Graphics and Applications, 29(1), 12–19. doi:10.1109/MCG.2009.6.

    Article  Google Scholar 

  • Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4), 1165–1188.

    Google Scholar 

  • Chen, J. V., Yen, D. C., Pornpriphet, W., & Widjaja, A. E. (2015). E-commerce web site loyalty: a cross cultural comparison. Information Systems Frontiers, 17(6), 1283–1299. doi:10.1007/s10796-014-9499-0.

    Article  Google Scholar 

  • Chesbrough, H. W. (2003a). The era of open innovation. MIT Sloan Management Review, 44(3), 35–41.

    Google Scholar 

  • Chesbrough, H. W. (2003b). Open innovation: The new imperative for creating and profiting from technology. Boston: Harvard Business School Publication Corp..

    Google Scholar 

  • Choudhury, M. D., Counts, S., & Czerwinski, M. (2011). Identifying relevant social media content: leveraging information diversity and user cognition. In Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia (pp. 161–170). New York, NY: ACM.

  • Chung, W., & Zeng, D. (2015). Social-media-based public policy informatics: Sentiment and network analyses of U.S. Immigration and border security. Journal of the Association for Information Science and Technology, 67(7), 1588–1606. doi:10.1002/asi.23449.

    Article  Google Scholar 

  • Clark, T. D. J., Jones, M. C., & Armstrong, C. P. (2007). The dynamic structure of management support systems: theory development, research focus, and direction. MIS Quarterly, 31(3), 579–615.

    Google Scholar 

  • Conboy, K. (2009). Agility from first principles: reconstructing the concept of agility in information systems development. Information Systems Research, 20(3), 329–354.

    Article  Google Scholar 

  • Conboy, K., Fitzgerald, G., & Mathiassen, L. (2012). Qualitative methods research in information systems: motivations, themes, and contributions. European Journal of Information Systems, 21(2), 113–118.

    Article  Google Scholar 

  • Cook, M., Harrison, T. M., Zhang, J., Puron-Cid, G., & Gil-Garcia, J. R. (2015). Using public value thinking for government IT planning and decision making: A case study. Information Polity, 20(2,3), 183–197. doi:10.3233/IP-150359.

    Article  Google Scholar 

  • Dang, Y., Zhang, Y., Hu, P. J.-H., Brown, S. A., & Chen, H. (2011). Knowledge mapping for rapidly evolving domains: a design science approach. Decision Support Systems, 50(2), 415–427. doi:10.1016/j.dss.2010.10.003

  • Dang, Y., Zhang, Y., Hu, P. J.-H., Brown, S. A., Ku, Y., Wang, J.-H., et al. (2014). An integrated framework for analyzing multilingual content in web 2.0 social media. Decision Support Systems, 61(0), 126–135. doi:10.1016/j.dss.2014.02.004.

    Article  Google Scholar 

  • Davis, G. B. (2005). Advising and Supervising. In D. E. Avison & J. Pries-Heje (Eds.), Research in Information Systems: A Handbook for Research Supervisors and Their Students (pp. 3–34). Amsterdam: Elsevier/Butterworth-Heinemann.

  • Dellarocas, C. (2003). The digitization of word of mouth: promise and challenges of online feedback mechanisms. Management Science, 49(10), 1407–1424.

    Article  Google Scholar 

  • Di Gangi, P. M., Wasko, M. M., & Hooker, R. E. (2010). Getting customers’ ideas to work for you: learning from Dell how to succeed with online user innovation communities. MIS Quarterly Executive, 9(4), 163–178.

    Google Scholar 

  • DiNucci, D. (1999). Fragmented Future. Print, 53(4), 32–35.

    Google Scholar 

  • Doan, A., Ramakrishnan, R., & Halevy, A. Y. (2011). Crowdsourcing systems on the world-wide web. Communications of the ACM, 54(4), 86–96. doi:10.1145/1924421.1924442.

    Article  Google Scholar 

  • Eastin, M. S., Daugherty, T., & Burns, N. M. (2011). Handbook of research on digital media and advertising: User generated content consumption. Hershey: Information Science Reference.

    Book  Google Scholar 

  • Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550.

    Google Scholar 

  • Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: opportunities and challenges. Academy of Management Journal, 50(1), 25–32.

    Article  Google Scholar 

  • Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3), 169–200. doi:10.1080/02699939208411068.

    Article  Google Scholar 

  • Eppler, M. J., & Platts, K. W. (2009). Visual strategizing: the systematic use of visualization in the strategic-planning process. Long Range Planning, 42(1), 42–74. doi:10.1016/j.lrp.2008.11.005.

    Article  Google Scholar 

  • Ertek, G., Tokdemir, G., Sevinç, M., & Tunç, M. M. (2015). New knowledge in strategic management through visually mining semantic networks. Information Systems Frontiers, 1–21. doi:10.1007/s10796-015-9591-0.

  • Estevez, E., Fillottrani, P., Janowski, T., & Ojo, A. (2012). Government Information Sharing. In Y.-C. Chen & P.-Y. Chu (Eds.), Electronic Governance and Cross-Boundary Collaboration (pp. 23–55). Hershey: IGI Global.

  • Etzelstorfer, S., Gegenhuber, T., & Hilgers, D. (2016). Opening up Government: Citizen Innovation and new modes of collaboration in Austria. In R. Egger, I. Gula, & D. Walcher (Eds.), Open Tourism: Open Innovation, Crowdsourcing and Collaborative Consumption Challenging the Tourism Industry (pp. 1–17). Berlin: Springer.

  • Fan, L., Zhang, Y., Dang, Y., & Chen, H. (2013). Analyzing sentiments in Web 2.0 social media data in Chinese: experiments on business and marketing related Chinese Web forums. Information Technology and Management, 14(3), 231–242. doi:10.1007/s10799-013-0160-2.

    Article  Google Scholar 

  • Fang, H., Zhang, J., Bao, Y., & Zhu, Q. (2013). Towards effective online review systems in the Chinese context: a cross-cultural empirical study. Electronic Commerce Research and Applications, 12(3), 208–220. doi:10.1016/j.elerap.2013.03.001.

    Article  Google Scholar 

  • Few, S. (2006). Information Dashboard Design. North Sebastopol: O’Reilly.

    Google Scholar 

  • Fliedl, G., Kop, C., & Vöhringer, J. (2010). Guideline Based Evaluation and Verbalization of OWL Class and Property Labels. Data & Knowledge Engineering, 69(4), 331–342. doi:10.1016/j.datak.2009.08.004.

    Article  Google Scholar 

  • Gao, G., McCullough, J. S., Agarwal, R., & Jha, A. K. (2010). Are doctors created equal? An investigation of online ratings by patients. In Proceedings of the Workshop on Information Systems and Economics (WISE) (pp. 1–6). St. Louis, MO.

  • Gelman, I. A., & Wu, N. (2011). Combining Structured and Unstructured Information Sources for a Study of Data Quality: A Case Study of Zillow.com. In Proceedings of the 44th Hawaii International Conference on System Sciences (pp. 1–12). Kauai, HI.

  • Glass, R. L. (2000). On design. Journal of Systems and Software, 52(1), 1–2. doi:10.1016/S0164-1212(99)00127-2.

    Article  Google Scholar 

  • Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37(2), 337–355.

    Google Scholar 

  • Hagen, L., Harrison, T. M., Uzuner, Ö., Fake, T., Lamanna, D., & Kotfila, C. (2015). Introducing textual analysis tools for policy informatics. In K. Mossberger, N. Helbig, J. Zhang, & Y. Kim (Eds.), Proceedings of the 16th Annual International Conference on Digital Government Research (pp. 10–19). Phoenix, AZ.

  • Hand, E. (2010). Citizen Science: People power. Nature, 466(7307), 685–687. doi:10.1038/466685a.

    Article  Google Scholar 

  • Hevner, A. R., & Chatterjee, S. (2010). Design research in information systems: Theory and practice. New York: Springer.

    Book  Google Scholar 

  • Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.

    Google Scholar 

  • Hilgers, D., & Ihl, C. (2010). Citizensourcing – Applying the Concept of Open Innovation to the Public Sector. International Journal of Public Participation (IJP2), 4(1), 67–88.

    Google Scholar 

  • Hoffman, P. E., & Grinstein, G. G. (2002). A survey of visualizations for high dimensional data mining. In U. M. Fayyad, G. G. Grinstein, & A. Wierse (Eds.), Information visualization in data mining and knowledge discovery. (pp. 47–82). San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Homburg, C. (2015). Marketingmanagement: Strategie, Instrumente, Umsetzung, Unternehmensführung (5th ed.). Wiesbaden: Springer.

    Book  Google Scholar 

  • Hu, W., Almansoori, A., Kannan, P. K., Azarm, S., & Wang, Z. (2012). Corporate dashboards for integrated business and engineering decisions in oil refineries: an agent-based approach. Decision Support Systems, 52(3), 729–741. doi:10.1016/j.dss.2011.11.019.

    Article  Google Scholar 

  • Huang, S., Ward, M. O., & Rundensteiner, E. A. (2005). Exploration of Dimensionality Reduction for Text Visualization. In Proceedings of the Third International Conference on Coordinated and Multiple Views in Exploratory Visualization (pp. 63–74). Los Alamitos, CA.

  • Janssen, M., Estevez, E., & Janowski, T. (2014). Interoperability in Big, Open, and Linked Data - Organizational Maturity, Capabilities, and Data Portfolios. IEEE Computer, 47(10), 44–49. doi:10.1109/MC.2014.290.

    Article  Google Scholar 

  • Janssen, M., Mäntymäki, M., Hidders, J., Klievink, B., Lamersdorf, W., van Loenen, B., et al. (Eds.) (2015a). Open and Big Data Management and Innovation (LNCS). Cham: Springer.

    Google Scholar 

  • Janssen, M., Matheus, R., & Zuiderwijk, A. (2015b). Big and Open Linked Data (BOLD) to Create Smart Cities and Citizens: Insights from Smart Energy and Mobility Cases. In E. Tambouris, M. Janssen, H. J. Scholl, M. A. Wimmer, K. Tarabanis, M. Gascó, et al. (Eds.), Electronic Government (Vol. 9248, pp. 79–90, LNCS). Cham: Springer.

    Google Scholar 

  • Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In Proceedings of the 10th European Conference on Machine Learning (pp. 137–142). London, UK: Springer.

  • Jones, G. J. F. (2011). Integrating social media with existing knowledge and information for crisis response. In Proceedings of the 3rd Workshop on Social Web Search and Mining (pp. 1–2). Beijing.

  • Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59–68. doi:10.1016/j.bushor.2009.09.003.

    Article  Google Scholar 

  • Karpf, D. (2009). Blogosphere research: a mixed-methods approach to rapidly changing systems. IEEE Intelligent Systems, 24(5), 67–70.

    Google Scholar 

  • Kawamura, R. (2010). Social media's impact on BI starts with web data services. http://kapowsoftware.com/blog/index.php/social-media-impact-on-bi-starts-with-web-data-services.

  • Keim, D. A. (2002). Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 8(1), 1–8. doi:10.1109/2945.981847.

    Article  Google Scholar 

  • Keller, K. L. (2014). MSI 2014–2016 Research Priorities. http://www.msi.org/uploads/files/MSI_RP14-16.pdf. Accessed 19 December 2015.

  • Kornai, A. (2013). Digital language death. PloS One, 8(10), 1–11. doi:10.1371/journal.pone.0077056.

    Article  Google Scholar 

  • Krishnaraju, V., Mathew, S. K., & Sugumaran, V. (2016). Web personalization for user acceptance of technology: an empirical investigation of E-government services. Information Systems Frontiers, 18(3), 579–595. doi:10.1007/s10796-015-9550-9.

    Article  Google Scholar 

  • Kuechler, W., & Vaishnavi, V. (2012). A framework for theory development in design science research: Multiple perspectives. Journal of the Association of Information Systems, 13(6), 395–423.

    Google Scholar 

  • Kvale, S., & Brinkmann, S. (2015). InterViews: Learning the craft of qualitative research interviewing. Thousand Oaks: Sage.

    Google Scholar 

  • Lau, R. Y. K., Liao, S. Y., Kwok, R. C.-W., Xu, K., Xia, Y., & Li, Y. (2011). Text mining and probabilistic language modeling for online review spam detection. ACM Transactions on Management Information Systems, 2(4), 1–30. doi:10.1145/2070710.2070716.

    Article  Google Scholar 

  • Lee, A. S., & Baskerville, R. L. (2003). Generalizing generalizability in information systems research. Information Systems Research, 14(3), 221–243.

    Article  Google Scholar 

  • Lee, T. Y., & Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research (JMR), 48(5), 881–894.

    Article  Google Scholar 

  • Lee, H., & Choi, B. (2003). Knowledge management enablers, processes, and organizational performance: an integrative view and empirical examination. Journal of Management Information Systems, 20(1), 179–228.

    Google Scholar 

  • Lee, G., & Kwak, Y. H. (2012). An open government maturity model for social media-based public engagement. Government Information Quarterly, 29(4), 492–503. doi:10.1016/j.giq.2012.06.001.

    Article  Google Scholar 

  • Leukel, J., Müller, M., & Sugumaran, V. (2014). The State of Design Science Research within the BISE Community: An Empirical Investigation. In Proceedings of the 35th International Conference on Information Systems. Auckland, New Zealand.

  • Li, N., & Wu, D. D. (2010). Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decision Support Systems, 48(2), 354–368. doi:10.1016/j.dss.2009.09.003.

    Article  Google Scholar 

  • Lipenkova, J. (2015). A system for fine-grained aspect-based sentiment analysis of Chinese. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. Beijing.

  • Lusch, R. F., Liu, Y., & Chen, Y. (2010). The phase transition of markets and organizations: the new intelligence and entrepreneurial frontier. IEEE Intelligent Systems, 25(1), 71–75.

    Google Scholar 

  • March, S. T., & Smith, G. F. (1995). Design and natural science research on information technology. Decision Support Systems, 15(4), 251–266. doi:10.1016/0167-9236(94)00041-2.

    Article  Google Scholar 

  • March, S. T., & Storey, V. C. (2008). Design science in the information systems discipline: an introduction to the special issue on design science research. MIS Quarterly, 32(4), 725–730.

    Google Scholar 

  • Marland, A., Lewis, J. P., & Flanagan, T. (2016). Governance in the Age of Digital Media and Branding. Governance. doi:10.1111/gove.12194.

  • Marshall, B., McDonald, D., Chen, H., & Chung, W. (2004). EBizPort: collecting and analyzing business intelligence information. Journal of the American Society for Information Science and Technology, 55(10), 873–891. doi:10.1002/asi.20037.

    Article  Google Scholar 

  • Matheus, R., & Janssen, M. (2015). Transparency Dimensions of Big and Open Linked Data. In M. Janssen, M. Mäntymäki, J. Hidders, B. Klievink, W. Lamersdorf, B. van Loenen, et al. (Eds.), Open and Big Data Management and Innovation (Vol. 9373, pp. 236–246, LNCS). Cham: Springer.

  • Mergel, I. (2013). A framework for interpreting social media interactions in the public sector. Government Information Quarterly, 30(4), 327–334. doi:10.1016/j.giq.2013.05.015.

    Article  Google Scholar 

  • Miles, M. B., Huberman, A. M., & Saldaña, J. (2013). Qualitative data analysis: A methods sourcebook. Thousand Oaks: Sage.

  • Miniwatts Marketing Group. (2016). Number of Internet Users by Language: Internet World Stats. http://www.internetworldstats.com/stats7.htm. Accessed 13 March 2016.

  • Moody, D. L., & Shanks, G. G. (2003). Improving the quality of data models: empirical validation of a quality management framework. Information Systems, 28(6), 619–650. doi:10.1016/S0306-4379(02)00043-1.

    Article  Google Scholar 

  • Muñoz, L. A., & Bolívar, M. P. R. (2015). Theoretical Support for Social Media Research A Scientometric Analysis. In E. Tambouris, M. Janssen, H. J. Scholl, M. A. Wimmer, K. Tarabanis, M. Gascó, et al. (Eds.), Electronic Government (Vol. 9248, pp. 59–75, LNCS). Cham: Springer.

    Google Scholar 

  • Neff, A. A., Hamel, F., Herz, T. P., Uebernickel, F., Brenner, W., & vom Brocke, J. (2014). Developing a maturity model for service systems in heavy equipment manufacturing enterprises. Information & Management, 51(7), 895–911. doi:10.1016/j.im.2014.05.001.

    Article  Google Scholar 

  • Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: market-structure surveillance through text mining. Marketing Science, 31(3), 521–543. doi:10.1287/mksc.1120.0713.

    Article  Google Scholar 

  • Nicolai, A., & Seidl, D. (2010). That's Relevant! Different forms of practical relevance in management science. Organization Studies, 31(9–10), 1257–1285. doi:10.1177/0170840610374401.

    Article  Google Scholar 

  • Noesselt, N. (2014). Microblogs and the adaptation of the Chinese party-State's governance strategy. Governance, 27(3), 449–468. doi:10.1111/gove.12045.

    Article  Google Scholar 

  • Nunamaker, J. F., & Briggs, R. O. (2011). Toward a broader vision for information systems. ACM Transactions on Management Information Systems, 2(4), 1–12. doi:10.1145/2070710.2070711.

    Article  Google Scholar 

  • Nunamaker, J. F., Chen, M., & Purdin, T. D. M. (1990). Systems development in information systems research. Journal of Management Information Systems, 7(3), 89–106. doi:10.2307/40397957.

    Article  Google Scholar 

  • OECD (2010). Denmark: Efficient e-Government for Smarter Public Service Delivery, OECD Publishing, Paris. DOI:10.1787/9789264087118-en

  • O’Reilly, T. (2005). What is Web 2.0? Design Patterns and Business Models for the Next Generation of Software. http://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html. Accessed 14 November 2015.

  • Offermann, P., Levina, O., Schönherr, M., & Bub, U. (2009). Outline of a design science research process. In Vijay K. Vaishnavi & Sandeep Purao (Eds.), Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology (pp. 1–11). Philadelphia, PA.

  • Österle, H., Becker, J., Frank, U., Hess, T., Karagiannis, D., Krcmar, H., et al. (2011). Memorandum on design-oriented information systems research. EJIS, 20(1), 7–10. doi:10.1057/ejis.2010.55.

    Google Scholar 

  • Paris, C., & Wan, S. (2011). Listening to the community: social media monitoring tasks for improving government services. In D. Tan, S. Amershi, B. Begole, W. A. Kellogg, & M. Tungare (Eds.), Vancouver, Canada (p. 2095). doi:10.1145/1979742.1979878

  • Peffers, K. E., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77.

    Article  Google Scholar 

  • Qin, J., Zhou, Y., Chau, M., & Chen, H. (2006). Multilingual web retrieval: an experiment in English–Chinese business intelligence. Journal of the American Society for Information Science and Technology, 57(5), 671–683. doi:10.1002/asi.20329.

    Article  Google Scholar 

  • Recker, J. C., & Rosemann, M. (2010). A measurement instrument for process modeling research: development, test and procedural model. Scandinavian Journal of Information Systems, 22(2), 3–30.

    Google Scholar 

  • Roberts, N. C. (2011). Tracking and disrupting dark networks: challenges of data collection and analysis. Information Systems Frontiers, 13(1), 5–19. doi:10.1007/s10796-010-9271-z.

    Article  Google Scholar 

  • Roitman, H., Barkai, G., Konopnicki, D., & Soffer, A. (2014). Measuring the Effectiveness of Multi-channel Marketing Campaigns Using Online Chatter. In Proceedings of the 23rd International Conference on World Wide Web (pp. 143–146). Seoul, Republic of Korea.

  • Rouibah, K., & Ould-ali, S. (2002). PUZZLE: a concept and prototype for linking business intelligence to business strategy. The Journal of Strategic Information Systems, 11(2), 133–152. doi:10.1016/S0963-8687(02)00005-7.

    Article  Google Scholar 

  • Santiago-Rivera, D., & Shanks, G. (2015). A dashboard to support management of business analytics capabilities. Journal of Decision Systems, 24(1), 73–86. doi:10.1080/12460125.2015.994335.

    Article  Google Scholar 

  • Sashi, C. M. (2012). Customer engagement, buyer-seller relationships, and social media. Management Decision, 50(2), 253–272. doi:10.1108/00251741211203551.

    Article  Google Scholar 

  • Schedler, K., & Proeller, I. (2011). New public management (5th ed., UTB, 2132 : Public management, Betriebswirtschaft). Bern: Haupt.

    Google Scholar 

  • Schmidt, M., & Hollensen, S. (2006). Marketing Research: An international approach. Harlow: Prentice Hall/Financial Times.

    Google Scholar 

  • Schultze, U., & Leidner, D. E. (2002). Studying knowledge Management in Information Systems Research: discourses and theoretical assumptions. MIS Quarterly, 26(3), 213–242.

    Article  Google Scholar 

  • Schumaker, R. P. (2011). From data to wisdom: the progression of computational learning in text mining. Communications of the IIMA, 11(1), 39–53.

    Google Scholar 

  • Scott Morton, M. S. (1984). The State of the Art of Research in Management Support Systems. In F. W. McFarlan (Ed.), The Information Research Challenge (pp. 13–41). Boston: Harvard University Press.

    Google Scholar 

  • Sha, S., Huang, T., & Gabardi, E. (2013). Upward Mobility: The Future of China’s Premium Car Market. http://www.mckinsey.com/insights/asia-pacific/getting_to_know_chinas_premium-car_market. Accessed 14 November 2015.

  • Shareef, M. A., Kumar, V., Dwivedi, Y. K., & Kumar, U. (2016). Service delivery through mobile-government (mGov): driving factors and cultural impacts. Information Systems Frontiers, 18(2), 315–332. doi:10.1007/s10796-014-9533-2.

    Article  Google Scholar 

  • Sherchan, W., Nepal, S., & Paris, C. (2013). A survey of trust in social networks. ACM Computing Surveys, 45(4), 1–33. doi:10.1145/2501654.2501661.

    Article  Google Scholar 

  • Shneiderman, B. (1996). The eyes have it: a task by data type taxonomy for information visualizations. In Proceedings of the 1996 IEEE Symposium on Visual Languages (pp. 336–343). Boulder, CO.

  • Simon, H. A. (1996). The sciences of the artificial (3rd ed.). Cambridge: MIT Press.

    Google Scholar 

  • Sonnenberg, C., & vom Brocke, J. (2012a). Evaluation Patterns for Design Science Research Artefacts. In M. Helfert & B. Donnellan (Eds.), Practical Aspects of Design Science (Vol. 286, pp. 71–83, Communications in Computer and Information Science). Berlin: Springer.

  • Sonnenberg, C., & vom Brocke, J. (2012b). Evaluations in the Science of the Artificial - Reconsidering the Build-Evaluate Pattern in Design Science Research. In K. Peffers, M. Rothenberger, & B. Kuechler (Eds.), Design Science Research in Information Systems. (pp. 381–397). Las Vegas: Springer.

  • Spence, R. (2001). Information visualization. Harlow: Addison-Wesley.

  • Stevens, C. H. (2013). Many-to many communication. Charleston: Nabu Press.

    Google Scholar 

  • Stieglitz, S., & Dang-Xuan, L. (2013). Social media and political communication: a social media analytics framework. Social Network Analysis and Mining, 3(4), 1277–1291. doi:10.1007/s13278-012-0079-3.

    Article  Google Scholar 

  • Stindt, D., Nuss, C., Bensch, S., Dirr, M., & Tuma, A. (2014). An Environmental Management Information System for Closing Knowledge Gaps in Corporate Sustainable Decision-Making. In Proceedings of the 35th International Conference on Information Systems. Auckland, New Zealand.

  • Straub, D., & Ang, S. (2011). Editor's Comments: Rigor and relevance in IS research: redefining the debate and a call for future research. MIS Quarterly, 35(1), iii–ixi.

    Google Scholar 

  • Talvensaari, T., Juhola, M., Laurikkala, J., & Järvelin, K. (2007). Corpus-based cross-language information retrieval in retrieval of highly relevant documents. Journal of the American Society for Information Science and Technology, 58(3), 322–334. doi:10.1002/asi.20495.

    Article  Google Scholar 

  • Tambouris, E., Janssen, M., Scholl, H. J., Wimmer, M. A., Tarabanis, K., Gascó, M., et al. (Eds.) (2015). Electronic Government (LNCS). Cham: Springer.

    Google Scholar 

  • Thomas, J. C. (2003). The New Face of Government: Citizen-Initiated Contacts in the Era of E-Government. Journal of Public Administration Research and Theory, 13(1), 83–102.

    Article  Google Scholar 

  • Timsina, P., Liu, J., & El-Gayar, O. (2016). Advanced analytics for the automation of medical systematic reviews. Information Systems Frontiers, 18(2), 237–252. doi:10.1007/s10796-015-9589-7.

    Article  Google Scholar 

  • Torres, L., Pina, V., & Acerete, B. (2006). E-governance developments in European Union cities: reshaping Government's relationship with citizens. Governance, 19(2), 277–302.

    Article  Google Scholar 

  • Tremblay, M. C., Hevner, A. R., & Berndt, D. J. (2010). Focus groups for artifact refinement & evaluation in design research. Communications of the Association for Information Systems, 26(27), 599–618.

    Google Scholar 

  • van Aken, J. E. (2004). Management Research Based on the Paradigm of the Design Sciences: The Quest for Field-Tested & Grounded Technological Rules. Journal of Management Studies, 41(2), 219–246.

    Article  Google Scholar 

  • Vickery, G., & Wunsch-Vincent, S. (2007). Participative Web and user-created content: Web 2.0, wikis and social networking. Paris: Organisation for Economic Co-operation and Development.

    Google Scholar 

  • von Hippel, E. (2005). Democratizing Innovation. Cambridge: MIT Press.

    Google Scholar 

  • Vulić, I., Smet, W., de Tang, J., & Moens, M.-F. (2015). Probabilistic topic modeling in multilingual settings: an overview of its methodology and applications. Information Processing & Management, 51(1), 111–147. doi:10.1016/j.ipm.2014.08.003.

    Article  Google Scholar 

  • Walls, J. G., Widmeyer, G. R., & El Sawy, O. A. (1992). Building an information system design theory for vigilant EIS. Information Systems Research, 3(1), 36–59.

    Article  Google Scholar 

  • Ware, C. (2000). Information Visualization. San Francisco: Morgan Kaufmann.

  • Watson, H. J., & Frolick, M. N. (1993). Determining information requirements for an EIS. MIS Quarterly, 17(3), 255–269.

    Article  Google Scholar 

  • Wattal, S., Schuff, D., Mandviwalla, M., & Williams, C. B. (2010). Web 2.0 and politics: The 2008 U.S. presidential election and an e-politics research agenda. MIS Quarterly, 34(4), 669–688.

    Google Scholar 

  • Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: writing a literature review. MIS Quarterly, 26(2), 13–23.

    Google Scholar 

  • Winograd, T. (1996). Bringing design to software (ACM press books). Reading: Addison-Wesley.

    Google Scholar 

  • Winograd, T. (1998). The Design of Interaction. In P. J. Denning & R. M. Metcalfe (Eds.), Beyond calculation: The next fifty years of computing (pp. 149–162). New York: Copernicus.

    Google Scholar 

  • Wise, J. A. (1999). The ecological approach to text visualization. Journal of the American Society for Information Science, 50(13), 1224–1233.

    Article  Google Scholar 

  • Wise, S., Paton, R. A., & Gegenhuber, T. (2012). Value co-creation through collective intelligence in the public sector. Vine - The journal of information and knowledge management systems, 42(2), 251–276. doi:10.1108/03055721211227273.

    Google Scholar 

  • Yang, H., & Callan, J. (2009). OntoCop: constructing ontologies for public comments. IEEE Intelligent Systems, 24(5), 70–75.

    Google Scholar 

  • Yang, H.-L., & Chao, A. F. Y. (2014). Sentiment analysis for Chinese reviews of movies in multi-genre based on morpheme-based features and collocations. Information Systems Frontiers. doi:10.1007/s10796-014-9498-1.

    Google Scholar 

  • Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Beverly Hills: Sage.

    Google Scholar 

  • Yin, P., Wang, H., & Guo, K. (2013). Feature–opinion pair identification of product reviews in Chinese: a domain ontology modeling method. New Review of Hypermedia and Multimedia, 19(1), 3–24. doi:10.1080/13614568.2013.766266.

    Article  Google Scholar 

  • Zeng, D., Wei, D., Chau, M., & Wang, F. (2011). Domain-specific Chinese word segmentation using suffix tree and mutual information. Information Systems Frontiers, 13(1), 115–125. doi:10.1007/s10796-010-9278-5.

    Article  Google Scholar 

  • Zhan, J., Loh, H. T., & Liu, Y. (2009). Gather customer concerns from online product reviews – A text summarization approach. Expert Systems with Applications, 36(2, Part 1), 2107–2115. doi:10.1016/j.eswa.2007.12.039.

    Article  Google Scholar 

  • Zhou, Y., Qin, J., Chen, H., & Nunamaker, J. F. (2005). Multilingual Web Retrieval: An Experiment on a Multilingual Business Intelligence Portal. In 38th Annual Hawaii International Conference on System Sciences. Big Island, HI.

  • Zikopoulos, P., Eaton, C., De Roos, D., Deutsch, T., & Lapis, G. (2012). Understanding big data: Analytics for Enterprise class Hadoop and streaming data. New York: McGraw-Hill.

    Google Scholar 

  • Zuiderwijk, A. (2015). Open data infrastructures: The design of an infrastructure to enhance the coordination of open data use. Delft: Delft University of Technology.

    Google Scholar 

  • Zuiderwijk, A., Janssen, M., Zhang, J., Puron-Cid, G., & Gil-Garcia, J. R. (2015). Towards decision support for disclosing data: Closed or open data? Information Polity, 20(2,3), 103–117. doi:10.3233/IP-150358.

  • Zwass, V. (2010). Co-creation: toward a taxonomy and an integrated research perspective. International Journal of Electronic Commerce, 15(1), 11–48.

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    Aaron W. Baur

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Table 5 Description of case studies
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Baur, A.W. Harnessing the social web to enhance insights into people’s opinions in business, government and public administration. Inf Syst Front 19, 231–251 (2017). https://doi.org/10.1007/s10796-016-9681-7

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