Zusammenfassung
Mit unserem Forschungskommentar zeigen wir vielversprechende Forschungsrichtungen auf, die aus dem wechselseitigen Zusammenspiel von Social Media und Collective Intelligence hervorgehen. Wir konzentrieren uns auf sogenannte „Wicked Problems“ – eine Klasse von Problemen, „for which no single computational formulation of the problem is sufficient, for which different stakeholders do not even agree on what the problem really is, and for which there are no right or wrong answers, only answers that are better or worse from different points of view“ (Introne et al. in Künstl. Intell. 27:45–52, 2013). Wir argumentieren, dass insbesondere die Disziplin Wirtschaftsinformatik einen Beitrag zur Gestaltung geeigneter Systeme leisten kann und zwar aufgrund des Nutzens, der sich aus einer kombinierten Perspektive von Social Media und Collective Intelligence ableitet. Wir legen die Relevanz und Aktualität von Social Media und Collective Intelligence für die Wirtschaftsinformatik dar, schlagen erforderliche Funktionalitäten von Informationssystemen für Wicked Problems vor, beschreiben verwandte Themenfelder und Herausforderungen für die Forschung, identifizieren wissenschaftliche Methoden zu ihrer Lösung und führen konkrete Beispiele für erste Forschungsergebnisse an.
Abstract
The objective of this commentary is to propose fruitful research directions built upon the reciprocal interplay of social media and collective intelligence. We focus on „wicked problems“ – a class of problems that Introne et al. (Künstl. Intell. 27:45–52, 2013) call „problems for which no single computational formulation of the problem is sufficient, for which different stakeholders do not even agree on what the problem really is, and for which there are no right or wrong answers, only answers that are better or worse from different points of view“. We argue that information systems research in particular can aid in designing appropriate systems due to benefits derived from the combined perspectives of both social media and collective intelligence. We document the relevance and timeliness of social media and collective intelligence for business and information systems engineering, pinpoint needed functionality of information systems for wicked problems, describe related research challenges, highlight prospective suitable methods to tackle those challenges, and review examples of initial results.
Literatur
Ahuja MK, Carley K (1999) Network structure in virtual organizations. Organ Sci 10(6):741–757
Ahuja MK, Galletta DF, Carley KM (2003) Individual centrality and performance in virtual R&D groups: an empirical study. Manag Sci 49(1):21–38
Alavi M, Leidner DE (2001) Review: knowledge management and knowledge management systems: conceptual foundations and research issues. Manag Sci 25(1):107–136
Arrow KJ, Forsythe R, Gorham M, Hahn R, Hanson R, Ledyard JO, Levmore S, Litan R, Milgrom P, Nelson FD, Neumann GR, Ottaviani M, Schelling TC, Shiller RJ, Smith VL, Snowberg E, Sunstein CR, Tetlock PC, Tetlock PE, Varian HR, Wolfers J, Zitzewitz E (2008) Economics – the promise of prediction markets. Science 320(5878):877–878
Ashworth M, Carley K (2006) Who you know vs. what you know: the impact of social position and knowledge on team performance. J Math Sociol 30(1):43–75
Austen K (2013) Out of the lab and onto the streets. New Sci 218(2932):48–51
Barabasi A, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509
Baroni M, Bisi S (2004) Using cooccurrence statistics and the web to discover synonyms in a technical language. In: Proceedings of LREC, Lisbon, S 1725–1728
Baskerville R, Lyytinen K, Sambamurthy V, Straub D (2011) A response to the design-oriented information systems research memorandum. Eur J Inf Syst 20(1):11–15
Bishop CM (2006) Pattern recognition and machine learning, Bd 4. Springer, Heidelberg
Bjelland OM, Wood RC 2008 An inside view of IBM’s ‘innovation jam’. MIt Sloan Manag. Rev. Fall 50(1):32–40
Boll S, Jain R, Luo JB, Xu D (2011) Introduction to special issue on social media. ACM Trans Multimed Comput 7(Supplement):25
Bonabeau E (2009) Decisions 2.0: the power of collective intelligence. Sloan Manag Rev 50(2):45–52
Bonney R, Cooper C, Dickinson J, Kelling S, Phillips T, Rosenberg K, Shirk J (2009) Citizen science: a developing tool for expanding science knowledge and scientific literacy. BioScience 59(11):977–984
Borgatti SP, Foster PC (2003) The network paradigm in organizational research: a review and typology. J Manag 29(6):991–1013
Borgatti SP, Everett M, Freeman L (2002) Ucinet for Windows: software for social network analysis. Analytic Technologies, Harvard
Bothos E, Apostolou D, Mentzas G (2009) Collective intelligence for idea management with Internet-based information aggregation markets. Internet Res 19(1):26–41
Brandes U (2001) A faster algorithm for betweenness centrality. J Math Sociol 25(2):163–177
Brants T, Chen F, Tsochantaridis I (2002) Topic-based document segmentation with probabilistic latent semantic analysis. In: Proc of 11th international conference on information and knowledge management, New York, S 211–218
Brass DJ, Galaskiewicz J, Greve HR, Tsai WP (2004) Taking stock of networks and organizations: a multilevel perspective. Acad Manag J 47(6):795–817
Brynjolfsson E, McAfee A (2012) Winning the race with ever-smarter machines. Sloan Manag Rev 53(2):53–60
Buhl HU, Fridgen G, Müller G, Röglinger M (2012a) On dinosaurs, measurement ideologists, separatists, and happy souls – proposing and justifying a way to make the global IS/BISE community happy. Bus Inf Syst Eng 4(6):307–315
Buhl HU, Müller G, Fridgen G, Röglinger M (2012b) Business and information systems engineering: a complementary approach to information systems – what we can learn from the past and may conclude from present reflection on the future. J Assoc Inf Syst 13(4):236–253
Carvalho P, Sarmento L, Silva MJ, De Oliveira E (2009) Clues for detecting irony in user-generated contents: oh...!! It’s “so easy” ;-). In: Proc of 1st international CIKM workshop on topic-sentiment analysis for mass opinion. ACM, New York, S 53–56
Chen HC, Yang CC (2011) Special issue on social media analytics: understanding the pulse of the society. IEEE Trans Syst Man Cybern 41(5):826–827
Chen H, Chiang RHL, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Quart 36(4):1165–1188
Churchman CW (1967) Wicked problems. Manag Sci 14(4):B-141–B-142
Cohn JP (2008) Citizen science: can volunteers do real research? BioScience 58(3):192–207
Conti M, Das SK, Bisdikian C, Kumar M, Ni LM, Passarella A, Roussos G, Tröster G, Tsudik G, Zambonelli F (2012) Looking ahead in pervasive computing: challenges and opportunities in the era of cyber-physical convergence. Pervasive Mob Comput 8(1):2–21
Cortizo JC, Carrero FM, Gomez JM (2011) Introduction to the special issue: mining social media. Int J Electron Commer 15(3):5–7
Councill IG, McDonald R, Velikovich L (2010) What’s great and what’s not: learning to classify the scope of negation for improved sentiment analysis. In: Proc of workshop on negation and speculation in natural language processing, S 51–59. Association for Computational Linguistics
Cross R, Liedtka J, Weiss L (2005) A practical guide to social networks. Harv Bus Rev 83(3):124–132
Davenport TH, Prusak L (1998) Working knowledge. Harvard Business School Press, Boston
Davenport TH, De Long DW, Beers MC (1998) Successful knowledge management projects. Sloan Manag Rev 39(2):43–57
De Nooy W, Mrvar A, Batagelj V (2005) Exploratory social network analysis with Pajek. Cambridge University Press, New York
Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113
Deary IJ (2000) Looking down on human intelligence – from psychometrics to the brain. Oxford University Press, New York
Doreian P, Stokman FN (1997) Evolution of social networks. Gordon and Breach, Amsterdam
European Commission (2009) The world in 2025. http://ec.europa.eu/research/social-sciences/pdf/the-world-in-2025-report_en.pdf. Abruf am 2012-07-01
Feldman R, Sanger J (2006) The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press, New York
Finholt TA (2002) Collaboratories. Annu Rev Inf Sci Technol 36(1):73–107
Fischbach K, Gloor PA, Schoder D (2009) Analyse informeller Kommunikationsnetzwerke am Beispiel einer Fallstudie. WIRTSCHAFTSINFORMATIK 51(2):164–174
Floeck F, Putzke J, Steinfels S, Fischbach K, Schoder D (2011) Imitation and quality of tags in social bookmarking systems – collective intelligence leading to folksonomies. In: Bastiaens T, Baumöl U, Krämer B (Hrsg) Advances in soft computing, on collective intelligence, Bd 76. Heidelberg, Springer, S 75–91
Forsythe R, Nelson F, Neumann GR, Wright J (1992) Anatomy of an experimental political stock-market. Am Econ Rev 82(5):1142–1161
Furtado V, Ayres L, de Oliveira M, Vasconcelos E, Caminha C, D’Orleans J, Belchior M (2010) Collective intelligence in law enforcement – the WikiCrimes system. Inf Sci 180(1):4–17
Gregg D (2009) Developing a collective intelligence application for special education. Decis Support Syst 47(4):455–465
Gregg D (2010) Designing for collective intelligence. Commun ACM 53(4):134–138
Gruber T (2007) Ontology of folksonomy: a mash-up of apples and oranges. Int J Semantic Web Inf Syst 3(1):1–11
Gürkan A, Iandoli L, Klein M, Zollo G (2010) Mediating debate through on-line large-scale argumentation: evidence from the field. Inf Sci 180(19):3686–3702
Gutwin CA, Lippold M, Graham TCN (2011) Real-time groupware in the browser: testing the performance of web-based network. In: Proc of ACM 2011 conference on computer supported cooperative work (CSCW 2011), Hangzhou, S 167–176
Hahn R, Bizer C, Sahnwaldt C, Herta C, Robinson S, Burgle M, Duwiger H, Scheel U (2010) Faceted Wikipedia search. Lect Notes Bus Inf 47:1–11
Hiltz SR, Johnson K, Turoff M (1991) Group decision support: the effects of designated human leaders and statistical feedback in computerized conferences. J Manag Inf Syst 8(2):81–108
Hiltz SR, Diaz P, Mark G (2011) Introduction: social media and collaborative systems for crisis management. ACM Trans Comput-Hum Interact 18(4):18
Hsieh WT, Stu J, Chen YL, Chou SCT (2009) A collaborative desktop tagging system for group knowledge management based on concept space. Expert Syst Appl 36(5):9513–9523
Introne J, Laubacher R, Olson G, Malone T (2013) Solving wicked social problems with socio-computational systems. Künstl Intell 27:45–52
Jindal N, Liu B (2008) Opinion spam and analysis. In: Proc of international conference on web search and web data mining. ACM, New York, S 219–230
Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. Lect Notes Comput Sci 139:137–142
Junglas I, Niehaves B, Spiekermann S, Stahl BC, Weitzel T, Winter R, Baskerville R (2011) The inflation of academic intellectual capital: the case for design science research in Europe. Eur J Inf Syst 20(1):1–6
Kane GC, Alavi M, Labianca G, Borgatti SP (im Druck) What’s different about social media networks? A framework and research agenda. MIS Quarterly
Kapetanios E, Koutrika G (2010) Guest editorial: special issue on collective intelligence. Inf Sci 180(1):1–3
Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of social media. Bus Horiz 53(1):59–68
Kearns M (2012) Experiments in social computation. Commun ACM 55(10):58–67
Kessler B, Numberg G, Schütze H (1997) Automatic detection of text genre. In: Proc of 35th annual meeting of the association for computational linguistics and eighth conference of the European chapter of the association for computational linguistics, S 32–38. Association for Computational Linguistics
Kietzmann JH, Hermkens K, McCarthy IP, Silvestre BS (2011) Social media? Get serious! Understanding the functional building blocks of social media. Bus Horiz 54(3):241–251
Kittur A, Lee B, Kraut RE (2009) Coordination in collective intelligence: the role of team structure and task interdependence. In: Proceedings of the 27th annual chi conference on human factors in computing systems (Chi2009), Boston, S 1495–1504
Klein M (2012) Enabling large-scale deliberation using attention-mediation metrics. Comput Support Coop Work 21:449–473
Krempel L (2005) Visualisierung komplexer Strukturen: Grundlagen der Darstellung mehrdimensionaler Netzwerke. Campus, Frankfurt
Krombholz K, Merkl D, Weippl E (2012) Fake identities in social media: a case study on the sustainability of the Facebook business model. J Serv Sci Res 4(2):175–212
Lappin S, Leass HJ (1994) An algorithm for pronominal anaphora resolution. Comput Linguist 20(4):535–561
Lee WH, Tseng SS, Shieh WY (2010) Collaborative real-time traffic information generation and sharing framework for the intelligent transportation system. Inf Sci 180(1):62–70
Leimeister JM (2010) Collective intelligence. Bus Inf Syst Eng 2(4):245–248
Levy P (2010) From social computing to reflexive collective intelligence: the IEML research program. Inf Sci 180(1):71–94
Liang TP, Turban E (2011) Introduction to the special issue social commerce: a research framework for social commerce. Int J Electron Commer 16(2):5–13
Liu B (2007) Web data mining: exploring hyperlinks, contents, and usage data. Heidelberg, Springer
Liu B (2010) Sentiment analysis and subjectivity. In: Indurkhya N, Damerau F (eds) Handbook of natural language processing, 2. Aufl. CRC, London, S 627–666
Luo SL, Xia HX, Yoshida T, Wang ZT (2009) Toward collective intelligence of online communities: a primitive conceptual model. J Syst Sci Syst Eng 18(2):203–221
Lykourentzou I, Papadaki K, Vergados DJ, Polemi D, Loumos V (2010) CorpWiki: a self-regulating wiki to promote corporate collective intelligence through expert peer matching. Inf Sci 180(1):18–38
Mertens P, Barbian D (2013) Forschung über „Grand Challenges“ – Eine „Grand Challenge“. Arbeitspapier Nr 1/2013, Universität Erlangen-Nürnberg
Malone TW, Laubacher R, Dellarocas C (2010) The collective intelligence genome. Sloan Manag Rev 51(3):21–31
nA (2009) The lund decleration – Europe must focus on the grand challenges of our time. http://www.vr.se/download/18.7dac901212646d84fd38000336/1264064126033/Lund_Declaration.pdf. Abruf am 2012-07-01
Newman M (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103(23):8577
Österle H, Becker J, Frank U, Hess T, Karagiannis D, Krcmar H, Loos P, Mertens P, Oberweis A, Sinz EJ (2010) Memorandum on design-oriented information systems research. Eur J Inf Syst 20(1):7–10
Pang B, Lee L (2008) Opinion mining and sentiment analysis. Now Pub, Hannover
Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proc of EMNLP 2002, Philadelphia, S 79–86
Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10059–10072
Passant A, Laublet P (2008) Combining structure and semantics for ontology-based corporate wikis. Lect Notes Bus Inf 7:58–69
Robins G, Snijders T, Wang P, Handcock M, Pattison P (2007) Recent developments in exponential random graph (p∗) models for social networks. Soc Netw 29(2):192–215
Quinn A, Bederson B (2011) Human computation: a survey and taxonomy of a growing field. In: CHI 2007, Vancouver, S 1403–1412
Rittel HWJ, Weber MM (1973) Dilemmas in a general theory of planning. Policy Sci 4(2):155–169
Schoder D, Gloor P, Metaxas PT (2013a) Special issue on social media (editorial). Künstl Intell 27(1):5–8
Schoder D, Gloor P, Metaxas PT (2013b) Social media and collective intelligence – ongoing and future research streams. Künstl Intell 27(1):9–15
Scott J (2010) Social network anylsis – a handbook, 2nd ed. Sage Publications, Thousand Oaks.
Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1):1–47
Servan-Schreiber E, Wolfers J, Pennock DM, Galebach B (2004) Prediction markets: does money matter? Electron Mark 14(3):243–251
Snijders TAB, van de Bunt GG, Steglich CEG (2009) Introduction to stochastic actor-based models for network dynamics. Soc Netw 32(1):44–60
Soon WM, Ng HT, Lim DCY (2001) A machine learning approach to coreference resolution of noun phrases. Comput Linguist 27(4):521–544
Spann M, Skiera B (2003) Internet-based virtual stock markets for business forecasting. Manag Sci 49(10):1310–1326
Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proc of 40th annual meeting on association for computational linguistics, S 417–424. Association for Computational Linguistics
Tziralis G, Tatsiopoulos I (2007) Prediction markets: an extended literature review. J Predict Mark 1(1):75–91
Vanderhaeghen D, Fettke P, Loos P (2010) Organizational and technological options for business process management from the perspective of web 2.0 results of a design oriented research approach with particular consideration of self-organization and collective intelligence. Bus Inf Syst Eng 2(1):15–28
von Ahn L (2005) Human computation. PhD thesis, Carnegie Mellon University
Wasko M, Teigland R, Faraj S (2009) The provision of online public goods: examining social structure in an electronic network of practice. Decis Support Syst 47(3):254–265
Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge
Wasserman S, Pattison P (1996) Logit models and logistic regressions for social networks. I. An introduction to Markov graphs and p∗. Psychometrika 61(3):401–425
Watts D, Peretti J, Frumin M (2007) Viral marketing for the real world. Harv Bus Rev 85(5):22–23
Watts D, Strogatz S (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442
Woolley AW, Chabris CF, Pentland A, Hashmi N, Malone TW (2010) Evidence for a collective intelligence factor in the performance of human groups. Science 330(6004):686–688
Zott C, Amit R, Massa L (2011) The business model: recent developments and future research. J Manag 37(4):1019–1042
Danksagung
Die Autoren danken Marc Egger, Tim Majchrzak, Martin Petzold, Frank Piller und den drei anonymen Gutachtern für ihre Beiträge und hilfreiche Kommentare zu früheren Entwürfen dieses Artikels. Prof. Metaxas’ Forschung wurde teilweise unterstützt durch „NSF grant CNS-1117693“.
Author information
Authors and Affiliations
Corresponding author
Additional information
Angenommen nach zwei Überarbeitungen durch die Herausgeber des Schwerpunktthemas.
This article is also available in English via http://www.springerlink.com and http://www.bise-journal.org: Schoder D, Putzke J, Metaxas PT, Gloor PA, Fischbach K (2013) Information Systems for “Wicked Problems”. Research at the Intersection of Social Media and Collective Intelligence. Bus Inf Syst Eng. doi: 10.1007/s12599-013-0303-3.
Rights and permissions
About this article
Cite this article
Schoder, D., Putzke, J., Metaxas, P.T. et al. Informationssysteme für „Wicked Problems“. Wirtschaftsinf 56, 3–11 (2014). https://doi.org/10.1007/s11576-013-0395-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11576-013-0395-x