Creativity forward: a framework that integrates data analysis techniques to foster creativity within the creative process in user experience contexts
Abstract
The latest technological advancements allow users to generate a large volume of data related to their experiences and needs. However, the absence of an advanced methodology that links the big data and the creative process prevents the effective use of the data and extracting all its potential and knowledge in this context, which is crucial in offering user-centred solutions. Incorporating data creatively and critically as design material can help us learn and understand user needs better. Therefore, design can bring deeper meaning to data, just as data can enhance design practice. Accordingly, this work raises a reflection on whether designers could appropriate the workflow of data science in order to integrate it into the research process in the creative process within a framework of user experience analysis. The proposed model: data-driven design model, enhances the exploratory design of problem space and assists in the creation of ideas during the conceptual design phase. In this way, this work offers an integrated vision, enhancing creativity in industrial design as an instrument for the achievement of the proper and necessary balance between intuition and reason, design, and science.
Keyword : big data, creative process, creativity, data-driven design, design, methodology, user experience
How to Cite
Quiñones-Gómez, J. C. (2021). Creativity forward: a framework that integrates data analysis techniques to foster creativity within the creative process in user experience contexts. Creativity Studies, 14(1), 51-73. https://doi.org/10.3846/cs.2021.12933
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Adams, R. S., & Atman, C. J. (1999, 10–13 November). Cognitive processes in iterative design behaviour. In Proceedings of the 29th American Society for Engineering Education/Institute of Electrical and Electronics Engineers Frontiers in Education Conference. San Juan, Puerto Rico. Institute of Electrical and Electronics Engineers, 11a6-13–11a6-18.
Affetti, L., Tommasini, R., Margara, A., Cugola, G., & Della Valle, E. (2017). Defining the execution semantics of stream processing engines. Journal of Big Data, 4. https://doi.org/10.1186/s40537-017-0072-9
Agard, B., & Kusiak, A. (2004). Data-mining-based methodology for the design of product families. International Journal of Production Research, 42(15), 2955–2969. https://doi.org/10.1080/00207540410001691929
Ahmed, E., Yaqoob, I., Targio Hashem, I. A., Khan, I., Abdalla Ahmed, A. I., Imran, M., & Vasilakos, A. V. (2017). The role of big data analytics in internet of things. Computer Networks, 129(2), 459–471. https://doi.org/10.1016/j.comnet.2017.06.013
Akhtar, P., Frynas, J. G., Mellahi, K., & Ullah, S. (2019). Big Data-Savvy Teams’ skills, big data-driven actions and business performance. British Journal of Management, 30(2), 252–271. https://doi.org/10.1111/1467-8551.12333
Akhtar, P., Tse, Y. K., Khan, Z., & Rao-Nicholson, R. (2016). Data-driven and adaptive leadership contributing to sustainability: global agri-food supply chains connected with emerging markets. International Journal of Production Economics, 181(B), 392–401. https://doi.org/10.1016/j.ijpe.2015.11.013
Amabile, T. M. (1988). A model of creativity and innovation in organisations. Research in Organisational Behavior, 10, 123–167.
Amabile, T. M. (1996). Creativity in context. Routledge.
Amabile, T. M. (1983). The social psychology of creativity. Series: Springer Series in Social Psychology. R. F. Kidd (Advisory Ed.). Springer-Verlag. https://doi.org/10.1007/978-1-4612-5533-8
Anand, S. S., & Buchner, A. G. (1998). Decision support using data mining. Series: Financial Times Management Briefings (Information Technology). Financial Times/Prentice Hall.
Apple.com. (2019). ReserachKit and CareKit. https://www.apple.com/il/researchkit/
Barlacchi, G., Perentis, Ch., Mehrotra, A., Musolesi, M., & Lepri, B. (2017). Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors. EPJ Data Science, 6(27). https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0124-6
Boden, M. A. (Ed.). (1996). Dimensions of creativity. Massachusetts Institute of Technology.
Boden, M. A. (2004). The creative mind: myths and mechanisms. Routledge. https://doi.org/10.4324/9780203508527
Bogers, S., Frens, J., Kollenburg, van J., Deckers, E., & Hummels, C. (2016, 4–8 June). Connected baby bottle: a design case study towards a framework for data-enabled design. In DIS ‘16: Proceedings of the 2016 Association for Computing Machinery Conference on Designing Interactive Systems (pp. 301–311). Brisbane, Australia. Association for Computing Machinery. https://doi.org/10.1145/2901790.2901855
Bourgeois, J., Linden, van der J., Kortuem, G., Price, B. A., & Rimmer, Ch. (2014, 13–17 September). Conversations with my washing machine: An in-the-Wild study of demand shifting with selfgenerated energy. In UbiComp ‘14: The 2014 Association for Computing Machinery Conference on Ubiquitous Computing (pp. 459–470). Association for Computing Machinery. https://doi.org/10.1145/2632048.2632106
Bodker, S. (2006, 14–18 October). When second wave HCI meets third wave challenges. In A. Morch, K. Morgan, T. Bratteteig, G. Ghosh, & D. Svanaes (Eds.), NordiCHI ’06: Proceedings of the 4th Nordic Conference on Human-Computer Interaction: Changing Roles 2006 (pp. 1–8). Oslo, Norway. Association for Computing Machinery. https://doi.org/10.1145/1182475.1182476
Burnett, C. A., & Haydon, K. P. (2016). Do we need a revolutionary approach to bring creativity into education? In R. A. Beghetto & B. Sririman (Eds.), Creative contradictions in education: cross disciplinary paradoxes and perspectives. Series: Creativity Theory and Action in Education. Vol. 1 (pp. 201–220). Springer International Publishing. https://doi.org/10.1007/978-3-319-21924-0_12
Candy, L. (1996, 29–30 April). Understanding creativity: an empirical approach. In Proceedings 2nd International Symposium Creativity and Cognition (pp. 45–54). LUTCHI Research Centre, Loughborough University, United Kingdom. Loughborough University.
Cao, L. (2017). Data science: a comprehensive overview. ACM Computing Surveys, 50(3). https://doi.org/10.1145/3076253
Cao, G., Duan, Y., & El Banna, A. (2019). A dynamic capability view of marketing analytics: evidence from UK firms. Industrial Marketing Management, 76, 72–83. https://doi.org/10.1016/j.indmarman.2018.08.002
Caputo, A., Marzi, G., & Pellegrini, M. M. (2016). The internet of things in manufacturing innovation processes: development and application of a conceptual framework. Business Process Management Journal, 22(2). https://www.emerald.com/insight/content/doi/10.1108/BPMJ-05-2015-0072/full/pdf?casa_token=mvyV0qZVUokAAAAA:9BM9yiu_sJyEGt73cd-fvbVyX6e13leI_OTkAdOTPU4uTwzEeS3tBVRL-i1VIAyGRB2XCIy1VvgfN4I9GDLWIMG1mnu0-aGPvVs3nmGDpflVUjpTKHA
Casado, R., & Younas, M. (2015). Emerging trends and technologies in big data processing. Concurrency and Computation: Practice and Experience, 27(8), 2078–2091. https://doi.org/10.1002/cpe.3398
Casakin, H., & Goldschmidt, G. (1999). Expertise and the use of visual analogy: implications for design education. Design Studies, 20(2), 153–175. https://doi.org/10.1016/S0142-694X(98)00032-5
Chakrabarti, A., & Bligh, Th. P. (1994). An approach to functional synthesis of solutions in mechanical conceptual design. Part I: Introduction and Knowledge Representation. Research in Engineering Design, 6, 127–141. https://doi.org/10.1007/BF01607275
Chan, Ch.-Sh. (1990). Cognitive processes in architectural design problem solving. Design Studies, 11(2), 60–80. https://doi.org/10.1016/0142-694X(90)90021-4
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39. https://doi.org/10.1080/07421222.2015.1138364
Christiaans, H. H. C. M., & Dorst, K. H. (1992). Cognitive models in industrial design engineering: a protocol study. Design Theory and Methodology (ASME 1992), 42, 131–140.
Corazza, G. E. (2017). Organic creativity for well-being in the post-information society. European Journal of Psychology, 13(4), 599–605. https://doi.org/10.5964/ejop.v13i4.1547
Cross, N. (1996). Creativity in design: not leaping but bridging. In Conference Paper Presented at the 2nd International Symposium Creativity and Cognition. Loughborough, United Kingdom [unpublished source].
Davies, T. (2006). Creative teaching and learning in Europe: Promoting a new paradigm. The Curriculum Journal, 17(1), 37–57. https://doi.org/10.1080/09585170600682574
D’Ignazio, C., & Bhargava, R. (2016). DataBasic: design principles, tools and activities for data literacy learners. The Journal of Community Informatics, 12(3), 83–107. https://doi.org/10.15353/joci.v12i3.3280
Dong, W., Liao, Sh., & Zhang, Zh. (2018). Leveraging financial social media data for corporate fraud detection. Journal of Management Information Systems, 35(2), 461–487. https://doi.org/10.1080/07421222.2018.1451954
Dorst, K. (2017). Notes on design: how creative practice works. Ph. Crabill, L. Dorst, & L. Kaldor (Eds.). BIS Publishers.
Dorst, K. (2011). The core of “design thinking” and its application. Design Studies, 32(6), 521–532. https://doi.org/10.1016/j.destud.2011.07.006
Dorst, K., & Cross, N. (2001). Creativity in the design process: co-evolution of problem–solution. Design Studies, 22(5), 425–437. https://doi.org/10.1016/S0142-694X(01)00009-6
Dove, G., & Jones, S. (2014, 21–25 June). Using data to stimulate creative thinking in the design of new products and services. In DIS ‘14: Proceedings of the 2014 Conference on Designing Interactive Systems (pp. 443–452). Vancouver, Canada. Association for Computing Machinery. https://doi.org/10.1145/2598510.2598564
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904. https://doi.org/10.1016/j.jbusres.2015.07.001
Everly, R. (2018). How big data can help designers create a better UX. Paul Olyslager. https://www.paulolyslager.com/big-data-designers-create-ux/
Feinberg, M. (2017, 6–11 May). A design perspective on data. In CHI ‘17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 2952–2963). Denver, Colorado, United States. Association for Computing Machinery. https://doi.org/10.1145/3025453.3025837
Ferre, X., Villalba, E., Julio, H., & Zhu, H. (2017, September 25–29). Extending mobile app analytics for usability test logging. In R. Bernhaupt, G. Dalvi, A. Joshi, D. K. Balkrishan, J. O’Neill, & M. Wincler (Eds.), Human-Computer Interaction – INTERACT 2017: 16th IFIP TC 13 International Conference. Mumbai, India. Proceedings, Part III (pp. 114–131). Series: Lecture Notes in Computer Science. Vol. 10515. G. Goos, J. Hartmanis, & J. van Leeuwen (Series Eds.). Springer. https://doi.org/10.1007/978-3-319-67687-6_9
Frich, J., MacDonald Vermeulen, L., Remy, Ch., Biskjaer, M. M., & Dalsgaard, P. (2019, 5–9 May). Mapping the landscape of creativity support tools in HCI. In CHI ‘19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Paper No. 389 (pp. 1–18). Glasgow, United Kingdom. Association for Computing Machinery. https://doi.org/10.1145/3290605.3300619
Gabora, L. (2000). Toward a theory of creative inklings. In R. Ascott (Ed.), Art, technology, consciousness: Mind@Large (pp. 159–164). Intellect Books.
Gandomi, A., & Haider, M. (2015). Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Geng, X., Chu, X., & Zhang, Z. (2012). An association rule mining and maintaining approach in dynamic database for aiding product–service system conceptual design. The International Journal of Advanced Manufacturing Technology, 62, 1–13. https://doi.org/10.1007/s00170-011-3787-3
Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. S. (2016, 11–14 December). Generating valuable insights through data analytics: a moderating effects model. In Proceedings of the 37th 2016 International Conference on Information Systems (ICIS 2016), Vol. 6 (pp. 4150–4159). Dublin, Ireland. Association for Information Systems.
Giaccardi, E., Cila, N., Speed, Ch., & Caldwell, M. (2016, 4–8 June). Thing ethnography: doing design research with non-humans. In DIS ‘16: Proceedings of the 2016 Association for Computing Machinery Conference on Designing Interactive Systems (pp. 377–3870. Brisbane, Australia. Association for Computing Machinery. https://doi.org/10.1145/2901790.2901905
Goel, A. K. (1997). Design, analogy, and creativity. IEEE Expert, May/June, 62–70. https://doi.org/10.1109/64.590078
Grolinger, K., Hayes, M., Higashino, W. A., L’Heureux, A., Allison, D. S., & Capretz, M. A. M. (2014, 27 June–2 July). Challenges for MapReduce in Big Data. In Proceedings of the Institute of Electrical and Electronics Engineers 10th 2014 World Congress on Services (SERVICES 2014) (pp. 182–189). Anchorage, AK, United States. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SERVICES.2014.41
Guilford, J. P. (1967). The nature of human intelligence. Series: McGraw-Hill Series in Psychology. McGraw-Hill.
Han, J., Pei, J., Kamber, M., & Pei, J. (2012). Data mining: concepts and techniques. Series: The Morgan Kaufmann Series in Data Management Systems. Elsevier Inc.
Han, J., Shi, F., & Childs, P. R. N. (2016, 16–19 May). The combinator: a computer-based tool for idea generation. In D. Marjanović, M. Štorga, N. Pavković, N. Bojcetic, & Škec, S. (Eds.), DS 84: Proceedings of the DESIGN 2016 14th International Design Conference (pp. 639–648). Series: Design (Design Support Tools). Dubrovnik, Croatia. University of Zagreb.
Handte, M., Foell, S., Wagner, S., Kortuem, G., & Marron, P. J. (2016). An internet-of-things enabled connected navigation system for urban bus riders. IEEE Internet of Things Journal, 3(5), 735–744. https://doi.org/10.1109/JIOT.2016.2554146
Hazen, B. T., Boone, Ch. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: an introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72–80. https://doi.org/10.1016/j.ijpe.2014.04.018
Hybs, I., & Gero, J. S. (1992). An evolutionary process model of design. Design Studies, 13(3), 273–290. https://doi.org/10.1016/0142-694X(92)90216-W
Interaction Design Foundation. (2019). User Experience (UX) design. https://www.interaction-design.org/literature/topics/ux-design
Kitchens, B., Dobolyi, D., Li, J., & Abbasi, A. (2018). Advanced customer analytics: strategic value through integration of relationship-oriented Big Data. Journal of Management Information Systems, 35(2), 540–574. https://doi.org/10.1080/07421222.2018.1451957
Kolko, J. (2010). Abductive thinking and sensemaking: the drivers of design synthesis. Design Issues, 26(1), 15–28.
Krieter, Ph., & Breiter, A. (2018, 3–6 September). Analyzing mobile application usage: generating log files from mobile screen recordings. In MobileHCI ‘18: Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services. Barcelona, Spain. Association for Computing Machinery. https://dl.acm.org/doi/pdf/10.1145/3229434.3229450?casa_token=cZAl7kq-crIAAAAA:7vTnJGyh3DtMR8Lg7KFmwbm1_ruCemBs83uDlfBklbNuixpxVDhJ7exID9AUmfVV0yimBTfbzyEM
Kruger, C., & Cross, N. (2001). Modelling cognitive strategies in creative design. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.196.4477&rep=rep1&type=pdf
Landauer, M., Wurzenberger, M., Skopik, F., Settanni, G., & Filzmoser, P. (2018). Dynamic log file analysis: an unsupervised cluster evolution approach for anomaly detection. Computers and Security, 79, 94–116. https://doi.org/10.1016/j.cose.2018.08.009
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D., Christakis, N.; Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M. (2009). Computational Social Science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742
Li, J., Ni, X., Yuan, Y., & Wang, F.-Y. (2018). A hierarchical framework for ad inventory allocation in programmatic advertising markets. Electronic Commerce Research and Applications, 31, 40–51. https://doi.org/10.1016/j.elerap.2018.09.001
Literat, I., & Glăveanu, V. P. (2018). Distributed creativity on the internet: a theoretical foundation for online creative participation. International Journal of Communication, 12, 893–908.
Lozano, D. J. (2008). Metodología para la eco-innovación en el diseño para desensamblado de productos industriales. Castellon, Espana. "https://www.tesisenred.net/bitstream/handle/10803/10383/justel.pdf?sequence=1&isAllowed=y
Lubart, T. (2005). How can computers be partners in the creative process: classification and commentary on the special issue. International Journal of Human-Computer Studies, 63(4–5), 365–369. https://doi.org/10.1016/j.ijhcs.2005.04.002
Lutzenberger, J., Klein, P., Hribernik, K., & Thoben, K.-D. (2016). Improving product-service systems by exploiting information from the usage phase. A case study. Procedia CIRP, 47, 376–381. https://doi.org/10.1016/j.procir.2016.03.064
Lycett, M. (2013). “Datafication”: making sense of (Big) data in a complex world. European Journal of Information Systems, 22(4), 381–386. https://doi.org/10.1057/ejis.2013.10
Ma, J., Kwak, M., & Kim, H. M. (2014). Demand trend mining for predictive life cycle design. Journal of Cleaner Production, 68, 189–199. https://doi.org/10.1016/j.jclepro.2014.01.026
Manzini, E. (2015). Design, when everybody designs: an introduction to design for social innovation. The MIT Press. https://doi.org/10.7551/mitpress/9873.001.0001
Mellander, Ch., Florida, R., Asheim, B. T., & Gertler, M. (Eds.). (2014). The creative class goes global. Series: Regions and Cities. Routledge. https://doi.org/10.4324/9780203094945
Moran, S. (2010). The roles of creativity in society. In J. C. Kaufman & R. J. Sternberg (Eds.), The Cambridge handbook of creativity (pp. 74–90). Cambridge University Press. https://doi.org/10.1017/CBO9780511763205.006
Mortier, R., Haddadi, H., Henderson, T., McAuley, D., & Crowcroft, J. (2014). Human-data interaction: the human face of the data-driven society. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2508051
Nicholson, J. D., LaPlaca, P., Al-Abdin, A., Breese, R., & Khan, Z. (2018). What do introduction sections tell us about the intent of scholarly work: a contribution on contributions. Industrial Marketing Management, 73, 206–219. https://doi.org/10.1016/j.indmarman.2018.02.014
Nunan, D., Sibai, O., Schivinski, B., & Christodoulides, G. (2018). Reflections on “Social Media: influencing customer satisfaction in B2B Sales” and a research agenda. Industrial Marketing Management, 75, 31–36. https://doi.org/10.1016/j.indmarman.2018.03.009
Orlovska, J., Wickman, C., & Soderberg, R. (2018, 21–24 May). Big Data analysis as a new approach for usability attributes evaluation of user interfaces: an automotive industry context. In D. Marjanović, M. Štorga, S. Škec, N. Bojčetić, & N. Pavković (Eds.), DS 92: Proceedings of the DESIGN 2018 15th International Design Conference (pp. 1651–1662). Series: Design (Design Information and Knowledge). Dubrovnik, Croatia. University of Zagreb. https://doi.org/10.21278/idc.2018.0243
Osborn, A. F. (1979). Applied imagination: principles and procedures of creative problem-solving. Scribner.
Pahl, G., Beitz, W., Feldhusen, J., & Grote, K. H. (Eds.). (2007). Engineering design: a systematic approach. Springer-Verlag. https://doi.org/10.1007/978-1-84628-319-2
Pajo, S., Vandevenne, D., & Duflou, J. R. (2015, 27–30 July). Systematic online lead user identification –case study for electrical installations. In DS 80-10 Proceedings of the 20th International Conference on Engineering Design (ICED 15) (pp. 125–132). Vol. 10: Design Information and Knowledge Management. Ch. Weber, S. Husung, M. Cantamessa, G. Cascini, D. Marjanovic, & S. Venkataraman (Eds.). Milan, Italy. KU Leuven.
Perez, F. J., Espinach Orus, X., Verdaguer Pujades, N., & Tresserras Picas, J. (2002, 23–25 October). Metodología del diseño, historia y nuevas tendencias. VI Congreso Internacional de Proyectos de Ingenieria. Barcelona, Spain. https://www.aeipro.com/files/congresos/2002barcelona/ciip02_0386_0394.1915.pdf
Pink, D. H. (2006). A whole new mind: why right-brainers will rule the future. Penguin.
Plucker, J. A., Beghetto, R. A., & Dow, G. T. (2004). Why isn’t creativity more important to educational psychologists? Potentials, pitfalls, and future directions in creativity research. Educational Psychologist, 39(2), 83–96. https://doi.org/10.1207/s15326985ep3902_1
Prendiville, A., Gwilt, I., & Mitchell, V. (2017). Making sense of data through service design – opportunities and reflections. In D. Sangiorgi & A. Prendiville (Eds.), Designing for service: key issues and new directions (pp. 225–236). Bloomsbury Academic. https://doi.org/10.5040/9781474250160.ch-016
Pusala, M. K., Amini Salehi, M., Katukuri, J. R., Xie, Y., & Raghavan, V. (2016). Massive data analysis: tasks, tools, applications, and challenges. In S. Pyne, B. L. S. P. Rao, & S. B. Rao (Eds.). Big Data analytics: methods and applications (pp. 11–40). Springer International Publishing. https://doi.org/10.1007/978-81-322-3628-3_2
Quinones-Gomez, J. C. (2019). Moving away from the basic, adopting a new approach to the creative process. In F. Cavas-Martinez, B. Eynard, F. J. Fernandez Canavate, D. G. Fernandez-Pacheco, P. Morer, & V. Nigrelli (Eds.), Advances on Mechanics, Design Engineering and Manufacturing II: Proceedings of the International Joint Conference on Mechanics, Design Engineering and Advanced Manufacturing (JCM 2018) (pp. 670–679). Series: Lecture Notes in Mechanical Engineering. Springer International Publishing.
Quinones-Gomez, J. C. (2017, 28–30 September). The growing influence of design data in the design process through a methodological development. In R. Valušytė, A. Biamonti, & C. Cautela (Eds.). 4D · Designing Development/Developing Design: Conference Proceedings (pp. 178–187). Kaunas, Lithuania. Technologija.
Quinones Gomez, J. C. (2018). Supporting the creative process from data. CERN Ideasquare: Journal of Experimental Innovation, 2(2), 32–38.
Razmi, F. (2018, 6–7 September). Social network, a potential tool for UX research. In E. Bohemia, A. Kovacevic, L. Buck, P. Childs, S. Green, A. Hall, & A. Dasan (Eds.), DS 93: Proceedings of the 20th International Conference on Engineering and Product Design Education (E&PDE 2018) (pp. 86–91). Series: E&PDE (Design and Engineering Education Practices). London, United Kingdom. Oslo and Akershus University College of Applied Science.
Reinsel, D., Gantz, J., & Rydning, J. (2018). The digitization of the world: from edge to core. Data Age 2025. IDC White Paper. Doc# US44413318. https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf
Sandberg, J., & Alvesson, M. (2011). Ways of constructing research questions: gap-spotting or problematization? Organization, 18(1), 23–44. https://doi.org/10.1177/1350508410372151
Sanders, E. B.-N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. CoDesign: International Journal of CoCreation in Design and the Arts, 4(1), 5–18. https://doi.org/10.1080/15710880701875068
Santanen, E. L., Briggs, R. O., & Vreede, de G.-J. (2002, 10 January). Toward an understanding of creative solution generation. In Proceedings of the 35th Hawaii International Conference on System Sciences (HICSS) (pp. 2899–2908). Big Island, Hawaii, United States. Institute of Electrical and Electronics Engineers.
Sarkar, P., & Chakrabarti, A. (2011). Assessing design creativity. Design Studies, 32(4), 348–383. https://doi.org/10.1016/j.destud.2011.01.002
Sarkar, P., & Chakrabarti, A. (2007, 28–31 July). Development of a method for assessing design creativity. In J.-C. Bocquet (Ed.), DS 42: Proceedings of ICED 2007, the 16th International Conference on Engineering Design. Series: ICED (Innovation). Paris, France. https://www.designsociety.org/publication/25506/Development+of+a+Method+for+Assessing+Design+Creativity
Shah, J. J., Smith, S. M., & Vargas-Hernandez, N. (2003). Metrics for measuring ideation effectiveness. Design Studies, 24(2), 111–134. https://doi.org/10.1016/S0142-694X(02)00034-0
Sio, U. N., Kotovsky, K., & Cagan, J. (2015). Fixation or inspiration? A meta-analytic review of the role of examples on design processes. Design Studies, 39, 70–99. https://doi.org/10.1016/j.destud.2015.04.004
Song, Z., & Kusiak, A. (2009). Optimising product configurations with a data-mining approach. International Journal of Production Research, 47(7), 1733–1751. https://doi.org/10.1080/00207540701644235
Speed, Ch., & Oberlander, J. (2016, 27–30 June). Designing from, with and by data: introducing the ablative framework. In Proceedings of the 2016 50th Anniversary Conference of Design Research Society. Brighton, United Kingdom. https://static1.squarespace.com/static/55ca3eafe4b05bb65abd54ff/t/5752ccb42b8dde646d462f10/1465044150377/433+Speed.pdf
Srinivasan, V., & Chakrabarti, A. (2010). An integrated model of designing. Journal of Computing and Information Science in Engineering, 10. https://www.researchgate.net/publication/47757563_An_Integrated_Model_of_Designing
Targio Hashem, I. A., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: review and open research issues. Information Systems, 47, 98–115. https://doi.org/10.1016/j.is.2014.07.006
Taylor, I. A. (1959). The nature of the creative process. In P. Smith (Ed.), Creativity: An Examination of the Creative Process. A Report on the Third Communications Conference of the Art Directors Club of New York (pp. 51–82). Hastings House.
Taylor, I. A., Austin, G. D. & Sutton, D. F. (1974). A note on “instant creativity” at CPSI. Journal of Creative Behavior, 8(3), 208–210. https://doi.org/10.1002/j.2162-6057.1974.tb01126.x
Tullis, T., & Albert, B. (2013). Measuring the user experience: collecting, analyzing and presenting usabilitybmetrics. Elsevier Inc.
Uckelmann, D., Harrison, M., & Michahelles, F. (2011). An architectural approach towards the future internet of things. In D. Uckelmann, M. Harrison, & F. Michahelles (Eds.), Architecting the internet of things (pp. 1–24). Springer International Publishing. https://doi.org/10.1007/978-3-642-19157-2_1
Varshney, L. R., Pinel, F., Varshney, K. R., Bhattacharjya, D., Schorgendorfer, A., & Chee, Y.-M. (2019). A big data approach to computational creativity: the curious case of Chef Watson. IBM Journal of Research and Development, 63(1), 7:1–7:18. https://doi.org/10.1147/JRD.2019.2893905
Wallas, G. (2015). The art of thought. Solis Press.
Wan, J., Tang, Sh., Li, D., Wang, Sh., Liu, Ch., Abbas, H., & Vasilakos, A. V. (2017). A manufacturing Big Data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics, 13(4), 2039–2047. https://doi.org/10.1109/TII.2017.2670505
Ward, Th. B., Smith, S. M., & Vaid, J. (Eds.). (1997). Creative thought: an investigation of conceptual structures and processes. American Psychological Association. https://doi.org/10.1037/10227-000
Wickel, M. C., & Lindemann, U. (2015, 27–30 July). How to integrate information about past engineering changes in new change processes? In Proceedings of International Conference on Engineering Design, ICED 15. Milan, Italy. https://pdfs.semanticscholar.org/5c3e/7065d786939f9b4c62de6233f473540e165b.pdf
Xu, Zh., Frankwick, G. L., & Ramirez, E. (2016). Effects of Big Data analytics and traditional marketing analytics on new product success: a knowledge fusion perspective. Journal of Business Research, 69(5), 1562–1566. https://doi.org/10.1016/j.jbusres.2015.10.017
Yang, Y., See-To, E. W. K., & Papagiannidis, S. (2020). You Have not been archiving emails for no reason! Using Big Data analytics to cluster B2B interest in products and services and link clusters to financial performance. Industrial Marketing Management, 86, 16–29. https://doi.org/10.1016/j.indmarman.2019.01.016
Yuan, M., Price, R., Erp, van J., & Socha, J. A. O. (2018, 25–28 June). Designing with meaningful data: deep personalisation in the air travel context. In Proceedings of the Design Research Society Conference 2018. Limerick, Ireland. https://repository.tudelft.nl/islandora/object/uuid:4d942913-ec43-4c87-8ca6-de0bb66a39b9
Zaki, M. J., & Meira, W. Jr. (2014). Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press. https://doi.org/10.1017/CBO9780511810114
Zhu, F., Jiang, B., & Chou, C. C. (2016a, 12–14 April). On the development of a new design methodology for vehicle crashworthiness based on data mining theory. In Proceedings of Society of Automotive Engineers 2016 World Congress and Exhibition. Detroit, United States. Technical Paper 2016-01-1524.
Zhu, F., Kalra, A., Saif, T., Yang, Z., Yang, K. H., & King, A. I. (2016b). Parametric analysis of the biomechanical response of head subjected to the primary blast loading – a data mining approach. Computer Methods in Biomechanics and Biomedical Engineering, 19(10), 1053–1059. https://doi.org/10.1080/10255842.2015.1091887
Affetti, L., Tommasini, R., Margara, A., Cugola, G., & Della Valle, E. (2017). Defining the execution semantics of stream processing engines. Journal of Big Data, 4. https://doi.org/10.1186/s40537-017-0072-9
Agard, B., & Kusiak, A. (2004). Data-mining-based methodology for the design of product families. International Journal of Production Research, 42(15), 2955–2969. https://doi.org/10.1080/00207540410001691929
Ahmed, E., Yaqoob, I., Targio Hashem, I. A., Khan, I., Abdalla Ahmed, A. I., Imran, M., & Vasilakos, A. V. (2017). The role of big data analytics in internet of things. Computer Networks, 129(2), 459–471. https://doi.org/10.1016/j.comnet.2017.06.013
Akhtar, P., Frynas, J. G., Mellahi, K., & Ullah, S. (2019). Big Data-Savvy Teams’ skills, big data-driven actions and business performance. British Journal of Management, 30(2), 252–271. https://doi.org/10.1111/1467-8551.12333
Akhtar, P., Tse, Y. K., Khan, Z., & Rao-Nicholson, R. (2016). Data-driven and adaptive leadership contributing to sustainability: global agri-food supply chains connected with emerging markets. International Journal of Production Economics, 181(B), 392–401. https://doi.org/10.1016/j.ijpe.2015.11.013
Amabile, T. M. (1988). A model of creativity and innovation in organisations. Research in Organisational Behavior, 10, 123–167.
Amabile, T. M. (1996). Creativity in context. Routledge.
Amabile, T. M. (1983). The social psychology of creativity. Series: Springer Series in Social Psychology. R. F. Kidd (Advisory Ed.). Springer-Verlag. https://doi.org/10.1007/978-1-4612-5533-8
Anand, S. S., & Buchner, A. G. (1998). Decision support using data mining. Series: Financial Times Management Briefings (Information Technology). Financial Times/Prentice Hall.
Apple.com. (2019). ReserachKit and CareKit. https://www.apple.com/il/researchkit/
Barlacchi, G., Perentis, Ch., Mehrotra, A., Musolesi, M., & Lepri, B. (2017). Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors. EPJ Data Science, 6(27). https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-017-0124-6
Boden, M. A. (Ed.). (1996). Dimensions of creativity. Massachusetts Institute of Technology.
Boden, M. A. (2004). The creative mind: myths and mechanisms. Routledge. https://doi.org/10.4324/9780203508527
Bogers, S., Frens, J., Kollenburg, van J., Deckers, E., & Hummels, C. (2016, 4–8 June). Connected baby bottle: a design case study towards a framework for data-enabled design. In DIS ‘16: Proceedings of the 2016 Association for Computing Machinery Conference on Designing Interactive Systems (pp. 301–311). Brisbane, Australia. Association for Computing Machinery. https://doi.org/10.1145/2901790.2901855
Bourgeois, J., Linden, van der J., Kortuem, G., Price, B. A., & Rimmer, Ch. (2014, 13–17 September). Conversations with my washing machine: An in-the-Wild study of demand shifting with selfgenerated energy. In UbiComp ‘14: The 2014 Association for Computing Machinery Conference on Ubiquitous Computing (pp. 459–470). Association for Computing Machinery. https://doi.org/10.1145/2632048.2632106
Bodker, S. (2006, 14–18 October). When second wave HCI meets third wave challenges. In A. Morch, K. Morgan, T. Bratteteig, G. Ghosh, & D. Svanaes (Eds.), NordiCHI ’06: Proceedings of the 4th Nordic Conference on Human-Computer Interaction: Changing Roles 2006 (pp. 1–8). Oslo, Norway. Association for Computing Machinery. https://doi.org/10.1145/1182475.1182476
Burnett, C. A., & Haydon, K. P. (2016). Do we need a revolutionary approach to bring creativity into education? In R. A. Beghetto & B. Sririman (Eds.), Creative contradictions in education: cross disciplinary paradoxes and perspectives. Series: Creativity Theory and Action in Education. Vol. 1 (pp. 201–220). Springer International Publishing. https://doi.org/10.1007/978-3-319-21924-0_12
Candy, L. (1996, 29–30 April). Understanding creativity: an empirical approach. In Proceedings 2nd International Symposium Creativity and Cognition (pp. 45–54). LUTCHI Research Centre, Loughborough University, United Kingdom. Loughborough University.
Cao, L. (2017). Data science: a comprehensive overview. ACM Computing Surveys, 50(3). https://doi.org/10.1145/3076253
Cao, G., Duan, Y., & El Banna, A. (2019). A dynamic capability view of marketing analytics: evidence from UK firms. Industrial Marketing Management, 76, 72–83. https://doi.org/10.1016/j.indmarman.2018.08.002
Caputo, A., Marzi, G., & Pellegrini, M. M. (2016). The internet of things in manufacturing innovation processes: development and application of a conceptual framework. Business Process Management Journal, 22(2). https://www.emerald.com/insight/content/doi/10.1108/BPMJ-05-2015-0072/full/pdf?casa_token=mvyV0qZVUokAAAAA:9BM9yiu_sJyEGt73cd-fvbVyX6e13leI_OTkAdOTPU4uTwzEeS3tBVRL-i1VIAyGRB2XCIy1VvgfN4I9GDLWIMG1mnu0-aGPvVs3nmGDpflVUjpTKHA
Casado, R., & Younas, M. (2015). Emerging trends and technologies in big data processing. Concurrency and Computation: Practice and Experience, 27(8), 2078–2091. https://doi.org/10.1002/cpe.3398
Casakin, H., & Goldschmidt, G. (1999). Expertise and the use of visual analogy: implications for design education. Design Studies, 20(2), 153–175. https://doi.org/10.1016/S0142-694X(98)00032-5
Chakrabarti, A., & Bligh, Th. P. (1994). An approach to functional synthesis of solutions in mechanical conceptual design. Part I: Introduction and Knowledge Representation. Research in Engineering Design, 6, 127–141. https://doi.org/10.1007/BF01607275
Chan, Ch.-Sh. (1990). Cognitive processes in architectural design problem solving. Design Studies, 11(2), 60–80. https://doi.org/10.1016/0142-694X(90)90021-4
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39. https://doi.org/10.1080/07421222.2015.1138364
Christiaans, H. H. C. M., & Dorst, K. H. (1992). Cognitive models in industrial design engineering: a protocol study. Design Theory and Methodology (ASME 1992), 42, 131–140.
Corazza, G. E. (2017). Organic creativity for well-being in the post-information society. European Journal of Psychology, 13(4), 599–605. https://doi.org/10.5964/ejop.v13i4.1547
Cross, N. (1996). Creativity in design: not leaping but bridging. In Conference Paper Presented at the 2nd International Symposium Creativity and Cognition. Loughborough, United Kingdom [unpublished source].
Davies, T. (2006). Creative teaching and learning in Europe: Promoting a new paradigm. The Curriculum Journal, 17(1), 37–57. https://doi.org/10.1080/09585170600682574
D’Ignazio, C., & Bhargava, R. (2016). DataBasic: design principles, tools and activities for data literacy learners. The Journal of Community Informatics, 12(3), 83–107. https://doi.org/10.15353/joci.v12i3.3280
Dong, W., Liao, Sh., & Zhang, Zh. (2018). Leveraging financial social media data for corporate fraud detection. Journal of Management Information Systems, 35(2), 461–487. https://doi.org/10.1080/07421222.2018.1451954
Dorst, K. (2017). Notes on design: how creative practice works. Ph. Crabill, L. Dorst, & L. Kaldor (Eds.). BIS Publishers.
Dorst, K. (2011). The core of “design thinking” and its application. Design Studies, 32(6), 521–532. https://doi.org/10.1016/j.destud.2011.07.006
Dorst, K., & Cross, N. (2001). Creativity in the design process: co-evolution of problem–solution. Design Studies, 22(5), 425–437. https://doi.org/10.1016/S0142-694X(01)00009-6
Dove, G., & Jones, S. (2014, 21–25 June). Using data to stimulate creative thinking in the design of new products and services. In DIS ‘14: Proceedings of the 2014 Conference on Designing Interactive Systems (pp. 443–452). Vancouver, Canada. Association for Computing Machinery. https://doi.org/10.1145/2598510.2598564
Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data consumer analytics and the transformation of marketing. Journal of Business Research, 69(2), 897–904. https://doi.org/10.1016/j.jbusres.2015.07.001
Everly, R. (2018). How big data can help designers create a better UX. Paul Olyslager. https://www.paulolyslager.com/big-data-designers-create-ux/
Feinberg, M. (2017, 6–11 May). A design perspective on data. In CHI ‘17: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 2952–2963). Denver, Colorado, United States. Association for Computing Machinery. https://doi.org/10.1145/3025453.3025837
Ferre, X., Villalba, E., Julio, H., & Zhu, H. (2017, September 25–29). Extending mobile app analytics for usability test logging. In R. Bernhaupt, G. Dalvi, A. Joshi, D. K. Balkrishan, J. O’Neill, & M. Wincler (Eds.), Human-Computer Interaction – INTERACT 2017: 16th IFIP TC 13 International Conference. Mumbai, India. Proceedings, Part III (pp. 114–131). Series: Lecture Notes in Computer Science. Vol. 10515. G. Goos, J. Hartmanis, & J. van Leeuwen (Series Eds.). Springer. https://doi.org/10.1007/978-3-319-67687-6_9
Frich, J., MacDonald Vermeulen, L., Remy, Ch., Biskjaer, M. M., & Dalsgaard, P. (2019, 5–9 May). Mapping the landscape of creativity support tools in HCI. In CHI ‘19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. Paper No. 389 (pp. 1–18). Glasgow, United Kingdom. Association for Computing Machinery. https://doi.org/10.1145/3290605.3300619
Gabora, L. (2000). Toward a theory of creative inklings. In R. Ascott (Ed.), Art, technology, consciousness: Mind@Large (pp. 159–164). Intellect Books.
Gandomi, A., & Haider, M. (2015). Beyond the hype: big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144. https://doi.org/10.1016/j.ijinfomgt.2014.10.007
Geng, X., Chu, X., & Zhang, Z. (2012). An association rule mining and maintaining approach in dynamic database for aiding product–service system conceptual design. The International Journal of Advanced Manufacturing Technology, 62, 1–13. https://doi.org/10.1007/s00170-011-3787-3
Ghasemaghaei, M., Ebrahimi, S., & Hassanein, K. S. (2016, 11–14 December). Generating valuable insights through data analytics: a moderating effects model. In Proceedings of the 37th 2016 International Conference on Information Systems (ICIS 2016), Vol. 6 (pp. 4150–4159). Dublin, Ireland. Association for Information Systems.
Giaccardi, E., Cila, N., Speed, Ch., & Caldwell, M. (2016, 4–8 June). Thing ethnography: doing design research with non-humans. In DIS ‘16: Proceedings of the 2016 Association for Computing Machinery Conference on Designing Interactive Systems (pp. 377–3870. Brisbane, Australia. Association for Computing Machinery. https://doi.org/10.1145/2901790.2901905
Goel, A. K. (1997). Design, analogy, and creativity. IEEE Expert, May/June, 62–70. https://doi.org/10.1109/64.590078
Grolinger, K., Hayes, M., Higashino, W. A., L’Heureux, A., Allison, D. S., & Capretz, M. A. M. (2014, 27 June–2 July). Challenges for MapReduce in Big Data. In Proceedings of the Institute of Electrical and Electronics Engineers 10th 2014 World Congress on Services (SERVICES 2014) (pp. 182–189). Anchorage, AK, United States. Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SERVICES.2014.41
Guilford, J. P. (1967). The nature of human intelligence. Series: McGraw-Hill Series in Psychology. McGraw-Hill.
Han, J., Pei, J., Kamber, M., & Pei, J. (2012). Data mining: concepts and techniques. Series: The Morgan Kaufmann Series in Data Management Systems. Elsevier Inc.
Han, J., Shi, F., & Childs, P. R. N. (2016, 16–19 May). The combinator: a computer-based tool for idea generation. In D. Marjanović, M. Štorga, N. Pavković, N. Bojcetic, & Škec, S. (Eds.), DS 84: Proceedings of the DESIGN 2016 14th International Design Conference (pp. 639–648). Series: Design (Design Support Tools). Dubrovnik, Croatia. University of Zagreb.
Handte, M., Foell, S., Wagner, S., Kortuem, G., & Marron, P. J. (2016). An internet-of-things enabled connected navigation system for urban bus riders. IEEE Internet of Things Journal, 3(5), 735–744. https://doi.org/10.1109/JIOT.2016.2554146
Hazen, B. T., Boone, Ch. A., Ezell, J. D., & Jones-Farmer, L. A. (2014). Data quality for data science, predictive analytics, and big data in supply chain management: an introduction to the problem and suggestions for research and applications. International Journal of Production Economics, 154, 72–80. https://doi.org/10.1016/j.ijpe.2014.04.018
Hybs, I., & Gero, J. S. (1992). An evolutionary process model of design. Design Studies, 13(3), 273–290. https://doi.org/10.1016/0142-694X(92)90216-W
Interaction Design Foundation. (2019). User Experience (UX) design. https://www.interaction-design.org/literature/topics/ux-design
Kitchens, B., Dobolyi, D., Li, J., & Abbasi, A. (2018). Advanced customer analytics: strategic value through integration of relationship-oriented Big Data. Journal of Management Information Systems, 35(2), 540–574. https://doi.org/10.1080/07421222.2018.1451957
Kolko, J. (2010). Abductive thinking and sensemaking: the drivers of design synthesis. Design Issues, 26(1), 15–28.
Krieter, Ph., & Breiter, A. (2018, 3–6 September). Analyzing mobile application usage: generating log files from mobile screen recordings. In MobileHCI ‘18: Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services. Barcelona, Spain. Association for Computing Machinery. https://dl.acm.org/doi/pdf/10.1145/3229434.3229450?casa_token=cZAl7kq-crIAAAAA:7vTnJGyh3DtMR8Lg7KFmwbm1_ruCemBs83uDlfBklbNuixpxVDhJ7exID9AUmfVV0yimBTfbzyEM
Kruger, C., & Cross, N. (2001). Modelling cognitive strategies in creative design. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.196.4477&rep=rep1&type=pdf
Landauer, M., Wurzenberger, M., Skopik, F., Settanni, G., & Filzmoser, P. (2018). Dynamic log file analysis: an unsupervised cluster evolution approach for anomaly detection. Computers and Security, 79, 94–116. https://doi.org/10.1016/j.cose.2018.08.009
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.-L., Brewer, D., Christakis, N.; Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & Van Alstyne, M. (2009). Computational Social Science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742
Li, J., Ni, X., Yuan, Y., & Wang, F.-Y. (2018). A hierarchical framework for ad inventory allocation in programmatic advertising markets. Electronic Commerce Research and Applications, 31, 40–51. https://doi.org/10.1016/j.elerap.2018.09.001
Literat, I., & Glăveanu, V. P. (2018). Distributed creativity on the internet: a theoretical foundation for online creative participation. International Journal of Communication, 12, 893–908.
Lozano, D. J. (2008). Metodología para la eco-innovación en el diseño para desensamblado de productos industriales. Castellon, Espana. "https://www.tesisenred.net/bitstream/handle/10803/10383/justel.pdf?sequence=1&isAllowed=y
Lubart, T. (2005). How can computers be partners in the creative process: classification and commentary on the special issue. International Journal of Human-Computer Studies, 63(4–5), 365–369. https://doi.org/10.1016/j.ijhcs.2005.04.002
Lutzenberger, J., Klein, P., Hribernik, K., & Thoben, K.-D. (2016). Improving product-service systems by exploiting information from the usage phase. A case study. Procedia CIRP, 47, 376–381. https://doi.org/10.1016/j.procir.2016.03.064
Lycett, M. (2013). “Datafication”: making sense of (Big) data in a complex world. European Journal of Information Systems, 22(4), 381–386. https://doi.org/10.1057/ejis.2013.10
Ma, J., Kwak, M., & Kim, H. M. (2014). Demand trend mining for predictive life cycle design. Journal of Cleaner Production, 68, 189–199. https://doi.org/10.1016/j.jclepro.2014.01.026
Manzini, E. (2015). Design, when everybody designs: an introduction to design for social innovation. The MIT Press. https://doi.org/10.7551/mitpress/9873.001.0001
Mellander, Ch., Florida, R., Asheim, B. T., & Gertler, M. (Eds.). (2014). The creative class goes global. Series: Regions and Cities. Routledge. https://doi.org/10.4324/9780203094945
Moran, S. (2010). The roles of creativity in society. In J. C. Kaufman & R. J. Sternberg (Eds.), The Cambridge handbook of creativity (pp. 74–90). Cambridge University Press. https://doi.org/10.1017/CBO9780511763205.006
Mortier, R., Haddadi, H., Henderson, T., McAuley, D., & Crowcroft, J. (2014). Human-data interaction: the human face of the data-driven society. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2508051
Nicholson, J. D., LaPlaca, P., Al-Abdin, A., Breese, R., & Khan, Z. (2018). What do introduction sections tell us about the intent of scholarly work: a contribution on contributions. Industrial Marketing Management, 73, 206–219. https://doi.org/10.1016/j.indmarman.2018.02.014
Nunan, D., Sibai, O., Schivinski, B., & Christodoulides, G. (2018). Reflections on “Social Media: influencing customer satisfaction in B2B Sales” and a research agenda. Industrial Marketing Management, 75, 31–36. https://doi.org/10.1016/j.indmarman.2018.03.009
Orlovska, J., Wickman, C., & Soderberg, R. (2018, 21–24 May). Big Data analysis as a new approach for usability attributes evaluation of user interfaces: an automotive industry context. In D. Marjanović, M. Štorga, S. Škec, N. Bojčetić, & N. Pavković (Eds.), DS 92: Proceedings of the DESIGN 2018 15th International Design Conference (pp. 1651–1662). Series: Design (Design Information and Knowledge). Dubrovnik, Croatia. University of Zagreb. https://doi.org/10.21278/idc.2018.0243
Osborn, A. F. (1979). Applied imagination: principles and procedures of creative problem-solving. Scribner.
Pahl, G., Beitz, W., Feldhusen, J., & Grote, K. H. (Eds.). (2007). Engineering design: a systematic approach. Springer-Verlag. https://doi.org/10.1007/978-1-84628-319-2
Pajo, S., Vandevenne, D., & Duflou, J. R. (2015, 27–30 July). Systematic online lead user identification –case study for electrical installations. In DS 80-10 Proceedings of the 20th International Conference on Engineering Design (ICED 15) (pp. 125–132). Vol. 10: Design Information and Knowledge Management. Ch. Weber, S. Husung, M. Cantamessa, G. Cascini, D. Marjanovic, & S. Venkataraman (Eds.). Milan, Italy. KU Leuven.
Perez, F. J., Espinach Orus, X., Verdaguer Pujades, N., & Tresserras Picas, J. (2002, 23–25 October). Metodología del diseño, historia y nuevas tendencias. VI Congreso Internacional de Proyectos de Ingenieria. Barcelona, Spain. https://www.aeipro.com/files/congresos/2002barcelona/ciip02_0386_0394.1915.pdf
Pink, D. H. (2006). A whole new mind: why right-brainers will rule the future. Penguin.
Plucker, J. A., Beghetto, R. A., & Dow, G. T. (2004). Why isn’t creativity more important to educational psychologists? Potentials, pitfalls, and future directions in creativity research. Educational Psychologist, 39(2), 83–96. https://doi.org/10.1207/s15326985ep3902_1
Prendiville, A., Gwilt, I., & Mitchell, V. (2017). Making sense of data through service design – opportunities and reflections. In D. Sangiorgi & A. Prendiville (Eds.), Designing for service: key issues and new directions (pp. 225–236). Bloomsbury Academic. https://doi.org/10.5040/9781474250160.ch-016
Pusala, M. K., Amini Salehi, M., Katukuri, J. R., Xie, Y., & Raghavan, V. (2016). Massive data analysis: tasks, tools, applications, and challenges. In S. Pyne, B. L. S. P. Rao, & S. B. Rao (Eds.). Big Data analytics: methods and applications (pp. 11–40). Springer International Publishing. https://doi.org/10.1007/978-81-322-3628-3_2
Quinones-Gomez, J. C. (2019). Moving away from the basic, adopting a new approach to the creative process. In F. Cavas-Martinez, B. Eynard, F. J. Fernandez Canavate, D. G. Fernandez-Pacheco, P. Morer, & V. Nigrelli (Eds.), Advances on Mechanics, Design Engineering and Manufacturing II: Proceedings of the International Joint Conference on Mechanics, Design Engineering and Advanced Manufacturing (JCM 2018) (pp. 670–679). Series: Lecture Notes in Mechanical Engineering. Springer International Publishing.
Quinones-Gomez, J. C. (2017, 28–30 September). The growing influence of design data in the design process through a methodological development. In R. Valušytė, A. Biamonti, & C. Cautela (Eds.). 4D · Designing Development/Developing Design: Conference Proceedings (pp. 178–187). Kaunas, Lithuania. Technologija.
Quinones Gomez, J. C. (2018). Supporting the creative process from data. CERN Ideasquare: Journal of Experimental Innovation, 2(2), 32–38.
Razmi, F. (2018, 6–7 September). Social network, a potential tool for UX research. In E. Bohemia, A. Kovacevic, L. Buck, P. Childs, S. Green, A. Hall, & A. Dasan (Eds.), DS 93: Proceedings of the 20th International Conference on Engineering and Product Design Education (E&PDE 2018) (pp. 86–91). Series: E&PDE (Design and Engineering Education Practices). London, United Kingdom. Oslo and Akershus University College of Applied Science.
Reinsel, D., Gantz, J., & Rydning, J. (2018). The digitization of the world: from edge to core. Data Age 2025. IDC White Paper. Doc# US44413318. https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf
Sandberg, J., & Alvesson, M. (2011). Ways of constructing research questions: gap-spotting or problematization? Organization, 18(1), 23–44. https://doi.org/10.1177/1350508410372151
Sanders, E. B.-N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. CoDesign: International Journal of CoCreation in Design and the Arts, 4(1), 5–18. https://doi.org/10.1080/15710880701875068
Santanen, E. L., Briggs, R. O., & Vreede, de G.-J. (2002, 10 January). Toward an understanding of creative solution generation. In Proceedings of the 35th Hawaii International Conference on System Sciences (HICSS) (pp. 2899–2908). Big Island, Hawaii, United States. Institute of Electrical and Electronics Engineers.
Sarkar, P., & Chakrabarti, A. (2011). Assessing design creativity. Design Studies, 32(4), 348–383. https://doi.org/10.1016/j.destud.2011.01.002
Sarkar, P., & Chakrabarti, A. (2007, 28–31 July). Development of a method for assessing design creativity. In J.-C. Bocquet (Ed.), DS 42: Proceedings of ICED 2007, the 16th International Conference on Engineering Design. Series: ICED (Innovation). Paris, France. https://www.designsociety.org/publication/25506/Development+of+a+Method+for+Assessing+Design+Creativity
Shah, J. J., Smith, S. M., & Vargas-Hernandez, N. (2003). Metrics for measuring ideation effectiveness. Design Studies, 24(2), 111–134. https://doi.org/10.1016/S0142-694X(02)00034-0
Sio, U. N., Kotovsky, K., & Cagan, J. (2015). Fixation or inspiration? A meta-analytic review of the role of examples on design processes. Design Studies, 39, 70–99. https://doi.org/10.1016/j.destud.2015.04.004
Song, Z., & Kusiak, A. (2009). Optimising product configurations with a data-mining approach. International Journal of Production Research, 47(7), 1733–1751. https://doi.org/10.1080/00207540701644235
Speed, Ch., & Oberlander, J. (2016, 27–30 June). Designing from, with and by data: introducing the ablative framework. In Proceedings of the 2016 50th Anniversary Conference of Design Research Society. Brighton, United Kingdom. https://static1.squarespace.com/static/55ca3eafe4b05bb65abd54ff/t/5752ccb42b8dde646d462f10/1465044150377/433+Speed.pdf
Srinivasan, V., & Chakrabarti, A. (2010). An integrated model of designing. Journal of Computing and Information Science in Engineering, 10. https://www.researchgate.net/publication/47757563_An_Integrated_Model_of_Designing
Targio Hashem, I. A., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: review and open research issues. Information Systems, 47, 98–115. https://doi.org/10.1016/j.is.2014.07.006
Taylor, I. A. (1959). The nature of the creative process. In P. Smith (Ed.), Creativity: An Examination of the Creative Process. A Report on the Third Communications Conference of the Art Directors Club of New York (pp. 51–82). Hastings House.
Taylor, I. A., Austin, G. D. & Sutton, D. F. (1974). A note on “instant creativity” at CPSI. Journal of Creative Behavior, 8(3), 208–210. https://doi.org/10.1002/j.2162-6057.1974.tb01126.x
Tullis, T., & Albert, B. (2013). Measuring the user experience: collecting, analyzing and presenting usabilitybmetrics. Elsevier Inc.
Uckelmann, D., Harrison, M., & Michahelles, F. (2011). An architectural approach towards the future internet of things. In D. Uckelmann, M. Harrison, & F. Michahelles (Eds.), Architecting the internet of things (pp. 1–24). Springer International Publishing. https://doi.org/10.1007/978-3-642-19157-2_1
Varshney, L. R., Pinel, F., Varshney, K. R., Bhattacharjya, D., Schorgendorfer, A., & Chee, Y.-M. (2019). A big data approach to computational creativity: the curious case of Chef Watson. IBM Journal of Research and Development, 63(1), 7:1–7:18. https://doi.org/10.1147/JRD.2019.2893905
Wallas, G. (2015). The art of thought. Solis Press.
Wan, J., Tang, Sh., Li, D., Wang, Sh., Liu, Ch., Abbas, H., & Vasilakos, A. V. (2017). A manufacturing Big Data solution for active preventive maintenance. IEEE Transactions on Industrial Informatics, 13(4), 2039–2047. https://doi.org/10.1109/TII.2017.2670505
Ward, Th. B., Smith, S. M., & Vaid, J. (Eds.). (1997). Creative thought: an investigation of conceptual structures and processes. American Psychological Association. https://doi.org/10.1037/10227-000
Wickel, M. C., & Lindemann, U. (2015, 27–30 July). How to integrate information about past engineering changes in new change processes? In Proceedings of International Conference on Engineering Design, ICED 15. Milan, Italy. https://pdfs.semanticscholar.org/5c3e/7065d786939f9b4c62de6233f473540e165b.pdf
Xu, Zh., Frankwick, G. L., & Ramirez, E. (2016). Effects of Big Data analytics and traditional marketing analytics on new product success: a knowledge fusion perspective. Journal of Business Research, 69(5), 1562–1566. https://doi.org/10.1016/j.jbusres.2015.10.017
Yang, Y., See-To, E. W. K., & Papagiannidis, S. (2020). You Have not been archiving emails for no reason! Using Big Data analytics to cluster B2B interest in products and services and link clusters to financial performance. Industrial Marketing Management, 86, 16–29. https://doi.org/10.1016/j.indmarman.2019.01.016
Yuan, M., Price, R., Erp, van J., & Socha, J. A. O. (2018, 25–28 June). Designing with meaningful data: deep personalisation in the air travel context. In Proceedings of the Design Research Society Conference 2018. Limerick, Ireland. https://repository.tudelft.nl/islandora/object/uuid:4d942913-ec43-4c87-8ca6-de0bb66a39b9
Zaki, M. J., & Meira, W. Jr. (2014). Data mining and analysis: fundamental concepts and algorithms. Cambridge University Press. https://doi.org/10.1017/CBO9780511810114
Zhu, F., Jiang, B., & Chou, C. C. (2016a, 12–14 April). On the development of a new design methodology for vehicle crashworthiness based on data mining theory. In Proceedings of Society of Automotive Engineers 2016 World Congress and Exhibition. Detroit, United States. Technical Paper 2016-01-1524.
Zhu, F., Kalra, A., Saif, T., Yang, Z., Yang, K. H., & King, A. I. (2016b). Parametric analysis of the biomechanical response of head subjected to the primary blast loading – a data mining approach. Computer Methods in Biomechanics and Biomedical Engineering, 19(10), 1053–1059. https://doi.org/10.1080/10255842.2015.1091887