Data-driven innovation to capture user-experience product design: An empirical study for notebook visual aesthetics design

Computers and Industrial Engineering

Volumn 99, Issue , 2016, Pages 162-173

  Chien, Chen Fu ^a   Kerh, Rhoann ^a   Lin, Kuo Yi ^b   Yu, Annie Pei I ^c  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^b ASIA UNIVERSITY   (Taiwan)

^c NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

Author keywords

Data mining; Data driven innovation; Notebook; Rough set theory; User experience; Visual aesthetics design

Indexed keywords

CUSTOMER SATISFACTION; DATA MINING; DESIGN; ROUGH SET THEORY; SALES; USER INTERFACES;

CONSUMER PREFERENCES; DATA DRIVEN; ELECTRONICS MANUFACTURING SERVICES; NOTEBOOK; PRODUCT CHARACTERISTICS; USER EXPERIENCE; USER EXPERIENCES (UX); VISUAL AESTHETICS;

PRODUCT DESIGN;

EID: 84979707878     PISSN: 03608352     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cie.2016.07.006     Document Type: Article

References (57)

1. Altintas, N., Trick, M., A data mining approach to forecast behavior. Annals of Operations Research 216:1 (2014), 3–22.
2. Ayetiran, E.F., Adeyemo, A.B., A data mining-based response model for target selection in direct marketing. International Journal of Information Technology and Computer Science 4:1 (2012), 9–18.
3. Bae, J.K., Kim, J., Product development with data mining techniques: A case on design of digital camera. Expert Systems with Applications 38:8 (2011), 9274–9280.
4. Baumann, C., Elliott, G., Burton, S., Modeling customer satisfaction and loyalty: Survey data versus data mining. Journal of Services Marketing 26:3 (2012), 148–157.
5. Berry, M.J., Linoff, G., Data mining techniques: For marketing, sales, and customer relationship management. 2004, John Wiley & Sons, New York, USA.
6. Bloch, P.H., Brunel, F.F., Arnold, T.J., Individual differences in the centrality of visual product aesthetics: Concept and measurement. Journal of Consumer Research 29:4 (2003), 551–565.
7. Chen, L.F., Chien, C.-F., Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries. Flexible Services and Manufacturing Journal 23:3 (2011), 263–289.
8. Chen, M.C., Chiu, A.L., Chang, H.H., Mining changes in customer behavior in retail marketing. Expert Systems with Applications 28:4 (2005), 773–781.
9. Chien, C.-F., Chen, L.F., Using rough set theory to recruit and retain high-potential talents for semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing 20:4 (2007), 528–541.
10. Chien, C.-F., Chen, L.F., Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications 34:1 (2008), 280–290.
11. Chien, C.-F., Hsu, C.Y., A novel method for determining machine subgroups and backups with an empirical study for semiconductor manufacturing. Journal of Intelligent Manufacturing 17:4 (2006), 429–439.
12. Chien, C.-F., Hsu, C.Y., Chen, P.N., Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence. Flexible Services and Manufacturing Journal 25:3 (2013), 367–388.
13. Chien, C.-F., Lin, K.Y., Yu, A.P.I., User-experience of tablet operating system: An experimental investigation of Windows 8, iOS 6, and Android 4.2. Computers & Industrial Engineering 73 (2014), 75–84.
14. Chien, C.F., Wang, W.C., Cheng, J.C., Data mining for yield enhancement in semiconductor manufacturing and an empirical study. Expert Systems with Applications 33:1 (2007), 192–198.
15. Creusen, M.E.H., Research opportunities related to consumer response to product design. Journal of Product Innovation Management 28:3 (2011), 405–408.
16. D'Haen, J., Van den Poel, D., Thorleuchter, D., Predicting customer profitability during acquisition: Finding the optimal combination of data source and data mining technique. Expert Systems with Applications 40:6 (2013), 2007–2012.
17. Ewert, A., Baker, D., Standing for where you sit: An exploratory analysis of the relationship between academic major and environment beliefs. Environment and Behavior 33:5 (2001), 687–707.
18. Feng, Z., Yangjian, J., Jiao, R.J., Prospect-theoretic modeling of customer affective-cognitive decisions under uncertainty for user experience design. IEEE Transactions on Human-Machine Systems 44:4 (2014), 468–483.
19. Hassenzahl, M., Tractinsky, N., User experience – A research agenda. Behaviour & Information Technology 25:2 (2006), 91–97.
20. Hoegg, J., Alba, J.W., Seeing is believing (too much): The influence of product form on perceptions of functional performance. Journal of Product Innovation Management 28:3 (2011), 346–359.
21. Hou, T.H., Huang, C.C., Application of fuzzy logic and variable precision rough set approach in a remote monitoring manufacturing process for diagnosis rule induction. Journal of Intelligent Manufacturing 15:3 (2004), 395–408.
22. Hsu, C.Y., Chien, C.-F., Lin, K.Y., Chien, C.Y., Data mining for yield enhancement in TFT-LCD manufacturing: An empirical study. Journal of the Chinese Institute of Industrial Engineers 27:2 (2010), 140–156.
23. Kang, K., Kang, C., Hong, Y.S., Data-driven optimized vehicle-level engineering specifications. Industrial Management & Data Systems 114:3 (2014), 338–364.
24. Kass, G.V., An exploratory technique for investigating large quantities of categorical data. Applied Statistics 29 (1980), 119–127.
25. Khan, R.A., Data mining: Applications in marketing. Data Mining and Knowledge Engineering 6:3 (2014), 89–93.
26. Kuo, C.J., Chien, C.-F., Chen, J.D., Manufacturing intelligence to exploit the value of production and tool data to reduce cycle time. IEEE Transactions on Automation Science and Engineering 8:1 (2011), 103–111.
27. Kusiak, A., Rough set theory: A data mining tool for semiconductor manufacturing. IEEE Transactions on Electronics Packaging Manufacturing 24:1 (2001), 44–50.
28. Kusiak, A., Data mining: Manufacturing and service applications. International Journal of Production Research 44:18–19 (2006), 4175–4191.
29. Kusiak, A., Kern, J.A., Kernstine, K.H., Tseng, B.T.L., Autonomous decision-making: A data mining approach. IEEE Transactions on Information Technology in Biomedicine 4:4 (2000), 274–284.
30. Li, S., Nahar, K., Fung, B.M., Product customization of tablet computers based on the information of online reviews by customers. Journal of Intelligent Manufacturing 26:1 (2015), 97–110.
31. Li, Y.L., Tang, J.F., Chin, K.S., Han, Y., Luo, X.G., A rough set approach for estimating correlation measures in quality function deployment. Information Sciences 189 (2012), 126–142.
32. Liao, S.-H., Hsieh, C.-L., Huang, S.-P., Mining product maps for new product development. Expert Systems with Applications 34:1 (2008), 50–62.
33. Lin, K.-Y., Chien, C.-F., Kerh, R., UNISON framework of data-driven innovation for extracting user experience of product design of wearable devices. Computers & Industrial Engineering, 2016, 1–16, 10.1016/j.cie.2016.05.023.
34. Liou, J.J.H., Tzeng, G.-H., A dominance-based rough set approach to customer behavior in the airline market. Information Sciences 180:11 (2010), 2230–2238.
35. Liu, H., Hussain, F., Tan, C., Dash, M., Discretization: An enabling technique. Data Mining and Knowledge Discovery 6:4 (2002), 393–423.
36. Mak, B., Munakata, T., Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3. European Journal of Operational Research 136 (2002), 212–229.
37. Okudan, G., Chiu, M.C., Kim, T.H., Perceived feature utility-based product family design: A mobile phone case study. Journal of Intelligent Manufacturing 24:5 (2013), 935–949.
38. Pawlak, Z., Rough sets. International Journal of Computer and Information Sciences, 11(5), 1982, 15.
39. Pawlak, Z., Rough set approach to knowledge-based decision support. European Journal of Operational Research 99:1 (1997), 48–57.
40. Peng, J.T., Chien, C.-F., Tseng, T.L.B., Rough set theory for data mining for fault diagnosis on distribution feeder. IEE Proceedings – Generation, Transmission and Distribution 151:6 (2004), 689–697.
41. Radford, S.K., Bloch, P.H., Linking innovation to design: Consumer responses to visual product newness. Journal of Product Innovation Management 28:s1 (2011), 208–220.
42. Sangelkar, S., Cowen, N., McAdams, D., User activity – Product function association based design rules for universal products. Design Studies 33:1 (2012), 85–110.
43. Sangelkar, S., McAdams, D.A., Mining functional model graphs to find product design heuristics with inclusive design illustration. Journal of Computing and Information Science in Engineering 13:4 (2013), 041008-1–041008-13.
44. Shao, X.Y., Wang, Z.H., Li, P.G., Feng, C.X.J., Integrating data mining and rough set for customer group-based discovery of product configuration rules. International Journal of Production Research 44:14 (2006), 2789–2811.
45. Shi, F., Sun, S., Xu, J., Employing rough sets and association rule mining in KANSEI knowledge extraction. Information Sciences 196 (2012), 118–128.
46. Song, Z., Kusiak, A., Optimising product configurations with a data-mining approach. International Journal of Production Research 47:7 (2009), 1733–1751.
47. Strohmeier, S., Piazza, F., Domain driven data mining in human resource management: A review of current research. Expert Systems with Applications 40:7 (2013), 2410–2420.
48. Sundar, S.S., Bellur, S., Oh, J., Xu, Q., Jia, H., User experience of on-screen interaction techniques: An experimental investigation of clicking, sliding, zooming, hovering, dragging, and flipping. Human-Computer Interaction 29:2 (2013), 109–152.
49. Thanh-Trung, N., Viet-Long Huu, N., Phi-Khu, N., Identifying customer characteristics by using rough set theory with a new algorithm and posterior probabilities. 2012 fourth international conference on computational and information sciences (ICCIS), 2012, 594–597.
50. Tuarob, S., Tucker, C., Quantifying product favorability and extracting notable product features using large scale social media data. Journal of Computing and Information Science in Engineering 15:3 (2015), 1–12.
51. Tucker, C.S., Kim, H.M., Data-driven decision tree classification for product portfolio design optimization. Journal of Computing and Information Science in Engineering 9:4 (2009), 041004-1–041004-14.
52. Wang, C.H., Incorporating customer satisfaction into the decision-making process of product configuration: A fuzzy Kano perspective. International Journal of Production Research 51:22 (2013), 6651–6662.
53. Wang, C.H., A market-oriented approach to accomplish product positioning and product recommendation for smart phones and wearable devices. International Journal of Production Research 53:8 (2015), 2542–2553.
54. Xue, L., Yen, C.C., Towards female preferences in design – A pilot study. International Journal of Design 1:3 (2007), 11–27.
55. Yang, X., Yang, J., Wu, C., Yu, D., Dominance-based rough set approach and knowledge reductions in incomplete ordered information system. Information Sciences 178:4 (2008), 1219–1234.
56. Zhai, L.Y., Khoo, L.P., Fok, S.C., Feature extraction using rough set theory and genetic algorithms—An application for the simplification of product quality evaluation. Computers & Industrial Engineering 43:4 (2002), 661–676.
57. Zhai, L.Y., Khoo, L.P., Zhong, Z.W., A dominance-based rough set approach to Kansei Engineering in product development. Expert Systems with Applications 36:1 (2009), 393–402.