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

A framework of data-driven product design is developed to capture user-experience effectively.An empirical study was conducted to derive useful rules for notebook visual aesthetics design.Specific rules are employed to support product design and validated the proposed approach in real settings. Visual aesthetics is a critical factor of new product design to capture customer attention and create positive emotional reaction to enhance the customer satisfaction. Understanding user preferences in terms of product visual aesthetics and the factors affecting user experience (UX) is crucial for the product designers to enhance customer satisfaction. However, few studies have been done to identify the relationship between product characteristics of visual aesthetics and the UX reaction. This study aims to propose a framework of data-driven product design for capturing product visual aesthetics UX to effectively identify the useful design concepts from consumer preferences to consumer response. In order to validate the proposed framework, an empirical study in cooperation with a world leading electronics manufacturing service (EMS) company was conducted. The derived rules can assist the designers to design notebook visual aesthetics and develop promotion strategies to corresponding segments of different customers. The results have shown the practical feasibility of the proposed framework that has been implemented in this case company.

[1]  Dirk Thorleuchter,et al.  Predicting customer profitability during acquisition: Finding the optimal combination of data source and data mining technique , 2013, Expert Syst. Appl..

[2]  Qian Xu,et al.  User Experience of On-Screen Interaction Techniques: An Experimental Investigation of Clicking, Sliding, Zooming, Hovering, Dragging, and Flipping , 2014, Hum. Comput. Interact..

[3]  Chris Baumann,et al.  Modeling customer satisfaction and loyalty: survey data versus data mining , 2012 .

[4]  Tung-Hsu Hou,et al.  Application of fuzzy logic and variable precision rough set approach in a remote monitoring manufacturing process for diagnosis rule induction , 2004, J. Intell. Manuf..

[5]  Conrad S. Tucker,et al.  Data-Driven Decision Tree Classification for Product Portfolio Design Optimization , 2009, J. Comput. Inf. Sci. Eng..

[6]  Daniel A. McAdams,et al.  Mining Functional Model Graphs to Find Product Design Heuristics With Inclusive Design Illustration , 2013, J. Comput. Inf. Sci. Eng..

[7]  Mariëlle E. H. Creusen.  Research Opportunities Related to Consumer Response to Product Design , 2011 .

[8]  Conrad S. Tucker,et al.  Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data , 2015, J. Comput. Inf. Sci. Eng..

[9]  Gwo-Hshiung Tzeng,et al.  A Dominance-based Rough Set Approach to customer behavior in the airline market , 2010, Inf. Sci..

[10]  Chen-Fu Chien,et al.  Rough set theory for data mining for fault diagnosis on distribution feeder , 2004 .

[11]  Chen-Fu Chien,et al.  UNISON framework of data-driven innovation for extracting user experience of product design of wearable devices , 2016, Comput. Ind. Eng..

[12]  Michael J. A. Berry,et al.  Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management , 2004 .

[13]  Yi Han,et al.  A rough set approach for estimating correlation measures in quality function deployment , 2012, Inf. Sci..

[14]  Gül E. Okudan Kremer,et al.  Perceived feature utility-based product family design: a mobile phone case study , 2013, J. Intell. Manuf..

[15]  Huan Liu,et al.  Discretization: An Enabling Technique , 2002, Data Mining and Knowledge Discovery.

[16]  Chen-Fu Chien,et al.  User-experience of tablet operating system: An experimental investigation of Windows 8, iOS 6, and Android 4.2 , 2014, Comput. Ind. Eng..

[17]  Xinyu Shao,et al.  Integrating data mining and rough set for customer group-based discovery of product configuration rules , 2006 .

[18]  Mu-Chen Chen,et al.  Mining changes in customer behavior in retail marketing , 2005, Expert Syst. Appl..

[19]  Chen-Fu Chien,et al.  Manufacturing intelligence for class prediction and rule generation to support human capital decisions for high-tech industries , 2011 .

[20]  Jae Kwon Bae,et al.  Product development with data mining techniques: A case on design of digital camera , 2011, Expert Syst. Appl..

[21]  Feng Zhou,et al.  Prospect-Theoretic Modeling of Customer Affective-Cognitive Decisions Under Uncertainty for User Experience Design , 2014, IEEE Transactions on Human-Machine Systems.

[22]  Andrew Kusiak,et al.  Rough set theory: a data mining tool for semiconductor manufacturing , 2001 .

[23]  Shraddha Sangelkar,et al.  User activity – product function association based design rules for universal products , 2012 .

[24]  Peter H. Bloch,et al.  Linking Innovation to Design: Consumer Responses to Visual Product Newness* , 2011 .

[25]  Chih-Hsuan Wang,et al.  Incorporating customer satisfaction into the decision-making process of product configuration: a fuzzy Kano perspective , 2013 .

[26]  Chen-Fu Chien,et al.  Using Rough Set Theory to Recruit and Retain High-Potential Talents for Semiconductor Manufacturing , 2007, IEEE Transactions on Semiconductor Manufacturing.

[27]  Toshinori Munakata,et al.  Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3 , 2002, Eur. J. Oper. Res..

[28]  Ching Chiuan Yen,et al.  Towards Female Preferences in Design - A Pilot Study , 2007 .

[29]  Chen-Fu Chien,et al.  Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence , 2012, Flexible Services and Manufacturing Journal.

[30]  Chia-Yu Hsu,et al.  Data mining for yield enhancement in TFT-LCD manufacturing: an empirical study , 2010 .

[31]  Chen-Fu Chien,et al.  A novel method for determining machine subgroups and backups with an empirical study for semiconductor manufacturing , 2006, J. Intell. Manuf..

[32]  Thanh-Trung Nguyen,et al.  Identifying Customer Characteristics by Using Rough Set Theory with a New Algorithm and Posterior Probabilities , 2012, 2012 Fourth International Conference on Computational and Information Sciences.

[33]  Andrew Kusiak,et al.  Autonomous decision-making: a data mining approach , 2000, IEEE Transactions on Information Technology in Biomedicine.

[34]  Eniafe Festus Ayetiran,et al.  A Data Mining-Based Response Model for Target Selection in Direct Marketing , 2012 .

[35]  Todd J. Arnold,et al.  Individual Differences in the Centrality of Visual Product Aesthetics: Concept and Measurement , 2003 .

[36]  Chih-Hsuan Wang,et al.  A market-oriented approach to accomplish product positioning and product recommendation for smart phones and wearable devices , 2015 .

[37]  G. V. Kass.  An Exploratory Technique for Investigating Large Quantities of Categorical Data , 1980 .

[38]  Alan Ewert,et al.  Standing for Where You Sit , 2001 .

[39]  Yoo Suk Hong,et al.  Data-driven optimized vehicle-level engineering specifications , 2014, Ind. Manag. Data Syst..

[40]  Joseph W. Alba,et al.  Seeing Is Believing (Too Much): The Influence of Product Form on Perceptions of Functional Performance , 2011 .

[41]  Benjamin C. M. Fung,et al.  Product customization of tablet computers based on the information of online reviews by customers , 2013, Journal of Intelligent Manufacturing.

[42]  Marc Hassenzahl,et al.  User experience - a research agenda , 2006, Behav. Inf. Technol..

[43]  Shu-Hsien Liao,et al.  Mining product maps for new product development , 2008, Expert Syst. Appl..

[44]  Andrew Kusiak,et al.  Optimising product configurations with a data-mining approach , 2009 .

[45]  Rafi Ahmad Khan.  Data Mining: Applications in Marketing , 2014 .

[46]  Z. Pawlak.  Rough set approach to knowledge-based decision support , 1997 .

[47]  Chen-Fu Chien,et al.  Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry , 2008, Expert Syst. Appl..

[48]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[49]  Jing-Yu Yang,et al.  Dominance-based rough set approach and knowledge reductions in incomplete ordered information system , 2008, Inf. Sci..

[50]  Li Pheng Khoo,et al.  Feature extraction using rough set theory and genetic algorithms--an application for the simplification of product quality evaluation , 2002 .

[51]  Michael A. Trick,et al.  A data mining approach to forecast behavior , 2014, Ann. Oper. Res..

[52]  Jiang Xu,et al.  Employing rough sets and association rule mining in KANSEI knowledge extraction , 2012, Inf. Sci..

[53]  Wen-Chih Wang,et al.  Data mining for yield enhancement in semiconductor manufacturing and an empirical study , 2007, Expert Syst. Appl..

[54]  Li Pheng Khoo,et al.  A dominance-based rough set approach to Kansei Engineering in product development , 2009, Expert Syst. Appl..

[55]  Chen-Fu Chien,et al.  Manufacturing Intelligence to Exploit the Value of Production and Tool Data to Reduce Cycle Time , 2011, IEEE Transactions on Automation Science and Engineering.

[56]  Stefan Strohmeier,et al.  Domain-Driven Data Mining in Human Resource Management: A Review , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[57]  Andrew Kusiak,et al.  Data mining: manufacturing and service applications , 2006 .