It is difficult for the existing research methods to describe the nonlinear relationship and influence path among the users’ multiple perception constructs during the product usage. This may lead that the user perception model is not real and accurate enough. Therefore, a new method combining structural equation model (SEM) with artificial neural network (ANN) is proposed for user perception modeling. Firstly, based on the results of SEM analysis, main factors that influence user perception and the causal relationship between user perception constructs are identified; Then, the result of SEM analysis is converted to the topology of the ANN model, so that a structured artificial neural network model for user perception is established, in order to get the connection weights between the network nodes the BP (back propagation) algorithm is used to train the model; Finally, the validity of the proposed method is demonstrated by a case study of smart phone user perception modeling, the results show that the SEM-ANN model with good goodness of fit and interpretability can more accurately and quantitatively express the relationship between user perception constructs and the factors that influence user perception constructs.
YAN Bo,CHU Xuening,ZHANG Lei
. User Perception Modeling by Combining Structural Equation
Model and Artificial Neural Network[J]. Journal of Shanghai Jiaotong University, 2019
, 53(7)
: 830
-837
.
DOI: 10.16183/j.cnki.jsjtu.2019.07.009
［1］张艳河, 杨颖, 罗仕鉴, 等. 产品设计中用户感知意象的思维结构［J］. 机械工程学报, 2010, 46(2): 178-184.
ZHANG Yanhe, YANG Ying, LUO Shijian, et al. Mental construction of user perception image in pro-duct design［J］. Journal of Mechanical Engineering, 2010, 46(2): 178-184.
［2］CHEN M C, CHANG K C, HSU C L, et al. Applying a Kansei engineering-based logistics service design approach to developing international express services［J］. International Journal of Physical Distribution & Logistics Management, 2015, 45(6): 618-646.
［3］BORDAGARAY M, DELL’OLIO L, IBEAS A, et al. Modelling user perception of bus transit quality considering user and service heterogeneity［J］. Transportmetrica A Transport Science, 2014, 10(8): 705-721.
［4］王宇琪. 感性工学在可穿戴设备设计中的应用研究——以健康手环为例［D］. 上海: 华东理工大学, 2015.
WANG Yuqi. The application of kansei engineering in the design of wearable equipment——A case study in healthy bracelet［D］.Shanghai: East China University of Science and Technology, 2015.
［5］周美玉, 龚丽娜, 刘飞. 聚类模型在感性设计中的应用［J］. 机械工程学报, 2008, 44(7): 248-252.
ZHOU Meiyu, GONG Lina, LIU fei. Application of cluster model in kansei design［J］. Chinese Journal of Mechanical Engineering, 2008, 44(7): 248-252.
［6］WANG K C. A hybrid kansei engineering design expert system based on grey system theory and support vector regression［J］. Expert Systems with Applications, 2011, 38(7): 8738-8750.
［7］USHADA M, OKAYAMA T, SUYANTOHADI A, et al. Kansei engineering-based artificial neural network model to evaluate worker performance in small-medium scale food production system［J］. International Journal of Industrial and Systems Engineering, 2017, 27(1): 28-47.
［8］孙逸文, 王丽亚, 孟竹. 基于结构方程模型的功能疲劳对客户资产的影响研究［J］. 机械制造, 2012, 50(6): 1-5.
SUN Yiwen, WANG Liya, MENG Zhu. An effect resulted from feature fatigue to customer equity ana-lysis approach based on structural equation model［J］. Machinery, 2012, 50(6): 1-5.
［9］XIONG B, SKITMORE M, XIA B, et al. Examining the influence of participant performance factors on contractor satisfaction: A structural equation mo-del［J］. International Journal of Project Management, 2014, 32(3): 482-491.
［10］GHOSH D, OLEWNIK A, LEWIS K, et al. Cyber-empathic design: A data-driven framework for pro-duct design［J］. Journal of Mechanical Design, 2017, 139(9): 091401.
［11］TUU H H, OLSEN S O. Nonlinear effects between satisfaction and loyalty: An empirical study of different conceptual relationships［J］. Journal of Targeting, Measurement and Analysis for Marketing, 2010, 18(3/4): 239-251.
［12］耿秀丽, 徐士东, 叶春明. 顾客需求驱动的产品服务系统模块选配方法［J］. 计算机集成制造系统, 2016, 22(1): 55-61.
GENG Xiuli, XU Shidong, YE Chunming. Module configuration approach for product service system design driven by customer requirements［J］. Computer Integrated Manufacturing Systems, 2016, 22(1): 55-61.
［13］SHEN Y, BAINGANA B, GIANNAKIS G B. Nonlinear structural equation models for network topology inference［C］//2016 Annual Conference on Information Science and Systems (CISS). Princeton, NJ: IEEE, 2016: 163-168.
［14］MAYER A, UMBACH N, FLUNGER B, et al. Effect analysis using nonlinear structural equation mixture modeling［J］. Structural Equation Modeling: A Multidisciplinary Journal, 2017, 24(4): 556-570.
［15］LIN C A, KIM T. Predicting user response to sponsored advertising on social media via the technology acceptance model［J］. Computers in Human Behavior, 2016, 64: 710-718.
