[Objective] This paper identifies the dimensions of customer interest in wearable healthcare devices and their impact on satisfaction, aiming to inspire businesses to optimize their products and services. [Methods] First, we retrieved 11,349 online reviews from Amazon.com as the corpus. Then, we used the LDA model to identify customer satisfaction dimensions. Finally, we constructed a satisfaction model using machine learning algorithms. [Results] The satisfaction model constructed with the Multi-Layer Perceptron (MLP) had the best prediction effect (F1=0.6534). Customers’ attention on products focused on 13 dimensions across seven comprehensive attributes: functionality attributes, service attributes, quality attributes, value attributes, ease of use attributes, social attributes, usefulness attributes. Functionality attributes was the most important product feature for customers. Social, quality, and service attributes had a negative impact on customer satisfaction and should be the priority for businesses to improve products and services. [Limitations] We did not consider the reviews’ authenticity and in future will include cases of false and malicious reviews in the analysis process. [Conclusions] This paper identifies the dimensions of customer attention to products, their impact on satisfaction, and the order in which improvement should be made, providing management insights for business.
Iqbal S M A, Mahgoub I, Du E, et al. Advances in Healthcare Wearable Devices[J]. npj Flexible Electronics, 2021, 5: Article No.9.
[2]
Li H, Wu J, Gao Y, et al. Examining Individuals’ Adoption of Healthcare Wearable Devices: An Empirical Study from Privacy Calculus Perspective[J]. International Journal of Medical Informatics, 2016, 88: 8-17.
doi: 10.1016/j.ijmedinf.2015.12.010
pmid: 26878757
[3]
Fortune Business Insights. Wearable Medical Devices Market Size, Share and Industry Analysis by Product, by Application, by Distribution, and Regional Forecast 2019-2026[EB/OL]. https://www.fortunebusinessinsights.com/industry-reports/wearable-medical-devices-market-101070.
[4]
Lee S M, Lee D. Healthcare Wearable Devices: An Analysis of Key Factors for Continuous Use Intention[J]. Service Business, 2020, 14(4): 503-531.
doi: 10.1007/s11628-020-00428-3
[5]
Huarng K H, Yu T H K, Lee C F. Adoption Model of Healthcare Wearable Devices[J]. Technological Forecasting and Social Change, 2022, 174: 121286.
doi: 10.1016/j.techfore.2021.121286
(Sha Jing, Zeng Gongli, et al. Personalized Game Recommendation Method Based on Implicit Feedback[J]. Journal of System Simulation, 2021, 33(4): 809-817.)
doi: 10.16182/j.issn1004731x.joss.19-0636
(Wang Fudong, Yin Qianqian, Liu Fengtao. The Identification Method of Implicit Product Feature in the Online Reviews[J]. Journal of Donghua University (Natural Science), 2019, 45(3): 451-456.)
(Wu Jiang, Zhou Lusha, Liu Guanjun, et al. The Study of Topic Mining on Online Reviews of Wearable Devices Based on LDA Model[J]. Journal of Information Resources Management, 2017, 7(3): 24-33.)
doi: 10.4018/IRMJ
(Wu Jiang, Li Shanshan, Hu Xian, et al. An Empirical Study of Users’ Engagement Intention on Healthy Wearable Devices[J]. Journal of Information Resources Management, 2017, 7(2): 22-30.)
[10]
Youm D S, Yu S Y. Factors Affecting Satisfaction of Products Implemented by Wearable Devices: Focused on Product Attributes and Customer Attributes International[J]. Journal of Pure and Applied Mathematics, 2018, 120(6): 5351-5369.
[11]
李淑芸. 可穿戴设备顾客满意度分析[D]. 武汉: 中南财经政法大学, 2019.
[11]
(Li Shuyun. Customer Satisfaction Analysis of Wearable Device[D]. Wuhan: Zhongnan University of Economics and Law, 2019.)
[12]
Kim M. Conceptualization of E-Servicescapes in the Fitness Applications and Wearable Devices Context: Multi-Dimensions, Consumer Satisfaction, and Behavioral Intention[J]. Journal of Retailing and Consumer Services, 2021, 61: 102562.
doi: 10.1016/j.jretconser.2021.102562
[13]
Liu Y P, Han M L. Determining the Key Factors of Wearable Devices Consumers’ Adoption Behavior Based on an MADM Model for Product Improvement[J]. IEEE Transactions on Engineering Management, 2022, 69(6): 4036-4051.
doi: 10.1109/TEM.2019.2960499
[14]
Jeng M Y, Yeh T M, Pai F Y. A Performance Evaluation Matrix for Measuring the Life Satisfaction of Older Adults Using eHealth Wearables[J]. Healthcare (Basel), 2022, 10(4): 605.
[15]
Jeng M Y, Yeh T M, Pai F Y. Analyzing Older Adults' Perceived Values of Using Smart Bracelets by Means-End Chain[J]. Healthcare (Basel), 2020, 8(4): 494.
[16]
Guo Y, Barnes S J, Jia Q. Mining Meaning from Online Ratings and Reviews: Tourist Satisfaction Analysis Using Latent Dirichlet Allocation[J]. Tourism Management, 2017, 59: 467-483.
doi: 10.1016/j.tourman.2016.09.009
[17]
Tirunillai S, Tellis G J. Mining Marketing Meaning from Online Chatter: Strategic Brand Analysis of Big Data Using Latent Dirichlet Allocation[J]. Journal of Marketing Research, 2014, 51(4): 463-479.
doi: 10.1509/jmr.12.0106
[18]
Archak N, Ghose A, Ipeirotis P G. Deriving the Pricing Power of Product Features by Mining Consumer Reviews[J]. Management Science, 2011, 57(8): 1485-1509.
doi: 10.1287/mnsc.1110.1370
[19]
Bi J W, Liu Y, Fan Z P, et al. Modelling Customer Satisfaction from Online Reviews Using Ensemble Neural Network and Effect-Based Kano Model[J]. International Journal of Production Research, 2019, 57(22): 7068-7088.
doi: 10.1080/00207543.2019.1574989
[20]
Bi J W, Liu Y, Fan Z P, et al. Wisdom of Crowds: Conducting Importance-Performance Analysis (IPA) Through Online Reviews[J]. Tourism Management, 2019, 70: 460-478.
doi: 10.1016/j.tourman.2018.09.010
[21]
Farhadloo M, Patterson R A, Rolland E. Modeling Customer Satisfaction from Unstructured Data Using a Bayesian Approach[J]. Decision Support Systems, 2016, 90: 1-11.
doi: 10.1016/j.dss.2016.06.010
[22]
Farhadloo M, Rolland E. Multi-Class Sentiment Analysis with Clustering and Score Representation[C]// Proceedings of the 13th International Conference on Data Mining Workshops. IEEE, 2014: 904-912.
[23]
Qi J, Zhang Z, Jeon S, et al. Mining Customer Requirements from Online Reviews: A Product Improvement Perspective[J]. Information & Management, 2016, 53(8): 951-963.
doi: 10.1016/j.im.2016.06.002
[24]
Joung J, Kim H M. Explainable Neural Network-Based Approach to Kano Categorisation of Product Features from Online Reviews[J]. International Journal of Production Research, 2022, 60(23): 7053-7073.
doi: 10.1080/00207543.2021.2000656
[25]
Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[26]
Liu X, Burns A C, Hou Y J. An Investigation of Brand-Related User-Generated Content on Twitter[J]. Journal of Advertising, 2017, 46(2): 236-247.
doi: 10.1080/00913367.2017.1297273
[27]
Deb Roy S, Mei T, Zeng W J, et al. Towards Cross-Domain Learning for Social Video Popularity Prediction[J]. IEEE Transactions on Multimedia, 2013, 15(6): 1255-1267.
doi: 10.1109/TMM.2013.2265079
[28]
Lee S M, So W Y, Youn H S. Importance-Performance Analysis of Health Perception Among Korean Adolescents During the COVID-19 Pandemic[J]. International Journal of Environmental Research and Public Health, 2021, 18(3): 1280.
doi: 10.3390/ijerph18031280
[29]
Lee S I, Yoo W J, Park H S, et al. An Empirical Study on Acceptance Intention Towards Healthcare Wearable Device[J]. The Journal of Information Systems, 2016, 25(2): 27-50.
doi: 10.5859/KAIS.2016.25.2.27
[30]
Wang A N, Zhang Q, Zhao S Y, et al. A Review-Driven Customer Preference Measurement Model for Product Improvement: Sentiment-Based Importance-Performance Analysis[J]. Information Systems and e-Business Management, 2020, 18(1): 61-88.
doi: 10.1007/s10257-020-00463-7
[31]
Ibrahim N F, Wang X J. A Text Analytics Approach for Online Retailing Service Improvement: Evidence from Twitter[J]. Decision Support Systems, 2019, 121(C): 37-50.
[32]
Mou J, Ren G, Qin C X, et al. Understanding the Topics of Export Cross-Border E-Commerce Consumers Feedback: An LDA Approach[J]. Electronic Commerce Research, 2019, 19(4): 749-777.
doi: 10.1007/s10660-019-09338-7
[33]
Anderson E W, Fornell C, Lehmann D R. Customer Satisfaction, Market Share, and Profitability: Findings from Sweden[J]. Journal of Marketing, 1994, 58(3): 53.
[34]
Oliver R L. A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions[J]. Journal of Marketing Research, 1980, 17(4): 460-469.
doi: 10.1177/002224378001700405
[35]
Woodruff R B, Cadotte E R, Jenkins R L. Modeling Consumer Satisfaction Processes Using Experience-Based Norms[J]. Journal of Marketing Research, 1983, 20(3): 296-304.
doi: 10.1177/002224378302000308
[36]
Zhou F, Ayoub J, Xu Q L, et al. A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems[J]. Journal of Mechanical Design, 2020, 142(1): 011101.
doi: 10.1115/1.4044435
[37]
Groves R M. Survey Nonresponse[M]. New York: Wiley, 2002.
[38]
Groves R M. Nonresponse Rates and Nonresponse Bias in Household Surveys[J]. Public Opinion Quarterly, 2006, 70(5): 646-675.
doi: 10.1093/poq/nfl033
[39]
Culotta A, Cutler J. Mining Brand Perceptions from Twitter Social Networks[J]. Marketing Science, 2016, 35(3): 343-362.
doi: 10.1287/mksc.2015.0968
[40]
Xiao S, Wei C P, Dong M. Crowd Intelligence: Analyzing Online Product Reviews for Preference Measurement[J]. Information & Management, 2016, 53(2): 169-182.
doi: 10.1016/j.im.2015.09.010
