Customer Satisfaction Modelling for Healthcare Wearable Devices Through Online Reviews

doi:10.11925/infotech.2096-3467.2022.0420

Data Analysis and Knowledge Discovery

2023, Vol. 7

Issue (5): 145-154 DOI: 10.11925/infotech.2096-3467.2022.0420

Current Issue | Archive | Adv Search

Customer Satisfaction Modelling for Healthcare Wearable Devices Through Online Reviews

Lin Weizhen,Liu Hongwei,Chen Yanjun,Wen Zhanming(

),Yi Minqi

School of Management, Guangdong University of Technology, Guangzhou 510520, China

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Abstract

[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.

Key words： Healthcare Wearable Devices Customer Satisfaction Online Reviews Topic Models Machine Learning

Received: 02 May 2022 Published: 04 July 2023

ZTFLH:	TP393
	G250

Fund:National Natural Science Foundation of China(71671048);National Education Science Planning Youth Project of the Ministry of Education(EIA210424);14th Five-Year Plan of Philosophy and Social Sciences of Guangdong Province 2022 Regular Projects(GD22YJY13)

Corresponding Authors: Wen Zhanming，ORCID：0000-0002-5001-334X，E-mail：wenzhanming@gdut.edu.cn。

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	Weizhen Lin
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Cite this article:

Lin Weizhen, Liu Hongwei, Chen Yanjun, Wen Zhanming, Yi Minqi. Customer Satisfaction Modelling for Healthcare Wearable Devices Through Online Reviews. Data Analysis and Knowledge Discovery, 2023, 7(5): 145-154.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0420 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I5/145

Research Review on Satisfaction with HWDs

Research Framework

Distribution of Customer Ratings

Coherence Values for Different Topic Models

CSD	关键词	参考文献	研究品牌
步数追踪	step， count， time， work， sleep， stair， walk， accurate， take， track	吴江等^[8]（2017）；吴江等^[9]（2017）； Jeng等^[14] （2022）	华为、小米、Fitbit
配送服务	delivery， family， complaint， rash， satisfied， push， fantastic， accountable， deliver	吴江等^[8]（2017）	华为、小米、Fitbit
易用性	exactly， easy， expect， work， arrive， motivator， fast， good， phone， thank	吴江等^[8]（2017）；吴江等^[9]（2017）； Kim^[12]（2021）；Youm等^[10]（2018）； Jeng等^[14-15]（2022，2020）	华为、小米、Fitbit
社交属性	gift， daughter， birthday， purchase， present， instruction， receive， friend， happy， time	吴江等^[9]（2017）；Kim^[12]（2021）	未提及
提醒功能	sleep， alarm， step， track， walk， night， wake， many， time， work	吴江等^[8]（2017）	华为、小米、Fitbit
表带卡扣	fall， band， clasp， lose， come， secure， apart， motivational， design， worth	吴江等^[8]（2017）；Kim^[12]（2021）	华为、小米、Fitbit
价格	lose， money， pound， want， spend， waste， extra， since， shape， change	吴江等^[8]（2017）；吴江等^[9]（2017）； Jeng等^[14] （2022）	华为、小米、Fitbit、荣耀
售后服务	band， month， fall， work， replacement， customer， replace， time， break， come	吴江等^[8]（2017）；吴江等^[9]（2017）； Jeng等^[14] （2022）	华为、小米、Fitbit、荣耀
续航	work， battery， month， stop， last， week， long， return， hold， life	吴江等^[8]（2017）；吴江等^[9]（2017）；李淑芸^[11]（2019）；Youm等^[10]（2018）； Jeng等^[15]（2020）	华为、小米、Fitbit
尺寸	small， large， wrist， size， band， wish， water， flex， waterproof， wear	吴江等^[8]（2017）	华为、小米、Fitbit
睡眠追踪	track， help， sleep， activity， calorie， step， exercise， weight， daily， food	吴江等^[8]（2017）；Kim^[12]（2021）	华为、小米、Fitbit
心率追踪	heart， rate， monitor， accurate， step， track， work， wear， seem	吴江等^[8]（2017）；吴江等^[9]（2017）； Jeng等^[14] （2022）	华为、小米、Fitbit
效用	everyday， motivate， move， challenge， wear， goal， work， step， friend， enjoy	吴江等^[8]（2017）；吴江等^[9]（2017）； Jeng等^[14] （2022）	华为、小米、Fitbit、荣耀

Topic Modelling Summarization of CSD

综合属性	CSD	$W j$
功能属性	步数追踪	0.108 4
	睡眠追踪	0.073 7
	心率追踪	0.180 8
	提醒功能	0.067 6
服务属性	配送服务	0.035 9
服务属性	售后服务	0.110 8
质量属性	续航	0.076 9
	尺寸	0.066 5
	表带卡扣	0.048 1
社交属性	社交属性	0.042 6
易用属性	易用性	0.054 5
价值属性	价格	0.037 4
效用属性	效用	0.096 6

Summary of Combined Attributes

Weight Distribution of CSD

Combined Attributes in Order of Customer’s Concern

模型	训练集	测试集
模型	准确率（交叉验证）	准确率	$F 1$ 值
MNLogit	0.514 8	0.505 3	0.227 6
Bayesian Ridge	0.290 3	0.321 6	0.245 7
SVM	0.498 7	0.512 7	0.222 5
DT	0.386 2	0.390 3	0.275 6
AdaBoost	0.505 0	0.501 3	0.290 1
MLP	0.740 6	0.738 6	0.653 4

Model Predictive Performance

Feature Weights Based on the MLP Customer Satisfaction Model

条件	描述	符号
I	当 $e > 0$ ，表示 $C S D i$ 提升顾客满意度	$+$
II	当 $e < 0$ ，表示 $C S D j$ 抑制顾客满意度	$-$

Conditions for Analysis of Factors Influencing Customer Satisfaction

综合属性	CSD	综合属性关注度	CSD与满意度关系
综合属性	CSD	综合属性关注度	本研究	现有文献结论	文献来源
功能属性	睡眠追踪	1	$+$	$+$	吴江等^[8]（2017）
	提醒功能	1	$+$	$+$	吴江等^[8]（2017）
	心率追踪	1	$+$	$+$	吴江等^[8]（2017）
	步数追踪	1	$+$	$+$	吴江等^[8]（2017）
服务属性	配送服务	2	$-$	$-$	李淑芸^[11]（2019）
服务属性	售后服务	2	$-$	$-$	李淑芸^[11]（2019）
效用属性	效用	3	$+$	$+$ $+$	吴江等^[8]（2017） Youm等^[10]（2018）
质量属性	尺寸	4	$+$	$-$	李淑芸^[11]（2019）
	表带卡扣	4	$-$	$-$	李淑芸^[11]（2019）
	续航	4	$-$	$-$	李淑芸^[11]（2019）
易用属性	易用性	5	$+$	$+ - - +$	Youm等^[10]（2018）吴江等^[9]（2017） Kim^[12]（2021） Jeng等^[14]（2022）吴江等^[8]（2017）
社交属性	社交属性	6	$-$	$+$ $-$	吴江等^[9]（2017） Kim^[12]（2021）
价值属性	价格	7	$+$	$+$ $-$	李淑芸^[11] （2019） Jeng等^[14]（2022）

Analysis of Factors Influencing Customer Satisfaction Based on Comprehensive Attribute

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