Analyzing Continuance Intention of Health APP Users Based on Information Ecology
Zhang Min1(), Luo Meifen1, Nie Rui1, Zhang Yan2
1Center for Studies of Information Resources, Wuhan University, Wuhan 430072, China 2School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China
[Objective] This paper aims to explore the factors affecting the continuance intention of mobile health application users. [Methods] From the perspective of information ecology, we first analysed information, users, technology and information environment factors. Then we proposed a new research hypotheses model based on the expectation confirmation model (ECM). [Results] We collected user behaviour data from server logs of multiple mobile health applications and questionnaires. A total of 288 valid samples were obtained and examined with SmartPLS2.0. We found that, all original relationships from the ECM existed in the mobile environment. The accuracy and consensus of information, perceived health threats, responding time and ease of use, as well as the direct / indirect network externality of the environment all positively correlated to the confirmation and perceived usefulness of mobile health applications. The eHealth literacy of users increased confirmation but restrained perceived usefulness. [Limitations] The sample size needed to be expanded, and the conclusions should to be promoted. [Conclusions] User’s continuance behaviour of mobile health APP is influenced by the information, users, technology and environment.
张敏, 罗梅芬, 聂瑞, 张艳. 信息生态视域下移动医疗APP用户持续使用意愿分析*[J]. 数据分析与知识发现, 2017, 1(4): 46-56.
Zhang Min,Luo Meifen,Nie Rui,Zhang Yan. Analyzing Continuance Intention of Health APP Users Based on Information Ecology. Data Analysis and Knowledge Discovery, 2017, 1(4): 46-56.
Johnston & Warkentin (2010)[33]; Sun, et al. (2013)[34]
电子健康素养(eHealth Literacy, EHLI)
8
Norman, et al. (2006)[35]
响应性(Responsiveness, RESP)
4
Parasuraman, et al. (1988)[26]
易用性(Perceived Ease of Use, PEOU)
4
Davis(1989)[18]
直接网络外部性(Direct Network Externalities, DNE)
3
Zhou (2015)[30]
间接网络外部性(Indirect Network Externality, INE)
3
Zhou (2015)[30]
问卷变量设计
类别
选项
人数
比例(%)
性别
男
155
53.8
女
133
46.2
年龄
18岁以下
17
5.9
18-25岁
228
79.2
26-35岁
18
6.3
36-45岁
12
4.2
46-60岁
11
3.8
60岁以上
2
0.7
教育程度
初中及以下
8
2.8
高中或高职高专
52
18.1
大专
29
10.1
本科
187
64.9
硕士
9
3.1
博士及以上
3
1.0
样本描述统计
变量
问项
因子负载
AVE
CR
$\alpha $值
持续使用意愿CONT
CONT1
0.9559
0.9000
0.9643
0.9444
CONT2
0.9468
CONT3
0.9434
电子健康素养EHLI
EHLI1
0.8604
0.7765
0.9653
0.9588
EHLI2
0.8970
EHLI3
0.8987
EHLI4
0.8986
EHLI5
0.8922
EHLI6
0.8926
EHLI7
0.8426
EHLI8
0.8655
期望确认EXPE
EXPE1
0.9343
0.8587
0.9480
0.9177
EXPE2
0.9165
EXPE3
0.9291
一致性CONS
CONS1
0.9156
0.8269
0.9348
0.8953
CONS2
0.8972
CONS3
0.9152
准确性ACCU
ACCU1
0.9152
0.8634
0.9499
0.9208
ACCU2
0.9341
ACCU3
0.9381
间接网络外部性INE
INE1
0.9165
0.8316
0.9368
0.8988
INE2
0.9052
INE3
0.9142
易用性PEOU
PEOU1
0.8935
0.8420
0.9552
0.9373
PEOU2
0.9192
PEOU3 PEOU4
0.9420 0.9151
感知健康 威胁PHT
PHT1
0.7830
0.6443
0.9156
0.8896
PHT2
0.8553
PHT3
0.7964
PHT4
0.7723
PHT5
0.7805
PHT6
0.8252
感知有用性PU
PU1
0.9053
0.8367
0.9389
0.9024
PU2
0.9273
PU3
0.9114
响应性RESP
RESP1
0.8894
08117
0.9452
0.9226
RESP2
0.9058
RESP3
0.9230
RESP4
0.8851
直接网络外部性DNE
DNE1
0.8782
0.8341
0.9378
0.9006
DNE2
0.9331
DNE3
0.9277
满意SATI
SATI1
0.9184
0.8532
0.9458
0.9139
SATI2
0.9174
SATI3
0.9352
测量模型信度和聚合效度相关指标
CONT
EHLI
EXPE
CONS
ACCU
INE
PEOU
PU
RESP
DNE
SATI
PHT
CONT
0.9487
EHLI
0.5584
0.8812
EXPE
0.8428
0.6356
0.9267
CONS
0.6347
0.6877
0.7204
0.9093
ACCU
0.6761
0.6566
0.7664
0.8001
0.9292
INE
0.5748
0.5718
0.6408
0.6949
0.6845
0.9119
PEOU
0.5543
0.7137
0.6504
0.7680
0.7228
0.6295
0.9176
PU
0.7197
0.6005
0.7677
0.8315
0.8201
0.7306
0.7292
0.9147
RESP
0.6319
0.6438
0.7419
0.7841
0.7785
0.7442
0.7135
0.7959
0.9009
DNE
0.6242
0.5182
0.6400
0.6378
0.6173
0.6475
0.4931
0.6532
0.6386
0.9133
SATI
0.8295
0.7095
0.9046
0.7564
0.8236
0.7038
0.7047
0.8101
0.7809
0.6495
0.9237
PHT
0.5384
0.4538
0.5253
0.5260
0.4729
0.5006
0.3752
0.5341
0.5151
0.4977
0.5142
0.8027
测量模型区别效度指标
模型检验 (注: ***: p<0.001, **: p<0.01, *: p<0.05)
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