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The impact of mobility, risk, and cost on the users’ intention to adopt mobile payments

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Abstract

With the development of mobile communication technology and the wide application of intelligent devices, mobile payments with great commercial potential have been born. However, the penetration rate of mobile payment is not satisfactory. In order to explore user acceptance of mobile payments, this study proposes a new research model based on the technology acceptance model, which integrates the characteristics of mobile payments (i.e., mobility) and inhibiting factors (i.e., risk and cost). Partial least squares was performed to analyse measurement and structural models on the data collected from 245 survey samples. The results indicated that perceived mobility has a positive and direct impact on perceived ease of use and perceived usefulness, as well as an indirect impact on adoption intention; however, perceived risk and perceived cost negatively affect a user’s intention to use mobile payments. Finally, the research provides empirical evidence for practitioners to enhance the adoption of mobile payments.

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Authors and Affiliations

  1. College of Management Science, Chengdu University of Technology, Chengdu, 610059, Sichuan, China

    Yong Liu, Meng Wang, Danyu Huang, Qiang Huang, Hua Yang & Zhigang Li

Authors
  1. Yong Liu
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Appendix: Total variance explained

Appendix: Total variance explained

Component

Total

Initial eigenvalues

Extraction sums of squared loadings

Rotation sums of squared loadings

% of variance

Cumulative%

Total

% of variance

Cumulative%

Total

% of variance

Cumulative%

1

6.848

40.285

40.285

6.848

40 285

40.285

2.592

15.248

15.248

2

2.204

12.963

53.248

2.204

12.963

53.248

2.456

14.447

29.695

3

1.419

8.346

61.594

1.419

8.346

61.594

2.339

13.761

43.456

4

1.199

7.052

68.646

1.199

7.052

68.646

2.263

13.312

56.768

5

0.984

5.790

74.436

0.984

5.790

74.436

2.085

12.266

69.034

6

0.900

5.295

79.731

0.900

5.295

79.731

1.818

10.697

79.731

7

0.587

3.454

83.184

      

8

0.474

2.788

85.973

      

9

391

2.300

88.273

      

10

0.363

2.137

90.410

      

11

0.342

2.013

92.423

      

12

0.280

1.648

94.071

      

13

0.246

1.449

95.520

      

14

0.229

1.346

96.866

      

15

0.212

1.248

98.114

      

16

0.179

1.050

99.164

      

17

0.142

0.836

100.000

      
  1. Extraction method: principal component analysis

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Liu, Y., Wang, M., Huang, D. et al. The impact of mobility, risk, and cost on the users’ intention to adopt mobile payments. Inf Syst E-Bus Manage 17, 319–342 (2019). https://doi.org/10.1007/s10257-019-00449-0

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