A study of user’s m-wallet usage behavior: The role of long-term orientation and perceived value

Figures & data

Figure 1. Proposed research framework

Table 1. Confirmatory factor analysis for measurement items

Factors/Items	Factor loadings
Personal innovativeness (PI) (α = .752, CR = .760, AVE = .515) PI1. I like to experience with new mobile apps PI2. I would look for ways to experience new mobile apps if I heard about it PI3. I am hesitant to try out new mobile apps Source: Adapted from Karjaluoto et al., 2019	621 613 865
Perceived risk (PR) (α = .849, CR = .851, AVE = .536) PR1. It is not completely secure when using a m-wallet PR2. Using m-wallet increases the risk of misusing my information PR3. It is not safe when making transactions with m-wallet PR4. I would worry about the m-wallet’s reliability PR5. M-wallet would not provide me the benefits I expected Source: Adapted from Karjaluoto et al., 2019; Singh et al., 2020	805 702 815 656 665
Perceived ease of use (PEOU) (α = .826, CR = .828, AVE = .547) PEOU1. It would be easy to use a m-wallet PEOU2. Using a m-wallet is understandable PEOU3. Using a mobile wallet is as easy as using an actual payment card PEOU4. It would be easy to learn how to use a m-wallet Source: Adapted from Shaw, 2014; Singh et al., 2020	787 763 634 724
Long-term orientation (LO) (α = .854, CR = .856, AVE = .599) LO1. I use my money carefully LO2. I work hard to be successful in the future LO3. I prefer making long-term plans LO4. Careful money management is important to me Source: Adapted from Nguyen et al., 2017; Sreen et al., 2018	681 730 760 892
Functional value (EV) (α = .812, CR = .814, AVE = .524) FV1. This m-wallet is helpful FV2. This m-wallet is necessary FV3. I can save time when I use m-wallet services FV4. I complete my payment more quickly when using m-wallet Source: Adapted from Karjaluoto et al., 2019; Ozturk et al., 2017	590 835 769 627
Social value (SV) (α = .783, CR = .783, AVE = .547) SV1. I make a good impression on others when using m-wallet SV2. I feel accepted by others when using m-wallet SV3. I use m-wallet that my relatives select Source: Adapted from Toufani et al., 2017; Pura, 2005	752 758 695
Emotional value (EV) (α = .857, CR = .858, AVE = .548) EV1. I feel happy when using m-wallet EV2. I feel good when using m-wallet EV3. I feel good that my m-wallet is superior to other m-wallet EV4. I feel my life is better since I used m-wallet EV5. Being noticed by others while using m-wallet is important to me Source: Adapted from Toufani et al., 2017; Pura, 2005	718 832 771 645 689
Commitment (COM) (α = .819, CR = .820, AVE = .534) COM1.Using m-wallet’s services makes me proud COM2. I am a loyal patron of m-wallet services COM3. M-wallet services means a lot to me COM4. I consider the m-wallet my first choice to do this type of services Source: Adapted from Karjaluoto et al., 2019; Pura, 2005	740 640 740 688
Recommendation to use (RECOMD) (α = .871, CR = .876, AVE = .639) RECOMD1. I would recommend m-wallet to my friends and family RECOMD2. If I have valueable experiences with m-wallet, I will recommend my relatives to use RECOMD3. If anyone asks me about my m-wallet, I would recommend it RECOMD4. I will talk about the strengths of m-wallets with everyone Source: Adapted from Singh et al., 2020	661 910 825 745

Table 2. Mean, correlation matrix and square roots of AVE on the diagonal^a

Constructs	Mean	SD	1	2	3	4	5	6	7	8	9
SV	0.783	0.547	0.739^a
EV	0.814	0.524	0.198	0.724
PR	0.851	0.536	−0.014	−0.109	0.732
RECOMD	0.875	0.639	0.273	0.333	−0.020	0.800
LO	0.856	0.599	0.280	0.254	0.308	0.177	0.774
PEOU	0.828	0.547	0.264	0.212	−0.071	0.358	0.100	0.739
FV	0.815	0.526	0.058	0.411	−0.061	0.306	0.302	0.244	0.725
COM	0.820	0.534	0.255	0.577	0.000	0.271	0.283	0.299	0.501	0.731
PI	0.760	0.515	0.279	0.275	−0.024	0.254	0.211	0.409	0.277	0.422	0.718

^aNumbers in bold represent the square roots of AVE

Figure 2. Structural model results

-—- not significant, *p < 0.05, **p < 0.01, ***p < 0.001

Table 3. Hypothesis testing results and fit indices of the structural model

Hypothesized path		Standardized coefficients	Results
H1a	PI -> FV	.177	Supported
H1b	PI -> SV	.160	Supported
H1c	PI -> EV	.199	Supported
H2a	PR -> FV	−.137	Supported
H2b	PR -> SV	−.067	Not supported
H2c	PR -> EV	−.137	Supported
H3a	PEOU -> FV	.148	Supported
H3b	PEOU -> SV	.184	Supported
H3c	PEOU -> EV	.112	Not supported
H4a	LO -> FV	.299	Supported
H4b	LO -> SV	.244	Supported
H4c	LO -> EV	.271	Supported
H5a	FV -> COM	.340	Supported
H6a	FV -> RECOMD	.212	Supported
H5b	SV -> COM	.159	Supported
H6b	SV -> RECOMD	.227	Supported
H5c	EV -> COM	.440	Supported
H6c	EV -> RECOMD	.218	Supported
Model fit indices		Measurement model fit: X^2/df = 1.125, GFI = 0.904, CFI = 0.985, TLI = 0.983, RMSEA = 0.020

Note. Chi-Square (χ2); df (Degrees of Freedom); Goodness of Fit Index (GFI); Comparative Fit Index (CFI); Tucker–Lewis Index (TLI); Root Mean Square EPRr Approximation (RMSEA)

Karjaluoto, H., Shaikh, A. A., Saarijärvi, H., & Saraniemi, S. (2019). How perceived value drives the use of mobile financial services apps. International Journal of Information Management, 47, 252–261. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2018.08.014

 Google Scholar

Singh, N., Sinha, N., & Liébana-Cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50, 191–205. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2019.05.022

 Google Scholar

Shaw, N. (2014). The mediating influence of trust in the adoption of the mobile wallet. Journal of Retailing and Consumer Services, 21(4), 449–459. https://doi.org/https://doi.org/10.1016/j.jretconser.2014.03.008

 Google Scholar

Nguyen, T. N., Lobo, A., & Greenland, S. (2017). Marketing Intelligence & Planning The influence of cultural values on green purchase behaviour The influence of cultural values on green purchase behaviour. Marketing Intelligence & Planning Food Journal Iss Environmental Quality: An International Journal, 35(4), 377–396. https://doi.org/10.1108/

 Google Scholar

Sreen, N., Purbey, S., & Sadarangani, P. (2018). Impact of culture, behavior and gender on green purchase intention. Journal of Retailing and Consumer Services, 41, 177–189. https://doi.org/https://doi.org/10.1016/j.jretconser.2017.12.002

 Google Scholar

Ozturk, A. B., Bilgihan, A., Salehi-Esfahani, S., & Hua, N. (2017). Understanding the mobile payment technology acceptance based on valence theory. International Journal of Contemporary Hospitality Management, 29(8), 2027–2049. https://doi.org/https://doi.org/10.1108/IJCHM-04-2016-0192

 Google Scholar

Toufani, S., Stanton, J. P., & Chikweche, T. (2017). The importance of aesthetics on customers’ intentions to purchase smartphones. Marketing Intelligence & Planning, 35(3), 316–338. https://doi.org/https://doi.org/10.1108/MIP-12-2015-0230

 Google Scholar

Pura, M. (2005). Linking perceived value and loyalty in location-based mobile services. Managing Service Quality, 15(6), 509–538. https://doi.org/https://doi.org/10.1108/09604520510634005

 Google Scholar