Goodbye wallet, Hello smartphone?!

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Goodbye wallet, Hello smartphone?!

Anastasia Nanou - S3701301 - 28/06/2019

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Short description here

01

02

03

04

05

06

07 Introduction

_Discussion

Results

Conceptual model

Limitations- Future research

Theoretical framework

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13 %

37%

19 %

Europe*

Asia/Pacific

*

Africa*

 Mobile payment services globally came

up to 290 million in 2016 and are

forecasted to rise to 663 million users in

2021 (Statista 2016).

*Nielsen 2016

Introduction

01

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Introduction-

Contribution

Purpose To further examine the factors that motivate or hinder the intention to use m-payments.

 Theoretical contribution:

o Proposing a new research model based on the Technology Acceptance Model (TAM)

o Research in Europe is still in progress It was intriguing to focus on Dutch market

 Managerial contribution:

o Guide merchants and businesses on how to encourage users to adopt mobile payments.

o Improve customers’ overall shopping experience.

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Theoretical framework

02 

Technology Acceptance Model (TAM)

o

_{A framework for investigating intentions to adopt new technology (Aboelmaged and Gebba, 2013).}

o

_{Develops a relationship among ‘‘belief”, ‘‘attitude”, “ intention”, and behavior that explores the reasons}

behind the acceptance or rejection of computer technology in an organization, considering the given

benefits of computer systems (Davis et al. 1989)

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Theoretical framework

02 

Focus in the individual behavioral and social dimensions:(Mehrad & Mohammadi, 2016) using:

o

WOM: has a robust persuasive impact on consumer's purchase intention, which is associated with

credibility” (Lee et al., 2008), especially in services.

o

Technology readiness (Optimism, Innovativeness, Discomfort, Insecurity): favorable or unfavorable

perceptions, feelings, and beliefs an individual holds towards high-tech products and services

(Parasuraman, 2000).

o

Privacy concerns: lack of control on the collection and use of information acquired through online

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Theoretical framework

02 Therefore the main focus of the research is:

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Conceptual model

Control variables:

Gender

Early/late adopters

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Results- method

04 100 (94)

respondents,

students aged between

22 and 27 years

→ Use of SmartPLS method (PLS-SEM) structural

equation modelling

Why?

o Useful approach.

o No need for separate regressions.

o Offers a better understanding.

o Relatively small sample appropriate method.

Hair et al. (2014), Hair et al. (2011), Garson (2016)

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Loadings PEOU_1 0.605 PEOU_2 0.942 PEOU_3 0.953 PEOU_4 0.889 PU_1 0.860 PU_2 0.908 PU_3 0.788 PU_4 0.700 PrC_1 0.856 PrC_2 0.937 PrC_3 0.914 dis1 0.614 dis2 0.825 dis3 0.815 innovativ1 0.666 innovativ2 0.965 innovativ3 0.760 optimism1 0.711 optimism2 0.587 optimism3 0.897 insec1 0.849 insec2 0.679 insec3 0.761 wom_1 0.521 wom_2 0.831 wom_3 0.890 int_1 0.890 int_2 0.910 int_3 0.927 early_adopters_1 0.791 early_adopters_2 0.480 early_adopters_3 -0.295 early_adopters_4 0.074 late_adopters_1 0.110 late_adopters_2 0.614 late_adopters_3 0.592 late_adopters_4 0.928 sex 1.000

Results

04 → Measurement model

(Indicator reliability)

*not significant increase in Composite reliability or

AVE after deletion

Removed < 0.4

Retained ≈ 0.5-0.6

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Variables

Cronbach’s

alpha

Composite

reliability

Average Variance

Extracted (AVE)

WOM

0.652

0.801

0.585 Perceived ease of use

0.875

0.916

0.738 Perceived usefulness

0.837

0.889

0.669 Optimism

0.613

0.782

0.552 Innovativeness

0.795

0.845

0.651 Discomfort

0.639

0.799

0.574 Insecurity

0.685

0.809

0.587 Privacy concerns

0.901

0.930

0.815 Sex

1.000

1.000 Early adopters

0.489

0.796

0.661 Late adopters

0.686

0.769

0.536 Table 1: Cronbach’s alpha, Composite reliability & AVE of the constructs.

Results

04 → Measurement model

(Reliability - Validity)

 Composite reliability preferred than Cronbach’s

alpha higher estimates of actual reliability.

value

≥ 0.70  sufficient reliability

(Nunnally and Bernstein, 1994)

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Results

04  The average variance extracted for

each latent variable is indeed higher

than the variable’s highest square

correlations with any of the other

latent variables.

Fornell-Lacker

criterion

is

also

satisfied, thus discriminant validity is

satisfied.

→ Measurement model

(Discriminant Validity)

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Results

04 → Structural model

Variables Intention to

use

Perceived ease of

use Perceived usefulness

WOM 1.426 1.144 1.144

Perceived ease of use

1.844 - -Perceived usefulness 1.861 - -Optimism 1.418 1.319 1.319 Innovativeness 1.457 1.220 1.220 Discomfort 1.214 1.069 1.069 Insecurity 1.258 1.073 1.073 Privacy concerns 1.254 - -Sex 1.316 - -Early adopters 1.378 - -Late adopters 1.249 -

-Inner VIF scores

 No multicollinearity issues in the

inner model

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Results

04 → Structural model

R-square

R-square adjusted

Intention

0.519

0.459 Perceived ease of use

0.247

0.207 Perceived usefulness

0.252

0.212 Q-square

Intention

0.361 Perceived ease of use

0.139 Perceived usefulness

0.136  R-square should be > 0.1 (Falk and Miller, 1992)

→ Represent the amount of explained variance of the endogenous constructs in the structural model

.

 Q-square should be > 0 (Hair et al., 2014)

→ The model’s predictive relevance regarding the endogenous latent variables is supported

Weak

Moderate to high value

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Results

04 → Hypotheses

Significant

Not significant

 H1a, H1b, H1c H1b supported (full

mediation through PU), p= 0.010

 Among the variables of Technology

readiness:

o H2d (innovativeness PEOU):

positive and significant (p=0.051)

supported

o H2iii (discomfort Int): positive

and significant (p=0.058)



rejected

(opposed to literature)

o H2g (insecurity PU): negative

and significant (p=0.085)

 supported

o H2iv (insecurityInt): negative

and significant (p=0.084)

supported

 H3 (PU intention):positive and

significant (p=0.000)

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Conclusion-

implications

05  WOM has an effect on the Intention to use, when Perceived usefulness is present.

Thus, managers need to promote on their campaigns the usefulness of their service, in order

to instill positive thoughts to consumers.

 The positive effect of discomfort to intention was unexpected (main finding)

 Managers need to emphasize in the advantages of m-payment usage.

 Insecurity has a negative effect with PU and intention to use, as it assumed

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Limitations-Future research

06 Usage of convenience sampling method More diverse sample in terms of age,

education and employment.

Usage of quantitative methods  Field experiments and secondary data

.

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References

07

• Aboelmaged, M., Gebba, T.R., (2013), Mobile banking adoption: an examination of the technology acceptance model and the theory of planned behavior. Int. J. Bus. • Acquisti, A., & Gross, R. (2006). Imagined communities: Awareness, information sharing, and privacy on Facebook. Lecture Notes in Computer Science, 4258, 36–58.

• Bagozzi, R. (2007). The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift. Journal of the Association for Information Systems, 8(4), pp.244-254.

• BCG- Boston Consulting Group (2017). Global payments 2017- Deepening customer relationship. [online] Boston, USA. Available at: http://image-src.bcg.com/Images/BCG-Global-Payments-2017-Oct-2017_tcm108-173047.pdf [Accessed 15 Mar. 2019].

• Castañeda, Alberto & Ríos, Francisco. (2007). The effect of Internet general privacy concern on customer behavior. Electronic Commerce Research. 7.

• Davis F.D. (1989), Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q., 13(3):319–340. doi: 10.2307/249008.

• Deloitte (2017). [Available at:https://www2.deloitte.com/content/dam/Deloitte/nl/Documents/technology-media-telecommunications/2017%20GMCS%20Dutch%20Edition.pdf • Falk, R.F. and Miller, N.B. (1992), A Primer for Soft Modeling. University of Akron Press, Akron.

• Fornell, C., Larcker, D.F., (1981), Structural equation models with unobservable variables and measurement error: algebra and statistics. J. Mark. Res. 18 (3), 382–388 • Garson, G. D. (2016), Partial Least Squares: Regression and Structural Equation Models. Asheboro, NC: Statistical Associates Publishers

• Gfk.com. (2019). Smartphone steeds vaker portemonnee, minder telefoon. [online] Available at: https://www.gfk.com/insights/press-release/smartphone-steeds-vaker-portemonnee-minder-telefoon/.

• Hair, J. F.; Ringle, C. M.; & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet.

• Hair, J.F.; Hult, T.M.; Ringle, C.M. e Sarstedt, M (2014), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles: SAGE • Hair, J.F.; Hult, T.M.; Ringle, C.M. e Sarstedt, M (2014), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Los Angeles: SAGE

• Lee, J., Park, D.-H., & Han, I. (2008). The Effect of Negative Online Consumer Reviews on Product Attitude: An Information Processing View. Electronic Commerce Research and Applications, 7(3), 341-352. doi:10.1016/j.elerap

• Mehrad, D., Mohammadi, S., (2016), Word of Mouth impact on the adoption of mobile banking in Iran. Telemat. Informat.,http://dx.doi.org/10.1016/j.tele.2016.08.009. • Nunnally, J.C. and Bernstein, I.H. (1994), The Assessment of Reliability. Psychometric Theory, 3, 248-292.

• Parasuraman, A., 2000. Technology readiness index (tri): a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research 2 (4), 307–320.) Res. Dev. 2 (1), 35–50.

• Statista (2016a): Mobile Payments – Highlights Germany. Available:https://www.statista.com/outlook/331/137/mobilepayments/germany#market-users.

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Goodbye wallet, Hello smartphone?!