Tapping the Next Purchase: Embracing the Wave of Mobile Payment

Tew, Hui-Ting; Tan, Garry Wei–Han; Loh, Xiu-Ming; Lee, Voon‐Hsien; Lim, WeiLee; Ooi, Keng‐Boon

doi:10.1080/08874417.2020.1858731

Cited by 49 publications

(37 citation statements)

References 39 publications

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“…Meanwhile, discriminant validity is also examined using the Fornell-Larcker criterion (refer to Table 4 ) which requires the AVE of each construct to be compared to the squared inter-construct correlation of the same and other constructs in the model [11] , [12] , [13] . This is also confirmed in Table 5 displaying the individual items of the construct's outer loadings are higher than the cross-loadings of other constructs [ 10 , 14 ]. Issues for collinearity are also checked by examining the variance inflation factor (VIF) where constructs should display a value lower than 5 as recommended [ 5 , 15 ].…”

Section: Data Descriptionsupporting

confidence: 58%

“…The independent variables examined are perceived ubiquity (PQ), perceived usefulness (PU), and perceived ease of use (PEOU) while the mediator is perceived enjoyment (PE), and the dependent variable is the intention to adopt m-commerce (INT). The reliability analysis found the constructs to be above the 0.70 thresholds indicating internal consistency reliability [4] , [5] , [6] , [7] while convergent validity is established with AVE value above 0.5 [8] , [9] , [10] . Meanwhile, discriminant validity is also examined using the Fornell-Larcker criterion (refer to Table 4 ) which requires the AVE of each construct to be compared to the squared inter-construct correlation of the same and other constructs in the model [11] , [12] , [13] .…”

Section: Data Descriptionmentioning

confidence: 95%

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M-commerce adoption among youths in Malaysia: Dataset article

et al. 2022

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Section: Data Descriptionsupporting

confidence: 58%

Section: Data Descriptionmentioning

confidence: 95%

M-commerce adoption among youths in Malaysia: Dataset article

et al. 2022

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“…However, Tan and Ooi ( 2018 , p. 1627) mentioned that an “outer loading between 0.4 and 0.7 can be accepted if other indicators with high loading can explain 50% of the AVE”. Therefore, all items were retained as all AVE values exceeded 0.50 (Tew et al, 2021 ). Moreover, this study investigated the discriminant validity (DV) by assessing the Hetero-Trait-Mono-Trait ratio of correlations (HTMT .85 ) (Henseler et al, 2015 ).…”

Section: Discussionmentioning

confidence: 99%

The Dark Side of Mobile Learning via Social Media: How Bad Can It Get?

et al. 2021

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As the COVID-19 pandemic continues to spread at an unprecedented rate, many universities around the world halted physical forms of teaching and learning to stop the spread of the virus. As a result, many university students were forced to utilize online learning through channels such as mobile social media. Due to the novelty of this situation, there are many unknowns particularly with the negative influences of mobile learning via social media on university students. Thus, this study looks to examine this subject matter from the perspective of the stimulus–organism–response theory. The uniquely developed research model included four stimuli (i.e., social overload, information overload, life invasion, and privacy invasion), two organisms (i.e., technostress and exhaustion) as well as a response in terms of reduced intention to use mobile learning via social media. The responses were collected from 384 university students via an online survey and analyzed with the Partial-Least-Square-Structural-Equation-Modelling. It was found that the antecedents for both technostress and exhaustion were able to account for more than half of their respective variances. Furthermore, technostress and exhaustion were significant facilitators of the students’ reduced intention to use mobile learning via social media. In addition to the practical insights for stakeholders in the education industry, this study also posited several theoretical implications for researchers.

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“…With reference to Table 3, all Ra values were statistically significant at p < 0.001 whereas Ra 2 (0.771) is higher than Rb 2 (0.027). Hence, CMB was determined to be absent (Tew et al, 2021). 4, the constructs were found to be reliable as all values for Cronbach's alpha, rho_A and composite reliability are above 0.7 (Yan et al, 2021).…”

Section: Common Methods Bias (Cmb)mentioning

confidence: 98%

The cognitive-affective nexus on mobile payment continuance intention during the COVID-19 pandemic

Loh

Lee

Hew

et al. 2022

IJBM

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PurposeThis study examines the antecedents of continuance intention to use mobile payment in the midst of a pandemic. In general, the cognitive-affective-conative (CAC) framework was used as the theoretical base. More specifically, the dynamic interrelationships between the cognitive and affective constructs were derived from a penta-dimensional perspective.Design/methodology/approachAn online survey yielded 307 responses from youths who were utilizing mobile payment through an online survey which were then analyzed using structural equation modeling (SEM) and artificial neural network (ANN).FindingsBoth positive and negative affective constructs significantly influence continuance intention and can also serve as mediators to the cognitive variables. Interestingly, price savings and referent network size were revealed to be sources of technostress. In addition, despite not having a significant direct influence, price savings should not be overlooked given its indirect significance on continuance intention.Originality/valueBased on the CAC framework, the constructs were conceptualized according to the respective dimensions to develop this study's research model. It was then used to examine their influences on the continuance intention to use mobile payment in the midst of a pandemic. Moreover, a few novel hypotheses were proposed, and the findings serve to increase the understanding of this subject matter.

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Tapping the Next Purchase: Embracing the Wave of Mobile Payment

Cited by 49 publications

References 39 publications

M-commerce adoption among youths in Malaysia: Dataset article

M-commerce adoption among youths in Malaysia: Dataset article

The Dark Side of Mobile Learning via Social Media: How Bad Can It Get?

The cognitive-affective nexus on mobile payment continuance intention during the COVID-19 pandemic

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