Counteracting the Impact of Online Fake News on Brands

Cham, Tat-Huei; Cheng, Boon Liat; Aw, Eugene Cheng-Xi; Tan, Garry Wei‐Han; Loh, Xiu-Ming; Ooi, Keng‐Boon

doi:10.1080/08874417.2023.2191350

Cited by 10 publications

(2 citation statements)

References 112 publications

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“…In order to verify that CMB is not an issue, the unmeasured latent method construct approach was conducted. It was confirmed that CMB is absent as every value of Ra was significant (p < 0.001) while Ra 2 had an average (0.778) which is significantly greater than that of Rb 2 (0.018) as shown in Table 3 Opposing forces of continuance intention rho_A, and Composite Reliability were more than 0.70 (Cham et al, 2023). Besides, all average variance extracted values were above 0.50; thereby establishing convergent validity (Leong et al, 2023).…”

Section: Common Methods Bias (Cmb)mentioning

confidence: 56%

“…The results for reliability and convergent validity are shown in Table 4. More precisely, reliability was confirmed as all values for Cronbach’s Alpha, rho_A, and Composite Reliability were more than 0.70 (Cham et al ., 2023). Besides, all average variance extracted values were above 0.50; thereby establishing convergent validity (Leong et al ., 2023).…”

Section: Resultsmentioning

confidence: 94%

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Understanding the opposing forces of continuance intention: a hybrid SEM-ANN approach

Loh,

Lee,

Leong

2024

IMDS

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PurposeThis study looks to understand the opposing forces that would influence continuance intention. This is significant as users will take into account the positive and negative use experiences in determining their continuance intention. Therefore, this study looks to highlight the opposing forces of users’ continuance intention by proposing the Expectation-Confirmation-Resistance Model (ECRM).Design/methodology/approachThrough an online survey, 411 responses were obtained from mobile payment users. Subsequently, a hybrid approach comprised of the Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) was utilized to analyze the data.FindingsThe results revealed that all hypotheses proposed in the ECRM are supported. More precisely, the facilitating and inhibiting variables were found to significantly affect continuance intention. In addition, the ECRM was revealed to possess superior explanatory power over the original model in predicting continuance intention.Originality/valueThis study successfully developed and validated the ECRM which captures both facilitators and inhibitors of continuance intention. Besides, the relevance and significance of users’ innovative resistance to continuance intention have been highlighted. Following this, effective business and research strategies can be developed by taking into account the opposing forces that affect users’ continuance intention.

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Section: Common Methods Bias (Cmb)mentioning

confidence: 56%

Section: Resultsmentioning

confidence: 94%