ABSTRACT Mobile payment is already the main financial transaction method for travellers, whereas cryptocurrency payment has emerged more recently. This study aimed to compare and reveal the intentions and research implications of the adoption of the two payment methods. The novelty of this study lies in the inclusion of the technological dimensions, perceived security, perceived risk, and perceived convenience that influence Korean and Chinese consumers’ behavioural intentions in the technology acceptance model (TAM). The results of this empirical study will help expand the industry’s perspective on and understanding of this innovation in technology, and facilitate the development of related policies in the tourism and hospitality industry.
Note 1: AVE: Average variance extracted, CR: Composite reliability. Note 2: INNO: innovativeness, COM: compatibility-mobile payment, PUM: perceived usefulness of mobile payment, PEUM: perceived ease of use of mobile payment, PSM: perceived security of mobile payment, PRM: perceived risk of mobile payment, PCM: perceived convenience of mobile payment, AM: attitude of mobile payment, IUM: intention to use mobile payment while travelling, COC: compatibility-cryptocurrency payment, PUC: perceived usefulness of cryptocurrency payment, PEUC: perceived ease of use of cryptocurrency payment, PSC: perceived security of cryptocurrency payment, PRC: perceived risk of cryptocurrency payment, PCC: perceived convenience of cryptocurrency payment, AC: attitude of cryptocurrency payment, IUC: intention to use cryptocurrency payment while travelling.
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121, CFI = 0.989, NFI = 0.911, TLI = 0.987, and RMSEA = 0.019; and Chinese samples: 2 = [df = 288, p < 0.01], 2/df = 1.285, CFI = 0.966, NFI = 0.934, TLI = 0.963, and RMSEA = 369.986, 0.024) and cryptocurrency payment (Korean samples: 2 = 348.514 [df = 288, p < 0.01], 2/df = 1.210, CFI = 0.987, NFI = 0.930, TLI = 0.984, and RMSEA = 0.025; and Chinese samples: 2 = 325.861 [df = 288, p < 0.05], 2/df = 1.131, CFI = 0.965, NFI = 0.924, TLI = 0.961, and RMSEA = 0.021) provided favourable model t statistics. All measures for both sample groups exhibited excellent factor loadings (ranging from .672 to .904). The average variance extracted (AVE) and composite condence values (CR) derived from the factor loadings were greater than the .500 and .700 thresholds recommended by Hair et al. (2021) (AVE: .504 to .780, CR: .753 to .914). The square root of AVE coecients derived from average variance extracted values ranged from .710 to .893, which was higher than the correlation coecients betw
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Thus, the measurement model oered adequate internal consistency, convergence, and discriminant validity (Fornell & Larcker, 1981; Hair et al.,2021).
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4.2. Assessment of the structural model, hypotheses testing, and indirect/total eects examination The constructed structural model was tested with an SEM analysis. The outcomes revealed that both mobile payment (Korean samples: 2 = 733.795 [df = 308, p < 0.01], 2/df = 2.382, GFI = CURRENT ISSUES IN TOURISM
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