Crystal T. Lee and Ling-Yen Pan
Sellers view facial recognition mobile payment services (FRMPS) as a convenient and cost-saving way to receive immediate payments from customers. For consumers, however, these…
Abstract
Purpose
Sellers view facial recognition mobile payment services (FRMPS) as a convenient and cost-saving way to receive immediate payments from customers. For consumers, however, these biometric identification technologies raise issues of usability as well as privacy, so FRMPS are not always preferable. This study uses the stressor–strain–outcome (S–S–O) framework to illuminate the underlying mechanism of FRMPS resistance, thereby addressing the paucity of research on users' negative attitudes toward FRMPS.
Design/methodology/approach
Drawing from the stressor–strain–outcome (S–S–O) framework, the purpose of this study is to illuminate the underlying mechanism of FRMPS resistance. To this end, they invited 566 password authentication users who had refused to use FRMPS to complete online survey questionnaires.
Findings
The findings enrich the understanding of FRMPS resistance and show that stressors (i.e. system feature overload, information overload, technological uncertainty, privacy concern and perceived risk) aggravate the strain (i.e. technostress), which then leads to users’ resistance behaviors and negative word of mouth.
Originality/value
Advances in payment methods have profoundly changed consumers’ consumption and payment habits. Understanding FRMPS resistance can provide marketers with strategies for dealing with this negative impact. This study theoretically confirms the S–S–O paradigm in the FRMPS setting and advances it by proposing thorough explanations of the major stressors that consumers face. Building on their findings, the authors suggest ways service providers can eliminate the stressors, thereby reducing consumers’ fear and preventing resistance or negative word-of-mouth behaviors. This study has valuable implications for both scholars and practitioners.
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Yung-Cheng Shen, Crystal T. Lee, Ling-Yen Pan and Chung-Yuan Lee
Dealing with online rumors or fake information on social media is growing in importance. Most academic research on online rumors has approached the issue from a quantitative…
Abstract
Purpose
Dealing with online rumors or fake information on social media is growing in importance. Most academic research on online rumors has approached the issue from a quantitative modeling perspective. Less attention has been paid to the psychological mechanisms accounting for online rumor transmission behavior on the individual level. Drawing from the theory of stimulus–organism–response, this study aims to explore the nature of online rumors and investigate how the informational characteristics of online rumors are processed through the mediation of psychological variables to promote online rumor forwarding.
Design/methodology/approach
An experimental approach to this issue was taken; the researchers investigated how the informational characteristics of online rumors and the psychological mediators promote online rumor transmission.
Findings
Four information characteristics (sense-making, funniness, dreadfulness and personal relevance) and three psychological motivators (fact-finding, relationship enhancement and self-enhancement) promote online rumor-forwarding behavior.
Originality/value
Because any online rumor transmitted on social media can go viral, companies may eventually encounter social media-driven crises. Thus, understanding what drives rumor-forwarding behavior can help marketers mitigate and counter online rumors.
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Crystal T. Lee, Ling-Yen Pan and Sara H. Hsieh
This study investigates the determinants of effective human and artificial intelligence (AI) relationship-building strategies for brands. It explores the antecedents and…
Abstract
Purpose
This study investigates the determinants of effective human and artificial intelligence (AI) relationship-building strategies for brands. It explores the antecedents and consequences of consumers' interactant satisfaction with communication and identifies ways to enhance consumer purchase intention via AI chatbot promotion.
Design/methodology/approach
Microsoft Xiaoice served as the focal AI chatbot, and 331 valid samples were obtained. A two-stage structural equation modeling-artificial neural network approach was adopted to verify the proposed theoretical model.
Findings
Regarding the IQ (intelligence quotient) and EQ (emotional quotient) of AI chatbots, the multi-dimensional social support model helps explain consumers' interactant satisfaction with communication, which facilitates affective attachment and purchase intention. The results also show that chatbots should emphasize emotional and esteem social support more than informational support.
Practical implications
Brands should focus more on AI chatbots' emotional and empathetic responses than functional aspects when designing dialogue content for human–AI interactions. Well-designed AI chatbots can help marketers develop effective brand promotion strategies.
Originality/value
This research enriches the human–AI interaction literature by adopting a multi-dimensional social support theoretical lens that can enhance the interactant satisfaction with communication, affective attachment and purchase intention of AI chatbot users.
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Crystal T. Lee and Ling-Yen Pan
Financial technology (FinTech) is undergoing a transformation as a result of robotics and artificial intelligence. FinTech service providers are embracing contactless technology…
Abstract
Purpose
Financial technology (FinTech) is undergoing a transformation as a result of robotics and artificial intelligence. FinTech service providers are embracing contactless technology, including the development and widespread adoption of innovative payment service. Among the many types of contactless payment services, facial recognition payment (FRP) has gained in popularity. To capitalize on this rising popularity, comprehending the mechanisms underlying continuous usage intention toward FRP is essential. Drawing from the stimulus–organism–response (S-O-R) model, this study investigates how FRP attributes facilitate continuous usage intention.
Design/methodology/approach
In total, 321 Chinese FRP users completed an online survey. Partial least squares structural equation modeling analyzed the results of the survey.
Findings
The results reveal that relative advantage and compatibility, user-interface attractiveness and perceived security (stimuli) promote performance expectancy, effort expectancy and positive emotion (organism), which in turn foster FRP continuous usage intention (response).
Originality/value
This research presents an S-O-R model that incorporates several attributes from DOI theory, the UTAUT model and the AIDUA framework to elucidate the antecedents of consumers' continuous usage intention toward FRP. The findings corroborate the significance of the S-O-R mechanism in FRP, setting the groundwork for the acceptance and development of biometric authentication technologies in service contacts and banks. In addition, the study highlights opportunities and essential aspects for FinTech service developers and providers to consider in terms of their practical significance.
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Tai-Yi Yu, Jeou-Shyan Horng, Chih-Hsing Liu, Sheng-Fang Chou, Yung-Chuan Huang, Quoc Phong La and Yen-Ling Ng
This study aims to explore post-COVID-19 tourism digital transformation, study innovative service delivery and provide insights for industry leaders and policy-makers to nurture…
Abstract
Purpose
This study aims to explore post-COVID-19 tourism digital transformation, study innovative service delivery and provide insights for industry leaders and policy-makers to nurture robust sector growth amid evolving consumer demands.
Design/methodology/approach
This study used anonymous questionnaires and explored views on digital technology in sports centers and entertainment venues. Structural equation modeling explores latent variable interactions with respect to mediating and moderating effects.
Findings
Digital transformation practices influence decision-making indirectly through perceived behavior control, attitudes and service innovation, with differentiation strategies moderating this relationship.
Research limitations/implications
This study focuses on the recreation sector; future efforts should include insights, attitudes and actions from experts and government policy-makers.
Practical implications
This study enhances the literature on recreation professionals, offering guidance for navigating the evolving landscape of digital dynamics in the leisure and recreation sector.
Originality/value
The rise of digital technology highlights the importance of analyzing customer decisions influenced by digital behavior within the leisure and recreation industry.
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Sheng-Fang Chou, Jeou-Shyan Horng, Chih-Hsing Liu, Tai-Yi Yu, Yung-Chuan Huang, Quoc Phong La and Yen-Ling Ng
Since the COVID-19 epidemic, the number of restaurant service quality studies has continued to increase. However, until now, there has not been an overall perspective or accurate…
Abstract
Purpose
Since the COVID-19 epidemic, the number of restaurant service quality studies has continued to increase. However, until now, there has not been an overall perspective or accurate instructions for research on restaurant service quality and experiential value enhancement. This study conducts multiple comparison studies to discover differences between consumer-perceived service quality and satisfaction perspectives on hotel fine dining and chain restaurants.
Design/methodology/approach
This study integrates a hotel’s fine dining and chain restaurant to obtain 636 participants (e.g. Study 1 has 318 hotel fine dining customers; Study 2 has 318 chain restaurant customers), mainly expanding the SERVQUAL model and stimulus–organism–response (S–O–R) theory.
Findings
The results of Study 1 show that value co-creation has a mediating effect on the relationship between service quality and satisfaction. In addition, customer experiences have a significant moderating effect on customer satisfaction. The outcomes of Study 2 showed that experiential value has a significant mediating effect on the relationship between service quality and satisfaction. In addition, customer relationship quality is a critical criterion in regulating the process of experience value delivery.
Practical implications
Hotels’ fine dining should pay attention to the item risk in the value co-creation factor, while chain restaurants should enhance the item service excellence in the experiential value factor to satisfy the changing customer requirements.
Originality/value
This study provides several alternative models to verify the robustness of the empirical results.
Highlights
This research has brought clarity to the diverse mediation-moderation models that compare of hotel fine dining and chain restaurant consumer perceived service quality and satisfaction predictions.
These models delve into how different service quality requirements after the epidemic that affect customer satisfaction, as perceived by customers consumed in hotel fine dining and chain restaurant.
Value cocreation and experiential value emerge as pivotal factors, they act as mediators between service quality and satisfaction.
They demonstrate a moderation effect of customer experiences between value cocreation and satisfaction, as well as customer relationship quality between experiential value and satisfaction.
This research has brought clarity to the diverse mediation-moderation models that compare of hotel fine dining and chain restaurant consumer perceived service quality and satisfaction predictions.
These models delve into how different service quality requirements after the epidemic that affect customer satisfaction, as perceived by customers consumed in hotel fine dining and chain restaurant.
Value cocreation and experiential value emerge as pivotal factors, they act as mediators between service quality and satisfaction.
They demonstrate a moderation effect of customer experiences between value cocreation and satisfaction, as well as customer relationship quality between experiential value and satisfaction.
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Keywords
Atanu Roy, Sabyasachi Pramanik, Kalyan Mitra and Manashi Chakraborty
Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO…
Abstract
Purpose
Emissions have significant environmental impacts. Hence, minimizing emissions is essential. This study aims to use a hybrid neural network model to predict carbon monoxide (CO) and nitrogen oxide (NOx) emissions from gas turbines (GTs) to enhance emission prediction for GTs in predictive emissions monitoring systems (PEMS).
Design/methodology/approach
The hybrid model architecture combines convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM) networks called CNN-BiLSTM with modified extrinsic attention regression. Over five years, data from a GT power plant was uploaded to Google Colab, split into training and testing sets (80:20), and evaluated using test matrices. The model’s performance was benchmarked against state-of-the-art emissions prediction methodologies.
Findings
The model showed promising results for GT CO and NOx emissions. CO predictions had a slight underestimation bias of −0.01, with root mean-squared error (RMSE) of 0.064, mean absolute error (MAE) of 0.04 and R2 of 0.82. NOx predictions had an RMSE of 0.051, MAE of 0.036, R2 of 0.887 and a slight overestimation bias of +0.01.
Research limitations/implications
While the model demonstrates relative accuracy in CO emission predictions, there is potential for further improvement in future research.
Practical implications
Implementing the model in real-time PEMS and establishing a continuous feedback loop will ensure accuracy in real-world applications, enhance GT functioning and reduce emissions, fuel consumption and running costs.
Social implications
Accurate GT emissions predictions support stricter emission standards, promote sustainable development goals and ensure a healthier societal environment.
Originality/value
This paper presents a novel approach that integrates CNN and Bi-LSTM networks. It considers both spatial and temporal data to mitigate previous prediction shortcomings.