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This chapter examines three common fintech use cases transforming the financial industry. First, the chapter introduces fintech's role in enhancing financial services and promoting financial inclusion, especially through digital platforms. Second, it investigates various fintech applications that support financial institution management by harnessing the power of artificial intelligence (AI) and machine learning (ML). Finally, the chapter explores fintech use cases related to the regulatory environment, including regulatory technology (regtech), blockchain technology, and cryptocurrencies. The insights presented in this chapter cater to researchers and practitioners keen on better understanding fintech's diverse applications in the ever-evolving financial industry landscape.

Experts may adjust their assessments through communication and mutual influence, and this dynamic evolution relies on the spread of internal trust relationships. Due to differences in educational backgrounds and knowledge experiences, trust relationships among experts are often incomplete. To address such issues and reduce decision biases, this paper proposes a probabilistic linguistic multi-attribute group decision consensus model based on an incomplete social trust network (InSTN).

In this paper, we first define the new trust propagation operators based on the operations of Probability Language Term Set (PLTS) with algebraic t-conorm and t-norm, which are combined with trust aggregation operators to estimate InSTN. The adjustment coefficients are then determined through trust relations to quantify their impact on expert evaluation. Finally, the particle swarm algorithm (PSO) is used to optimize the expert evaluation to meet the consensus threshold.

This study demonstrates the feasibility of the method through the selection of treatment plans for complex cases. The proposed consensus model exhibits greater robustness and effectiveness compared to traditional methods, mainly due to the effective regulation of trust relations in the decision-making process, which reduces decision bias and inconsistencies.

This paper introduces a novel probabilistic linguistic multi-attribute swarm decision consensus model based on an InSTN. It proposes a redefined trust propagation and aggregation approach to estimate the InSTN. Moreover, the computational efficiency and decision consensus accuracy of the proposed model are enhanced by using PSO optimization.

Social live streaming services (SLSS) have infused gamification into interface design and feature applications. Firms adopt gamification mechanisms to win customer loyalty in the live streaming and SLSS markets. Based on the mechanics-dynamics-aesthetics (MDA) framework and uses and gratifications 2.0 theory (UGT 2.0), this study aims to investigate the effects of game mechanics (mechanics) on enjoyment and user retention (aesthetics) through rewards and social interaction (dynamics) in the context of SLSS.

This study used an online survey via Google Forms, SurveyCake and social media platforms like Facebook, Instagram and Line to collect data from 232 SLSS users in Taiwan. Partial least squares structural equation modeling (PLS-SEM) was adopted to analyze the data.

The results validated the relationships between game mechanics and dynamic elements (rewards and social interaction) that triggered aesthetic elements (enjoyment feelings) among users. In addition, users experienced a sense of enjoyment that led to usage retention when using the gamified SLSS. Further, this study found enjoyment crucial for users to stay interactive with gamified services.

Driven by UGT 2.0, this study closed the gaps by integrating the MDA framework into the SLSS context and better understanding how game mechanics are connected to rewards and social interaction, leading to enjoyment and user retention when using SLSS. This study provides fresh insights into gamification-oriented SLSS practices. It offers significant theoretical and managerial implications and provides guidelines for SLSS platform operators on fostering user retention.

Compared to traditional (domestic) e-commerce consumers, cross-border electronic commerce (CBEC) consumers may face greater information asymmetry in the CBEC purchase process. Given this background, however, the literature has paid limited attention to the informational antecedents that influence consumers' perceptions of transaction costs and their CBEC purchase intentions. To fill this gap, this study integrates the elaboration likelihood model (ELM) and transaction cost theory (TCT) to develop a model for exploring how product (website informativeness, product diagnosticity and website interactivity as the central route) and external (country brand, website policy and vendor reputation as the peripheral route) informational antecedents affect consumers’ evaluations of transaction costs in terms of uncertainty and asset specificity and their CBEC purchase intentions.

This study employs a survey approach to validate the model with 766 Generation Z CBEC consumers based on judgment sampling. The partial least squares (PLS) technique is adopted for data analysis.

The results show that all the proposed central and peripheral informational antecedents reduce consumers’ perceptions of uncertainty and asset specificity, which in turn negatively influences their CBEC purchase intentions.

Through this investigation, this study increases our understanding of how product and external informational antecedents affect consumers’ evaluations of transaction costs, which subsequently determine their CBEC purchase decisions. This study offers theoretical contributions to existing CBEC research and has practical implications for CBEC organizations and managers.

The purpose of this study is to investigate the effect of brand experiences through cafés or art spaces in luxury fashion flagships on consumers’ buying behavior toward authorized shopping channels.

Online questionnaires are used by adapting measurements from prior research. We test whether positive relationships exist between multi-faceted (i.e. sensory, emotional, intellectual, and relational) experiences, consumer’s revisiting intention toward the experiential spaces, and the purchase intention of luxury fashion goods from authorized channels, especially focusing on the mediation effect of the intention to revisit. We also include the experiential space type (cafés vs. art exhibitions) as moderator.

The results confirm that sensory, emotional, intellectual, and relational experiences in cafés or art exhibitions of luxury fashion flagships have a positive impact on the intention to revisit. This revisit intention to experience space has a significant effect on purchase intention from authorized shopping channels. Specifically, sensory experiences in an art space could lead to a positive revisit intention for consumers. Furthermore, relational experiences in cafés could create positive revisit intention in consumers.

This is the first study to compare consumers' perceptions by categorizing extended brand spaces and assessing experiential marketing for authorized shopping channels.

This paper aims to identify the role of coproduction and value-in-use in the overall hotel experience value, and its effects on cognitive-affective satisfaction and loyalty. The country chosen is Spain, in the context of the “new normal,” where the increase in tourist flow after the COVID-19 pandemic has been very relevant, setting up the chance to better assess and discuss value creation in a post-pandemic era.

The causal model is validated with partial least squares (PLS) in a panel of 405 hotel guests (applying sampling quotas by sex, age and region).

The chain of effects is verified with stronger links at the end and with a greater weight of value-in-use compared to coproduction. Hotels’ efforts to adapt to the pandemic situation work better if a customized, unique experience is offered and if intensive communication is with and not just to the guest. The role of experience value is key for the cocreation to produce results for satisfaction and loyalty.

This study contributes to the value creation literature by highlighting the respective roles of coproduction and value-in-use as drivers of the satisfaction−loyalty chain, and by introducing the guest’s overall hotel experience value into this chain. This is viewed against the backdrop of the contextual changes that have occurred since 2020 in the Spanish hotel industry. The post-pandemic context “forces” the client to actively participate. Analysis of the implications of this kind of new cocreation on the overall customer experience is relevant for both tourism scholars and managers.

This study aims to apply the theory of consumption value to analyze the utilization of Metaverse technologies within hospitality and tourism while examining the factors that impact consumer intentions to use the Metaverse.

This paper aims to consider an extensive study spanning the period October 2021 to March 2023 was conducted to understand the shifts in an individual's intention to use Metaverse technologies in hospitality.

The findings of this study confirm that individual attitudes to the Metaverse and trust in Metaverse technologies significantly impact their intention to use the Metaverse.

The study aims to provide fresh insights into how individuals perceive Metaverse technologies in the context of choosing hotels and resorts, which enriches the understanding of consumer behaviors around Metaverse technology in hospitality.

This study aims to consider not only tourist intentions to use the Metaverse but also how diverse consumption values impact user attitudes, an area currently underresearched.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

Based on upper echelon theory and signaling theory, we aim to examine the impact of returnee executives on firms’ relative exploratory innovation focus and the moderating effect of economic policy uncertainty on this relationship.

Using panel data of Chinese listed companies from 2009 to 2020, we obtained empirical evidence to support our arguments.

Returnee executives positively influence firms’ relative exploratory innovation focus. This means that firms with returnee executives will shift the focus of their innovation activities toward exploratory innovation more than exploitative innovation. In addition, we find that economic policy uncertainty strengthens this relationship.

First, by showing how returnee executives positively influence firms’ shift in focus to exploratory rather than exploitative innovation, we expand our understanding of firms’ trade-offs between exploratory and exploitative innovation. Second, this study examines how returnee executives influence the relative importance that firms place on exploratory and exploitative innovation, allowing us to build a realistic and nuanced view of how returnee executives influence firms’ strategic choices. Finally, this study expands the strategic leadership literature and responds directly to the call for studies focusing on how institutional environmental conditions and executive characteristics work together to shape firm outcomes.

Although visual prototypicality in fashion is an observed phenomenon, empirical examinations of the link between fashion products' design prototypicality and consumer evaluations still need to be included. The present study analyzes the influence of the visual prototypicality of fashion products on consumer-perceived product values and brand preference.

An online survey adopting the fashion product images with significantly differing levels of visual prototypicality was used to collect data from 456 US consumers. The hypothesized relationships among visual prototypicality, product values and brand preference were analyzed through multi-group analysis.

Perceived visual typicality of fashion product designs significantly increased the hedonic and utilitarian value of the product and only indirectly increase brand preference. The hypothesized positive relationship between visual prototypicality and the product’s social value was found to be significant only in the low-price levels but became insignificant in the high-price levels.

The findings of this study contribute to the extant literature by first providing an initial analysis of the mechanism of visual prototypicality in the fashion product design field. The results confirm that visual prototypicality indirectly influences consumers' brand evaluations by the product’s perceived value. This relationship was previously assumed but not empirically proven only in non-fashion product categories. The study also presents additional new points, further enriching the understanding of visual typicality. Additionally, the results show the complex relationship between the visual prototypicality of fashion product designs and the perceived social value of the product, which varies depending on the price range.