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The rapid growth of Fintech presents a growing challenge for banking institutions, particularly those with more traditional, service backgrounds. This paper aims to examine the relationship between Fintech innovation and bank performance by exploiting novel Chinese market data.

Guided by the work of Dietrich and Wanzenried (2011, 2014) and Phan et al. (2019), the authors construct a regression model to investigate the effect of Fintech innovation on the profitability of Chinese listed banks. The authors include their measures of Fintech innovation in each of their selected structures.

Results indicate that Fintech innovation is negatively associated with bank performance and that state-owned banks, joint-stock commercial banks and long-established banks are more negatively impacted by Fintech innovation relative to city and rural commercial banks and younger banks.

Risk tolerance levels, internal structure and efficiency and recent debt repayment performance channels are each shown to be significant, robust explanatory factors underpinning such results.

This study aims to explore the consumer perceptions of cognition and intention to visit pilot zone of international medical tourism as emerging, developed medical tourism destinations.

Using a survey-based quantitative method, based on a survey of 439 tourists who have cross-border travel experience, the partial least squares approach was performed to test the hypotheses.

The results show that internal factors had a stronger influence on destination image compared to external factors. Among different factors, preferential policies had the greatest impact on intention to visit. Perceived quality had a stronger effect on intention to visit than preference. Geographical distance had a varied effect, with those furthest away in Northeast China showing greater intention to visit compared to closer regions.

This study explores the impact of multidimensional destination perception on medical tourists’ behavioural intention in emerging destinations by integrating the push-pull theory and theory of planned behaviour and tests how geographical distance affects intention to visit emerging destinations. Using China international medical tourism pilot area as a typical case of medical tourism emerging destinations for empirical analysis. This research offers guidance for branding and marketing strategies, contributes to a deeper understanding of medical tourists’ destination choices, enriches the theoretical explanation of emerging destination choice in medical tourism and provides valuable insights for destination recovery.

Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.

This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.

The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.

Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.

Advances in material agency driven by artificial intelligence (AI) have facilitated breakthroughs in material adaptivity enabling smart objects to autonomously provide individualized smart services, which makes smart objects act as social actors embedded in the real world. However, little is known about how material adaptivity fosters the infusion use of smart objects to maximize the value of smart services in customers' lives. This study examines the underlying mechanism of material adaptivity (task and social adaptivity) on AI infusion use, drawing on the theoretical lens of social embeddedness.

This study adopted partial least squares structural equation modeling (PLS-SEM), mediating tests, path comparison tests and polynomial modeling to analyze the proposed research model and hypotheses.

The results supported the proposed research model and hypotheses, except for the hypothesis of the comparative effects on infusion use. Besides, the results of mediating tests suggested the different roles of social embeddedness in the impacts of task and social adaptivity on infusion use. The post hoc analysis based on polynomial modeling provided a possible explanation for the unsupported hypothesis, suggesting the nonlinear differences in the underlying influencing mechanisms of instrumental and relational embeddedness on infusion use.

The formation mechanisms of AI infusion use based on material adaptivity and social embeddedness help to develop the business strategies that enable smart objects as social actors to exert a key role in users' daily lives, in turn realizing the social and economic value of AI.

This study advances the theoretical research on material adaptivity, updates the information system (IS) research on infusion use and identifies the bridging role of social embeddedness of smart objects as agentic social actors in the AI context.

This study aims to investigate the influence of parent company personnel embedding on the stock price crash risk (SPCR) of listed companies, along with the moderating effect of disparate locations between parent and subsidiary companies and other major shareholders.

This research empirically tests hypotheses based on a sample of listed subsidiaries in China during the period between 2006 and 2021.

Our results demonstrate that personnel embeddedness in the parent company significantly alleviates SPCR in subsidiaries. This effect is even more substantial when the parent and subsidiary companies are in different places. However, other major shareholders in the subsidiary company weaken it. Our additional analysis indicates that, relative to executive embeddedness, director embeddedness exerts a stronger effect on the SPCR of the subsidiary. Mechanism examination reveals that the information asymmetry and the level of internal control (IC) within the subsidiary are significant channels through which the personnel embeddedness from the parent company influences the SPCR of the subsidiary.

This study expands the literature on how personnel arrangements in corporate groups within emerging countries influence SPCR. We have extended the traditional concept of interlocking directorates to corporate groups, thereby broadening the understanding of the governance effects of interlocking directors and executives from a group perspective.

This paper aims to examine the existing literature on firms’ power through the lens of the supply chain and highlights some gaps that could be covered by future research.

This study uses a systematic framework-based review combining the insights of the antecedents, decisions and outcomes (ADO) and theories, contexts and methods (TCM) frameworks. The review was carried out using a sample of 108 articles published between 1984 and 2022 in 25 prestigious journals.

The ADO framework maps out the state of the art of the antecedents of power (i.e. sources and types of firm power), the decision to use power and the effect that exercising power over other firms may have on firm performance and the quality of inter-firm relationships. In addition, this framework highlights factors that mediate or moderate the decision to exercise power and the factors that mediate or moderate the outcomes of exercising power or power asymmetry. The TCM framework provides insights into the theories, contexts (i.e. countries, industries, level of analysis and sources of data) and methods used by the existing literature. The content analysis using the aforementioned frameworks provides the basis to elaborate propositions for future research on power in the supply chain from the perspective of gender differences.

This systematic literature review offers a comprehensive guide for researchers to understand the antecedents, decisions and outcomes of firm power in the supply chain, as well as the TCM used in the literature. The content analysis using frameworks provides a road map to investigate the proposed factors that might moderate the decision to exercise power and the outcome of exercising power or power asymmetry from the perspective of gender differences. In addition, based on content analysis, the authors make propositions about TCM that could be applied in future research.

From a practical perspective, this systematic literature review may help managers to better understand the sources and consequences of their firm’s power. This would allow managers to make better decisions when negotiating with their supply chain parties, which could potentially lead to better performance for their firms and the whole supply chain.

To the best of the authors’ knowledge, this study is the first to conduct a comprehensive systematic literature review of the different dimensions of firms’ power in the supply chain.

Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。

The purpose of this paper is to investigate the factors influencing Palestinian consumer attitudes toward artificial intelligence (AI)-powered online banking, focusing on performance expectancy, effort expectancy, social influence and facilitating conditions while considering the moderating role of trust in financial institutions.

To test the hypotheses, an empirical study with a questionnaire was carried out. The study was completed by 362 Palestinian customers who use online banking services.

The findings of this paper show that performance expectancy, effort expectancy, social influence and facilitating conditions significantly influence consumer attitudes toward AI-powered online banking. Furthermore, trust in financial institutions as a moderating variable strengthens the impact of performance expectancy, effort expectancy, social influence and facilitating conditions on consumer attitudes toward AI-powered online banking. Therefore, more studies should focus on certain fields and cultural contexts to get a more thorough grasp of the variables influencing adoption and acceptability.

The study's findings may be specific to the Palestinian context, limiting generalizability. The reliance on self-reported data and a cross-sectional design may constrain the establishment of causal relationships and the exploration of dynamic attitudes over time. In addition, external factors and technological advancements not captured in the study could influence Palestinian consumer attitudes toward AI-powered online banking.

Financial institutions can leverage the insights from this research to tailor their strategies for promoting AI-powered online banking, emphasizing factors like perceived security and ease of use. Efforts to build and maintain trust in financial institutions are crucial for fostering positive consumer attitudes toward AI technologies. Policymakers can use these findings to inform regulations and initiatives that support the responsible adoption of AI in the financial sector, ensuring a more widespread and effective implementation of these technologies.

This research delves into Palestinian consumer attitudes toward AI-powered online banking, focusing on trust in financial institutions. It aims to enrich literature by exploring this under-explored area with meticulous examination, robust methodology and insightful analysis. The study embarks on a novel journey into uncharted terrain, seeking to unearth unique insights that enrich the existing literature landscape. Its findings offer valuable insights for academia and practitioners, enhancing understanding of AI adoption in Palestine and guiding strategic decisions for financial institutions operating in the region.

The purpose of this paper is to present the influence of inter-turn short circuit faults (ITSF) on electromagnetic vibration in high-voltage line-starting permanent magnet synchronous motor (HVLSPMSMS).

In this paper, the ampere–conductor wave model of HVLSPMSM after ITSF is established. Second, a mathematical model of the magnetic field after ITSF is established, and the influence law of the ITSF on the air-gap magnetic field is analyzed. Further, the mathematical expression of the electromagnetic force density is established based on the Maxwell tensor method. The impact of HVLSPMSM torque ripple frequency, radial electromagnetic force spatial–temporal distribution and rotor unbalanced magnetic tension force by ITSF is revealed. Finally, the electromagnetic–mechanical coupling model of HVLSPMSM is established, and the vibration spectra of the motor with different degrees of ITSF are solved by numerical calculation.

In this study, it is found that the 2np order flux density harmonics and (2 N + 1) p order electromagnetic forces are not generated when ITSF occurs in HVLSPMSM.

By analyzing the multi-harmonics of HVLSPMSM after ITSF, this paper provides a reliable method for troubleshooting from the perspective of vibration and torque fluctuation and rotor unbalanced electromagnetic force.

Contemporary changes have occurred in country-level policies and tourists’ intentions in recent years. The role of maintaining a country’s image is trendy in crisis control but has not yet been discussed in domestic tourism research. Extending the Stimulus Organism Response model, this study aims to focus on “trustable WOM creation” in China. In addition, it aimed to discover how behavioral changes encourage domestic tourism intention (DTI).

This study explored the mediating role of DTI and the moderating role of maintenance of country image (MCI) for trustable word of mouth (WOM) creation. Using the snowball sampling technique, a structural equation modeling analysis (Smart PLS-4) was employed to analyze the data of 487 Chinese tourists.

Findings confirm that behavioral changes positively encourage domestic tourism and discourage international tourism, with significant negative moderation by MCI. MCI has an insignificant positive moderating effect between government-media trust and DTI. Furthermore, DTI positively and directly affects the creation of trustable WOM. In addition, it had a 20% mediation effect (VAF%) between behavioral changes and WOM creation, higher than the rejected mediation effect (12%), in the causal relationship between government-media trust and WOM creation.

WOM creation varies from different behavioral changes, but findings suggest that government-media trust and DTI influenced it significantly. Based on the study findings, the government and media can enhance domestic tourism by maintaining the country’s image. These findings both encourage and control the recovery of tourism.

This study provides a theoretical explanation for tourists' behavioral changes during the pandemic. Moreover, it shows that despite avoiding international tourism due to behavioral changes and government-media trust, MCI moderation with the mediation effect of DTI can create trustable WOM. To the best of the authors’ knowledge, this is the first study to theoretically promote tourism through DTI-induced psychology as a mediator and an organism affect prevailing among Chinese tourists.