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This study aims to gain insight into the motivations behind the decision to use high-cost payday loans by households who possess mainstream credit and to determine whether this behavior has changed over time.

Using data from Statistics Canada’s Surveys of Financial Security, probit models are used to examine the sociodemographic and financial indicators associated with payday loan use.

The analysis uncovers the sociodemographic and financial characteristics of payday loan-user households with access to lower-cost short-term loans. The findings indicate that the likelihood of payday loan use has risen over time. Additional analysis reveals that indicators of financial instability are positively associated with payday loan use among this group.

This research highlights the dichotomy of payday loan users and recommends policymakers tailor solutions to the specific needs of different types of payday loan users.

This research highlights the distinguishing sociodemographic and financial characteristics of payday loan user households and recommends policymakers tailor solutions to the specific needs of different types of payday loan users.

This is the first study, to our knowledge, to focus analysis on payday loan use of those with access to lower-cost short-term credit alternatives in Canada and to include measures of financial instability in the analysis. This research is timely given the current economic environment of high interest rates and high levels of household debt.

Introduction: When people need to take decisions, being economic decisions or otherwise, their decisions tend to rely on information the brain has already processed, and this includes the resources that the person has already invested. This is called sunk cost bias in the behavioural economics literature. On the other hand, mental practices could lead to the mental accounting bias, where people allocate a different value to a fixed amount of money, depending on circumstances.

Purpose: In this chapter, both biases mental accounting and sunk cost are investigated for the tourism industry in Turkey.

Methodology: The topic is researched through scenario-based questions and the Chi-square Automatic Interaction Detector (CHAID) method is applied.

Findings: As a result, it could be reported that people, regardless of gender, fall into sunk cost and mental accounting biases in decisions relating to their vacations. Mental accounting biases can be primarily explained using the scenario questions posed rather than gender, education, and income while sunk cost bias is explained by status, ‘being s university student’ and ‘income level’.

Practical implications: Rapid price changes in the tourism industry can disturb consumers who are mental accounting and sunk cost biased. So, they can change their holiday preferences or be dissatisfied with it and give negative feedback.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

After the effective date of International Financial Reporting Standard (IFRS) 15, the accounting treatment of credit card rewards programmes (CCRPs) is no longer explicitly prescribed. Uncertainty regarding what constitutes faithful representation, and the inconsistent accounting practices observed, has created a need for guidance on the appropriate accounting treatment of CCRP transactions. Accounting theory has the potential to provide the foundation for this guidance. As a result, the objective of this study was to develop a theoretical model for the accounting treatment of CCRP transactions using accounting theory.

This non-empirical qualitative conceptual study utilised document analysis, focussing specifically on accounting theory, to construct an accounting treatment model.

Applying the relevant accounting theory (International Accounting Standards Board's (IASB's) Conceptual Framework), a theoretical model for the accounting treatment of CCRP transactions was developed, which emphasises the importance of understanding the economic phenomenon (the CCRP transaction) and determining how management views the transaction (in isolation as marketing or as an integral part of the credit card transaction).

Addressing the problem of accounting for CCRP transactions with reference to accounting theory (which is the main element of scholarly activity in accounting) distinguishes this study from previous research on the topic. The CCRP accounting treatment theoretical model could assist CCRP management in faithfully accounting for a CCRP transaction and reduce uncertainty and inconsistency in practice. Moreover, this study identified the procedures to be employed when using accounting theory to determine the appropriate accounting treatment of business transactions. These procedures could be employed by accountants when faced with other transactions not covered by specific accounting standards.

This study aims to examine the impact and contribution of priority and non-priority sectors, as well as their sub-sectors, on the gross non-performing assets of public, private and foreign sector banks.

The Reserve Bank of India's database on the Indian economy is used to retrieve data over 13 years (2008–2021). Public sector (12), private sector (22) and foreign sector (44) banks are represented in the sample. Two-way ANOVA, multiple regression and panel regression statistical techniques are used in SPSS and EViews to examine the data. Further, the results are also validated by using robustness testing by applying the fully modified ordinary least square (FMOLS) and dynamic least square (DOLS) regression.

The results showed that, for private and foreign banks, the non-priority sector makes up the majority of the total gross non-performing assets, although both the priority and non-priority sectors are substantial for public sector banks. The largest contributors to the total gross non-performing assets in public, private and foreign banks are industries, agriculture and micro and small businesses. The FMOLS displays robustness results that are qualitatively similar to the baseline result.

Based on the study's findings about the patterns of non-performing assets originating from these specific industries, banks might improve the way in which these advanced loans are managed.

There has not been much research done on the subject of sub-sector-specific non-performing assets and how they affect total gross non-performing assets across the three sector banks. The study's primary focus will be on the issue of non-performing assets in the priority’s and non-priority’s sub-sectors, namely, agricultural, micro and small businesses, food credit, industries, services, retail loans and other priority and non-priority sectors.

The purpose of the study is to identify cybersecurity threats that hinder the adoption of digital banking and provide sustainable strategies to combat cybersecurity risks in the banking industry.

Systematic literature review guidelines were used to conduct a quantitative synthesis of empirical evidence regarding the impact of cybersecurity threats and risks on the adoption of digital banking.

A total of 84 studies were initially examined, and after applying the selection and eligibility criteria for this systematic review, 58 studies were included. These selected articles consistently identified identity theft, malware attacks, phishing and vishing as significant cybersecurity threats that hinder the adoption of digital banking.

With the country’s banking sector being new in this area, this study contributes to the scant literature on cyber security, which is mostly in need due to the myriad breaches that the industry has already suffered thus far.

This study aims to highlight that security and flexibilities remain the main points of contention in the cordiality business. This research points to planning a framework that empowers hotel users to get to the room using a mobile access key. Advancing secured facilities, mobile phone “Near Field Communication” (NFC) innovation as the entrance device by carrying out an application containing an imitated mobile key for explicit verification access is used.

The proposed system is evaluated by triangulation of experimental, numerical and rational evaluation using partial least square structural equation modeling (PLS-SEM) with Malaysian hotel guests and employees.

The discoveries with the hypothesis supported validated that the suggested solution can eliminate physical cards, boost protection and encourage a contactless ecosystem. Theoretical, management and societal contributions are discussed here.

This experiment comes with the constraints that it was conducted in only two hotels and does not fully reflect the choices of a wider range of travellers. Secondly, the cost of existing NFC smart locks is still relatively high, and along with the development of technology, the price will decrease when supply exceeds demand.

To promote high-security attributes, NFC technology as the access system by implementing an application containing an emulated smart key for specific authentication access is used. The host-card emulation enables cost-effectiveness profit and initiating a defence system in the pandemic era.

To promote high-security attributes, NFC technology is used as the access system by implementing an application containing an emulated smart key for specific authentication access. The host-card emulation enables cost-effectiveness profit and initiating a defence system in the pandemic era.

The novelty of this study comes from the use of commonly available smartphone NFC features that are yet to be applied in the tourism ecosystem. The research provokes the applied concept of mobile smartkeys.

In October 2016, Timothy Sloan, the newly appointed CEO of American banking giant Wells Fargo, faced a massive public-relations crisis. A few weeks earlier, a United States government agency had announced the results of its regulatory review of the bank and exposed a shocking practice common in the retail division, in which aggressive community bankers had created more than a million fraudulent accounts and credit card applications on behalf of unaware customers for the past several years. Over the next few weeks, the bank—and Sloan's predecessor, John Stumpf, in particular—suffered from harsh criticism from politicians, journalists, and former employees alike, ultimately forcing Stumpf's resignation. As Sloan sought to minimize the public-image backlash and restore general trust in Wells Fargo, he struggled to construct the best communication strategy for the bank's next chapter.