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In 2018, the International Financial Reporting Standard 9-Financial Instruments became mandatory, effectively changing the underlying accounting principles of financial instruments. This paper systematically reviews the academic literature on the implementation effects of IFRS 9, providing a coherent picture of the state of the empirical literature on IFRS 9.

The study thrives on a systematic review approach by analyzing existing academic studies along the following three broad categories: adoption and implementation, impact on financial reporting, and risk management and provisioning. The study concludes by providing research prospects to fill the identified gaps.

We document data-related issues, forecasting uncertainties and the interaction of IFRS 9 with other regulatory standards as implementation challenges encountered. Also, we observe cross-country heterogeneity in reporting quality. Furthermore, contrary to pre-implementation expectations, we find improvement in risk management. This suggests that despite the complexities of the new regulatory standard on financial instruments, it appears to be more successful in achieving the intended objective of enhancing better market discipline and transparency rather than being a regulatory overreach.

As the literature on IFRS 9 is burgeoning, we provide state-of-the-art guidance and direction for researchers with a keen interest in the economic significance and implications of IFRS 9 adoption. The study identifies gaps in the literature that require further research, specifically, IFRS 9 adoption and firm’s hedging activities, IFRS 9 implications on non-financial firms. Lastly, existing studies are mostly focused on Europe and underscore the need for more research in under-researched jurisdictions, particularly in Asia and Africa. Also, to standard setters, policymakers and practitioners, we provide some insight to aid the formulation and application of standards.

Smart tourism has become an inevitable trend in future tourism development. However, despite significant investment in its technological foundation, little is known about whether and when tourists are willing to be involved in smart tourism. This study explores tourists' willingness to contribute to smart tourism development by empirically examining their intention to share personal information and use smart technology.

Based on construal level theory (CLT), a 2 (far/near spatial distance) × 2 (gain/loss persuasive information frame) × 2 (altruistic/egoistic value orientation) laboratory experiment with different contextual features was designed to examine tourists' willingness to contribute to smart tourism.

Tourists are most willing to share personal information and use smart technologies when spatial distance aligns with information framing, spatial distance aligns with value orientation and information framing aligns with value orientation.

This study provides essential insights for destination management organizations (DMOs) about tourists' perceptions of smart tourism, enabling DMOs to develop more precise marketing strategies to encourage tourists to contribute to smart tourism development and enrich tourists' travel experiences.

This study enriches theoretical knowledge of DMOs' boundaries in encouraging tourists to contribute to smart tourism and provides critical insights into future smart tourism development for researchers and practitioners.

This paper aims to examine the impact of the mandatory adoption of (International Financial Reporting Standards [IFRS] 9) on loan provisions, nonperforming loans (NPL) and impairment loan loss in Gulf banks. This study also investigates potential variations in outcomes compared to prior models and explores the use of the Callaway and Sant’Anna (2021) estimator for difference-in-differences (DiD) with multiple time periods.

The research is based on a sample of 53 Gulf banks covering the period from 2012 to 2020. The study analyzes the changes in loan provisions, impairment loss and NPL following the implementation of IFRS 9. It uses statistical analysis and the DiD method to compare the outcomes between the experimental group (treated by IFRS 9) and the control group (not treated).

The findings reveal a statistically insignificant increase in loan provisions, impairment loss and NPL after the adoption of IFRS 9. These results align with previous studies and suggest that Gulf banks were proactive in anticipating and mitigating the impact of the new standard. The study also observes a synchronization of provisioning practices across Gulf countries and a certain level of consistency in recognizing loan losses.

The practical implications of this study suggest that Gulf banks have successfully absorbed the impact of IFRS 9 and have implemented collaborative approaches.

The study offers some new sight into IFRS9 outcomes in developing countries and opens the door for implementing a novel DiD estimation in future research studies.

This study aims to examine how financial analysts evaluate other comprehensive income (OCI) information with a focus on the information content and economic substance of OCI gain and loss.

This study conducted a 2 × 2 between-subject experiment by manipulating profitability (net profit or net loss) and OCI (OCI gain or loss). A total of 103 equity research analysts participated in the experiment.

The results show that when the company suffers a net loss, the presence of unrealized gain in OCI appears to cause concern for analysts, in that they assigned a lower valuation to the OCI gain company than the OCI loss company. However, in the cases where the company is profitable, analysts appeared to respond to the direction of OCI (i.e. gain or loss) and incorporated the directional information in their valuation judgment.

The experimental results complement prior archival research on OCI valuation. This study extends prior work on OCI’s decision usefulness, improves understanding of the impact of OCI on firm valuation and contributes to the ongoing debate about whether OCI is viewed as a performance measure. The findings indicate that the effect of OCI gains or losses is most pronounced when the company experiences a loss. During such instances, analysts may interpret a combination of net loss and OCI gain as a potential indicator of earnings management opportunities. Consequently, they may perceive it as a signal of deteriorating future financial performance.

The study attempts to investigate the relationship between emotional biases (loss aversion bias, overconfidence bias, and regret aversion bias) and investment decisions through a meta-analysis approach.

A meta-correlation analysis was done using sample size and correlation (r) data from several relevant studies that look at how emotional biases (loss aversion bias, regret aversion bias, and overconfidence bias) affect investment decisions. Additionally, beta coefficients (ß) were also converted to correlation coefficients (r) from six studies.

This study analysed 31 empirical studies and found a significant positive correlation between emotional biases and investment decisions [loss aversion bias (r = 0.492), regret aversion bias (r = 0.401), and overconfidence bias (r = 0.346)]. We set the statistical significance threshold at 0.05.

The review covered 31 online research publications that showed significant heterogeneity, possibly influenced by various methodological, population, or other factors. Furthermore, the use of correlational data restricts the ability to establish causation.

This is a novel attempt to integrate the results of various studies through meta-analysis on the relation between these emotional biases (loss aversion, overconfidence, and regret aversion) and investment decisions.

While the fashion industry is beginning to comprehend the commercial imperative for sustainability, it is struggling to address the issues of overconsumption and accompanying packaging. Research indicates that framing of marketing messages makes an impact on consumers’ choices, particularly when it comes to supporting sustainable initiatives from brands. This study aims to investigate the impact of message framing, reference to perceived benefits and green consumer values on their choice of packaging reduction initiatives in the context of online retailing and the subsequent impact on brand attitude.

A 2 (frame: gain/loss) × 2 (reference to perceived benefits: personal/societal) × 2 (green consumer value: high/low) mixed method online experiment was conducted.

Results indicated that how the message is referenced in terms of benefits (personal gain/loss or societal gain/loss) and green consumer values act as moderators between message frame and attitude toward the packaging initiatives, which in turn impact brand attitude.

Overall, the findings contribute to message architecture, insight on consumer behavior, and add to the business case for sustainable packaging for fashion/apparel companies.

Motivated by recent research indicating the significant challenges posed by imbalanced datasets in industrial settings, this paper presents a novel framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis, aiming to improve fault detection accuracy and reliability.

This study addressing the challenge of imbalanced datasets in predicting hard drive failures is both innovative and comprehensive. By integrating data enhancement techniques with cost-sensitive methods, the research pioneers a solution that directly targets the intrinsic issues posed by imbalanced data, a common obstacle in predictive maintenance and reliability analysis.

In real industrial environments, there is a critical demand for addressing the issue of imbalanced datasets. When faced with limited data for rare events or a heavily skewed distribution of categories, it becomes essential for models to effectively mine insights from the original imbalanced dataset. This involves employing techniques like data augmentation to generate new insights and rules, enhancing the model’s ability to accurately identify and predict failures.

Previous research has highlighted the complexity of diagnosing faults within imbalanced industrial datasets, often leading to suboptimal predictive accuracy. This paper bridges this gap by introducing a robust framework for Industrial Data-driven Modeling for Imbalanced Fault Diagnosis. It combines data enhancement and cost-sensitive methods to effectively manage the challenges posed by imbalanced datasets, further innovating with a bagging method to refine model optimization. The validation of the proposed approach demonstrates superior accuracy compared to existing methods, showcasing its potential to significantly improve fault diagnosis in industrial applications.

Millennials are a vital generational cohort of the Indian population, and understanding their motivation to participate in the stock market is crucial. This study aims to understand the investment decision-making behavior among millennials in the Indian Stock Market.

Using a cross-sectional research design that entails in-depth personal interviews, this study aims to understand the equity investment behavior of millennials. Verbatim texts from interview transcripts were used to analyze the content and arrive at themes.

The study investigated the motivation to enter the stock market and gained insights into how individuals make equity investment decisions considering economic and behavioral dimensions. The basis for stock selection was predominantly on the self-analysis of investors. Multiple stock selection priorities are also discussed. In addition, informants ensured asset diversification and exercised various strategies to overcome emotions. Furthermore, they suffered from various behavioral biases.

Individual investors are the least informed and most impacted stakeholders in the stock markets; therefore, this study contributes fresh insights to enhance their financial security. The paper also examines some noticeable behavioral tendencies retail investors exhibit and gathers helpful strategies for mitigating behavioral biases.

The uniqueness of the research lies in its adoption of a qualitative methodology that uses the investment experience of millennial investors to reveal the components of decision-making behavior and investor psychology. The findings are thereby unique and have significant managerial implications.

This paper aims to propose a solution for dissolving the “privacy paradox” in social networks, and explore the feasibility of adopting a synergistic mechanism of “deep-learning algorithms” and “differential privacy algorithms” to dissolve this issue.

To validate our viewpoint, this study constructs a game model with two algorithms as the core strategies.

The “deep-learning algorithms” offer a “profit guarantee” to both network users and operators. On the other hand, the “differential privacy algorithms” provide a “security guarantee” to both network users and operators. By combining these two approaches, the synergistic mechanism achieves a balance between “privacy security” and “data value”.

The findings of this paper suggest that algorithm practitioners should accelerate the innovation of algorithmic mechanisms, network operators should take responsibility for users’ privacy protection, and users should develop a correct understanding of privacy. This will provide a feasible approach to achieve the balance between “privacy security” and “data value”.

These findings offer some insights into users’ privacy protection and personal data sharing.

This paper aims to examine how fear of missing out (FOMO) and investment intention mediate the relationship between behavioral biases and investment decisions of retail investors in the Indian stock market.

The present research comprises two cross-sectional quantitative studies, where Study A involves data from 405 self-employed and Study B involves 393 salaried investors. Data was attained through questionnaires – the partial least squares structural equation modeling was used for data analysis.

The outcomes show that herding, overconfidence and loss aversion bias significantly impact investment intention and FOMO on both studies. Furthermore, the outcomes also indicate that herding and loss aversion bias significantly influence investment decisions in studies (A and B); however, overconfidence bias insignificantly affects the investment decisions in Study A. Besides, the results also reveal a substantial relationship between FOMO, investment intention and investment decision.

The findings of this paper assist practitioners (financial analysts and retail investors) in considering the various ways of analyzing investment decision outcomes by considering the joint effect of several biases.

This paper is an initial attempt to propose a new theoretical framework and empirically examine the impact of behavioral biases on investment decisions by considering the FOMO and investment intention among self-employed and salaried investors. This study also contributes to the behavioral finance literature; other researchers may find it valuable to attain their goals.