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Islamic financial technology (fintech), primarily peer-to-peer (P2P) lending, plays a substantial role in funding the unbanked population and small and medium enterprises (SMEs) by offering streamlined financial services through online digital technology. In addition, Islamic fintech lending offers a promising return rate for individual and institutional investors, and therefore, it is considered a worthy investment alternative for diversification. This study aims to examine the determinants of project returns of SMEs on Islamic fintech lending platforms, taking the case study of one Islamic fintech lending platform registered at the Financial Service Authority in Indonesia.

Project return information and other information, such as the name of the SME raising fund, project duration, location, contract (aqad) and value (amount of money) to be raised, were extracted from the Islamic fintech lending platform. Furthermore, a regression analysis was performed using the completed projects as sample data (n = 122) on the platform.

The results show that the rate of return is significantly affected by project duration and type of Sharia-compliant contract. Location and project value are, however, found to be statistically insignificant. This study’s overall results align with the Signaling theory, indicating the importance of information for decision-making.

Due to limited access to the data, our study uses data from one of seven Islamic fintech lending platforms; thus, the study results may not be generalized to the general population.

The results suggest that investors aspiring to invest their funds in SME projects on Islamic fintech lending platforms should consider the project duration and contractual agreement since these factors significantly influence the return. Additionally, society may consider the Islamic fintech lending platform a viable investment instrument since its return rate follows the risk-return principle in classical and established finance theories. That is why Islamic fintech lending platforms are competitive compared to the more established ones, such as the Islamic stock market.

To the best of the authors’ knowledge, this study is the first study using an empirical approach to reveal the project return determinants of SMEs on Islamic fintech lending platform.

The purpose of this study is to identify the structural determinants of e-returns service interactions, examine their impact on online shoppers' loyalty and propose returns service interventions from an interactive marketing perspective to facilitate consumer interaction and repeat purchase intentions with e-retailers.

This study empirically tests the research hypotheses based on cross-sectional survey data collected from Chinese online consumers who experienced interactions during the product returns process.

E-return service interaction includes three determinants: access support, friendly interaction and communication support. These interactions positively impact ease of return, returns satisfaction and customer loyalty. Returns satisfaction is a key mediator in the relationship between return service interaction and loyalty. Access support and friendly interaction have both direct and indirect effects on loyalty, while communication support has only an indirect effect.

This study contributes to understanding e-returns service interaction by analyzing its structural determinants, providing a robust scale foundation and analytical framework for future empirical research. Additionally, this study explores the driving role of e-returns service interaction in forming e-customer loyalty, offering a theoretical basis for the service recovery function of e-returns service interaction. It enriches the application of service recovery theory and relationship marketing theory in the field of interactive marketing.

This study aims to identify which types of information quality influence purchase intentions the most in live streaming commerce and to examine the role of network size in this context.

We propose a model to investigate the correlation among the quality of different information in live streaming commerce, consumer trust, network size and purchase intention. An empirical analysis of 505 questionnaires was conducted by constructing a structural equation model.

The empirical findings indicate that information quality can directly enhance purchase intention and exert an indirect influence through the mediating factors of trust in products and streamers. Perceived network size positively moderates the relationship between information quality and trust in products. Of the five types of information, the quality of bullet-screen comments information is most important to consumers.

This study represents the first systematic analysis of how the quality of multiple types of information in live streaming commerce influences consumer trust and purchase intention, integrated within a unified framework. It uniquely introduces network size as a moderating variable, offering both theoretical insights and practical guidance for balancing information quality with network size in live streaming commerce environments.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

This study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.

The study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.

First, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.

This study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.

This study explores the association between market efficiency and speculation. The government of India temporarily banned the futures trading of various commodities several times citing the presence of speculation. Many controversies exist about this topic; thus, this study clarifies the association between market efficiency and speculation and investigates whether market reforms altered this association.

The data for nine commodities is collected from the National Commodity and Derivative Exchange (NCDEX) for 2005–2022. Regression analysis and Automatic Variance Ratio (AVR) were adopted to inspect the informational efficiency and influence of speculation in the commodity market. Furthermore, this study uses different sub-samples to understand the changes in the market microstructure and its effects on market quality.

The results confirm an inverse and significant relationship between information efficiency and speculation and a deviation from the random walk process observed. Therefore, return predictability exists in the market. This study confirms that market reforms do not reduce the influence of speculation on market efficiency. The study concludes that the market is not weak-form efficient.

This study has certain limitations, since this study is empirical in nature, it may possess the limitations of empirical research.

This paper has dual novelty. First, this study investigates the effects of market reforms. Second, this study captures the influence of speculation in the Indian agricultural commodity market by considering the market microstructure aspects.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

Supply chain revamping (SCR) is an important strategy for firms to improve their supply chain operations in a rapidly changing environment. The purpose of this study is to shed light on the impact of SCR on shareholder value.

Based on Signaling Theory and 184 SCR announcements published by US-listed firms from 2013 to 2018, this study employs event study methodology and empirically examines three issues: Antecedents of SCRs; Primary purposes and actions of SCRs; In addition to the impact of SCRs on shareholder value using stock returns, we also examined the factors that can influence the extent of stock returns.

Firstly, our results indicate that SCRs are primarily driven by firms’ poor prior performance, CEO turnover and external control threats (ECTs). Secondly, the stock market favors SCRs aiming to meet customer needs and those accomplished through network remodel. However, the market reacts negatively to SCRs aiming at cutting costs, improving poor performance, and those implemented through network trim. Finally, the cross-sectional analysis indicates that shareholders prefer firms operating in more competitive or faster-growing industries and those adopting an expansionist strategy than those adopting a streamlining strategy.

Our study provides managers with valuable insights into when firms can benefit from initiating SCRs not only by examining the purposes and actions of SCRs but also by examining the industry- and strategy-specific moderators. Our study illuminates the conditions under which SCR will positively affect shareholder value. Additionally, this study contributes to the existing literature by deepening the understanding of the impact of supply chain decisions on firm performance and identifying the marginal conditions under which the stock market will react positively to SCR announcements.

This study introduces a new type of platform recommendation about mutual funds and draws on the signaling theory to conduct a quasi-experimental design to investigate how the platform recommendation influences investors’ investment decisions. Moreover, the authors examine the combined effect of star ratings and the platform recommendation on fund flow and test the investment value of recommended funds.

This study implements a quasi-experimental design based on 1,295 mutual funds traded on Alipay’s online platform to test the hypotheses.

The empirical results show that the recommended funds received higher fund flows from investors when the platform recommendation was established. Moreover, a substitution effect between tag recommendation and star ratings on fund flow was identified. We also uncovered that investing in platform-recommended funds can yield significant and higher fund returns for investors than those without platform recommendations.

Our findings shed new insights into the role of platform recommendations in helping fund investors make investment decisions and contribute to the business of online mutual fund transactions by investigating the effect of platform recommendations on fund flow and performance.

The research aims to provide empirical evidence on the relationship between financial statement comparability (FSC) and cost of equity (COE) in an emerging market.

Specifically, this study examines the relationship between FSC and COE of Vietnamese listed firms. The research uses the System Generalized Method of Moments regression techniques for a panel data set of 454 companies for the period 2015–2022.

The authors find that firms with high comparability of financial statements have lower COE. To confirm the research findings, the authors conduct the robustness test by using different proxies for the cost of equity. Consistent results are found.

The study contributes to the overall understanding of the relationship between FSC and COE, and suggests policy implications for relevant stakeholders such as managers, regulatory bodies and investors. Especially, regarding policymakers, this study could provide more insight into how the accounting convergence process impacts the effectiveness of a firm’s capital allocation.