While algorithmic decision-making technology can greatly improve efficiency at work, it has the potential to promote unethical decision making in humans who work with the technology. In this paper, we test how the moral disengagement mechanism, displacement of responsibility arises, as an...

A large body of research across management, psychology, accounting, and economics shows that subjective performance evaluations are systematically biased: ratings cluster near the midpoint of scales and are often excessively lenient. As organizations increasingly adopt large language models...

Can algorithms enhance bureaucrats' work in developing countries? In data-poor environments, bureaucrats often exercise discretion over key decisions, such as audit selection. Exploiting newly digitized micro-data, this study conducted an at-scale field experiment whereby half of Senegal's...

This work introduces a novel method for sentiment analysis in the stock market by combining Deep Neural Networks (DNN) and an improved Firefly Algorithm (FA). It is essential to comprehend investor sentiment in financial markets in order to forecast stock price movements and make wise investment...

This study investigates the impact of competition on credit risk in the Turkish banking system, focusing on Islamic (participation) and conventional banking under the same regulatory conditions regarding credit risk. The credit risk model is trained using CatBoost, extreme gradient boosting...

The financial sector faces mounting challenges in processing vast volumes of high-velocity data to support intelligent, real-time decision-making. Traditional machine learning models often fall short in accuracy, scalability, and responsiveness when dealing with large, dynamic financial...

Purpose This study explores the potential of artificial intelligence (AI) in advancing sustainability within business practices. It focuses on three key areas: optimising energy efficiency, developing sustainable products and services, and improving waste and resource management. By integrating...

In this paper, we study the multidimensional claims problem and introduce the eating algorithm to this problem. It is shown that a solution is efficient if and only if it is the outcome of the eating algorithm with a profile of specific eating functions. Moreover, we adapt three classical...

Innovations in big data and algorithms are enabling new approaches to target interventions at scale. We compare the accuracy of three different systems for identifying the poor to receive benefit transfers - proxy means-testing, nominations from community members, and an algorithmic approach...

Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applications. In particular, in the last few years, Raman spectroscopy analysis of biological samples has been successfully applied for early-stage...

Sustainable Supplier Selection and Order Allocation (SSSOA) are critical strategic decisions in supply chain management. The decision-making process becomes complex under uncertainty, especially in a multi-supplier, multi-item, and multi-period environment. This study proposes a four-stage...

This paper explores classical and Bayesian approaches to the estimation of unknown parameters and reliability functions for the power Rayleigh distribution. The maximum likelihood estimator (MLE) method is considered in classical estimation. The Bayesian estimation, on the other hand uses...

An adaptive analytics workflow is presented for customer churn prediction, combining Principal Component Analysis for dimensionality reduction, a hybrid Modified Particle Swarm Gravitational Search Optimization (MPSO-GSO) for feature selection and hyperparameter tuning, and an ensemble learning...

Data is an essential fuel for artificial intelligence (AI) to power the underlying machine learning (ML) algorithms. Missing data is common in most real-world datasets due to measurement errors, non-responses, and human errors during the data collection, which can ultimately lead to reduced...

In today's globalised trade, supply chains form complex networks spanning multiple organisations and even countries, making them highly vulnerable to disruptions. These vulnerabilities, highlighted by recent global crises, underscore the urgent need for improved visibility and resilience of the...

Artificial intelligence (AI) is transforming the way businesses operate, enabling entrepreneurs to achieve diagnoses that were once only possible for large companies. This transformation is evident in digital advertising, where AI not only enables advanced analytics, but also offers the...

This study uses natural language processing (NLP) techniques to analyze the political discourse of the surprise presidential candidate, focusing on linguistic patterns, sentiment distribution, and recurring themes. This study addresses the problem of how TikTok virality and algorithmic...

This work looks for the optimal allocation of different assets, namely, the G7 stock indices, commodities (gold and WTI crude oil), cryptocurrencies (Bitcoin and Ripple), and S&P Green Bond, over four periods: before the COVID-19 crisis, during the COVID-19 crisis and before the Russia-Ukraine...

This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH). Traditional data sampling methods, such as time bars, often fail to capture the nuances of...

Recently, internet users have significantly increased their use of search engines, and market investors are no exception. As a result, predictive models that incorporate scattered web-based information are developing as an area of forecasting. The objective of this research is to compare the...

The study conducts a bibliometric review of artificial intelligence applications in two areas: the entrepreneurial finance literature, and the corporate finance literature with implications for entrepreneurship. A rigorous search and screening of the web of science core collection identified...

The estimation of the probability of informed trading (PIN) model and its extensions poses significant challenges owing to various computational problems. To address these issues, we propose a novel estimation method called the expectation-conditional-maximization (ECM) algorithm, which can...