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This paper seeks to present a fully digital, real‐time (RT) hardware‐in‐the‐loop (HIL) simulator on PC‐cluster, of electric systems and drives for research and education purposes; to use the developed system to conduct several motor drives implementation and to evaluate the motor and the control algorithm performance in RT.

This simulator was developed with the aim of meeting the simulation needs of electromechanical drives and power electronics systems while solving the limitations of traditional RT simulators. This simulator has two main subsystems, software and hardware. The two subsystems were coordinated together to achieve the RT simulation. The software subsystem includes MATLAB/Simulink environment, a C++ compiler and RT shell. The hardware subsystem includes FPGA data acquisition card, the control board, the sensors, and the controlled motor.

The complexity of RT implementation of motor drives is greatly reduced by utilizing this simulator. The detailed operation and implementation of this simulator are presented, together with test results and comparisons with simulated virtual environment for a permanent magnet dc and induction motors (IM). The simulator performance is adequate for both open and closed loops motor drives. The simulation time step is limited by the system Master/Target CPU's speed, the communication network type, and the complexity of the control algorithm.

A typical application for this system is to select and evaluate the performance of electric motors for a hybrid electric vehicle in a real vehicle environment without actually installing that component in the real vehicle.

The use of the developed RT simulator to achieve HIL simulation allows rapid prototyping, converter‐inverter topologies testing, motors testing, and control strategies evaluation. The transition from simulated virtual environment to the HIL mode can be performed by replacing the model of the physical system (e.g. motor) with the DAQ blocks to represent the channels connected to the physical system sensors. The use of a single environment for both simulation and HIL control provides a quick experimentation and performance comparison between the real and simulated systems.

The current research inspects the moderation role of ownership concentration on chief executive officer (CEO) characteristics and real earnings management (REM) relationship in Jordan.

Driscoll–Kraay regressions were run using data from 348 firm-year observations for companies listed on the Amman Stock Exchange between 2013 and 2018.

Driscoll–Kraay regressions demonstrate that CEO experience, tenure and political connections improve REM practices. Ownership concentration diminishes and limits REM practices when combined with CEO experience, tenure and political connections, since all three have a negative and significant link with REM.

Initial constraints include the study’s lack of generalisability due to a small number of CEO-related parameters. Second, critics of the ideal model for judging EM have a foreseeable flaw. No generally accepted model is perfect.

This study’s conclusions are crucial for industry participants, including companies, policymakers, investors and the general public. These findings will help investors, practitioners and regulators understand that businesses with significant ownership concentrations and experienced CEOs have superior earnings and low REM practises.

The findings of this study have an optimistic impact on the existing body of knowledge. The current literature has yet to properly inspect the moderation role that ownership concentration has on the connotation between CEO characteristics and EM.

Despite several research studies in both developed and developing nations, ownership concentration has been almost virtually neglected. The current study could fill a hole in earlier research, rendering it a novel study.

This study aims to investigate the impact of artificial intelligence (AI) on customer loyalty in the luxury fashion market. It explores how AI-driven tools influence customer trust, satisfaction, commitment and engagement, which in turn affect loyalty. By examining these relationships, the study provides insights into the acceptance and effectiveness of AI technologies in enhancing customer loyalty within the luxury fashion sector.

This study employs structural equation modelling (SEM) to analyse data collected from 406 luxury consumers in Iran. The data was gathered using a targeted sampling procedure, leveraging DigiKala’s e-commerce platform. A comprehensive literature review informed the measurement items, and a seven-point Likert scale was used. The methodology includes confirmatory factor analysis (CFA) to assess the reliability and validity of the constructs, followed by hypothesis testing through SEM.

The study reveals that AI significantly enhances customer loyalty in the luxury fashion market by positively influencing trust, satisfaction, commitment and engagement. Satisfaction and engagement were found to be key mediators between AI and loyalty, while trust had no direct impact on loyalty. The results underscore the importance of AI-driven personalized experiences in fostering stronger customer relationships and loyalty.

This study is one of the first to explore the impact of AI on customer loyalty in the luxury fashion market, using a comprehensive model that includes trust, satisfaction, commitment and engagement as mediators. It extends the stimulus-organism-response (SOR) and technology acceptance model (TAM) frameworks, offering valuable insights for luxury brands on how AI can be leveraged to enhance customer relationships and loyalty.

The purpose of this paper is to problematise the concept of corruption as it is used in the African context by exposing the weaknesses in the business model used to define corruption and resource the massive yet incompetent anti‐corruption effort. The paper then aims to follow this critique by considering an alternative way of dealing with the awesome dimensions of African corruption.

The paper utilises in‐depth secondary source analysis, applying critical theory.

Corruption's main interpretive framework, neo‐liberalism, is exposed as dominating, business‐centric and non‐utilitarian. A new paradigm with a strong ethnographic texture is presented.

The paper for the first time co‐analyses two contending paradigms for the construction of African corruption in the context of the global economic crisis.

This study aims to measure the effect of customers’ technology readiness and the quality of electronic services on customer satisfaction.

The statistical sample included 410 respondents from 24 branches of a private bank. The sampling method was questionnaire. Because of the structural and organizational similarity of private banks and the elimination of nuisance variables, a bank with the most branches and customers has been selected. To test the model, equation modeling was performed to test the hypotheses. Data were collected through a self-developed structured questionnaire, which served as the measurement tool as well.

The results of the study showed that technology readiness has a significant and positive effect on customer satisfaction through the quality of self-service. Moreover, the intermediate role of perceived value in this regard was confirmed; however, the role of trust was not confirmed.

Previous studies have considered technology readiness as an effective factor in the quality of self-service and customer satisfaction. In this study, apart from quality of service in self-service banking and customer satisfaction, two variables of trust and perceived value have been investigated. An attempt has also been made to address some questions, including “what the effect of customer technology readiness on perceived value of self-services as well as customer satisfaction is?” and “how it is possible to improve self-service quality in modern banking based on customer expectations?” or “what effects variables such as trust and perceived value have on customer satisfaction?” Having a glance at the studies done before, it can be understood that so far, there has been no study done using a mixture of these variables, yet societies’ demands for self-service operations grow day by day. It is, therefore, mandatory to study the prerequisites associated with any actions before one is taken. The paper contributes in the following way: trust and perceived value are added to the the study because of their role in customer satisfaction. In addition, for the first time, variables have been studied, which had never been under focus in any studies in developing countries before.

Cause-related B2B marketing programs involve sponsoring organisations working with B2B suppliers with the help of non-profit organisations (NPOs) on practises relating to environmental friendliness, workforce diversity, human rights, safety, philanthropy and business ethics. The study aims to identify the combinatory factors driving the adoption of Digital B2B platforms for managing cause-related B2B marketing programs.

The study adopts an innovative approach of fuzzy-set qualitative comparative analysis (fsQCA) on data collated from top corporations in India supporting cause-related B2B marketing programs. Sponsoring organisations and NPO dyads (i.e. survey both) filled out an email survey on 264 cause-related B2B marketing programs.

The study establishes that the combination of technological, organisational and environmental factors would lead to the adoption of Digital B2B platforms in managing cause-related B2B marketing programs. The study identifies six combinations of these factors for adopting Digital B2B platforms within and across sponsoring organisations and NPOs.

The study findings would aid cause-related B2B marketers in developing Digital B2B platforms’ capabilities by understanding the different combinations of factors driving adoption. Digital B2B platforms’ capabilities can improve market performance if developed as core competencies.

The study findings would enable improvements in the implementation and performance of cause-related B2B marketing programs. Better management of cause-related B2B marketing programs would help increase beneficiary coverage and the realisation of societal goals.

To the author’s knowledge, this is the first study to apply the TOE framework in conjunction with complexity theory to explain the diffusion of adoption of Digital B2B platforms for managing cause-related B2B marketing programs.

This paper reviews 105 Scopus-indexed articles to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of human resource management (HRM).

A semi-systematic approach has been used in this review. It allows for a more detailed analysis of the literature which emerges from multiple disciplines and uses different methods and theoretical frameworks. Since ML research comes from multiple disciplines and consists of several methods, a semi-systematic approach to literature review was considered appropriate.

The review suggests that HRM has embraced ML, albeit it is at a nascent stage and is receiving attention largely from technology-oriented researchers. ML applications are strongest in the areas of recruitment and performance management and the use of decision trees and text-mining algorithms for classification dominate all functions of HRM. For complex processes, ML applications are still at an early stage; requiring HR experts and ML specialists to work together.

Given the current focus of organizations on digitalization, this review contributes significantly to the understanding of the current state of ML integration in HRM. Along with increasing efficiency and effectiveness of HRM functions, ML applications improve employees' experience and facilitate performance in the organizations.

The COVID-19 pandemic has disrupted various systems that drove people to adapt to certain technologies, such as electronic government services, for daily survival and to meet social distancing requirements. Therefore, this study aims to determine the antecedents of e-government use based on prospect theory and modified unified theory of acceptance use of technology (UTAUT) during a pandemic.

Convenience sampling of 368 respondents from Vietnam was conducted, and questionnaires were distributed personally or by email. The data were analyzed following a two-stage structural equation modeling (SEM) using SPSS v23 and AMOS v23. The validity and reliability of the instrument were tested and ensured.

Results show that perceived severity drives government support and social influence while perceived security drives government support, social influence and trust. Social influence enhances government support and trust, which both drives e-government use. Mediation analysis shows that government support mediates perceived the influence of perceived severity on e-government use.

The integration of prospect theory and UTAUT brings into light what will drive the adoption of e-government in the context of Vietnam. Supporting mechanisms, such as security measures, trust-building, government support and social influence, will drive citizens to adapt to technologies provided by the government but would also rely on the perceived risks and benefits.

This study integrates prospect theory and a modified version of UTAUT to explain the drivers of e-government use. The results reveal that under uncertainties, government support is critical in driving the use of e-government for people to manage the daily lives for survival.

This study aims to assess the use of the Internet of Things (IoT) in retail stores to improve supply chain visibility and integration.

This study employed a qualitative methodology with data collected using semi-structured interviews from a sample selected using purposive sampling. The population consists of 48 employees, of which 6 were selected for the sample as they worked directly with IoT and supply chain issues. Participants were from a SPAR franchise store (Samenwerken Profiteren Allen Regalmatig).

Thematic analysis was used to analyse the transcribed data from the interviews. The themes identified include supply chain visibility, supply chain integration and IoT. The findings indicate that the main IoT used is an organisational-wide system, the SIGMA (SPAR Integrated Goods Management Application) system. Other technologies that aid supply chain visibility and integration are geotags, the internet, WhatsApp social media applications, emails and scanners.

From the findings, this study recommends that IoT systems should be frequently updated to reflect current trends and that IoT systems should enable the integration of small and medium Enterprises (SMEs) suppliers.

The Fourth Industrial Revolution has ushered in new technologies that revolutionise business operations. Among these technologies is the IoT, which has ushered in a new connectivity area. However, there is little research on the use of IoT for supply chain visibility and integration in the South African retail sector. It provides sector-specific insights and recommendations for retailers, which might not be covered in general supply chain management literature.

The aim of this paper is to experimentally evaluate the effectiveness of the state-of-the-art printed Arabic text recognition systems to determine open areas for future improvements. In addition, this paper proposes a standard protocol with a set of metrics for measuring the effectiveness of Arabic optical character recognition (OCR) systems to assist researchers in comparing different Arabic OCR approaches.

This paper describes an experiment to automatically evaluate four well-known Arabic OCR systems using a set of performance metrics. The evaluation experiment is conducted on a publicly available printed Arabic dataset comprising 240 text images with a variety of resolution levels, font types, font styles and font sizes.

The experimental results show that the field of character recognition for printed Arabic still requires further research to reach an efficient text recognition method for Arabic script.

To the best of the authors’ knowledge, this is the first work that provides a comprehensive automated evaluation of Arabic OCR systems with respect to the characteristics of Arabic script and, in addition, proposes an evaluation methodology that can be used as a benchmark by researchers and therefore will contribute significantly to the enhancement of the field of Arabic script recognition.