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AbstractOver the last decade, rapid advances in information systems (ISs) have greatly reshaped and changed the nature of doing business and how its performance is measured, with Electronic Auditing (E-auditing) emerging as a pivotal element in improving organizational efficiency. This study addresses the challenges faced in manually implementing audits and underscores the necessity for transitioning to electronic audit systems. The manual approach has limitations regarding the accuracy of operations, so to enhance performance, E-auditing is now imperative. The purpose of the study is to evaluate E-auditing in the public sector of Saudi Arabia, utilizing DeLone and McLean’s information system model (DM ISM). The focus is on vital factors including information quality, system quality, service quality, system usage and user satisfaction and their influence on the performance of internal audit departments, particularly during the challenges posed by the recent COVID-19 pandemic. This research employs a quantitative approach, utilizing a self-administered survey questionnaire to collect data from E-auditing users in the Saudi public sector. The study applies partial least squares structural equation modelling (PLS-SEM) to validate the gathered data. Findings reveal that information quality and system quality significantly influence E-auditing usage. While service quality exhibits no marked effect on usage, the study establishes a strong relationship between E-auditing usage and user satisfaction. Effective E-auditing usage and satisfied users contribute convincingly to the improved performance of internal audit departments. The paper concludes with implications, limitations, and suggestions for future studies.

AbstractThe concept of country branding, also referred to as nation or place branding, involves the creation and management of a nation’s image and products to promote various aspects of its identity. This study conducted a systematic review of country branding studies based on articles published between 2010 and 2020. A total of 59 papers were obtained from electronic databases such as Scopus, search engines like Google Scholar, and publishers including Taylor and Francis, Emerald, Elsevier, and Wiley online, using a specific search criterion. Of the 59 articles, only 44 met the inclusion criteria and were included in this review. The results were organised using the theory, context, characteristics, and methods (TCCM) organising approach for systematic literature reviews. The study revealed two dominant research themes: ‘national branding campaign’ and ‘country of origin (COO). Most studies did not use any specific model and the qualitative method was the dominant research method used. The findings of this study provide a roadmap for understanding the literature on country branding and offer directions for future theory building.

AbstractChinese agriculture grapples with contemporary challenges involving resource scarcity, environmental concerns, and transformations in agricultural labor structures. This work endeavors to address these challenges and foster sustainable agricultural development by advocating the principles of agricultural green development and sustainability in China. The application principles of digital technology are first expounded alongside a discussion on the foundational concepts of fuzzy systems. Subsequently, an in-depth analysis is undertaken on the composition and trajectory of China’s agricultural total factor productivity (ATFP), culminating in the construction of a Green ATFP model. The findings illuminate the heavy reliance on labor and resources in current agricultural development, with the transformation of labor structure emerging as a pivotal factor influencing progress. Notably, the trends of feminization, aging, and urbanization in rural labor have precipitated a substantial shortage, impeding agricultural development. Furthermore, under the guidance of digital technology and fuzzy systems, this paper establishes the theoretical framework of green ATFP. Evaluation results disclose a noteworthy decrease in the contribution rates of various indicators under the Green ATFP model, with the highest contribution rate approximating 1.23. This implies that the existing measures for China’s green ATFP are still imperfect, signaling a need for greater emphasis on environmental considerations in agricultural development. This work furnishes valuable insights for advancing agricultural development and contributes significantly to the ongoing efforts in promoting the green evolution of Chinese agriculture.

Influence maximization is an important research area in social network analysis where researchers are concerned with detecting influential nodes. The detection of influential nodes is of great interest in several disciplines including computer science, opinion propagation, political movements, or economics, where systems are often modeled as graphs. The Influence Maximization problem is proved NP-hard. This computational complexity is justified by two main factors. The first factor is about the important size of social networks. Modern social networks like TikTok and Facebook have reached an unprecedented number of users. Dynamic social networks, whose topology or/and informational content is able to evolve, represent the second factor. Maximizing influence in such networks remains a significant task. In this light, several methods have been proposed. Being motivated by this fact, we provide in this paper a detailed survey of influence maximization approaches. Our main concern is to provide a taxonomy of existing models in both static and dynamic networks. In addition, we provide a comparison of the state-of-the-art approaches according to a clear categorization. New trends for detecting influential nodes are also discussed. We provide then some challenges as well as future directions. • Taxonomy of existing IM models in both static and dynamic networks is provided. • A comparison of existing approaches according to a clear categorization is presented. • New trends for detecting influential nodes are also discussed. [ABSTRACT FROM AUTHOR]

The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence in particular. Increasing the capacity of road networks is not always possible, thus a more efficient use of the available transportation infrastructure is required. Another issue is that many problems in traffic management and control are inherently decentralized and/or require adaptation to the traffic situation. Hence, there is a close relationship to multiagent reinforcement learning. However, using reinforcement learning poses the challenge that the state space is normally large and continuous, thus it is necessary to find appropriate schemes to deal with discretization of the state space. To address these issues, a multiagent system with agents learning independently via a learning algorithm was proposed, which is based on estimating Q-values from k-nearest neighbors. In the present paper, we extend this approach and include transfer of experiences among the agents, especially when an agent does not have a good set of k experiences. We deal with traffic signal control, running experiments on a traffic network in which we vary the traffic situation along time, and compare our approach to two baselines (one involving reinforcement learning and one based on fixed times). Our results show that the extended method pays off when an agent returns to an already experienced traffic situation. [ABSTRACT FROM AUTHOR]

In the present paper, we propose a new approach on 'distributed systems': the processes are represented through total orders and the communications are characterized by means of biorders. The resulting distributed systems capture situations met in various fields (such as computer science, economics and decision theory). We investigate questions associated to the numerical representability of order structures, relating concepts of economics and computing to each other. The concept of 'quasi-finite partial orders' is introduced as a finite family of chains with a communication between them. The representability of this kind of structure is studied, achieving a construction method for a finite (continuous) Richter–Peleg multi-utility representation. [ABSTRACT FROM AUTHOR]