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This study aims to present a methodology and a system to support the technical and managerial issues involved in anomaly detection within the reverse logistics process of an e-commerce company.

A case study approach is used to document the company’s experience, with interviews of key stakeholders and integration of obtained evidence with secondary data.

The paper presents an algorithm and a system to support a more efficient and smart management of reverse logistics based on a set of anticipatory actions, and continuous and automatic monitoring of returned goods. Improvements are described in terms of a number of key performance indicators.

The analysis and the developed system need further applications and validations in other organizational contexts. However, the research presents a roadmap and a research agenda for the reverse logistics transformation in Industry 4.0, by also providing new insights to design a multidimensional performance dashboard for reverse logistics.

The paper describes a replicable experience and provides checklists for implementing similar initiatives in the domain of reverse logistics, in the aim to increase the company’s performance along four key complementary dimensions, i.e. time savings, accuracy, completeness of data analysis and interpretation and cost efficiency.

The main novelty of the study stays in carrying out a classification of anomalies by type and product category, with related causes, and in proposing operational recommendations, including process monitoring and control indicators that can be included to design a reverse logistics performance dashboard.

The sustainable development goal (SDG 17) is to strengthen the means of implementation and revitalise the global partnership for sustainable development. Partnership is a key strategy for acquiring skills, information and private finance for developing critical infrastructure projects. Existing studies have mostly concentrated on identifying critical success factors (CSFs) for successfully delivering public–private partnerships (PPPs) projects, but the interrelationships among CSFs expected from stakeholders have garnered less attention. The purpose of this study is to examine the interconnections among CSFs of PPP projects while keeping the public in mind, as well as the influencing routes that contribute to success in the PPP implementation process.

The study uses a mixed-method approach that involves both quantitative and qualitative methods of data collection. The data for the study were collected through a well-structured questionnaire from the respondents who are directly or indirectly associated with the PPP project implementation process. The CSF’s framework was built using a literature study and expert interviews; nine hypotheses were developed and evaluated using structural equation modelling. Random and purposive sampling techniques were used for the respondents who were directly or indirectly associated with the PPP project implementation process.

The findings of this study reveal that the relationship between public and private partners is more adherent than collaborative, with unequal responsibilities, powers and resources distributed among the parties involved. The relevance of state rules, the PPP directive and policies in PPP implementation is supported by the positive connection between favourable environments (Fav_Env) and government attributes (Gov_Atr; the path coefficient is 0.405). Moreover, government attributes have a favourable influence on private company attributes (Pvt_Atr; the path coefficient is 0.198), showing that the state plays a substantial role in the private sector’s decision-making process in PPP. The long-term sustainability and success of a PPP project are dependent on the established interrelationships between CSFs.

The established interrelationships between CSFs will contribute to the long-term viability and success of a PPP project. Since PPP is still in its infancy in India, the study of CSFs in PPPs and their interrelationships will help policymakers and other stakeholders better understand the major individual factors impacting the success or failure of PPP initiatives.

Because PPP is still in its infancy in India, the study of CSFs in PPPs and their interrelationships will help policymakers and other stakeholders to better understand the major individual factors impacting the success or failure of PPP initiatives. A key implication of the study borders on providing a better understanding of the relationships between CSFs and public engagement. This is also critical for the long-term viability of PPPs and the enhancement of public service quality.

This study aims to develop and validate the scale of the Behavioral Regulation for the behavior of the Financial Statement Fraudster, from the perspective of the Organismic Integration Theory (OIT).

To achieve the objective of this article, an exploratory and quantitative study was developed. The instrument developed followed all the steps recommended by Koufteros (1999) and MacKenzie et al. (2011), from the elaboration of the constructs based on the theory to the factorial validation of reliability.

The tests applied reveal that the instrument has statistical validity and can be applied to models that seek to explain the individual motivations for committing accounting fraud.

We did not develop a mathematical model. As a suggestion for future studies, it is recommended to focus on developing a mathematical model relating the motivations to commit accounting fraud with variables capable of measuring the quality of governance or related to performance. In addition, study factors that may moderate these relationships.

The validated instrument can be used by auditors and gatekeepers to detect the risk of fraudulent behavior.

The instrument validated here may be useful to researchers who wish to test the motivations for committing fraud in structural models.

There is little research on accounting fraud on how to define theoretical constructs (as far as the literature review has reached, to the best of the authors’ knowledge, none has been identified). In addition, few studies have been identified that suggest the OIT as an adequate theoretical lens to illuminate the phenomenon of accounting fraud.

The purpose of this study was a longitudinal study on knowledge organization (KO) realm structure and cluster concepts and emerging KO events based on co-occurrence analysis.

This longitudinal study uses the co-occurrence analysis. This research population includes keywords of articles indexed in the Web of Science Core Collection 1975–1999 and 2000–2018. Hierarchical clustering, multidimensional scaling and co-occurrence analysis were used to conduct the present research. SPSS, UCINET, VOSviewer and NetDraw were used to analyze and visualize data.

The “Information Technology” in 1975–1999 and the “Information Literacy” in 2000–2018, with the highest frequency, were identified as the most widely used keywords of KO in the world. In the first period, the cluster “Knowledge Management” had the highest centrality, the cluster “Strategic Planning” had the highest density in 2000–2018 and the cluster “Information Retrieval” had the highest centrality and density. The two-dimensional map of KO’s thematic and clustering of KO topics by cluster analysis method indicates that in the periods examined in this study, thematic clusters had much overlap in terms of concept and content.

The present article uses a longitudinal study to examine the KO’s publications in the past half-century. This paper also uses hierarchical clustering and multidimensional scaling methods. Studying the concepts and thematic trends in KO can impact organizing information as the core of libraries, museums and archives. Also, it can scheme information organizing and promote knowledge management. Because the results obtained from this article can help KO policymakers determine and design the roadmap, research planning, and micro and macro budgeting processes.

The authors propose the concept of “Autonomic Cycle for innovation processes,” which defines a set of tasks of data analysis, whose objective is to improve the innovation process in micro-, small and medium-sized enterprises (MSMEs).

The authors design autonomic cycles where each data analysis task interacts with each other and has different roles: some of them must observe the innovation process, others must analyze and interpret what happens in it, and finally, others make decisions in order to improve the innovation process.

In this article, the authors identify three innovation sub-processes which can be applied to autonomic cycles, which allow interoperating the actors of innovation processes (data, people, things and services). These autonomic cycles define an innovation problem, specify innovation requirements, and finally, evaluate the results of the innovation process, respectively. Finally, the authors instance/apply the autonomic cycle of data analysis tasks to determine the innovation problem in the textile industry.

It is necessary to implement all autonomous cycles of data analysis tasks (ACODATs) in a real scenario to verify their functionalities. Also, it is important to determine the most important knowledge models required in the ACODAT for the definition of the innovation problem. Once determined this, it is necessary to define the relevant everything mining techniques required for their implementations, such as service and process mining tasks.

ACODAT for the definition of the innovation problem is essential in a process innovation because it allows the organization to identify opportunities for improvement.

The main contributions of this work are: For an innovation process is specified its ACODATs in order to manage it. A multidimensional data model for the management of an innovation process is defined, which stores the required information of the organization and of the context. The ACODAT for the definition of the innovation problem is detailed and instanced in the textile industry. The Artificial Intelligence (AI) techniques required for the ACODAT for the innovation problem definition are specified, in order to obtain the knowledge models (prediction and diagnosis) for the management of the innovation process for MSMEs of the textile industry.

This study examines the dynamics of multidimensional poverty in Nigeria from 2003 to 2018, focusing on women and children, to understand the reality of poverty in Nigeria, where poverty reduction has been stagnant.

This study employed the first-order dominance (FOD) methodology to conduct a multidimensional analysis of poverty among households, women and children in Nigeria, using data from the Demographic and Health Surveys (DHS) conducted in 2003, 2008, 2013 and 2018. We examined how the relative position of multidimensional poverty in each zone has changed for approximately 20 years.

The results indicated that the north-south poverty gap in Nigeria persisted as of 2018 and, regarding within the north and south, changes in the relative pecking order of poverty between the zones have occurred considerably over the past two decades. Different trends were also observed for child and female poverty, suggesting the influence of the unique dimensions of poverty and cultural differences.

This study is the first poverty analysis to apply the FOD approach to children and women in Nigeria, the country with the highest poverty, over a relatively long period of 2003–2018.

This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

The purpose of this study is to examine the extent to which using accounting as a multidimensional practice that encompasses technical, social and moral dimensions facilitates the instigation and advancement of a culture of sustainable development.

A qualitative approach was used to analyse the case of Waratah Coal Pty Ltd vs Youth Verdict Ltd – a dispute over a lease to establish a coal mine. The study draws on Carnegie et al.’s (2021a, 2021b) multidimensional definition of accounting and the Carnegie et al.’s (2023) framework for analysis to explore how different parties drew on accounting concepts to support their position over the sustainability of the mining lease proposal.

A multidimensional perspective on accounting appears to have clear transformative potential and can be used to champion a culture of sustainable development. This approach also has broad societal, environmental and moral implications that transcend Western financial metrics. This study shows that relying solely on accounting as a technical practice to pursue economic benefits can result in contested arguments. Overall, this analysis illustrates how the wider public, and notably First Nations communities, might challenge accounting methodologies that marginalise cultural and social narratives.

This paper expands accounting research by demonstrating how fully embracing accounting’s capacities can create a space for hearing multiple voices, including those silenced by Western accounting practices. Specifically, this study presents a unique case in which the authors incorporate the voices and views of those affected by accounting-based decisions.

The aim of the study is to understand the transformative impact of ChatGPT on artificial intelligence (AI) research, its applications, implications, challenges and potential to shape future AI trends. The study also seeks to assess the relevance and quality of research output through citation and bibliographic coupling analysis.

This study employed a comprehensive bibliometric analysis using Biblioshiny and VOSviewer to investigate the research trends, influential entities and leading contributors in the domain of AI, focusing on the ChatGPT model.

The analysis revealed a high prevalence of AI-related terms, indicating a significant interest in and engagement with ChatGPT in AI studies and applications. “Nature” and “Thorp H.H.” emerged as the most cited source and author, respectively, while the USA surfaced as the leading contributor in the field.

While the findings provide a comprehensive overview of the ChatGPT research landscape, it is important to note that the conclusions drawn are only as current as the data used.

The study highlights potential collaboration opportunities and signals areas of research that might benefit from increased focus or diversification. It serves as a valuable resource for researchers, practitioners and policymakers for strategic planning and decision-making in AI research, specifically in relation to ChatGPT.

This study is one of the first to provide a comprehensive bibliometric analysis of the ChatGPT research domain, its multidimensional impact and potential. It offers valuable insights for a range of stakeholders in understanding the current landscape and future directions of ChatGPT in AI.

This study aims to develop an econometric analysis of how modern agriculture can be a fundamental instrument for reducing the levels of multidimensional poverty in Uganda. It demonstrates the importance of agriculture in reducing inequalities amongst the poor while focusing on the relationship between increasing productions from modern agricultural practices and the poverty level across the country.

The study explores Box–Jenkins approach to cereal production data with the use of econometric analysis as the main tool to determine the implications of modern agricultural practices in Uganda. Most poor people around the world are in marginalized rural environments, and agriculture provides for their livelihoods. This makes agricultural development crucial for reducing multidimensional poverty on a large scale and needs development within agriculture to be enhanced. Education, health and standard of living are the three dimensions considered from the weighted indicators, amounting to 30%, to be categorized poor in the three dimensions.

Modernization of agriculture is an ultimate solution to multidimensional poverty reduction in Uganda through employment generation and the effects of food prices. Shreds of evidence support the theories that agricultural incomes together with the actual wages increase with a general rise in the rural non-agricultural economy. Results depict a close correlation between national income and GDP per capita which is a very significant indication that more application of agricultural technology would lead to a sub sequential improvement of livelihoods engaged in agricultural practices.

Agriculture remains a vital sector that employs a greater portion of the population in Uganda’s economy. Major roles have been played by the sector in the economy including employment opportunities, rural household incomes, food supplies and a reduction in poverty from a multidimensional front. Exploring the behavior of poverty level using modern agriculture as an indicator and its relationship with the poverty level arising from improved agricultural practices could provide a meaningful display of variation in poverty across the regions at the country level.