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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 relative success of East Asian countries in controlling the spread of COVID-19 at its onset was widely attributed to their capacity to learn from previous experience of epidemics, their preparedness to deal with new threats to health, and public acceptance of the need to comply unquestioningly with stringent measures to contain the virus. The conditions were very different when Europe was recognised as the epicentre of the pandemic in March 2020. In a climate of uncertainty, intensified by inconsistent scientific advice and intractable political dilemmas, European governments embarked on a steep learning curve. They experimented with packages of measures based on limited and often conflicting evidence about their effectiveness in preventing transmission of the disease and high excess death rates, amid growing concern about the collateral damage being caused to public health, and to social and economic life.

Drawing on a wide range of multi-disciplinary published materials and official statistics, research articles, reports, briefings and academic debates, as well as media headlines and commentary, this briefing assesses policy learning during the first wave of the pandemic in Europe and asks:

What lessons did European countries, individually and collectively, draw from their own experiences and from the policy responses of neighbouring countries when Europe was the epicentre of the COVID-19 pandemic?

Were decision-takers better prepared to contain further waves of the disease and to improve outcomes?

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.

At present, the scale of China's floating population has reached 376 million people. Compared with the local inhabitants, the poverty problem of the floating population is more complex, and this problem should also attract the attention of all sectors of society. This paper aims to measure and analyze the multidimensional poverty of the floating population in China.

The data used in this paper are the data of the China Migrants Dynamic Survey (CMDS) in China. This survey is a large-scale national migratory population sampling survey organized by the China National Health Commission, covering 31 provinces (autonomous regions and cities) and other autonomous regions. This paper uses the dynamic monitoring and Alkire and Foster (A-F) method to study the multidimensional poverty problem of the floating population.

This study finds that income poverty is no longer the main type of poverty faced by the floating population. The multidimensional poverty of the floating population mainly occurs in the social security and education dimensions, of which social security has become the most severe poverty dimension of the floating population. From the perspective of group differences, compared with the floating population in urban areas, the multidimensional poverty of migrant workers is more serious. However, the poverty of migrant workers is mainly concentrated in one-dimensional poverty and two-dimensional poverty.

In the future, the authors should focus on the social security of the floating population in the place of influx and the education of the floating population.

Through the review of the existing literature, the authors find that the current research on the multidimensional poverty of the floating population is mainly concentrated on the migrant worker groups that move from rural areas to urban areas. However, insufficient attention is paid to the urban floating population groups moving between cities.

This paper aims to examine the mismatch between multidimensional deprivation and monetary poverty in identifying the poor in Egypt and investigates their determinants empirically.

The paper uses the Alkire-Foster multidimensional poverty measurement method using data from Egypt’s 2017/2018 Household Income, Expenditure and Consumption Survey (HIECS 2017/2018). Using a logistic regression model, the paper assesses the empirical relationship between multidimensional and monetary poverty and their determinants at the aggregate level and by dimension.

The paper demonstrates a significant mismatch between multidimensional and monetary poverty measures, underscoring their complementary nature. Statistics indicate that both measures overlap in classifying 35.81% of Egyptians, whereas monetary poverty ignores 63.12% of multidimensionally poor in at least one dimension. Regression estimates show a significant moderate negative association between expenditure per capita and multidimensional poverty and its dimensions. Moreover, they show that household head’s gender, age, education attainment, marital status, job proficiency, household size and location affect poverty mismatch and match in Egypt.

This paper offers Egyptian policymakers the multidimensional poverty index that enables more efficient designing and targeting of poverty alleviation programs and assessing current poverty alleviation programs to modify them if needed.

To the best of the authors’ knowledge, this study is the first to examine the mismatch between both poverty measures in Egypt, using the recent full data set of HIECS 2017/2018. This paper confirms that depending only on monetary measures can send inaccurate insights for crafting effective social policies. Also, it offers policymakers a comprehensive insight into the country’s poverty landscape, which enable more efficient design, targeting of poverty alleviation programs and monitoring their effectiveness.

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.

This study aims to measure the status of rural household food security across regions using multidimensional indicators. It also aims to identify the determinants of rural household food security in Ethiopia.

The study adopted descriptive and explanatory designs. It used data from the fourth wave of the Ethiopian socioeconomic survey that has 3,115 respondents. The authors constructed household food security index using variables that capture availability, access, utilization and stability dimensions of food security. The authors categorized households into relative food security groups, namely, alarming and moderately food insecure, as well as moderately and highly food secure. Beta regression model, which is widely used to analyze response variables that assume values between 0 and 1, is used to estimate the determinants of food security.

The study finds that 77.7% of rural households are food insecure. Of this, 90% are moderately food insecure. Regional variations in magnitude of food security showed that Harari, Gambella and Benshanguel Gumuz regional states are relatively better-off than other regions in Ethiopia. The study identified sex, education level, marital status, location and wealth status of households as significant determinants of food security.

This study sheds light on regional variations in multidimensional food security in Ethiopia. It thus challenged previous estimates of food security using uni-dimensional indicator. It highlighted the need for region-specific analysis of determinants and a follow up of tailored regional interventions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0139