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The purpose of this study is to investigate how the process of becoming an entrepreneur in the cooperation and poverty contexts takes place.

This study adopted a phenomenological approach for data collection purposes. Autoscopy, which is a methodological device of reflective nature, was herein applied to a group of interlocutors comprising seven representatives of waste pickers’ cooperatives (RC). Data analysis focused on defining the meaning of participants’ speech was conducted in compliance with Interpretative Phenomenological Analysis guidelines.

The current findings have shown that becoming a representative of cooperatives involves mobilization toward empowerment and a sense of collectively doing on behalf of community interests. Moreover, these RCs become entrepreneurs in the poverty context, as they perceive opportunities, are persistent and take risks pursuing alternatives for both the survival and improvement of theirs own living conditions, and of others.

Adopting a reflective approach associated with an ontology of becoming could have led to deeper results if the current research was a longitudinal study, rather than a cross-sectional one.

Training programs provided for waste pickers should take into consideration that their learning process is mainly based on practice.

Behaviors disclosed by participants toward fostering collective and entrepreneurial actions in the poverty context may be an inspiration for future changes.

The methodological option for adopting a reflective approach resulted in a contribution device that is barely applied to research in the management field; thus, the current investigation can introduce a new pathway for further research.

Cooperatives are used as one of the strategic tools to reach smallholder farmers and reduce household poverty through augmenting access to inputs, technologies, farm productivity and markets. Our study aims to investigate the impacts of the Meki Batu Cooperative Union on reducing household poverty.

This study employed a cross-sectional survey of households in the Dugda district of the East Shewa zone in the Oromia region. A two-stage sampling design was applied, where four rural Kebeles were first randomly picked, followed by stratified random samples of 217 producers comprising 100 members and 117 non-members of cooperatives. The standard probit model was estimated with a set of observable factors. Propensity score matching (PSM), doubly robust inverse probability weighting and treatment effect estimation were performed along with matching techniques.

The results reveal that education, livestock assets, access to irrigation and extension contact positively determine participation in cooperatives. As the income-based poverty attests, the poverty gap was reduced by 5.9–6.3% and the severity of poverty by 3.7–3.8% due to the cooperative membership.

The investigation suggests the need for continued and comprehensive social services to address development challenges through the facilitation of producers’ engagement in collective actions and agribusinesses.

Existing research evidence is inconclusive with the view of impacts of collective actions on housed welfare in Ethiopia. This study empirically tested the impacts in connection to the production and marketing of high-value crops.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2024-0231

The present study undertakes an extensive review of the causes of service failures in artificial intelligence (AI) technology literature.

A hybrid review has been employed which includes descriptive analysis, and bibliometric analysis with content analysis of the literature approach to synthesizing existing research on a certain topic. The study has followed the SPAR-4-SLR protocol as outlined by Paul et al. (2021). The search period encompasses the progression of service failure in AI from 2001 to 2023.

From identified theories, theoretical implications are derived, and thematic maps direct future research on topics such as data mining, smart factories, and among others. The key themes are being proposed incorporates technological elements, ethical deliberations, and cooperative endeavours.

This research study makes a valuable contribution to understanding and reducing service defects in AI by providing insights that can inform future investigations and practical implementations. Six key future research directions are derived from the thematic and cluster discussions presented in the content analysis.

The purpose of this paper is to examine the challenges faced by rural women entrepreneurs in Lebanon and explore how stakeholder engagement strategies, guided by stakeholder theory, can mitigate these challenges. By analyzing the interactions between stakeholders and women entrepreneurs, the study aims to uncover effective solutions that contribute to the sustained success of women entrepreneurs in Lebanon's rural areas.

This study uses participatory action research (PAR) and narrative inquiry. PAR involves women entrepreneurs as active collaborators, fostering participation and enabling marginalized voices to address business challenges. Narrative inquiry delves into their experiences deeply, accessing multiple perspectives and insights.

This study uncovers challenges in resource accessibility, societal norms and market limitations for rural women entrepreneurs in Lebanon. Stakeholder influence, especially community support and tailored training programs, proves crucial. However, governmental involvement remains limited, relying more on nongovernmental organizations (NGOs) and expert mentorship. Targeted interventions and policies are essential for inclusive growth and gender equality in entrepreneurship.

This paper offers a unique perspective by focusing on rural women entrepreneurs in Lebanon, exploring their specific challenges within the country's socioeconomic landscape. Its interdisciplinary approach and actionable recommendations for practical strategies, along with a comprehensive stakeholder analysis, provide novel insights into women's entrepreneurship in rural settings.

The United Nations Convention on the Rights of Persons with Disabilities (2006) recognizes the right of disabled people to access work. Against this legislative backdrop, this study explores the strengths and weaknesses of the Italian system of targeted placement for disabled people, based on Law 68/1999, which delegates to regional authorities the management of the labor market. The examination centers on the perspective of companies, the primary stakeholders in the inclusion of persons with disabilities within organizational structures.

The article discusses the results of focus groups conducted with 28 managers of large, medium and small enterprises in Lombardy (Italy). Qualitative analysis was employed, and the results were structured using a simplified strengths, weaknesses, opportunities and threats (SWOT) analysis, incorporating practical recommendations.

The analysis leads to practical suggestions to improve the entire targeted placement process at the regional level, from selection and accompaniment to evaluation, such as improving the networking of local stakeholders who deal with the inclusion of disabled people, homogeneity of the procedures in different regions, making all employees aware of diversity management, etc. The territorial network and the welfare environment are particularly important in achieving a successful targeted placement and to promote an inclusive corporate culture.

This study is not representative of Italy as a whole, as it remains a qualitative investigation focused on a single region.

This contribution accomplishes an in-depth study of the law of labor inclusion of people with disabilities observed from the point of view of companies, which are still usually reluctant to integrate people with disabilities into their organizations or encounter difficulties in doing so.

Economic evaluations often utilise individual-patient data (IPD) to calculate probabilities of events based on observed proportions. However, this approach is limited when interest is in the likelihood of extreme biomarker values that vary by observable characteristics such as blood glucose in gestational diabetes mellitus (GDM). Here, instead of directly calculating probabilities using the IPD, we utilised flexible parametric models that estimate the full conditional distribution, capturing the non-normal characteristics of biomarkers and enabling the derivation of tail probabilities for specific populations. In the case study, we used data from the Born in Bradford study (N = 10,353) to model two non-normally distributed GDM biomarkers (2-hours post-load and fasting glucose). First, we applied fully parametric maximum likelihood to estimate alternative flexible models and information criteria for model selection. We then integrated the chosen distributions in a probabilistic decision model that estimates the cost-effective diagnostic thresholds and the expected costs and quality-adjusted life years (QALYs) of the alternative strategies (‘Testing and Treating’, ‘Treat all’, ‘Do Nothing’). The model adopts the ‘payer’ perspective and expresses results in net monetary benefits (NMB). The log-logistic and Singh-Maddala distributions offered the optimal fit for the 2-hours post-load and fasting glucose biomarkers, respectively. At £13,000 per QALY, maximum NMB with ‘Test and Treat’ (−£330) was achieved for a diagnostic threshold of fasting glucose >6.6 mmol/L, 2-hours post-load glucose >9 mmol/L, identifying 2.9% of women as GDM positive. The case study demonstrated that fully parametric approaches can be implemented in healthcare modelling when interest lies in extreme biomarker values.

Financial inclusion implies providing the access to finance for the people at the bottom of the pyramid. The financial inclusion of the rural people remains the challenge because the poor people, especially the tribal people, do not have knowledge and are financially illiterate. They cannot also bank and require specialised support to access financial capabilities. The marginalised people do not have access to finance, and the social collateral or the social capital enables the marginalised members to get access to finance.

The study aims to enhance the detection and classification of conjunctival eye diseases' severity through the development of ConjunctiveNet, an innovative deep learning framework. This model incorporates advanced preprocessing techniques and utilizes a modified Otsu’s method for improved image segmentation, aiming to improve diagnostic accuracy and efficiency in healthcare settings.

ConjunctiveNet employs a convolutional neural network (CNN) enhanced through transfer learning. The methodology integrates rescaling, normalization, Gaussian blur filtering and contrast-limited adaptive histogram equalization (CLAHE) for preprocessing. The segmentation employs a novel modified Otsu’s method. The framework’s effectiveness is compared against five pretrained CNN architectures including AlexNet, ResNet-50, ResNet-152, VGG-19 and DenseNet-201.

The study finds that ConjunctiveNet significantly outperforms existing models in accuracy for detecting various severity stages of conjunctival eye conditions. The model demonstrated superior performance in classifying four distinct severity stages – initial, moderate, high, severe and a healthy stage – offering a reliable tool for enhancing screening and diagnosis processes in ophthalmology.

ConjunctiveNet represents a significant advancement in the automated diagnosis of eye diseases, particularly conjunctivitis. Its originality lies in the integration of modified Otsu’s method for segmentation and its comprehensive preprocessing approach, which collectively enhance its diagnostic capabilities. This framework offers substantial value to the field by improving the accuracy and efficiency of conjunctival disease severity classification, thus aiding in better healthcare delivery.