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The purpose of the article is to explore the perceptions of Brazilian game developers about the power relations between them and the sponsors of digital game platforms. It also aims to identify forms of collective action that developers can use to counteract the asymmetry of power.

The research employed an abductive approach, seeking empirical evidence that would challenge consolidated theory. To achieve this, semi-structured interviews were conducted with 25 Brazilian developers. The data were analyzed qualitatively using NVivo software. The aim was to resolve theoretical ambiguities identified in the literature review and to explore unexpected findings.

The study explores Brazilian game developers' perceptions through interviews, revealing their experiences within the industry’s concentrated structure and their use of collective action to navigate power dynamics.

The study's focus on Brazil limits the generalizability of its findings to the broader game development industry.

The study suggests Brazilian game devs can leverage collective action to counteract power imbalance with platforms, collaborate through events and projects and facilitate internationalization of their games.

The study suggests collective action could empower developers to challenge platform dominance and foster a stronger community among Brazilian game developers.

The article’s value lies in examining Brazilian devs' experiences within their specific industry context and highlighting collective action as a potential strategy for developers.

Diabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.

In this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.

By conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.

In this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

Digitalization is one of the major factors that fosters economic growth across the world. However, the level of digitalization varies significantly between developed and developing countries, with the latter often lagging behind. To bridge this gap, it is crucial to pinpoint the drivers of digitalization, specifically from the macroeconomic and country-level governance dimensions. Therefore, this study aims to investigate the determinants of digitalization, particularly for countries in Asia and the Pacific region.

Our study utilizes unbalanced panel data from 46 Asian and Pacific countries for the period of 2001–2021. Initially, we analyzed the data using conventional econometric methods, such as pooled ordinary least squares (POLS), random-effects model (REM) and fixed-effects model (FEM). Moreover, we employed endogeneity-corrected techniques and alternative proxies to enhance the robustness and reliability of our findings.

Our findings reveal that economic development progress, government expenditure relative to country size and political stability are key drivers of digitalization. In contrast, corruption at the country level emerges as a significant impediment. Notably, our results remain robust to endogeneity-corrected techniques and alternative proxies of digitalization. Overall, these insights can inform policymakers, helping them to understand the macroeconomic and governance factors shaping digitalization and guide their decision-making toward effective policy interventions.

This study’s empirical findings add significant value to the existing literature by quantifying the impact of macroeconomic and governance factors on digitalization in selected countries. This offers valuable insights for policymakers, particularly in nations with lower levels of digitalization.

This study aims to apply the appreciative inquiry approach (AI) to develop a tourism strategy for poverty alleviation in marginalised communities. The focus is to provide practical insights for leveraging tourism to drive positive socio-economic change for the impoverished, using Rosetta, a port city in Egypt with cultural and historical significance, as a case study.

This qualitative applied study uses the four-D phases of AI and thematic analysis to strategise tourism development in Rosetta. Through interviews, focus groups and field visits, the study identifies tourism potential, stakeholder aspirations and actionable strategies for sustainable development. The approach prioritises a bottom-up, community-centric and stakeholder-involved process, aiming for inclusive and equitable growth.

The study revealed Rosetta’s underutilised tourism potential, emphasising heritage tourism. Although tourism offers some economic benefits, its impact on alleviating poverty in Rosetta remains limited. A holistic strategy for tourism development in Rosetta is proposed for economic growth and poverty reduction, focusing on sustainable management, local empowerment, enhanced marketing, improved infrastructure and diversified tourism offerings.

While AI is not new in qualitative studies, the novelty of this study lies in its application to tourism planning for poverty alleviation in a marginalised community like Rosetta, introducing a comprehensive tourism strategy with an original framework applicable to comparable destinations. The study’s significance is emphasised by providing actionable strategies for policymakers, valuable insights for practitioners and enriching the discourse and methodology on pro-poor tourism for academics, representing a step towards filling the gap between theoretical concepts and practical strategies.

The main purpose of this research is to examine the impact of institutional quality and sectoral employment on environmental degradation in developing countries. This paper also re-examined the validity of the Environmental Kuznets Curve (EKC) hypothesis and estimated the long run impact of explanatory variables on CO₂ emissions.

In this paper, the balanced panel data for the period 2002–2016 was used based on data availability and applied two-step SYS-GMM estimators.

The results showed that institutional quality such as government effectiveness (GE) and the rule of law (RL) reduce CO₂ emissions and promote environmental quality in developing countries. Interestingly, the authors found new evidence that employment in agriculture and industry has a positive impact on pollution, while employment in the service sector was negatively associated with CO₂ emissions, and the validity of the EKC hypothesis was confirmed. In addition, the research suggests that strong institutional frameworks and their effective implementation are the most important panacea and should be treated as a top priority to counteract environmental degradation and achieve the UN Sustainable Development Goals.

This is the first study to examine the short run and long run effects of institutional quality and sectoral employment on environmental degradation using the balanced panel data for a large sample of developing countries. This paper also used a special technique of Driscoll and Kraay standard error approach to confirm the robustness results and showed the different roles of sectoral employment on environmental quality.