High-quality implementation of cross-border mergers and acquisitions (cross-border M&As) is an important pathway for emerging-market multinational enterprises (EMNEs) to enhance their international competitiveness. However, in comparison to developed countries, cross-border M&As by EMNEs are often prohibited by the liability of origin caused by negative political coverage. How and why negative political coverage affect the completion of cross-border M&As by EMNEs? What are the contextual constraints that moderate the impact of negative political coverage on cross-border M&As completion? Based on the “liability of origin” theory, this paper addresses these questions using data from the Zephyr database on cross-border M&As by EMNEs in the United States from 2016 to June 2021 and employing a logit model for estimation. The research findings are as follows: (1) Negative political coverage leads to negative perceptions of emerging market countries by host country stakeholders, creating the liability of origin and stigmatizing the corporate nationality, thereby reducing the success rate of cross-border M&As by EMNEs. (2) Increasing geographical distance leads to information asymmetry, reinforcing the negative impact of negative political coverage on the completion of cross-border M&As by EMNEs. (3) Relevant mergers and acquisitions exacerbate the negative effect of negative political coverage on the success rate of cross-border M&As by EMNEs. (4) Being a publicly traded firm and having successful experience in cross-border M&As both intensify the negative impact of negative political coverage on the success rate of cross-border M&As by EMNEs.

The cars industry has undergone significant technological advancements, with data analytics and artificial intelligence (AI) reshaping its operations. This study aims to examine the revolutionary influence of artificial intelligence and data analytics on the cars sector, particularly in terms of supporting sustainable business practices and enhancing profitability. Technology-organization-environment model and the triple bottom line technique were both used in this study to estimate the influence of technological factors, organizational factors, and environmental factors on social, environmental (planet), and economic. The data for this research was collected through a structured questionnaire containing closed questions. A total of 327 participants responded to the questionnaire from different professionals in the cars sector. The study was conducted in the cars industry, where the problem of the study revolved around addressing artificial intelligence in its various aspects and how it can affect sustainable business practices and firms’ profitability. The study highlights that the cars industry sector can be transformed significantly by using AI and data analytics within the TOE framework and with a focus on triple bottom line (TBL) outputs. However, in order to fully benefit from these advantages, new technologies need to be implemented while maintaining moral and legal standards and continuously developing them. This approach has the potential to guide the cars industry towards a future that is environmentally friendly, economically feasible, and socially responsible. The paper’s primary contribution is to assist professionals in the industry in strategically utilizing Artificial Intelligence and data analytics to advance and transform the industry.

Given the growing significance of the metaverse in research, it is crucial to understand its scope, relevance in the tourism industry, and the human-computer interaction it involves. The emerging field of metaverse tourism has a noticeable research gap, limiting a comprehensive understanding of the concept. This article addresses this gap by conducting a hybrid systematic review, including a variable-oriented literature review, to assess the extent and scope of metaverse tourism. A scrutiny on Scopus identified a reduced number of relevant documents. The analysis exposes theoretical and empirical gaps, along with promising opportunities in the metaverse and tourism intersection. These insights contribute to shaping a contemporary research agenda, emphasizing metaverse tourism. While this study offers an overview of current research in metaverse tourism, it is essential to recognize that this field is still in its early stages, marked by the convergence of technology and transformations in tourism. This exploration underscores the challenges and opportunities arising from the evolving narrative of metaverse tourism.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

The Sipongi System is essential in dealing with forest and land fires because this system provides real-time data that empowers stakeholders and communities to proactively overcome fire dangers. Its advantages are seen in its ability to provide detailed information regarding weather conditions, wind patterns, water levels in peatlands, air quality, and responsible work units. This data facilitates efficient decision-making and resource allocation for fire prevention and control. As an embodiment of Collaborative Governance, the Sipongi System actively involves various stakeholders, including government institutions, local communities, environmental organizations and the private sector. This cooperative approach fosters collective responsibility and accountability, improving fire management efforts. The Sipongi approach is critical in reducing forest and land fires in Indonesia by providing real-time data and a collaborative governance model. This results in faster response times, more effective fire prevention and better resource allocation. Although initially designed for Indonesia, the adaptable nature of the system makes it a blueprint for addressing similar challenges in other countries and regions, tailored to specific needs and environmental conditions. Qualitative research methods underlie this study, including interviews with key stakeholders and analysis of credible sources. Government officials, community leaders, environmental experts and organizational representatives were interviewed to comprehensively examine the mechanisms of the Sipongi System and its impact on forest and land fire management in Indonesia. Future research should explore the application of Sipongi Systems and collaborative governance in various contexts by conducting comparative studies across countries and ecosystems. Additionally, assessing the long-term impact and sustainability of the Sipongi System is critical to evaluating its effectiveness over time.

The melon culture is one of the Brazilian horticultural crops, due to its productive potential and socio-economic role. It is recommended for the State of Goiás and the Federal District for it is easy to plant and having need of zoning of climatic conditions and thus, being able to perform their sowing. The present work used the Sarazon program to perform the water balance of the melon crop, for the 2nd, 4th and 6th five-day sowing dates in August, September and October and in relation to the water reserves in the soil of 50 mm and 75 mm. The data were spatialized using the SPRING 4.3 program. It was observed that the producers are performing in practice what can be demonstrated in theory that the period October 16–20 is the most indicated for sowing in soils of 50 mm of water reserve and October 6–10 the beginning of sowing in soil of 75 mm of water reserve for the cultivation of melon and have adequate profitability.