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The aim of this study is to examine the motive of China's and India's engagement in African countries particularly in Ethiopia and to address the land grabbing and debt-trap diplomacy between Ethiopia and the Asian drivers, which creates challenges across the diverse social, political, economic and ecological contexts.

This study utilises both primary and secondary data. The available literature is also reviewed. The primary data were gathered through semi-structured interviews and discussions from (1) several authority offices in Ethiopia, sources close to authorities, information-rich informants, employees and (2) perspectives, perceptions and prospects from individual members of society.

The study unmasks the win-win cooperation strategy from the perspective of the members of society in Ethiopia, evaluates whether China and India have strings attached or land grabbing motives. The study also shows that whether China's and India's move was deliberate, the implications of debt-trap diplomacy and exploitation in Ethiopia are apparent. Additionally, this study investigated several considerable potential threats to Ethiopia that will persist unless significant measures are taken to control the relations with Asian drivers.

Some of the limitations of this paper pertain to the primary data collection process from the Ethiopian Investment Commission (EIC) and other authorities, which was very challenging because people can be punished for talking to journalists or researchers. Furthermore, some investors were not willing to participate in discussions because they were engaged in areas that are not related to their licenses. Many interviewees were also not willing to disclose their names, and the data are not exhaustive in the number of investment projects covered.

This study provides new evidence on the influence of Chinese and Indian investment, aid and trade on Ethiopia's social, political and economic spheres. Additionally, this study contributes to the ongoing debate on land grabbing and debt-trap diplomacy in Ethiopia.

This study focuses on the use of guanxi by African returnees from China in Africa. It explains how returnees understood and leveraged guanxi to collaborate with Chinese partners.

This study uses semi-structured interviews to document how guanxi is increasingly extending beyond Chinese borders. It focuses on Ethiopia, Kenya and Tanzania and analyzes the returnees' mobilization of guanxi in Sino–African business contexts.

African returnees play an increasingly important role in guanxi internationalization in Africa. Returnees' understanding of guanxi is shaped by their African traditions and their Chinese experiences, creating their new cultural capital and a dynamic Sino–African business mindset.

This paper reveals an emerging shift in the business mindset among African returnees once initiated in guanxi. In addition, guanxi is increasingly practiced by African organizations.

This study aims to investigate how the Chinese Belt and Road Initiative (BRI) and Chinese outward foreign direct investments (FDI) impact the Belt and Road countries (BRCs). It draws on postcolonial theory to investigate the (geo)political objectives behind the financial and economic means.

In line with the nature of postcolonial studies, the study applies a discourse analysis integrating it with empirical data on indebtedness and trade.

This study finds that FDI and the BRI, as a development project, need to be considered a double-edged sword for the receiving countries. The authors provide evidence that China has instrumentalized financial and economic means to gain political influence and pursue geopolitical ambitions. Moreover, investments into sensitive sectors (e.g. energy, infrastructure), combined with the BRCs’ inability to pay back loans, could eventually lead to China gaining control of these assets.

The study investigates the financial and economic means that are instrumentalized to gain political influence while not considering flows of technology and know-how. It also limits itself to the study of FDI coming from one specific country, i.e. China. Therefore, no comparison and evaluation are made of FDI from other countries, such as the USA or European countries.

By revealing noncommercial objectives and geopolitical ambitions that China pursues through the BRI, the authors derive policy implications for the BRCs, third countries and China.

The study contributes to postcolonial theory and neocolonialism by investigating how China uses financial and economic means to achieve noncommercial objectives and pursue geopolitical ambitions. Additionally, the authors enhance the understanding of FDI by highlighting more subtle aspects of the complex and contextual nature of FDI as a social phenomenon, which have been overlooked thus far. The authors challenge the predominant positive framing of FDI and provide a counterpoint to the way FDI is often coined.

Drawing on the resource dependence theory (RDT), this paper investigates ownership composition, export intensity, and industry class as moderating factors to investigate the role of imported raw materials in performance of inward foreign direct investment (IFDI) in Ethiopia.

The hypotheses were tested using secondary data obtained from the 2016 Central Statistical Agency (CSA) on Large- and Medium-Scale Manufacturing and Electricity Industries Survey. The data included basic quantitative information on the country's manufacturing industry. The data items for the 2016 manufacturing and electricity industries surveyed are the numbers of proprietors or establishments involved in various sectors. The report did not record small firms that employed fewer than 10 people and did not use power-driven machinery. Two-Stage least squares (2SLS) regression analysis was performed to test the proposed hypotheses.

The results of this study indicate that three moderators (ownership composition, export intensity, and industry classification) interact with the hypothetical relationships between imported raw materials and performance. These findings enrich the knowledge of IFDI firms' operations in Ethiopia and in other least-developed countries (LDCs). The findings could provide information for IFDI firms that are looking to invest in LDCs.

Like all social science research, this study has some limitations. First, the research was conducted with the data found in the Report on Large- and Medium-Scale Manufacturing and Electricity Industries Survey In 2016. This was the first year of the second five-year Growth and Transformation Plan (GTP II), a national development plan for the 2016–2020 period. Continual research on IFDI in Ethiopia in the following years will be needed to get a full picture of the effects of the determinants on IFDIs.

To IFDI investors, the result of this thesis demonstrates several alternatives to overcoming hurdles in manufacturing operation. The results find that J.V. firms make better use of imported raw materials than W.O. subsidiaries in order to achieve better performance. Concerning the choice between focusing on export or domestic markets, the study suggests that domestic market—oriented companies require less imported raw materials to achieve better performance. Concerning the comparative advantage on different industries, this study found the performance of firms in Industry 12 depended on imported raw materials. These findings highlight the challenges and opportunities for potential foreign investors. Ownership composition, market factors, and industry factors should be well considered in making investment decisions.

This is one of few studies on IFDI in Ethiopia, the most populous LDC. Ownership composition, export intensity, and industry class are used as moderating variables to investigate the difference between imported raw materials and the level of expatriate deployment to IFDI performance. For IFDI investors, the results of this study demonstrate several alternatives to overcoming hurdles in manufacturing operation.

The present study was designed to investigate eight research questions that are related to the analysis and the detection of dialectal Arabic hate speech that targeted African refugees and illegal migrants on the YouTube Algerian space.

The transfer learning approach which recently presents the state-of-the-art approach in natural language processing tasks has been exploited to classify and detect hate speech in Algerian dialectal Arabic. Besides, a descriptive analysis has been conducted to answer the analytical research questions that aim at measuring and evaluating the presence of the anti-refugee/migrant discourse on the YouTube social platform.

Data analysis revealed that there has been a gradual modest increase in the number of anti-refugee/migrant hateful comments on YouTube since 2014, a sharp rise in 2017 and a sharp decline in later years until 2021. Furthermore, our findings stemming from classifying hate content using multilingual and monolingual pre-trained language transformers demonstrate a good performance of the AraBERT monolingual transformer in comparison with the monodialectal transformer DziriBERT and the cross-lingual transformers mBERT and XLM-R.

Automatic hate speech detection in languages other than English is quite a challenging task that the literature has tried to address by various approaches of machine learning. Although the recent approach of cross-lingual transfer learning offers a promising solution, tackling this problem in the context of the Arabic language, particularly dialectal Arabic makes it even more challenging. Our results cast a new light on the actual ability of the transfer learning approach to deal with low-resource languages that widely differ from high-resource languages as well as other Latin-based, low-resource languages.

The purpose of this paper is to investigate the effect of including letter repetition commonly found within social media text and its impact in determining the sentiment scores for two major airlines in Malaysia.

A Sentiment Intensity Calculator (SentI-Cal) was developed by assigning individual weights to each letter repetition, and tested it using data collected from official Facebook pages of the airlines.

Evaluation metrics indicate that SentI-Cal outperforms the baseline tool Semantic Orientation Calculator (SO-CAL), with an accuracy of 90.7 percent compared to 58.33 percent for SO-CAL.

A more accurate sentiment score allows airline services to easily obtain a better understanding of the sentiments of their customers, hence providing opportunities in improving their airline services.

Proposed mechanism calculates sentiment intensity of social media text by assigning individual weightage to each repeated letter and exclamation mark thus producing a more accurate sentiment score.

Research on sentiment analysis were mostly conducted on product and services, resulting in scarcity of studies focusing on social issues, which may require different mechanisms due to the nature of the issue itself. This paper aims to address this gap by developing an enhanced lexicon-based approach.

An enhanced lexicon-based approach was employed using General Inquirer, incorporated with multi-level grammatical dependencies and the role of verb. Data on illegal immigration were gathered from Twitter for a period of three months, resulting in 694,141 tweets. Of these, 2,500 tweets were segregated into two datasets for evaluation purposes after filtering and pre-processing.

The enhanced approach outperformed ten online sentiment analysis tools with an overall accuracy of 81.4 and 82.3% for dataset 1 and 2, respectively as opposed to ten other sentiment analysis tools.

The study is novel in the sense that data pertaining to a social issue were used instead of products and services, which require different mechanism due to the nature of the issue itself.