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This study aims to identify the main challenges to achieving humanitarian logistics in the context of United Nations peace missions in sub-Saharan Africa and to present suggestions for overcoming the logistical gaps encountered.

The methodological approach of the work focuses on the comparative case study of the United Nations Mission in South Sudan, the United Nations Multidimensional Integrated Stabilisation Mission in the Central African Republic and The United Nations Organisation Stabilisation Mission in the Democratic Republic of Congo from 2014 to 2021. The approach combined a systematic literature review with the authors’ empirical experience as participant observers in each mission, combining theory and practice.

As a result, six common challenges were identified for carrying out humanitarian logistics in the three peace missions. Each challenge revealed a logistical gap for which an appropriate solution was suggested based on the best practices found in the case study of each mission.

This paper presents limitations when addressing the logistical analysis based on only three countries under the UN mission as a case study, as well as conceiving that certain flaws in the system, in the observed period, are already in the process of correction with the adoption of the 2016–2021 strategy by the UN Global Logistic Cluster. The authors suggest that further studies can be carried out by expanding the number of cases or using countries where other bodies (AU, NATO or EU) work.

To the best of the authors’ knowledge, this study is the first comparative case study of humanitarian logistics on the three principal missions of the UN conducted by academics and practitioners.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

The existing literature offers various perspectives on integrating cryptocurrencies into investment portfolios; yet, there is a gap in understanding the behaviours, attitudes and cross-investment links of individual investors. This study, grounded in the modern portfolio theory and the random walk theory, aims to add empirical insights that are specific to the UK context. It explores four hypotheses related to the influence of socio-demographics, digital adoption, cross-investment behaviours and financial attitudes on cryptocurrency owners.

This study uses a logistic regression model with secondary data from the Financial Lives Survey 2020 to assess the factors impacting cryptocurrency ownership. A total of 29 variables are used, categorized into four groups aligned with the hypotheses. Additionally, hierarchical clustering analysis was conducted to further explore the cross-investment links.

The study reveals a significant lack of diversification among UK cryptocurrency investors, a pronounced inclination towards high-risk investments such as peer-to-peer lending and crowdfunding, and parallels with gambling behaviours, including financial dissatisfaction and a propensity for risk-taking. It highlights the influence of demographic traits, risk tolerance, technological literacy and emotional attitudes on cryptocurrency investment decisions.

This study provides valuable insights into cryptocurrency regulation and retail investor protection, underscoring the necessity for tailored financial education and a holistic regulatory approach for investment products with comparable risk levels, with the aim of minimizing regulatory arbitrage. It significantly enhances our understanding of the unique dynamics of cryptocurrency investments within the evolving financial landscape.

As many businesses faced economic disruption due to the Covid-19 pandemic and sought financial relief, existing bank relationships became critical to getting a loan. This study examines factors associated with the development of personal relationships of rural small businesses with community bank representatives.

We applied a mixed-method approach. We employed descriptive statistics, principal factor analysis and logistic regression for data analysis. We distributed an online survey to rural small businesses in five states in the United States. Key informant interviews with community bank representatives supplemented the survey results.

A business owner’s trust in a banker was positively associated with the establishment of a business–bank relationship. However, an analysis of individual trust’s components revealed that the nature of trust is complex, and a failure of one or more components may lead to decreased trustworthiness in a banker. Small businesses that preferred personal communication with a bank were more inclined to relationship banking.

Due to the relatively small sample size and cross-sectional data, our results may not be conclusive but should be viewed as preliminary and as suggestions for future research. Bankers should be aware of the importance of trust for small business owners and of the actions that lead to increased trustworthiness.

The study extends the existing knowledge on the business–bank relationship by focusing mainly on social (instead of economic) factors associated with the establishment of the business–bank relationship in times of crisis and high uncertainty.

Color psychology theory reveals that complex images with very varied palettes and many different colors are likely to be considered unattractive by individuals. Notwithstanding, web content containing social signals may be more attractive via the initiation of a social connection. This research investigates a predictive model blending variables from these theoretical perspectives to determine crowdfunding success.

The research is based on data from 176,614 Kickstarter projects. A number of machine learning and artificial intelligence techniques were employed to analyze the listing images for color complexity and the presence of people, while specific language features, including socialness, were measured in the listing text. Logistic regression was applied, controlling for several additional variables and predictive model was developed.

The findings supported the color complexity and socialness effects on crowdfunding success. The model achieves notable predictive value explaining 56.4% of variance. Listing images containing fewer colors and that have more similar colors are more likely to be crowdfunded successfully. Listings that convey greater socialness have a greater likelihood of being funded.

This investigation contributes a unique understanding of the effect of features of both socialness and color complexity on the success of crowdfunding ventures. A second contribution comes from the process and methods employed in the study, which provides a clear blueprint for the processing of large-scale analysis of soft information (images and text) in order to use them as variables in the scientific testing of theory.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

This paper contributes insights into how different firm types in the emerging market (EM) of Turkey respond to upgrading pressures in terms of internationalization and the usage of domestic political support. It seeks to highlight how the usage of and the responses to different strategies, connections and policy instruments vary with firm types.

Binary logistic regression analysis is used to differentiate and identify characteristics of firms regarding market-seeking strategies and their usage of institutional and financial support. The analysis is based on survey data from firms located in the metro-region of Istanbul: advanced market multinational enterprises (AMNEs), Turkish MNEs (TMNEs) and domestic Turkish firms (DTFs).

Different types of firms within the population of innovative firms in the EM setting of Turkey show significant variety regarding the usage of and the responses to key factors affecting internationalization. AMNEs particularly benefit from investment and export incentives as well as from establishing political connections in Turkey. DTFs significantly use tax incentives and primarily seek advanced markets. TMNEs particularly benefit from investment and export incentives and prefer to target advanced markets.

Using Turkey as a single-country setting is a limitation to the generalizability of the results. Future studies could use more cases of AMNEs to compare different countries of origin. In addition, the intended focus on R&D-related firms produces specific outcomes for such companies.

National and regional policies need to pursue different strategies for the surveyed groups of firms to attract and maintain foreign direct investments (FDIs) of AMNEs as well as to support outward FDIs of domestic firms and EM MNEs. In particular, policies for market entries and knowledge sourcing in advanced markets are becoming a crucial factor for EM firms in overcoming a shortage of resources at home.

This paper’s findings challenge existing theories such as the concept of psychic distance or liabilities of foreignness, which do not always provide an adequate explanation for internationalization activities of EM firms. In addition, it is highly relevant to apply an eclectic or multidimensional concept when conducting research in EMs in order to capture the interrelated constructs of upgrading, internationalization and political support.

This study aims to determine the extent to which consumers perceive food-related information to be tied to the intrinsic characteristics of food, particularly safety.

The authors examined Japanese consumers’ preferences regarding domestic or foreign perishables, as well as how they interpret origin information, focusing on their perceptions of safety using descriptive analysis and binary logistic regression analysis.

The results indicated that a high proportion of Japanese consumers considered domestic foods to be safer; in particular, people affected by food safety information obtained through personal communication, books and magazines tended to choose domestically produced perishables rather than imported ones.

The belief that domestic foods are safer than imported ones was highly universal among Japanese consumers, although this belief is not necessarily grounded in scientific evidence. When considering strategies to improve consumers’ information literacy, factors that have fostered this belief must be discussed. For consumers to make logical food choices, both food businesses and consumers require scientific and informational literacy and policy support.

“Poor, Stressed, Drink (alcohol), and Gambling” is one of the campaigns for poverty eradication in Thailand. This study focuses on informal workers—gamblers—who belong to low-income groups and are not covered by the law as an employer. The main objective was to investigate the factors affecting financial stress among informal laborers and determine the factors that drive informal workers to buy lottery tickets (classified by economic, psychological and social motives).

The authors applied binary logistic regression to determine what factors affected financial stress and multinomial logistic regression was applied to examine the factors affecting the motives for buying the lottery.

According to the study's results, factors including education, income, gambling intensity, level of financial literacy, saving and knowledge about finance in general influenced both economic and psychological motives negatively. However, gender, status, age, level of risk tolerance, self-evaluated level of acceptable risk and knowledge about compound interest influenced both economic motives and psychological motives positively. It is worth noting that both the self-evaluation of their level of financial literacy and knowledge about inflation resulted in effects moving in different directions, with self-evaluation of their level of financial literacy and knowledge about inflation negatively affecting economic motives, but positively affecting psychological motives.

The results of this study are expected to help policymakers understand more about this issue since it will illustrate the relationships between financial stress and financial literacy, financial behaviors, financial attitudes and risk tolerance and gambling behaviors. After all, financial stress is a significant problem affecting individuals, their families and the community, and it stems from various complex factors. Therefore, the government and counseling agencies should apply active strategies to mitigate these issues and lessen the resulting financial stress by providing financial literacy projects, as well as financial counseling.

Low financial literacy, especially being inefficient at managing one's finances, unusually comes with unhealthy financial thought patterns, as well as a lack of systematic financial management. Furthermore, the lack of financial literacy can potentially lead to unfavorable circumstances. When one falls into uncontrollable situations, including divorce, becoming unemployed, having health problems, being in toxic relationships, loss of a breadwinner, an unexpected pregnancy, etcetera, they could easily find themselves failing to properly cope with these problems and become stressed. Finally, they are also more at risk to take illicit drugs or begin gambling more frequently.

One of the key elements that reduces financial stress is a person's finances, which is thought to have a significant role in reducing their betting behaviors. The findings of this study can be used to guide policy making intended to deter those who have never gambled from starting. Gambling is considered a risk-taking activity with a higher value reward in return. Money, enjoyment, socialization and excitement were all popular motives for gambling. These findings were consistent with what has been observed in Thai society related to the factors influencing individual to gamble, in other words, economic, psychological and social motives. The study focused on gamblers who were informal laborers. They are laborers without an employer according to the Thai labor law, do not have any social security from the government and, usually, have low incomes.