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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.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

Gamification design is considered an effective way of changing users' health behavior and improving their health management performance. Even though numerous studies have investigated the positive effect of gamification competition on users, little research has considered gamification's ineffectiveness and negative effects. In particular, how gamification competition affects users' technological exhaustion remains unclear.

According to flow theory and related research on gamification, this study discusses the nonlinear relationship between gamification competition and users' technological exhaustion. Furthermore, the authors analyze the moderating effect of user type (socializers and achievers) and users' health condition on this nonlinear relationship. Based on flow theory, the authors propose a series of research hypotheses. To test all research hypotheses, the authors collected information from 407 users via a questionnaire as the data for this study.

The empirical results found a U-shaped relationship between gamification competition and technological exhaustion. Technological exhaustion gradually decreases as competition increases until reaching the lowest point; after that, technological exhaustion gradually increases as competition increases. Further, being a socializer and health condition play a moderating role in the U-shaped relationship between competition and technological exhaustion.

This study's findings not only enrich the related research in flow theory and gamification, but also contribute to the effective design of gamification in health management platforms.

Foreign direct investment in the real estate (FDIRE) sector is required to bridge the gap between investment needed and domestic funds. Further, foreign direct investment is gaining importance because other sources of raising finance such as External Commercial Borrowing and foreign currency convertible bonds have been banned in the Indian real estate sector. Therefore, the objective of the study is to explore the determinants attracting foreign direct investment in real estate and to assess the impact of those variables on foreign direct investments in real estate.

Johansen cointegration test, vector error correction model along with variance decomposition and impulse response function are employed to understand the nexus of the relationship between various macroeconomic variables and foreign direct investment in real estate.

The results indicate that infrastructure, GDP and tourism act as drivers of foreign direct investment in real estate. However, interest rates act as a barrier.

This article aimed at exploring factors attracting FDIRE along with estimating the impact of identified variables on FDI in real estate. Unlike other studies, this study considers FDI in real estate instead of foreign real estate investments.

This study aims to highlight the impact of altruistic-self and hunger awareness on socially responsible food consumption through the lens of self-awareness and self-congruity theories due to the great challenge of Sustainable Development Goal 2: Zero Hunger.

A survey was conducted with a sample of 812 respondents. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) confirm each variable's structure through the measurement model and test the hypothesis to support a structural model.

The results highlight that the combination of altruistic-self and hunger awareness (AS-HA congruence) drives consumers to execute socially responsible food consumption. Meanwhile, consumers' food-saving attitude mediation translates to the attitude towards responsible and ethical use increasing socially responsible food consumption, a contextual development in the theory of congruence. Conversely, hunger awareness is not confirmed as significantly influencing socially responsible food consumption.

This research provides valuable insights for academicians and practitioners in developing food waste management strategies that can be implemented to reduce food wastage.

Food waste is a global concern and is challenging for many manufacturing, distribution and individual wastage levels. However, food wastage by consumers is one of the most critical problems which can be minimised with awareness and attitudinal changes in behaviour as a form of socially responsible consumption.

The COVID-19 pandemic provides researchers and practitioners with an opportunity to examine the effect of emergency policing on public trust in the police and augment our understanding. Therefore, the primary purpose of this study was to examine the effect of police enforcement of COVID-19 health measures on public trust in the police in Ghana.

A multivariate binary logistic regression was utilized to assess the effect of police enforcement of COVID-19 health measures on public trust in the police in Ghana using national representative data.

Our analysis suggests that emergency policing positively influences public trust in the police in Ghana. Additionally, we observed that police-related issues such as corruption and professionalism, as well as demographic factors of the public, influence trust in the police. These observations are helpful for emergency policing and policy development in Ghana.

This study is unique because it uses national representative data to assess the effect of police enforcement of COVID-19 health measures on public trust in the police in Ghana. Furthermore, this study is among the first or among the few from Ghana and the sub-region to examine the nexus between health emergencies and policing.

Efforts to reduce farmers' market risks and improve buyers' access to farm commodities have encouraged contract farming (CF) in Ghana's cashew sector in recent years. Consequently, the existence of CF shows that farmers who use it may be benefiting from it, as it is their economic responsibility to decide how to sell agricultural products. However, the magnitudes of these benefits or otherwise have been inadequately explored. This paper aims to empirically estimate the impact of CF on farm performance and welfare of smallholder cashew farmers.

The study used probit-two-stage least square (probit-2sls) as a primary estimator to account for self-selection bias and endogeneity that could arise from both observed and unobserved heterogeneities among farming households to estimate the causal effects of CF on farm performance and household welfare.

The results indicated that participation in CF contribute significantly to the gains in farm performance (price margins, yields and net farm revenue) and welfare (consumption expenditure per capita), and that the non-participants of CF would have benefited substantially if they had participated. An analysis of the farm size disaggregated into small, medium and large with regards to the outcome variables produces mixed results.

It can be concluded that participating in CF enhances farm performance and household welfare.

While many other studies do not account for changes in farm performance and welfare due to differences in farm size or other observed factors, this study fills a crucial void.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

The purpose of the chapter was to examine the impact of customer care chatbots on customer satisfaction levels in the mobile telephony industry in Zimbabwe, with a special focus on the company Econet Wireless. This chapter shows the conceptual framework used. An online questionnaire was administered to a sample of 100 Econet Wireless subscribers who were selected using probability stratified random sampling from Zimbabwe’s 10 provinces. The research data were collected and analysed for correlation, and a multiple regression analysis was carried out to identify the relationship between customer satisfaction and the three customer service improvements brought in by the introduction of customer service chatbots. The study discovered that there is a positive relationship between customer satisfaction levels and each of the three customer service improvements brought in by customer service chatbots, namely customer service convenience, speed of response, and omnichannel strategies. This study thereby proves that the introduction of customer service chatbots in the mobile telephony industry in Zimbabwe can lead to an improvement in customer satisfaction levels. However, addressing service quality only as a determinant of customer satisfaction in isolation is not sufficient to fully improve customer satisfaction levels. Therefore, organisations that seek to improve their customer satisfaction should consider strategies that address all determinants of customer satisfaction, namely price, product quality, service quality, situational factors, and personal factors. This study contributes to the body of knowledge, particularly regarding the use of artificial intelligence (AI) for customer service in developing economies.