This study aims to structure guidelines for an intervention model from the perspective of Integral Project Management to improve the competitiveness level of cacao associations in south region of Colombia. The research followed a mixed-method approach with a non-experimental cross-sectional design and a descriptive scope. The study employed a stage-based analytical framework which included: identifying the factors influencing the competitiveness of the cacao sector; grouping these factors under the six primary determinants of competitiveness with reference to Porter’s Diamond Model; and proposing guidelines for an intervention model to enhance the competitiveness of the studied associations through project management. The first stage was conducted via literature review. The second stage involved primary data collected through surveys and interviews with the associations, members, and cacao sector experts in Huila. The third stage entailed grouping the factors within the main determinants that promote and limit the competitiveness of the cacao sector in the context of Porter’s Diamond Model. Based on the analysis of the corresponding restrictive and promoting factors, strategic recommendations were formulated for the various sector stakeholders on the measures that can be adopted to address restrictive factors and maintain promoting factors to enhance and sustain the sector's competitiveness.

In most studies on hydroclimatic variability and trend, the notion of change point detection analysis of time series data has not been considered. Understanding the system is crucial for managing water resources sustainably in the future since it denotes a change in the status quo. If this happened, it is difficult to distinguish the time series data’s rising or falling tendencies in various areas when we look at the trend analysis alone. This study’s primary goal was to describe, quantify, and confirm the homogeneity and change point detection of hydroclimatic variables, including mean annual, seasonal, and monthly rainfall, air temperature, and streamflow. The method was employed using the four-homogeneity test, i.e., Pettitt’s test, Buishand’s test, standard normal homogeneity test, and von Neumann ratio test at 5% significance level. In order to choose the homogenous stations, the test outputs were divided into three categories: “useful”, “doubtful”, and “suspect”. The results showed that most of the stations for annual rainfall and air temperature were homogenous. It is found that 68.8% and 56.2% of the air temperature and rainfall stations respectively, were classified as useful. Whereas, the streamflow stations were classified 100% as useful. Overall, the change point detection analyses timings were found at monthly, seasonal, and annual time scales. In the rainfall time series, no annual change points were detected. In the air temperature time series except at Edagahamus station, all stations experienced an increasing change point while the streamflow time series experienced a decreasing change point except at Agulai and Genfel hydro stations. While alterations in streamflow time series without a noticeable change in rainfall time series recommend the change is caused by variables besides rainfall. Most probably the observed abrupt alterations in streamflow could result from alterations in catchment characteristics like the subbasin’s land use and cover. These research findings offered important details on the homogeneity and change point detection of the research area’s air temperature, rainfall, and streamflow necessary for the planers, decision-makers, hydrologists, and engineers for a better water allocation strategy, impact assessment and trend analyses.

Cassava’s adaptability to different agroecological conditions, high yield, as well as its ability to thrive under harsh climatic conditions, makes it an essential food security crop. In South Africa, the cassava value chain is currently uncoordinated and underdeveloped, with a couple of smallholder farmers growing the crop for household consumption and as a source of income. Other farmers regard it as a secondary crop and hardly any producers grow it for industrial purposes. Hence, this study sought to analyze the determinants of household participation in the cassava value chain in South Africa. The study employed the multivariate probit model to analyze the determinants of household participation in the cassava value chain in South Africa, using a primary dataset collected through a simple sample method from smallholder farmers in KwaZulu-Natal, Mpumalanga, and Limpopo provinces. Results show that livestock ownership has a positive and significant effect on the likelihood of farmers participating in the value chain by growing cassava for household food consumption. Also, findings reveal that hiring labour in cassava production and an increase in the yield during the previous season increases the probability of farmers’ interest in selling cassava tubers along the value chain. Hence, the positive and statistically significant influence of hiring labour during cassava production in driving the farmers’ interest in selling cassava tubers and cuttings implies that the development of the cassava value chain presents great opportunities for creating jobs (employment) in the country. Also, policy interventions that ensure land tenure security and empower farmers to increase their cassava yields are bound to encourage further participation in the value chain with an interest in selling fresh tubers, among other derived products to generate income. Lastly, programmes that empower and encourage youth participation in the cassava value chain can increase the number of farmers interested in selling cassava products.

Knowledge transfer, assimilation, transformation and exploitation significantly impact performing business activities, developing innovations and moving forward to new business models such as transferring to a circular economy. However, organizations’ decisions or willingness to transition to a circular economy are very often also influenced by the external environment. The study aims to determine the influence of the external environment on the transfer from a linear to a circular economy while mediating knowledge assimilation. The quantitative research involved 159 Nordic capital companies operating in Estonia and Lithuania. The survey has been performed by means of the CATI method. The analysis has been done also by applying structural equation modelling (SEM). In order to perform mediation analysis, IBM SPSS and a special PROCESS macro have been used. The study showed that knowledge assimilation partially mediates the relationship between the external environment and the transfer to the circular economy. Hence, the external environment’s direct effect is much more significant than the indirect. The added value of the study also consists in extending the concept of circular economy by including some aspects of absorptive capacity and the external environment.

Analysing external factors with a design-thinking approach is crucial for adaptation, identifying opportunities, and mitigating risks in native digital enterprises. This research introduces a framework rooted in design principles and future scenarios for external analysis, with the aim of meeting current market needs. The study employs a mixed qualitative-quantitative research approach, incorporating methods such as literature review, workshops, and surveys. These methods enable the collection and analysis of both qualitative and quantitative data, providing a comprehensive and accurate understanding of the research topic by using it in a DNVB case study. Developing a conceptual framework using a design-thinking approach which we call ASPECT contributes to a comprehensive interpretation of complexity, intertwining collective and individual factors. This reduces the risk of overlooking essential elements when making strategic decisions in ambiguous, uncertain, and volatile contexts. This method contrasts with traditional external analysis frameworks like CAME, Pestle, and SWOT. The document aims to contribute to the literature by exploring new models of external analysis based on the design process. This framework combines the conventional stages of a design thinking process with methodologies for future scenarios to identify relevant external factors for organizations. It provides an innovative conceptual framework for creating new business models and growth strategies for digital enterprises.

Using time series data covering the years 1980 to 2020, this study examines the effects of government spending, population growth, and economic expansion on unemployment in the context of South Africa. The study’s variables include government spending, population growth, and economic growth as independent factors, and unemployment as the dependent variable. To ascertain the study’s outcomes, basic descriptive statistics, the Vector Error Correction Model (VECM), the Johansen Cointegration Procedures, the Augmented Dicky-Fuller Test (ADF), and diagnostic tests were used. Since all the variables are stationary at the first difference, the ADF results show that there isn’t a unit root issue. According to the Johansen cointegration estimation, there is a long-term relationship amongst the variables. Hence the choice of VECM to estimate the outcomes. Our results suggests that a rise in government spending will result in a rise in South Africa’s unemployment rate. The findings also suggest that there is a negative correlation between unemployment and population growth. This implies that as the overall population grows, unemployment will decline. Additionally, the findings suggest that unemployment and economic growth in South Africa are positively correlated. This contradicts a number of economic theories, including Keynesian and Okuns Law, which hold that unemployment and economic growth are inversely correlated.