Search results

Customised capacity building is thought to be essential for organisations. However, empirical studies are lacking with respect to its effect on employee outcomes. This study aims to examine the effect of customised capacity building on employee outcomes including employee empowerment and employee engagement through employee learning in Ghanaian local government institutions.

Valid responses from 281 employees of Metropolitan, Municipal, and District Assemblies (MMDAs) in Ghana were collected through a survey. A structural equation model was used to analyse the data and test the hypotheses formulated.

The results showed that customised capacity building has an effect on employee learning, employee empowerment, and employee engagement. Employee learning also had an effect on employee empowerment and employee engagement. In addition, employee learning partially mediated the effect of customised capacity building on both employee empowerment and employee engagement.

This study is of particular relevance to public organisations. As there is a dearth of studies focusing on customised capacity building, this study provides insight into incorporating the phenomenon into public sector organisations to enhance employee learning, empowerment and engagement.

The study investigates the influence of financial transparency on citizens' trust and revenue paying behaviour of citizens of local governments in sub-Saharan Africa. It relies on the theories of stewardship and public choice in explaining the relationship between financial transparency, trust and willingness to pay.

The study applied a Partial Least Square Structural Equation Model (PLS-SEM) to survey data of 404 respondents selected from four Metropolitan and Municipal Assemblies of Ghana to test the hypotheses of the study.

It establishes the fact that financial transparency positively influences trust of citizens in local government and their willingness to pay taxes and levies. The study also found that both financial transparency and trust are low in the local governments of Ghana.

The study emphasises the importance of financial transparency in improving trust and willingness to pay. Thus, local governments are encouraged to seek innovative ways to enhance the quality and access to financial information by the citizens.

While prior studies focus on the measurement and determinant of financial transparency, this study links financial transparency to revenue mobilisation in the local government of sub-Saharan Africa.

This study aims to investigate the impact of seaport efficiency on economic growth in Ghana over the period 2006–2020.

Comprehensive methodology, diverse data analysis techniques, including Augmented Dickey–Fuller tests, autoregressive distributed lag (ARDL) modeling and Granger Causality, were applied to explore the intricate relationship between Seaport Efficiency and Economic Growth.

The findings reveal a statistically significant and positive association between seaport efficiency and GDP, underscoring the crucial role of efficient seaport operations in actively stimulating economic growth. Beyond seaport efficiency, influential factors such as capital, human capital, knowledge spillover and productive capacities were identified, contributing to the dynamics of economic growth.

The Granger Causality Test solidifies seaport efficiency as a robust predictor of GDP fluctuations, emphasizing its significance in economic forecasting. Notably, this study contributes to the existing body of knowledge with its nuanced exploration of the intricate relationship between seaport efficiency and economic growth in the specific context of Ghana.

This study’s implications extend beyond academia, offering invaluable guidance for policymakers and planners. It serves as a comprehensive roadmap for informed decision-making, emphasizing the pivotal role of efficient seaports in charting a trajectory for enduring and resilient economic progress in the nation.

While the broader theme has been explored in existing literature, the uniqueness of this study lies in its specific application to the Ghanaian context. The choice of Ghana, a nation where maritime transport handles over 90% of trade, underscores the significance of understanding seaport efficiency in this regional and economic setting. The study’s originality is reinforced by incorporating diverse economic variables, aligning with recommendations for a comprehensive analysis of factors influencing port performance.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

The purpose of this paper is to determine the effects of socio-economic factors of land redistribution for agricultural development project beneficiaries on savings in the North West Province, South Africa.

A binary logistic regression model was employed to determine the effects of socio-economic factors of project beneficiaries on their savings.

The results show that the average number of trainings attended by the beneficiaries, the proportion of youth per project and the average net farm income of the project positively and significantly influence the level of savings by the beneficiaries. About 62 percent of the beneficiaries did not have savings; thus, only 38 percent of beneficiaries had savings. Of the 38 percent who had savings, the majority (77 percent) had an annual net farm income of less than R1,000. Only 2 percent of the projects had an annual net farm income of more than R10,000.

The findings of this study are valuable to policymakers dealing with the issue of land reform and could shed some light on how land redistribution can achieve its intended purposes. These findings should be granted serious consideration when formulating policies aimed at improving savings within collective groups.

The findings of this study have revealed the importance of training and participation of youth in influencing savings. As well, the findings imply that an organization or household with a health income have a higher propensity of saving.

The research findings point out to the importance of saving. With savings, a household is in a better position to deal with situations that arises in case of emergency.

This paper is among the few studies to analyze the determinants of savings at a group or project level. Most studies are done at household or individual level.

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

To endure and cope in the rapidly changing environment, it is required of firms to gain a deeper acquisition of knowledge on market dynamics and subsequently concentrate on corporations' capacity to create, restructure and integrate their internal and external competences. Hence, the objective of this study is to investigate the influence of eco-dynamic capability (EDC) on the sustainability performance of small and medium-sized enterprises (SMEs).

Structured questionnaires were used to obtain primary data. The data were solicited from 500 employees and owner-managers of SMEs. The study’s hypotheses were tested using standard multiple regression through IBM SPSS Statistics (version 24).

The study revealed that EDC has a substantial positive effect on the economic, social and environmental sustainability performance dimensions.

The focus of this study is on EDC. Thus, although dynamic capability has been the subject of substantial study, little is known regarding the effect of EDC on the economic sustainability performance (ESP) (financial), environmental sustainability performance (ENSP) and social sustainability performance (SSP) of SMEs, predominantly amongst SMEs in emerging economies.

More complexity, less freedom, distrust and a lack of information seem to pose threats to the success of partner relationships in accounting firms, as approximately 70% of business partnerships fail globally, undermining SDG 17. The low competitive intensity in this industry seems not to help the current situation. Yet, the existing strategic alliance (SA) literature have been somewhat sluggish in adequately addressing how partnership attributes (PAs) affect partnership success (PS) and how brand competition (BC) impacts this relationship. In response, this conceptual work addresses the impact of PAs on PS in accounting firms. It further explores the BC conditions under which the PAs–PS connection may be intensified.

Incorporating theories and empirics on six distinct topics, this study presents a conceptual model and ten hypotheses that are worth testing in future research.

This research finds that PAs will be favourably linked to PS, and this favourable association will be positively moderated by BC such that the PAs–PS connection will be more pronounced if BC within the accounting industry is high than low.

Further research is needed to empirically test the suppositions made. Also, they could extend the proposed framework to cover other moderators like technological turbulence, market dynamism and government regulation.

Practical lessons for governments, shareholders, chief executive officers, consultants and other industry players, particularly those who are interested in the success of accounting partnership firms, are deliberated.

This study demonstrates how PAs and BC interact to foster PS. It also provides a baseline information for upcoming researchers to investigate other external factors under which the PAs–PS connection may be improved.

The much-trumpeted Green Climate Fund and several other official financial mechanisms for financing adaptation to climate change under the UN Framework Convention on Climate Change have fallen short in meeting adaptation needs. Many poorer people are still grappling with the scourge of climate change impacts. Consequently, there has been a dominant research focus on climate change financing emanating from official development assistance (ODA), Adaptation Fund, public expenditure and private sector support. However, there has been little attempt to examine how migrants’ remittances can close adaptation financing gaps at the local level, ostensibly creating a large research gap. This paper aims to argue that migrants’ remittances provide a unique complementary opportunity for financing adaptation and have a wider impact on those who are extremely vulnerable to climate change.

The paper is aligned to the qualitative research approach. Both secondary and primary data acquired through interviews and focus group discussions were used for the study. Multiple sampling methods were also used to select the respondents.

The findings show that remittances are used to finance both incremental costs of households’ infrastructure and consumption needs, as well as additional investment needs to be occasioned by ongoing or expected changes in climate.

In the wake of dwindling government/public revenue, ODA and poor commitment of Annex II countries to fulfil their financial obligations, the study makes the following recommendations: First, the financial infrastructure underpinning money transfers in both sending and recipient countries should be improved to make transfers attractive. Second, significant steps should be taken to reduce the fees on remittance services, especially for the small transfers typically made by poor migrants. Finally, adequate climatic information should be made available to local people to ensure that remittances are applied to the right adaptation option to avoid maladaptation.