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This paper aims to provide insights into the environment needed for advancing a digitally enabled circular plastic economy in Africa. It explores important technical and social paradigms for the transition.

This study adopted an interpretivist paradigm, drawing on thematic analysis on qualitative data from an inter-sectoral engagement with 69 circular economy stakeholders across the continent.

The results shows that, while substantial progress has been made with regard to the development and deployment of niche innovations in Africa, the overall progress of circular plastic economy is slowed due to relatively minimal changes at the regime levels as well as pressures from the exogenous landscape. The study highlights that regime changes are crucial for disrupting the entrenched linear plastic economy in developing countries, which is supported by significant sunk investment and corporate state capture.

The main limitation of this study is with the sample as it uses data collected from five countries. Therefore, while it offers a panoramic view of multi-level synergy of actors and sectors across African countries, it is limited in its scope and ability to illuminate country-specific nuances and peculiarities.

The study underlines the importance of policy innovations and regulatory changes in order for technologies to have a meaningful contribution to the transition to a circular plastic economy.

The study makes an important theoretical contribution by using empirical evidence from various African regions to articulate the critical importance of the regime dimension in accelerating the circular economy transition in general, and the circular plastic economy in particular, in Africa.

This paper aims to examine rising geopolitical tensions associated with the implementation of the US Creating Helpful Incentives to Produce Semiconductors (CHIPS) Act of 2022.

To provide background for the analysis, the authors conduct a theoretic analysis of the literature to introduce the current geopolitical environment while examining the rising threat of conflict, general factors driving change in the world order, and the critical role that the international semiconductor supply chain plays to all involved.

In this paper, the authors observe that in good times, economic forces drive capital-intensive industries toward a free-trade-inspired concentration of manufacturing in low-cost centers of production. In challenging economic and geopolitical times, however, the trend reverses toward retrenchment and what some call techno-nationalism. This occurs as nations struggle to secure access to critical sources of supply for themselves while stifling access for competitors and rivals.

The CHIPS Act of 2022 signifies a pivotal change in global trade dynamics, shifting away from liberal norms to techno-nationalism. This shift may spark supply chain hurdles as countries adopt nationalistic sourcing, potentially causing shortages in vital components like chips. Consequently, consumers may face disruptions as companies seek alternative suppliers, resulting in higher costs and lower-quality products. Supply chain disruptions may also delay product launches, and retaliatory trade actions could affect multiple industries, limiting access to lucrative markets.

The passage of the US CHIPS Act of 2022 has major implications related to global supply chain issues and potential geopolitical concerns. This study uses the threat of potential conflict as a lens for examining the international semiconductor supply chain.

There is a lot of talk about the electric car today, but these vehicles are not new. Indeed, thebeginning of the 20th century saw electricity and the automobile take hold in North American society, so that by 1910, the electric car was everywhere. Until the turn of the 1920s, a new era dawned for transportation in the USA, but without the electric car. The purpose of this study is to question Why did it happen.

This paper develops such a comparison, not of the cars themselves, through a detailed engineering analysis, but rather of the marketing of electric vehicles in the USA in 1910 and 2010, as it appeared in the marketing strategies of the manufacturers.

There are many technical and economic reasons for this, but not only; there are also commercial strategy reasons. The position of manufacturers, especially through advertising and the press, can tell us about this golden age of the electric car, what precipitated its fall, and its reappearance a century later.

It is a comparison of images, of how electric vehicles had been and are proposed to the public, through the exploration of mainly promotional material and newspaper articles.

The aim of the study is to explore the links between multiple personal income tax regimes, pay dissatisfaction, employee lateness and absenteeism. Accordingly, this paper examines the relationships between income tax policies, pay dissatisfaction and the work withdrawal behaviours of employees in the public service.

The study adopted a quantitative design, and data were collected through a structured questionnaire from a sample of 252 respondents from the Bayelsa State Civil Service in Nigeria. Data were analysed by applying multivariate regression and structural equation modelling through the use of Stata software version 12 and SmartPLS version 4.

The results demonstrated that there was a positive relationship between personal income tax regimes and pay dissatisfaction; there was a positive relationship between pay dissatisfaction and work withdrawal behaviour of employee tardiness and absenteeism and pay dissatisfaction mediated the relationships between personal income tax regimes and work withdrawal behaviours of public sector employees.

The study appears to be the first to explore the nexus between personal income tax regimes and pay dissatisfaction and withdrawal behaviours of employee tardiness and absenteeism as well as the mediating role of pay dissatisfaction in public service organisations.

This study examines the nexus of bond market development and economic growth from 2015 to 2022.

The system-generalized method of moments (GMM) is employed on economic growth, government market capitalization, corporate market capitalization, bond yield, interest rate spread, trade openness and investment level.

The findings show that the government bond market, corporate bond capitalization and bond yield positively impact the gross domestic product (GDP). The results equally reveal a causal link between the corporate bond market, bond yield and GDP.

Governments should emphasize creating, developing and sustaining bond markets in the economies of developing countries to boost economic activity by promoting structural transformation. Policymakers should improve the implementation of existing rules and regulations while complementing them with new ones since well-developed bond markets provide alternative sources of financing that make economies financially resilient. Policymakers should encourage the issuance of corporate bonds to enhance the efficiency of the capital markets and mobilize funds for economic growth stimulation. Governments and corporations should diversify their sources of funding into the bond markets since the bond yields are favorable to economic growth.

Earlier studies presented arguable results on the bond market development and economic growth nexus. Several findings indicate a positive link; others give a negative link between bond market development and economic growth. Some show causal directions, while other reveal none. The contradictory results motivate research. This research results contribute to the literature in that the government bond market, corporate bond capitalization and bond yield positively impact the GDP of developing nations.

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

Small-scale farmers are highly heterogeneous with regard to their types of farming, levels of technology adoption, degree of commercialization and many other factors. Such heterogeneous types, respectively groups of small-scale farming systems require different forms of government interventions. This paper applies a machine learning approach to analyze the typologies of small-scale farmers in South Africa based on a wide range of objective variables regarding their personal, farm and context characteristics, which support an effective, target-group-specific design and communication of policies.

A cluster analysis is performed based on a comprehensive quantitative and qualitative survey among 212 small-scale farmers, which was conducted in 2019 in the Limpopo Province of South Africa. An unsupervised machine learning approach, namely Partitioning Around Medoids (PAM), is applied to the survey data. Subsequently, the farmers' risk perceptions between the different clusters are analyzed and compared.

According to the results of the cluster analysis, the small-scale farmers of the investigated sample can be grouped into four types: subsistence-oriented farmers, semi-subsistence livestock-oriented farmers, semi-subsistence crop-oriented farmers and market-oriented farmers. The subsequently analyzed risk perceptions and attitudes differ considerably between these types.

This is the first typologisation of small-scale farmers based on a comprehensive collection of quantitative and qualitative variables, which can all be considered in the analysis through the application of an unsupervised machine learning approach, namely PAM. Such typologisation is a pre-requisite for the design of more target-group-specific and suitable policy interventions.

Product color names related to a consumption setting are commonly used in advertising to persuade. This study aims to use consumption imagery fluency as an underlying mechanism for assessing how such a naming tactic impacts product evaluation.

Three between-subjects experiments examine how product evaluation, in response to the use of color names containing consumption situation information, varies as a function of their accessibility (Study 1), and also test the role of a naming explanation (Study 2). How readily a consumer takes in consumption imagery is evaluated as a mediator. The studies further check if color attribute serves as a moderator of such color naming effect and that the naming factor contributes to consumption imagery fluency directly or indirectly alters such through their impact on comprehension fluency (Study 3).

Marketing products with color names related to the consumption setting is more effective than using generic names. Consumption imagery fluency mediates the results. This positive outcome is reduced when color names are less accessible. Fortunately, including an explanation to facilitate reasoning for product color names is helpful to reverse this disadvantage. The same patterns are not evident for highly accessible names. In addition, the effectiveness of consumption situation-related color names is restricted to the circumstance of color attribute as secondary, as opposed to primary. Furthermore, naming factors influence the ease of consumption of imagery whether or not facilitated by comprehension fluency.

This research provides evidence of consumers’ responses to product color naming that involves consumption situations and identifies consumption imagery fluency as a potential means for mediating the studied effect.

Naming a product color in consumption situation-related terms triggers consumption imagery, driving evaluation when color is the secondary attribute of a product.

This research contributes to understanding the influence of naming a product’s color in promotional communication and correlates to productive tactics for advertising messages.