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This paper empirically demonstrates the major organizational events of a rolling forecasting process and the roles of controllers therein. In particular, this study aims to investigate how the understanding of a “realistic forecast” is translated and questioned by various mediators in the rolling forecasting process and how it affects the quality of planning as the ultimate accuracy of forecasts is seen as important.

This study follows an actor-network theory (ANT) approach and maps the key points of translation in the rolling forecasting process by inspecting the roles of mediators. This qualitative case study is based on interviews with controllers and managers involved in the forecasting process in a single manufacturing company.

The paper identified two episodes of translation in the forecasting process, in which the forecast partially stabilized to create room for managerial discussion and debate. The abilities of controllers to infiltrate various functional groups and calculative practices appeared to be one way to control the accuracy of forecasting, although this was built on a façade of neutrality.

Prior literature identifies the aims of interactive planning processes as being to improve the quality of planning. The authors apply ANT to better understand the nature of mediators in constructing an entity called a “realistic rolling forecast”.

The purpose of this study is to examine how US farm sector performed in 2020, the first year of the pandemic. There were significant supply and demand shocks due to the pandemic. Furthermore, there was significant fluctuation in commodity prices and record high government payments in 2020. This study aims to examine the performance and position of US farm sector (financially) to system (and global economy) wide shocks.

The authors examine 2020 values for farm sector financial ratios before and after the onset of the Coronavirus (COVID-19) pandemic using the data from the United States Department of Agriculture to understand the financial position and performance of the US farm sector.

The authors find solvency ratios (which are indicators of the sector's ability to repay financial liabilities via the sale of assets) worsened in 2020 relative to pre-pandemic expectations. Efficiency ratios (which evaluate the conversion of assets into production and revenue) and liquidity ratios (which are indicators of the availability of cash to cover debt payments) showed mixed outcomes for the realized results in 2020 relative to the pre-pandemic forecasts. Four profitability ratios were stronger in 2020 relative to pre-pandemic expectations. All solvency, liquidity and profitability ratios plus 2 out of 5 efficiency ratios for 2020 were weaker than their respective average ratios obtained from 2000 to 2019 data.

This research is one of the first papers to use financial ratios to examine how the US farm sector performed in 2020 compared to expectations prior to the pandemic.

The purpose of this study is to examine how lenders alter their behavior when faced with real earnings management.

This study uses the incremental R-square approach as in Kim and Kross (2005) to examine how much lenders rely on income statement and balance sheet ratios as the degree of real earnings management increases.

As real earnings management affects mostly the income statement, the authors find that lenders rely less on income statement ratios in making credit decisions in the presence of real earnings management. The authors also find that lenders do not alter their reliance on balance sheet ratios when faced with real earnings management.

This paper is the first to study how lenders alter their reliance on financial statements in making credit decisions in the presence of real earnings management. The findings of this paper could help the regulators set standards to improve the usefulness of financial statements. The findings of this paper could also help practitioners (borrowers and lenders) understand how real earnings management affects credit decisions.

The continuous increase of energy demands is a critical worldwide matter. Jordan’s household sector accounts for 44% of overall electricity usage annually. This study aims to use artificial neural networks (ANNs) to assess and forecast electricity usage and demands in Jordan’s residential sector.

Four parameters are evaluated throughout the analysis, namely, population (P), income level (IL), electricity unit price (E$) and fuel unit price (F$). Data on electricity usage and independent factors are gathered from government and literature sources from 1985 to 2020. Several networks are analyzed and optimized for the ANN in terms of root mean square error, mean absolute percentage error and coefficient of determination (R²).

The predictions of this model are validated and compared with literature-reported models. The results of this investigation showed that the electricity demand of the Jordanian household sector is mainly driven by the population and the fuel price. Finally, time series analysis approach is incorporated to forecast the electricity demands in Jordan’s residential sector for the next decade.

The paper provides useful recommendations and suggestions for the decision-makers in the country for dynamic planning for future resource policies in the household sector.

Numerous policies are established in Mauritius to attract foreign direct investment, but at the same time, severe concerns were raised concerning the erosion of Mauritian tax base, which is witnessed by the decrease in the percentage of tax revenue to gross domestic product in recent years. To avoid these issues, in 2019, the Mauritian legislator has domesticated the Organisation for Economic Co-operation and Development (OECD) BEPS 2013 Action 3 on controlled foreign company (CFC) in its income tax legislation. As such, the purpose of this study is to critically assess the implications of CFC rules of Mauritius to reduce tax avoidance in the light of international tax competition.

To achieve the research objective, this study will adopt a black letter approach by analysing the rules and regulations of various jurisdiction as well as international standards on CFCs and other tax avoidance legal provisions. A comparative analysis will be conducted between Mauritian laws on CFCs and the corresponding legislation of the UK and the USA, which are selected to assess the developed world’s position on strict CFC rules.

A hasty implementation of CFC rules leads to various complexities like interpretation issues and diminishing the competitiveness of the country to multinationals. In this respect, there is the risk of a trade-off between tax collected and foreign direct investment in the country. Consequently, the research recommends that Mauritius reforms its CFC legislation by extending the scope of tax exemptions for intra-group financing income, for the first year of CFC’s operation with the possibility of offsetting foreign taxes and for the Mauritius Revenue Authority to establish detailed guidelines on the determination of CFC income and its attribution for tax purposes in Mauritius.

Existing literature has to a great extent focused on the role of CFC rules as a tax avoidance measure and on the divergence or convergence between domestic CFC legislation against the OECD recommendations (Dourado, 2015; Xu, 2018; Beebeejaun et al., 2023). However, limited literature is available on the evaluation of the purpose of CFC rules enacted by a developing country being Mauritius in the context of the global competitive market, to which this research aims at filling the gap.

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.

In recent years, private brands for e-commerce platforms have experienced rapid growth. However, whether these platforms developing private brands should share their demand information with others and how such information sharing affects the sales format selection of national brand manufacturers have puzzled firm managers in practice. This paper aims to investigate the information-sharing strategy for the e-commerce platform and its influence on the sales format selection in the presence of the private brand.

The authors use a game-theoretical model to examine the interaction between the information-sharing strategy and sales format selection in a supply chain consisting of a manufacturer and a platform that operates a private brand.

The equilibrium results show that when the commission rate is low, the manufacturer favors agency selling, and the platform shares demand information with the manufacturer; when the commission rate is high, the manufacturer prefers reselling, and the platform does not share the information. This preference is affected by information forecasting accuracy; as the information forecasting accuracy increases, the manufacturer prefers to adopt agency selling, and the platform tends to share the information. Interestingly, under agency selling, sharing information with the manufacturer can increase the platform’s profit from selling the private brand and achieve a win-win situation for them. Furthermore, we show that the manufacturer can inspire the platform to share the information with himself by adopting agency selling, whereas the platform sharing the information improves the probability that the manufacturer adopts agency selling. Moreover, the manufacturer may have a first-mover advantage. In particular, the manufacturer moving first increases the likelihood that the manufacturer chooses agency selling and the platform shares the information.

This paper contributes to sales format literature by exploring the effect of information sharing strategy on sales format selection in the presence of the private brand and can help manufacturers and platforms to make suitable decisions regarding information sharing and sales format selection.

The study aims at examining the relationship between the forms of misclassification practices, namely expense shifting and revenue shifting. In particular, the study aims at identifying the form of shifting that has been preferred by firms to meet the industry average profitability.

Core earnings and operating revenue expectation models are used to measure expense shifting and revenue shifting, respectively. The panel fixed-effects models are used to control for unobserved heterogeneity across industries and time.

Based on a sample of Bombay Stock Exchange-listed firms, the author finds that firms prefer expense shifting over revenue shifting to meet industry average profitability, implying that firms choose the shifting tool based on the relative advantage. Further, the findings deduced from the empirical results demonstrate that firm life cycle and mandatory adoption of International Financial Reporting Standards (IFRS) moderates the relationship between shifting forms and industry average profitability. However, the negative impact of IFRS on shifting practices is found to be less pronounced among BigN audit firms.

The study is among the pioneering attempt to document the substitution relationship between shifting forms. It is the first study that examines a form of classification shifting, where gross profit and core earnings both change as an effect of misclassification.

The purpose of this study is to explore how the personal traits of the informal entrepreneurs influence their formalization decisions.

This study adopted a qualitative approach using a multicase design in which 28 informal entrepreneurs situated in Kampala district, Uganda, were engaged. An interview guide, recorders and note books were used in data collection.

The results indicate that the traits of informal and semiformal entrepreneurs are distinct. Informal entrepreneurs have been noted to be more courageous and resilient, while their semiformal counterparts have greater passion for their businesses. It is thus observed that the formalization prospects are higher for the semiformal entrepreneurs than for their informal counterparts. Entrepreneurs that would be willing to formalize their businesses are discouraged by distance, technology and the cost of involving middlemen. Whereas the resilient entrepreneurs are noted to work through these challenges, the passive ones in both the informal and semiformal categories will not formalize their businesses by giving such excuses.

This study contributes to the extant literature on informal entrepreneurship by providing initial empirical evidence on how the personal traits of the entrepreneurs influence their formalization decisions specifically.

This study aims to investigate earnings management practice of classification shifting of revenues in Chinese-listed firms.

The study employs a dataset of 2,920 A-listed firms from Chinese stock exchanges of Shanghai and Shenzhen for the period of 2003–2019. We apply both univariate and panel regression analysis by using fixed effect estimation with robust standard errors.

Our findings reveal that firms misclassify revenues by taking advantage of the flexibility provided by applicable financial reporting standards. The empirical evidence obtained through regression analysis suggest that managers reclassify non-operating revenues as operating revenue to alter the economic reality while seeking the advantage of financial reports users’ vulnerability for valuing the upper half of income statement items more as compared to lower part. The results further indicate that international financial reporting standards adoption inhibits the earnings management practices using classification shifting of revenues. It is also concluded that firms, which are suffering losses or having low growth, are more persistently involved in misclassification of revenues.

The study is unique from the point of view that it investigates earnings management from the prospective of revenue’s classification in an emerging market characterized by various market imperfections such as lower investor protection and higher information asymmetry.