Search results

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

The increasing presence of traditional or new forms of robots at work demonstrates how the copresence of workers and robots might reframe work and workplaces and consequently arouse new human resource management (HRM) questions regarding how to manage the spatiotemporal change of work in organizations. Based on a spatiotemporal perspective, this conceptual article examines the implication of new spatiotemporal dynamics of work, which are generated by the interaction between workers and traditional or new forms of robots that are driven by advanced digital technologies, for HRM.

The article begins by carrying out a selective review focusing on the studies that enhanced the comprehension of the digital-driven spatiotemporal dynamics of work. It then presents a spatiotemporal framework from which it examines the implications of digital-driven spatiotemporal work boundaries for HRM. The article ends by underscoring the theoretical and empirical importance of taking more interest in new spatiotemporal forms of work for developing the HRM of the future.

By developing the notion of workuniverses, which denotes the spatiotemporal boundaries generated by the act of working through the interaction between workers and different forms of robots, this research first develops a theoretical framework that discerns three forms of spatiotemporal dynamics forming workuniverses at different levels and two spatiotemporal arrays for managing the spatiotemporal change of work in organizations. The HRM questions and ethical concerns generated by the formation of workuniverses are then revealed through four focuses: the management ethics in workuniverses, individuals' spatiotemporal well-being, collective spatiotemporal coordination and spatiotemporal change management in workuniverses.

This research provides an original perspective, which is the spatiotemporal perspective, to examine the new spatiotemporal dynamics that form workuniverses and the HRM questions and concerns generated by the increasing interaction between workers and different forms of digital-driven robots.

Differences in annual publication counts may reflect the dynamic of scientific progress. Declining annual numbers of publications may be interpreted as missing progress in field-specific knowledge.

In this paper, we present empirical results on dynamics of progress in economic fields (defined by Journal of Economic Literature (JEL), codes) based on a methodological approach introduced by Bornmann and Haunschild (2022). We focused on publications that have been published between 2012 and 2021 and identified those fields in economics with the highest dynamics (largest rates of change in paper counts).

We found that the field with the largest paper output across the years is “Economic Development”. The results reveal that the field-specific rates of changes are mostly similar. However, the two fields “Production and Organizations” and “Health” show point estimators which are clearly higher than the estimators for the other fields. We investigated the publications in “Production and Organizations” and “Health” in more detail.

Understanding how a discipline evolves over time is interesting both from a historical and a recent perspective. This study presents results on the dynamics in economic fields using a new methodological approach.

Prior institutional duality research asserts that ceremonial implementation of organisational practice protects multinational corporations’ subsidiaries. However, the temporal dynamics of the safeguarding function has been under researched. Public sector organisations have also been ignored. This research aims to explore how the safeguarding function is created, maintained and disrupted using the overseas offices (OOs) of a bilateral development agency (BDA) as a case.

A multi-case study, underpinned by neo-institutionalism, was conducted. Data obtained from in-depth remote interviews with 39 informants from the BDA OOs were analysed using the “asking small and large questions” technique, four analytical techniques, cross-case synthesis and theoretical propositions.

A three-phase process was identified. The first phase is the appearance of discrepancies due to institutional duality. The second is the emergence of ceremonial implementation as a solution. In the third phase, “the creation, maintenance and disruption of a safeguarding function” begins. When ceremonial implementation successfully protects the OOs, the safeguarding function is created. The OOs are likely to repeat ceremonial implementation, thus sustaining the function. Meanwhile, when conditions such as management staff change, ceremonial implementation may not take place, and the safeguarding function disappears.

The BDA OOs may not face strong host country regulative pressures because they are donors to aid-recipient countries. Hence, the findings may not directly apply to other public sector organisations.

Development cooperation practitioners should understand that ceremonial implementation is not exclusively harmful.

To the best of the author’s knowledge, this is the first institutional duality research that explores the temporal dynamics of safeguarding functions targeting public sector organisations.

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.

This study aims to examine the implications of short-term and long-term reputation change because of government agency responses to firm product defects.

This study’s findings have important implications for both scholars and practitioners. From a scholarly perspective, the authors create a more fine-grained examination of reputation that may be used to assess various performance dimensions. From a practice perspective, managers must realize that reputation can be one of an organization’s most important resources as it meets each of the valuable, rare, inimitable and nonsubstitutable criteria associated with those resources capable of providing sustainable competitive advantage.

Analysis of 17,879 product recalls from 15 automobile manufacturers in the US suggests that firms with higher long-term reputations are more likely to face regulator sanctions when a reputation-damaging event happens. On the other hand, firms with higher short-term reputations are less likely to face sanctions in such circumstances. Finally, firms whose short-term reputation exceeds their long-term reputation are less likely to be sanctioned by regulators when reputation-damaging events occur.

There are several limitations that should be addressed. First, as our reputation measure is based on government investigations of potential defects, vehicles that have never been inspected are not included in the sample. Although this number is likely extremely low, omitting vehicles that have never been inspected leaves out some high-reputation firms from the sample. In addition, the study relies on a single-firm stakeholder that is capable of punitive actions.

From a practical perspective, this study’s findings encourage managers to think about the temporal aspects associated with firm reputation, and to realize that stakeholders may react differently when their expectations are not met depending on an organization’s relative long- and short-term reputations. From a theoretic perspective, the primary contribution of this study is to illustrate how long-term and short-term changes in reputation can provide mixed signals to firm stakeholders regarding future performance.

This study explores the temporal aspects of firm reputation by examining how government sanctions vary depending on firms’ long-term (10 years) and short-term (1 year) reputation. The findings of this study contribute to current reputation research by illustrating the variation in government responses to product defects as a function of short-term and long-term reputation. In doing so, the important role of the timing of firm performance is considered.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

This paper aims to investigate how purchasing organizations implement supplier diversity (SD) initiatives over time.

A multiple case study approach was conducted. Data were collected through in-depth interviews with participants from purchasing organizations, intermediary organizations and diverse suppliers.

The research suggests that the SD journey encompasses three different, but interrelated stages before full implementation is achieved: structuring, operation and adaptation. The findings also provide evidence that SD implementation in Brazil is highly influenced by the lack of a consistent knowledge base and the lack of legitimized intermediary organizations.

Using a temporal approach to understand how different practices suggested by the literature have been managed by practitioners over time, this study contributes to the understanding of the path to effective SD implementation and how intra- and interorganizational context influences this journey.

By identifying which practices should be adopted during different phases of SD implementation and proposing ways to overcome some of the inherent challenges, managers can better plan and allocate resources for the adoption of a successful SD initiative.

This research demonstrates how organizations can promote diversity and reduce social and economic inequalities by buying from diverse suppliers.

Using a temporal approach, the research empirically investigates how different purchasing organizations have implemented and managed the known practices and dealt with the challenges faced when trying to adopt SD.

This research developed a model to understand and predict housing market dynamics and evaluate the significance of house permits data in the model’s forecasting capability.

The research uses a multilevel algorithm consisting of a machine-learning regression model to predict the independent variables and another regressor to predict the dependent variable using the forecasted independent variables.

The research establishes a statistically significant relationship between housing permits and house prices. The novel approach discussed in this paper has significantly higher prediction capabilities than a traditional regression model in forecasting monthly average prices (R-squared value: 0.5993), house price index prices (R-squared value: 0.99) and house sales prices (R-squared value: 0.7839).

The impact of supply, demand and socioeconomic factors will differ in various regions. The forecasting capability and significance of the independent variables can vary, but the methodology can still be applicable when provided with the considered variables in the model.

The resulting model is helpful in the decision-making process for investments, house purchases and construction as the housing demand increases across various cities. The methodology can benefit multiple players, including the government, real estate investors, homebuyers and construction companies.

Existing algorithms and models do not consider the number of new house constructions, monthly sales and inventory in the real estate market, especially in the United States. This research aims to address these shortcomings using current socioeconomic indicators, permits, monthly real estate data and population information to predict house prices and inventory.

Rotating machinery is a crucial component of large equipment, and detecting faults in it accurately is critical for reliable operation. Although fault diagnosis methods based on deep learning have been significantly developed, the existing methods model spatial and temporal features separately and then weigh them, resulting in the decoupling of spatiotemporal features.

The authors propose a spatiotemporal long short-term memory (ST-LSTM) method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

Through these two experiments, the authors demonstrate that machine learning methods still have advantages on small-scale data sets, but our proposed method exhibits a significant advantage due to the simultaneous modeling of the time domain and space domain. These results indicate the potential of the interactive spatiotemporal modeling method for fault diagnosis of rotating machinery.

The authors propose a ST-LSTM method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.