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We evaluate the hypothesis of temporal stability in risk preferences using two recent data sets from longitudinal lab experiments. Both experiments included a combination of decision tasks that allows one to identify a full set of structural parameters characterizing risk preferences under Cumulative Prospect Theory (CPT), including loss aversion. We consider temporal stability in those structural parameters at both population and individual levels. The population-level stability pertains to whether the distribution of risk preferences across individuals in the subject population remains stable over time. The individual-level stability pertains to within-individual correlation in risk preferences over time. We embed the CPT structure in a random coefficient model that allows us to evaluate temporal stability at both levels in a coherent manner, without having to switch between different sets of models to draw inferences at a specific level.

Temporal leadership is a new construct that predicts team outcomes. This study examines the mediating role of shared temporal cognitions and the moderating role of time pressure in the relationship between temporal leadership and project success within sustainable construction projects.

The multi-source and multi-wave data were collected via self-administered questionnaires from teams working on sustainable construction projects. The direct and mediating hypotheses were tested using multi-level structural equation modelling, while moderated mediation hypotheses were examined by applying the bootstrap method using SPSS Process Macro.

The results showed that temporal leadership enables project success via shared temporal cognitions. Temporal leadership is most beneficial for facilitating project success via shared temporal cognitions when teams experience high time pressure.

This is the first study examining shared temporal cognitions as a mediator of the relationship between temporal leadership and project success. Also, this is the first study that considered time pressure as a boundary condition that influences the relationships between temporal leadership, shared temporal cognitions and project success within sustainable construction projects. The study provides valuable advice to project managers and project-based construction organizations about using and managing time within projects.

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.

This study aims to investigate the individual factors that directly and indirectly favor (or hinder) employees’ attitudes toward women in management. Two sides of psychological ownership (PO), promotion-focused and prevention-focused PO, are studied as having a direct effect on employees’ attitudes toward women in management. Past and future temporal focuses are examined as possible antecedents to the sides of PO, and as indirectly affecting employees’ attitudes toward women in management.

Survey questionnaires were collected from 230 MBA and related program students who were currently working and enrolled in one of six different universities in Ethiopia. Confirmatory factor analysis was applied to validate all measurement scales, and structural equation modeling was used to test the study hypotheses using Mplus software.

Employees with promotion-focused PO and employees with prevention-focused PO had a favorable and unfavorable attitude, respectively, toward women in management. In addition, a future temporal focus had a significant positive effect on promotion-focused PO, and a past temporal focus had a significant positive effect on prevention-focused PO. Overall, this mediation model showed that promotion-focused PO partially mediates the relationship between future temporal focus and attitudes toward equal opportunity for women managers, whereas prevention-focused PO fully mediates the negative relationship between past temporal focus and attitudes toward women in management.

This study provides new insight for organizations by showing how employees’ temporal focus explains their side of PO and how that affects their reaction toward women in management.

A new mechanism for revealing gender inequality in organizations is introduced. Moreover, the relationship between temporal focus and PO is discovered. This study is novel in providing an understanding of the antecedent to and effect of prevention-focused PO, which are largely overlooked in extant research.

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.

Accurate segmentation of artificial assembly action is the basis of autonomous industrial assembly robots. This paper aims to study the precise segmentation method of manual assembly action.

In this paper, a temporal-spatial-contact features segmentation system (TSCFSS) for manual assembly actions recognition and segmentation is proposed. The system consists of three stages: spatial features extraction, contact force features extraction and action segmentation in the temporal dimension. In the spatial features extraction stage, a vectors assembly graph (VAG) is proposed to precisely describe the motion state of the objects and relative position between objects in an RGB-D video frame. Then graph networks are used to extract the spatial features from the VAG. In the contact features extraction stage, a sliding window is used to cut contact force features between hands and tools/parts corresponding to the video frame. Finally, in the action segmentation stage, the spatial and contact features are concatenated as the input of temporal convolution networks for action recognition and segmentation. The experiments have been conducted on a new manual assembly data set containing RGB-D video and contact force.

In the experiments, the TSCFSS is used to recognize 11 kinds of assembly actions in demonstrations and outperforms the other comparative action identification methods.

A novel manual assembly actions precisely segmentation system, which fuses temporal features, spatial features and contact force features, has been proposed. The VAG, a symbolic knowledge representation for describing assembly scene state, is proposed, making action segmentation more convenient. A data set with RGB-D video and contact force is specifically tailored for researching manual assembly actions.

Robots face fundamental challenges in achieving reliable and stable operations for complex home service scenarios. This is one of the crucial topics of robotics methods to imitate human beings’ advanced cognitive characteristics and apply them to solve complex tasks. The purpose of this study is to enable robots to have the ability to understand the scene and task process in complex scenes and to provide a reference method for robot task programming in complex scenes.

This paper constructs a task modeling method for robots in complex environments based on the characteristics of the perception-motor memory model of human cognition. In the aspect of episodic memory construction, the task execution process is included in the category of qualitative spatio-temporal calculus. The topology interaction of objects in a task scenario is used to define scene attributes. The task process can be regarded as changing scene attributes on a time scale. The qualitative spatio-temporal activity graphs are used to analyze the change process of the object state with time during the robot task execution. The tasks are divided according to the different values of scene attributes at different times during task execution. Based on this, in procedural memory, an object-centered motion model is developed by analyzing the changes in the relationship between objects in the scene episode by analyzing the scene changes before and after the robot performs the actions. Finally, the task execution process of the robot is constructed by alternately reconstructing episodic memory and procedural memory.

To verify the applicability of the proposed model, a scenario where the robot combines the object (one of the most common tasks in-home service) is set up. The proposed method can obtain the landscape of robot tasks in a complex environment.

The robot can achieve high-level task programming through the alternating interpretation of scenarios and actions. The proposed model differs from traditional methods based on geometric or physical feature information. However, it focuses on the spatial relationship of objects, which is more similar to the cognitive mechanism of human understanding of the environment.

Entrepreneurial intention (EI) has been studied prolifically, as a precursor to entrepreneurial action, and a desired outcome of entrepreneurship education. Yet, the paucity of extant studies that analyze its temporal stability has been noted. This paper aims to address this gap by studying the temporal stability of EI, investigating its persistence as an attitudinal state over time.

A series of intraindividual and group-level longitudinal analyses were undertaken, over an 11-year period, using a student sample from Portugal. The authors highlight the magnitude of EI change over time, where item-structure, relative and absolute stability and group and individual-level EI changes are all considered.

Results indicate an initially strong to moderate EI item-structure stability and relative stability over the first five years, with moderate signs of deterioration. This deterioration becomes even more pronounced across the full 11-year period. Regarding EI absolute stability, while college students (as a group) did not display a general tendency to develop higher or lower EI during the first five years, a small deterioration was found over the 11-year period. At the individual level, EI instability was detected, and this increased with time. Finally, the exploratory results suggest that entrepreneurship education may buffer the deterioration of EI.

The findings provide a more nuanced reasoning for dampened EI–entrepreneurial behavior associations and highlight key determinants of EI change, which can inform educational experts and policymakers.

The legitimacy of the EI field lays heavily on the existence of a stable EI construct and a strong relationship between intentions and behavior. The methodology provides a new and more complete picture of EI’s temporal stability.

The authors proposed a conceptual model by examining the influence of threats to their freedom on tourists’ psychological distance including social distance, spatial distance, and temporal distance, which effect psychological reactance and the consequent online Airbnb booking intentions. Furthermore, media intrusiveness as a moderator determines the boundary conditions between perceived threats to their freedom and social distance, spatial distance, and temporal distance.

Data was gathered from 491 Chinese travelers to provide empirical evidence. The authors performed data analysis in Amos 26.0 using structural equation modeling (SEM) and Hayes (2013) PROCESS macro.

The findings positively reinforced all the structural relationships of the study. Notably, media intrusiveness significantly moderates the association between perceived threats to their freedom and psychological distance (i.e. social distance, spatial distance, and temporal distance).

The findings contribute significantly to the field of social psychology, advertising, and consumer behavior derive prolific implications for policymakers and sharing economy platforms. Lastly, by identifying limitations, this research opens doors for future scholars.

Governments' acute precautionary measures in response to the COVID-19 outbreak have confined individual freedom across the globe. This study illuminates how tourists conceive these preventative measures as perceived threats to their freedom, and subsequently engage psychological reactance.

This paper aims to explore the spatio-temporal patterns of Chinese foreign direct investment (FDI) since the inception of the Belt and Road Initiative (BRI) in 2013 as an extended version of geographically weighted regression.

The panel data are used to examine spatial and temporal dynamics of the magnitude and the direction of China's outward FDI stock and its flow from 2011 to 2015 at a country level. Using the geographically and temporally weighted regression (GTWR), spatio-temporal distribution of FDI is explained through Logistic Performance Index, the size of gross domestic product (GDP), Shipping Linear Connectivity Index and Container Port Throughput.

A comparative analysis between participating and non-participating countries in the BRI shows that the size of GDP and Container Port Throughput of the participating countries have a positive effect on the increases of China's outward FDI Stock to Asia especially after 2013, while non-participating countries, such as North America, Western Europe and Western Africa, have no significant effect on it before and after the implementation of the BRI.

The findings, however, will not necessarily provide insight into the needs of China's outward FDI in certain countries to develop their economy. The findings provide the evidence to inform policy making to help identify the winners and losers of the investment, scale and direction of investment and the key drivers that shape the distributive investment patterns globally.

The study provides the empirical evidence to inform investment policy and strategic realignment by quantifying scale, direction and drivers that shape the spatio-temporal shifts of China's FDI.

The analysis also guides the Chinese government improve bilateral trade, build infrastructure and business partnerships with preferential countries participating in the BRI.

There is an urgent need to adopt a new perspective to unfold the spatial temporal complexity of FDI that incorporates space and time dependencies, and the drivers of the situated context to model their effects on FDI. The model is based on GTWR and an extended geographically weighted regression (GWR) allowing the simultaneous analysis of spatial and temporal decencies of exploratory variables.