Search results

This research aims to investigate whether and how differences may exist in children’s preferences of package design across cultures, with a focus on three aspects of package design: curvilinearity, figurativeness and complexity.

A large-scale questionnaire survey has been conducted in a face-to-face setting in the USA and China, generating valid responses from 763 American children and 837 Chinese children of age 3-12 years.

Unlike previous findings among adults, children from both cultures were found to unanimously prefer curved package design. Nevertheless, Chinese children showed greater preferences for figurative and complex package design than American children; these tendencies increased with age, suggesting significant age–culture interactions.

The surprising finding of the lack of cultural difference in children’s preferences of curved package design suggests that such cultural preferences established in studies of adults may not emerge through time via cultural/social learning until after age 12. The limited cultures, stimuli and factors included in the study call for replications of the study in more realistic and broader settings.

The findings provide package design guidelines for consumer product marketers and designers/innovators targeting the Chinese and American children’s markets. Curved package designs are preferred by children from both cultures. Nevertheless, marketers should choose figurative and complex package design in accordance with the target children’s age and cultural background.

This study contributes to the limited empirical consumer behavior research on package design, especially that of children’s products. It also extends the literature on cultural psychology, experimental aesthetics and developmental psychology.

The purpose of this research is to examine the role of worry versus sadness in influencing young consumers’ purchase decisions and to clarify the differences across the worry–consumption versus the sadness–consumption relationships.

Three studies were conducted. Study 1 was a 3 (emotion: worry vs sadness vs neutral) × 2 (brand perceptions: conflicting vs consistent) between-subject design. Study 2 was a 3 (emotion: sadness vs worry vs neutral) × 2 (product type: social status associated vs hedonic) mixed design. Study 3 was a questionnaire survey.

The results demonstrate that worry induces young consumers’ identification with peers, and is more related to youth’s purchase intention for social status associated products rather than hedonic products. Sadness induces young consumers to follow their own perceptions, and is more associated with purchase intention for hedonic rather than social status-associated products. The drivers of purchase behavior for expensive products also differ: worried young consumers’ purchase intention is driven by perceptions of social status value associated with these products, whereas sad consumers’ purchase intention is driven by perceptions of hedonic value.

This research has significant implications for marketing practitioners on strategic marketing and communication to young consumers. It also provides important suggestions to young consumers on how to effectively regulate negative emotions via socially accepted behavior (i.e. purchases).

This research contributes to the extant literature on emotion’s impact on consumer behavior by elaborating carryover effects of emotion varying in the overlooked personal- and social-focus dimension. It also extends the literature on peer influence among young consumers.

The purpose of this work is to obtain an overbounded broadcast sigma from actual (non-Gaussian) correction error distribution under the stringent navigation integrity requirements for aircraft precision approach and landing.

Approach is statistically to overbound satellite pseudorange correction error distribution with the use of numerical solution of Fisher-Z transformation. Inflation factors for overbounding broadcast sigma are extracted from Fisher-Z transformation based on measured correlation and counted independent identically distributed (iid) sample sizes of true empirical data.

New overbounded broadcast sigma values for eight long-pass satellites were obtained based on measured actual empirical data and ensured integrity risk at 10⁻⁸ probability level. Proposed methodology successfully overbounds ground reflection multipath-type systematic and temporal errors sources.

This paper introduced a new method of accounting for ground reflection multipath for local area augmentation system/ground-based augmentation system navigation integrity. The method is also applicable to statistically overbound any other serially correlated temporal variation in measured data if both correlation values and finite iid sample sizes are known.

The purpose of this paper is to extend prior supply chain research by describing the process of innovation knowledge increase in supply chain network. More specifically, this study investigates the role of network density, and views the knowledge increase as the process of knowledge diffusion and knowledge innovation.

A multi-agent model, which demonstrates the process of knowledge increase in supply chain network, was established, and simulated by using NetLogo simulation platform.

The results indicate that the network density will promote the knowledge increase of the supply chain when it is high or low. In the meantime, these results show that the inhibition of knowledge diffusion and knowledge innovation will appear when network density is moderate.

Although previous research has identified the importance of knowledge increase in promoting sustainable development of supply chain, far less attention was given to the study of the effect of network structure on the knowledge increase in supply chain. This study thus fulfills the research gap by providing a description of the process of knowledge increase with the consideration of network density. The conclusion is of great significance for the choice of network density for sustainable development of supply chain.

The purpose of this paper is to examine how knowledge search (KS) affects open innovation performance (OIP) in consideration of appropriability hazard in an emerging market context.

The authors identify two types of appropriability hazard related to KS activities in the improvement of OIP: partner-related and market-related hazards, and construct a model of KS to the OIP with the government-enterprise relationship and market focus (MF) as moderating factors of the analysis framework, using the hierarchical regression analysis based on a survey sample of 258 Chinese companies.

The authors find that KS has a stronger positive relationship with OIP in international market-focused enterprises compared with domestic market-focused enterprises. In addition, using a configuration approach, the authors find that KS is more positively related to OIP when enterprises have an international MF and a good relationship with the government, but less in other MF government and enterprises relationship configurations.

This paper contributes to previous research work by identifying two types of appropriability hazard related to KS activities in emerging market countries: partner-related and market-related hazards. The authors argue that a positive relationship between KS and OIP is more likely to occur if these appropriability hazards can be mitigated by export MF and a good relationship with the government which has not been demonstrated in previous research.

To address the issues of low localization and mapping accuracy, as well as map ghosting and drift, in indoor degraded environments using light detection and ranging-simultaneous localization and mapping (LiDAR SLAM), a real-time localization and mapping system integrating filtering and graph optimization theory is proposed. By incorporating filtering algorithms, the system effectively reduces localization errors and environmental noise. In addition, leveraging graph optimization theory, it optimizes the poses and positions throughout the SLAM process, further enhancing map accuracy and consistency. The purpose of this study resolves common problems such as map ghosting and drift, thereby achieving more precise real-time localization and mapping results.

The system consists of three main components: point cloud data preprocessing, tightly coupled inertial odometry based on filtering and backend pose graph optimization. First, point cloud data preprocessing uses the random sample consensus algorithm to segment the ground and extract ground model parameters, which are then used to construct ground constraint factors in backend optimization. Second, the frontend tightly coupled inertial odometry uses iterative error-state Kalman filtering, where the LiDAR odometry serves as observations and the inertial measurement unit preintegration results as predictions. By constructing a joint function, filtering fusion yields a more accurate LiDAR-inertial odometry. Finally, the backend incorporates graph optimization theory, introducing loop closure factors, ground constraint factors and odometry factors from frame-to-frame matching as constraints. This forms a factor graph that optimizes the map’s poses. The loop closure factor uses an improved scan-text-based loop closure detection algorithm for position recognition, reducing the rate of environmental misidentification.

A SLAM system integrating filtering and graph optimization technique has been proposed, demonstrating improvements of 35.3%, 37.6% and 40.8% in localization and mapping accuracy compared to ALOAM, lightweight and ground optimized lidar odometry and mapping and LiDAR inertial odometry via smoothing and mapping, respectively. The system exhibits enhanced robustness in challenging environments.

This study introduces a frontend laser-inertial odometry tightly coupled filtering method and a backend graph optimization method improved by loop closure detection. This approach demonstrates superior robustness in indoor localization and mapping accuracy.

This paper aims to describe a three-dimensional mathematical and numerical model based on finite volume method to simulate the fluid dynamics in weld pool, droplet transfer and keyhole behaviors in the laser-MIG hybrid welding process of Fe36Ni Invar alloy.

Double-ellipsoidal heat source model and adaptive Gauss rotary body heat source model were used to describe electric arc and laser beam heat source, respectively. Besides, recoil pressure, electromagnetic force, Marangoni force, buoyancy as well as liquid material flow through a porous medium and the heat, mass, momentum transfer because of droplets were taken into consideration in the computational model.

The results of computer simulation, including temperature field in welded plate and velocity field in the fusion zone were presented in this article on the basis of the solution of mass, momentum and energy conservation equations. The correctness of elaborated models was validated by experimental results and this proposed model exhibited close correspondence with the experimental results with respect to weld geometry.

It lays foundation for understanding the physical phenomena accompanying hybrid welding and optimizing the process parameters for laser-MIG hybrid welding of Invar alloy.

In the context of fire incidents within buildings, efficient scene perception by firefighting robots is particularly crucial. Although individual sensors can provide specific types of data, achieving deep data correlation among multiple sensors poses challenges. To address this issue, this study aims to explore a fusion approach integrating thermal imaging cameras and LiDAR sensors to enhance the perception capabilities of firefighting robots in fire environments.

Prior to sensor fusion, accurate calibration of the sensors is essential. This paper proposes an extrinsic calibration method based on rigid body transformation. The collected data is optimized using the Ceres optimization algorithm to obtain precise calibration parameters. Building upon this calibration, a sensor fusion method based on coordinate projection transformation is proposed, enabling real-time mapping between images and point clouds. In addition, the effectiveness of the proposed fusion device data collection is validated in experimental smoke-filled fire environments.

The average reprojection error obtained by the extrinsic calibration method based on rigid body transformation is 1.02 pixels, indicating good accuracy. The fused data combines the advantages of thermal imaging cameras and LiDAR, overcoming the limitations of individual sensors.

This paper introduces an extrinsic calibration method based on rigid body transformation, along with a sensor fusion approach based on coordinate projection transformation. The effectiveness of this fusion strategy is validated in simulated fire environments.

This study aims to address the fundamental question on how the major players in the economy dynamically interact with each other: among the central bank, the investors in the bond market and the firms and consumers that contribute to the economic growth, who gets information from whom, when and why?

To answer “who follows whom,” the authors apply a novel technique to examine the lead–lag relations between three time series, the federal funds rate, the treasury yield curve and the gross domestic product (GDP). To investigate “when and why,” the authors combine the lead–lag relations with principal component analysis to cluster economic states that are similar with respect to the eight macroeconomic variables.

The authors show that during the period 1977–2019, the bond market potentially obtained information from the federal funds rate (61% of the time) and less often (34% of time) from the changes in the GDP. Meanwhile, the funds rate decision by the Federal Reserve seems to lead the economic growth about 63% of the time. The analysis also suggests that the bond market obtained information directly from GDP when unemployment and inflation was high. In addition, the authors find that the federal funds rate was leading the GDP when the GDP deviated from the target value, consistent with the Federal Reserve’s policy of boosting and damping the economy when the GDP growth is low or high, respectively.

This study provides insights into the fundamental questions that have important implications for empirical work on the monetary policy, financial stability and economic activities.

This study aims to examine factors affecting Chinese adolescents’ purchase intention for local brands; this study focuses on the effects of perceived social status value and materialistic values.

A theoretical model relating perceived social status value of brands to purchase intention, including materialistic values as a moderator, was developed and tested, using a sample of 587 Chinese adolescent respondents. Another experimental study examined the variability of the moderation of materialistic values across different levels of peer pressure in a product usage occasion.

Perceived social status value associated with local and foreign brands significantly influences purchase intention for local brands. Moreover, influence of perceived social status value of local versus foreign brands on local brand purchase intention is greater for materialistic adolescents. Additionally, the moderation of materialistic values is found in a product usage occasion with high peer pressure, but not in an occasion with low peer pressure.

The findings show that perceived social status value associated with brands shapes purchase intention for local brands. The moderating effect of materialistic values is complex and suggests further research. The study’s scope is limited to Chinese adolescents.

The findings provide understanding of the drivers of purchase intention, and thus serve as a guideline for Chinese firms and foreign marketers seeking to enter the growing Chinese market, as well as consumer educators in China.

This study contributes to the limited empirical research into the factors shaping country-of-origin effects. Moreover, the findings suggest the need to consider the moderating role of materialistic values on purchase intention for local brands.