scholarly journals Influence of Interfirm Social Networks on Technological Innovation and its Time Lag Effect: a Meta-Analysis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 167019-167031
Author(s):  
Wei Chen ◽  
Xuan Wei ◽  
Ranran Liu
Keyword(s):  
Social Networks ◽  
Technological Innovation ◽  
Meta Analysis ◽  
Time Lag ◽  
Lag Effect

scholarly journals A Weighted-Time-Lag Method to Detect Lag Vegetation Response to Climate Variation: A Case Study in Loess Plateau, China, 1982–2013

2021 ◽  
Vol 13 (5) ◽  
pp. 923
Author(s):  
Qianqian Sun ◽  
Chao Liu ◽  
Tianyang Chen ◽  
Anbing Zhang
Keyword(s):  
Climate Change ◽  
Loess Plateau ◽  
Time Lag ◽  
New Method ◽  
Climate Factors ◽  
The Loess Plateau ◽  
Climate Conditions ◽  
Vegetation Growth ◽  
Lag Effect

Vegetation fluctuation is sensitive to climate change, and this response exhibits a time lag. Traditionally, scholars estimated this lag effect by considering the immediate prior lag (e.g., where vegetation in the current month is impacted by the climate in a certain prior month) or the lag accumulation (e.g., where vegetation in the current month is impacted by the last several months). The essence of these two methods is that vegetation growth is impacted by climate conditions in the prior period or several consecutive previous periods, which fails to consider the different impacts coming from each of those prior periods. Therefore, this study proposed a new approach, the weighted time-lag method, in detecting the lag effect of climate conditions coming from different prior periods. Essentially, the new method is a generalized extension of the lag-accumulation method. However, the new method detects how many prior periods need to be considered and, most importantly, the differentiated climate impact on vegetation growth in each of the determined prior periods. We tested the performance of the new method in the Loess Plateau by comparing various lag detection methods by using the linear model between the climate factors and the normalized difference vegetation index (NDVI). The case study confirmed four main findings: (1) the response of vegetation growth exhibits time lag to both precipitation and temperature; (2) there are apparent differences in the time lag effect detected by various methods, but the weighted time-lag method produced the highest determination coefficient (R2) in the linear model and provided the most specific lag pattern over the determined prior periods; (3) the vegetation growth is most sensitive to climate factors in the current month and the last month in the Loess Plateau but reflects a varied of responses to other prior months; and (4) the impact of temperature on vegetation growth is higher than that of precipitation. The new method provides a much more precise detection of the lag effect of climate change on vegetation growth and makes a smart decision about soil conservation and ecological restoration after severe climate events, such as long-lasting drought or flooding.

Information technology investment and innovation performance: does investment paradox exist?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Peinan Ji ◽  
Xiangbin Yan ◽  
Yan Shi
Keyword(s):  
Information Technology ◽  
Time Lag ◽  
Innovation Performance ◽  
Test Model ◽  
It Investment ◽  
Firm Innovation ◽  
Data Set ◽  
Content Type ◽  
Lag Effect ◽  
Enterprise Innovation

Purpose The purpose of this study is to deepen the understanding of the effects of information technology (IT) investment on firm innovation performance and examining the investment paradox effect in China. Design/methodology/approach Using a sample of China’ public firms IT investment data between 2010 and 2016, the authors establish a test model of IT investment and innovation performance. Findings The result indicates that IT investment in firms have no effect on innovation performance in the investment period. However, in the full sample and manufacturing sample, the IT investment has a significant positive effect on innovation performance in the post-investment years. In addition, this study finds that large companies and low-age companies may contribute more to innovation when firm investment in IT. Research limitations/implications There are several limitations in this research. First, the authors are failed to obtain a larger sample about the IT investment information data set in China, so this study was compelled to use limited sample data from China, hence, this could lead to errors of too early generalization. Second, the authors use the number of invention patent applications to represent the performance of enterprise innovation, which may not show enterprise innovation effectively. Third, the firms in the sample are all in China Listed Companies, so this may not accurately reflect the entire environment of firm innovation performance, and could possibly. Practical implications The research confirms that there is a paradox and time lag effect in IT investment, which enterprises should pay attention to. Originality/value Existing research confirms that corporate IT investments can bring new products or services. However, the authors still do not know whether IT investment has improved the company’s ability of innovation. This study will fill this gap and the industry effect and time lag effect of the influence of IT investment on innovative performance are also examined.

The Roles of Initial Mathematics, Reading, and Cognitive Skills in Subsequent Mathematics Performance: A Meta-Analytic Structural Equation Modeling Approach

2021 ◽  
pp. 003465432110545
Author(s):  
Xin Lin ◽  
Sarah R. Powell
Keyword(s):  
Working Memory ◽  
Structural Equation Modeling ◽  
Cognitive Skills ◽  
Structural Equation ◽  
Meta Analysis ◽  
Time Lag ◽  
Mathematics Performance ◽  
Equation Modeling ◽  
Word Problem Solving ◽  
Significant Moderator

In the present meta-analysis, we systematically investigated the relative contributions of students’ initial mathematics, reading, and cognitive skills on subsequent mathematics performance measured at least 3 months later. With one-stage meta-analytic structural equation modeling, we conducted analyses based on 580,437 students from 265 independent samples and 250 studies. Findings suggested fluency in both mathematics and reading, as well as working memory, yielded greater impacts on subsequent mathematics performance. Age emerged as a significant moderator in the model, such that the effects of comprehensive mathematics and working memory on subsequent mathematics increased with age, whereas attention and self-regulation’s impacts declined with age. Time lag between assessments also emerged as a significant moderator, such that the effects of word-problem solving and word recognition accuracy decreased as the time lag increased, whereas vocabulary, attention, and self-regulation’s effects increased as the time lag increased.

Numerical Investigations for the Effect of Slender Body on Dynamic Rolling Characteristics of a 80°/60° Double Delta Wing

2013 ◽  
Vol 444-445 ◽  
pp. 286-292
Author(s):  
Bing Han ◽  
Min Xu ◽  
Xi Pei ◽  
Xiao Min An
Keyword(s):  
Stokes Equations ◽  
Delta Wing ◽  
Time Lag ◽  
Slender Body ◽  
Vortical Flow ◽  
Navier Stokes ◽  
Rolling Motion ◽  
Navier Stokes Equations ◽  
Lag Effect ◽  
Double Delta Wing

The effect of slender body on the rolling characteristics of a double delta wing is found by comparing the numerical simulation results of the double delta wing and wing-body configuration. The coupled computation system solving the Navier-Stokes equations and the rolling motion equation alternatively to obtain the unsteady vortical flow around the two configurations while rolling. The results conclusively showed the upwash effect of the slender body enhanced the energy of strake vortex and merged vortex.The aerodynamic lag of double delta wing is weak, contrarily, the time lag effect of the wing-body configuration is significant. The asymmetry vortices structure nearby the trailing edge are believed to be the main reason for the unsteady time lag effect.

scholarly journals An SEIR Model for Assessment of Current COVID-19 Pandemic Situation in the UK

2020 ◽  
Author(s):  
Peiliang Sun ◽  
Kang Li
Keyword(s):  
Seasonal Influenza ◽  
Time Lag ◽  
Seir Model ◽  
Infected People ◽  
Proposed Model ◽  
Lag Effect ◽  
Novel Coronavirus ◽  
The Uk ◽  
Specific Symptoms ◽  
Limited Testing

AbstractThe ongoing COVID-19 pandemic spread to the UK in early 2020 with the first few cases being identified in late January. A rapid increase in confirmed cases started in March, and the number of infected people is however unknown, largely due to the rather limited testing scale. A number of reports published so far reveal that the COVID-19 has long incubation period, high fatality ratio and non-specific symptoms, making this novel coronavirus far different from common seasonal influenza. In this note, we present a modified SEIR model which takes into account the time lag effect and probability distribution of model states. Based on the proposed model, it is estimated that the actual total number of infected people by 1 April in the UK might have already exceeded 610,000. Average fatality rates under different assumptions at the beginning of April 2020 are also estimated. Our model also reveals that the R0 value is between 7.5–9 which is much larger than most of the previously reported values. The proposed model has a potential to be used for assessing future epidemic situations under different intervention strategies.

Coupling entropy of co-processing model on social networks

2015 ◽  
Vol 29 (25) ◽  
pp. 1550149
Author(s):  
Zhanli Zhang
Keyword(s):  
Social Networks ◽  
Information Flow ◽  
Structural Characteristics ◽  
Time Lag ◽  
Optimal Routing ◽  
Information Propagation ◽  
Time Processing ◽  
Scale Free ◽  
Routing Strategy ◽  
Processing Ability

Coupling entropy of co-processing model on social networks is investigated in this paper. As one crucial factor to determine the processing ability of nodes, the information flow with potential time lag is modeled by co-processing diffusion which couples the continuous time processing and the discrete diffusing dynamics. Exact results on master equation and stationary state are achieved to disclose the formation. In order to understand the evolution of the co-processing and design the optimal routing strategy according to the maximal entropic diffusion on networks, we propose the coupling entropy comprehending the structural characteristics and information propagation on social network. Based on the analysis of the co-processing model, we analyze the coupling impact of the structural factor and information propagating factor on the coupling entropy, where the analytical results fit well with the numerical ones on scale-free social networks.

Non-parametric treatment time-lag effect estimation

2021 ◽  
pp. 096228022110326
Author(s):  
Kristine Gierz ◽  
Kayoung Park ◽  
Peihua Qiu
Keyword(s):  
Survival Data ◽  
Treatment Time ◽  
Time Lag ◽  
Simulated Data ◽  
Real Data ◽  
Point Problem ◽  
Change Point Problem ◽  
Lag Effect ◽  
Effect Estimation ◽  
Non Parametric

In general, the change point problem considers inference of a change in distribution for a set of time-ordered observations. This has applications in a large variety of fields, and can also apply to survival data. In survival analysis, most existing methods compare two treatment groups for the entirety of the study period. Some treatments may take a length of time to show effects in subjects. This has been called the time-lag effect in the literature, and in cases where time-lag effect is considerable, such methods may not be appropriate to detect significant differences between two groups. In this paper, we propose a novel non-parametric approach for estimating the point of treatment time-lag effect by using an empirical divergence measure. Theoretical properties of the estimator are studied. The results from the simulated data and the applications to real data examples support our proposed method.

scholarly journals Revealing the Fingerprint of Climate Change in Interannual NDVI Variability among Biomes in Inner Mongolia, China

2020 ◽  
Vol 12 (8) ◽  
pp. 1332 ◽  
Author(s):  
Linghui Guo ◽  
Liyuan Zuo ◽  
Jiangbo Gao ◽  
Yuan Jiang ◽  
Yongling Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Interannual Variability ◽  
Inner Mongolia ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Lag ◽  
Growing Season ◽  
Climate Fluctuations ◽  
Temperature And Precipitation ◽  
Lag Effect

An understanding of the response of interannual vegetation variations to climate change is critical for the future projection of ecosystem processes and developing effective coping strategies. In this study, the spatial pattern of interannual variability in the growing season normalized difference vegetation index (NDVI) for different biomes and its relationships with climate variables were investigated in Inner Mongolia during 1982–2015 by jointly using linear regression, geographical detector, and geographically weighted regression methodologies. The result showed that the greatest variability of the growing season NDVI occurred in typical steppe and desert steppe, with forest and desert most stable. The interannual variability of NDVI differed monthly among biomes, showing a time gradient of the largest variation from northeast to southwest. NDVI interannual variability was significantly related to that of the corresponding temperature and precipitation for each biome, characterized by an obvious spatial heterogeneity and time lag effect marked in the later period of the growing season. Additionally, the large slope of NDVI variation to temperature for desert implied that desert tended to amplify temperature variations, whereas other biomes displayed a capacity to buffer climate fluctuations. These findings highlight the relationships between vegetation variability and climate variability, which could be used to support the adaptive management of vegetation resources in the context of climate change.

A Lag Effect of IT Investment on Firm Performance

2011 ◽  
pp. 246-273
Author(s):  
Sangho Lee ◽  
Soung Hie Kim
Keyword(s):  
Firm Performance ◽  
Time Lag ◽  
It Investment ◽  
Performance Factors ◽  
Distributed Lag ◽  
Distributed Lag Model ◽  
Lag Effect ◽  
Lag Model ◽  
Information Intensity ◽  
High Information

This chapter discusses the positive effects of IT investment on firm financial performance when a distinct range of characteristics is examined. The relationship between IT investment and firm performance considering the information intensity of the industry is explored using a distributed lag model. Findings indicate both a positive effect and a positive lag effect of IT investment. The effects of IT investment in the high information-intensive industry are significantly larger than in the low information-intensive industry. Furthermore, a lagged effect of IT investment is larger than an immediate effect, regardless of the information intensity of the industry. We conclude that firms in the high information-intensive industry need to be more cognizant of performance factors when investing in IT investment than in the low information-intensive industry. Moreover, it is necessary to consider the time lag between IT investment and firm performance.

Sign in / Sign up

Export Citation Format

Share Document