Response strategy to environment context factors using a lean and agile approach: Implications for firm performance

Based on the environment-strategy performance perspective and dynamic capabilities framework, we develop a theoretical model and hypotheses specifying how supply chain collaboration as a response to environment context factors – competitive intensity, supply uncertainty, technological turbulence and market turbulence, using a lean and agile strategy may influence firm performance. We test the model using partial least square structural equation modelling on data collected from a field survey with responses from 152 manufacturing firms representing a variety of industries. Empirical findings generally support the relationship between collaboration and firm performance using a lean and agile strategy. Also, for firms in industries that face environments characterised by high supply uncertainty and competitive intensity with, technological turbulence, the study finds evidence of a direct relationship between these environmental factors and supply chain collaboration. The findings provide an initial strategic response framework for appropriately aligning a lean and agile supply chain strategy through collaboration with environment context factors to achieve firm performance improvements.     

Modelling daily multivariate pollutant data at multiple sites

Summary. This paper considers the spatiotemporal modelling of four pollutants measured daily at eight monitoring sites in London over a 4-year period. Such multiple-pollutant data sets measured over time at multiple sites within a region of interest are typical. Here, the modelling was carried out to provide the exposure for a study investigating the health effects of air pollution. Alternative objectives include the design problem of the positioning of a new monitoring site, or for regulatory purposes to determine whether environmental standards are being met. In general, analyses are hampered by missing data due, for example, to a particular pollutant not being measured at a site, a monitor being inactive by design (e.g. a 6-day monitoring schedule) or because of an unreliable or faulty monitor. Data of this type are modelled here within a dynamic linear modelling framework, in which the dependences across time, space and pollutants are exploited. Throughout the approach is Bayesian, with implementation via Markov chain Monte Carlo sampling.     

论大学英语课堂评估方式及其对教学的反拨作用

适时、适量、适度的大学英语课堂评估能有效地促进大学英语教学.大学英语课堂评估的形式可分为测试性与非测试性两类,非测试性方式有课堂观察、学习档案、日志等,在当前比较注重学生英语运用能力的教学中,非测试手段尤为重要.在测试性方式中,随堂测试、诊断测试、成绩测试等都可选用.如果进行得恰当,课堂评估对教学会产生多方面的积极反拨作用.     

Quantifying uncertainty in the biospheric carbon flux for England and Wales

Summary.  A crucial issue in the current global warming debate is the effect of vegetation and soils on carbon dioxide (CO₂) concentrations in the atmosphere. Vegetation can extract CO₂ through photosynthesis, but respiration, decay of soil organic matter and disturbance effects such as fire return it to the atmosphere. The balance of these processes is the net carbon flux. To estimate the biospheric carbon flux for England and Wales, we address the statistical problem of inference for the sum of multiple outputs from a complex deterministic computer code whose input parameters are uncertain. The code is a process model which simulates the carbon dynamics of vegetation and soils, including the amount of carbon that is stored as a result of photosynthesis and the amount that is returned to the atmosphere through respiration. The aggregation of outputs corresponding to multiple sites and types of vegetation in a region gives an estimate of the total carbon flux for that region over a period of time. Expert prior opinions are elicited for marginal uncertainty about the relevant input parameters and for correlations of inputs between sites. A Gaussian process model is used to build emulators of the multiple code outputs and Bayesian uncertainty analysis is then used to propagate uncertainty in the input parameters through to uncertainty on the aggregated output. Numerical results are presented for England and Wales in the year 2000. It is estimated that vegetation and soils in England and Wales constituted a net sink of 7.55 Mt C (1 Mt C = 10¹² g of carbon) in 2000, with standard deviation 0.56 Mt C resulting from the sources of uncertainty that are considered.     

Bias Correction in the Dynamic Panel Data Model with a Nonscalar Disturbance Covariance Matrix

Approximation formulae are developed for the bias of ordinary and generalized Least Squares Dummy Variable (LSDV) estimators in dynamic panel data models. Results from Kiviet [Kiviet, J. F. (1995), on bias, inconsistency, and efficiency of various estimators in dynamic panel data models, J. Econometrics68:53-78; Kiviet, J. F. (1999), Expectations of expansions for estimators in a dynamic panel data model: some results for weakly exogenous regressors, In: Hsiao, C., Lahiri, K., Lee, L-F., Pesaran, M. H., eds., Analysis of Panels and Limited Dependent Variables, Cambridge: Cambridge University Press, pp. 199-225] are extended to higher-order dynamic panel data models with general covariance structure. The focus is on estimation of both short- and long-run coefficients. The results show that proper modelling of the disturbance covariance structure is indispensable. The bias approximations are used to construct bias corrected estimators which are then applied to quarterly data from 14 European Union countries. Money demand functions for M1, M2 and M3 are estimated for the EU area as a whole for the period 1991: I-1995: IV. Significant spillovers between countries are found reflecting the dependence of domestic money demand on foreign developments. The empirical results show that in general plausible long-run effects are obtained by the bias corrected estimators. Moreover, finite sample bias, although of moderate magnitude, is present underlining the importance of more refined estimation techniques. Also the efficiency gains by exploiting the heteroscedasticity and cross-correlation patterns between countries are sometimes considerable.     

Bayesian updating of atmospheric dispersion after a nuclear accident

Summary.  We consider a Bayesian forecasting system to predict the dispersal of contamination on a large scale grid in the event of an accidental release of radioactivity. The statistical model is built on a physical model for atmospheric dispersion and transport called MATCH. Our spatiotemporal model is a dynamic linear model where the state parameters are the (essentially, deterministic) predictions of MATCH; the distributions of these are updated sequentially in the light of monitoring data. One of the distinguishing features of the model is that the number of these parameters is very large (typically several hundreds of thousands) and we discuss practical issues arising in its implementation as a realtime model. Our procedures have been checked against a variational approach which is used widely in the atmospheric sciences. The results of the model are applied to test data from a tracer experiment.     

Missing data and forecasting in multivariate time series: An application of the common components dynamic linear model

Summary The paper deals with missing data and forecasting problems in multivariate time series making use of the Common Components Dynamic Linear Model (DLMCC), presented in Quintana (1985), and West and Harrison (1989). Some results are presented and discussed: exploiting the correlation between series, estimated by the DLMCC, the paper shows as it is possible to update state vector posterior distributions for the unobserved series. This is realized on the base of the updating of the observed series state vectors, for which the usual Kalman filter equations can be applied. An application concerning some Italian private consumption series provides an example of the model capabilities.     

嵌入式框架iframe在网页设计中的应用研究

本文对嵌入式框架iframe在网页设计中的应用技巧进行了研究。从iframe的一般应用方法入手,给出了嵌入式框架与父框架之间的相互访问方法,结合ASP和数据库实现了网页中两个下拉列表框的动态关联。通过网页实例和代码,对上述方法进行了具体阐述。     

How can David orchestrate resources to enhance firm performance? A dynamic approach to coping with resource constraints

Given that the possession of abundant resources does not always guarantee superior firm performance, this study proposes a dynamic approach to enhancing firm value. Building on the resource orchestration thesis, we investigate how firms facing resource constraints enhance their capabilities over time by orchestrating resources and how this resource orchestration can play a role in improving firm performance. Using the dynamic perspective, this study identifies four resource orchestration modes, which are specified based on how a given firm dynamically allocates its resources to technology (enacted by R&D activities) and marketing (enacted by advertising activities). Accordingly, we claim that firms with resource constraints can enhance their performance by escalating or altering their resources over time. To test our hypotheses, we used a sample of 4078 small and medium-sized enterprises in manufacturing industries between 1984 and 2018. We found that focus escalation for technology positively affects firm performance, captured by Tobin's q, and that focus alternation toward either technology or market is positively related to firm performance. Finally, we discuss the theoretical and practical implications of our findings.     

品牌延伸:资产价值转移与理论模型创建

本文通过对品牌延伸(Brand Extension)的研究综述和实证研究,提出决定品牌延伸的三大因子,并在此基础上构成品牌资产价值(Brand Asset Value),进而研究其在品牌延伸过程中的价值转移,从而构成了评估品牌延伸成败的标准,最终将各种因子和评估标准进行整合,提出了品牌延伸的理论模型。