Training and organisational innovations in a local industrial system: empirical evidence from Emilia‐Romagna1

The article studies the driving forces of firm training using a survey‐based dataset of manufacturing firms in the Emilia‐Romagna region, Northern Italy. The data are derived from the responses to a structured questionnaire administered in 2002 to the management of a representative sample of firms with more than 50 employees in the highly industrialised province of Reggio Emilia. Firms’ training choices are analysed using a theoretical/conceptual framework based on the notion of complementarity among productive factors. Training is provided as long as it favours the establishment of complementary relationships among the skills it develops and other inputs. The main factors associated with training include structural characteristics, HRM practices, workforce features, labour management and performance of the firm. Training activities emerge as being positively associated with organisational practices that affect the whole firm: workforce skill level, firm size, firm productivity and labour flexibility. The role of HRM practices in driving training is brought into question. These are key issues for the current debate on the development of local systems in the European and Italian context. The high and joint relevance of structural variables and labour demand‐related factors shows that regional industrial policies must support labour policies within an integrated policy effort aimed at increasing potential firm productivity.     

THE DETERMINANTS OF RURAL INDUSTRIAL ESTATE PERFORMANCE IN KOREA

Since the 1980s, a total of 221 small- and medium-sized rural industrial estates have been established primarily to induce economic growth into the rural areas in Korea. Despite a growing interest in the performance of the rural industrial estates, it has not been closely examined. The purpose of this paper is to find the plausible factors that have a significant influence on the performance of the rural industrial estates, thereby providing a general understanding of the structural relationship between the estate performance and its determinants. An econometric modeling approach, called LISREL, is used to carry out this evaluation. Of several influential determinants distinguished, seven variables appear to be of substantial significance. They include the urbanization and industrialization of the estate host community, the accessibility of the individual estates to the major cities, the distances from the estates to the large industrial complexes, the local tax rates, and the government loans and incentives. The findings imply that more favorable are the estates constructed within the labor market boundaries of the major cities and adjacent to large industrial complexes. In addition to the location and environmental factors, financial funds and incentives will continue to remain as an influential factor for the success or failure of the estates.     

An integral model for the implementation of environmental policy strategy

This article responds to demands for a more in‐depth study on how to implement an environmental policy strategy (EPS). Following the strategic choice framework, this study seeks to address this issue by testing a theoretical model of several managerial and organisational variables associated with a firm's EPS. The managerial variables examined include managers' perceptions of environmental pressures, managers' consciousness of environmental problems, and managers' perceptions of the potential benefits associated with the implementation of an EPS. The organisational variables that are investigated relate to the structure, systems, and processes of the representative organisations. Control variables include the firm's industrial sector, its country of origin, its size, and its international character. A model based on structural equations with the weighted least squares estimation method is used to test the hypotheses in a large sample of firms. The results confirm the need to consider a multidisciplinary view of the implementation of an EPS.     

城市化对碳排放量及强度的影响

基于1995-2009年中国各省的面板数据,利用STIRPAT模型建立了基本的分析框架。首先以全国各省整体为研究对象,研究了城市化对中国碳排放的整体作用方向,然后利用面板变系数模型和门槛面板模型分析了城市化对碳排放的影响的差异。结果表明:整体而言,中国的城市化对碳排放量和碳排放强度均具有正向的影响,且城市化对碳排放强度的影响比对碳排放量的影响大;就不同省份而言,城市化对碳排放的影响存在差异;人口规模、收入水平、产业结构以及城市化水平的变化,均会使城市化与碳排放的关系发生变化,且相对而言,人口规模和城市化水平对城市化与碳排放的关系影响更大。     

COMPUTER USE AND STRUCTURAL CONTROL: A STUDY OF AUSTRALIAN ENTERPRISES

This article investigates underlying relationships between the extent of use of computers and dimensions of organization structure. Empirical tests on a sample of 149 Australian work-units show strong associations between computer use and structural characteristics. Specifically, greater computer use is found to be related to less centralization and greater formalization and departmentalization. Further tests show that these associations are to some extent moderated by size. However, this moderating effect is most apparent in small organizations for formalization and departmentalization while the relationships involving centralization are more prominent in larger work-units. Factor analysis led to the diagnosis of two major types of computer use (i.e., informational and operational use). Tests on these categorized effects and the factor-analysed structural dimensions revealed that: (1) the effects on centralization are more strongly explained by the impact of operational use on the ‘centralization of operational decisions’ sub-measure and that of informational use on ‘financial decisions’; (2) formalization and departmentalization are found to be most strongly influenced by greater informational use. Here again, significant relationships were found between sub-measures, the most important of which is the relationship involving the informational factor of formalization with the informational use of computers and the relationship between departmentalization of core functions and the operational use of computing technology.     

THE REMUNERATION OF NON-OWNER MANAGERS IN SMALL AND MEDIUM-SIZED UK ENTERPRISES

Virtually all prior research on small and medium sized enterprise (SME) management has focused on owner managers. This article, however, empirically examines the determinants of managerial remuneration for a sample of 97 UK SME non-owning managerial employees. the empirical analysis, based upon data obtained from interviews with middle (i.e. non-director level) managers and the published financial records of their employing firms lodged at Companies House, first examines the influence of firm size and performance and then augments the empirical model to include pay composition, industrial sector, external labour market and human capital variables. The empirical results indicate that the average profitability of the employing firms is not a significant determinant of managerial remuneration. However, composition of pay appears to have a significant effect upon total remuneration since, even after controlling for other influences, managers in receipt of annual bonuses and/or profit-sharing bonuses are estimated to earn an additional £6,600. the managers’age and qualifications, and the asset size, industry and location of their employing firms are also significant factors and collectively are able to explain a large proportion of the cross-sectional variance in remuneration. Though there is a lack of previous empirical research on SME managerial pay.     

Determinants of environmental management systems standards implementation: evidence from Greek industry

This paper employs logistic regression analysis to test a model that predicts the implementation or non‐implementation of Environmental Management Systems Standards (EMSSs) by considering various factors as explanatory variables. The dependent variable is dichotomous: industrial firms either implementing or not implementing EMSSs. From past experience we identify 15 major variables contributing to implementation of EMSSs. A sample of 259 respondents (84 implementing and 175 not) is used to estimate the parameters of the logistic regression model employing maximum likelihood. The results show an overall significant model with four of the 15 variables significant. The significance of management perception of environmental issues on their decision to implement EMSS was confirmed with regards to their perception on win–win possibilities. Pressure on companies to improve their environmental performance does not result in higher uptake of the standards. Company image and size are important factors in its decision to implement EMSS. Copyright © 2002 John Wiley & Sons, Ltd. and ERP Environment     

ASSESSING THE SAMPLES OF PRIOR ORGANIZATIONAL RESEARCH

The sampling designs used in organizational research have been less than consistent across different reported studies. In this analysis we examine the reported relationships among several key organizational variables in ten separate previously published studies to determine the degree to which major differences in sample designs have influenced findings. We isolate that portion of reported associations due to unique characteristics of the samples and report the association these ‘sample design effects’ have with particular sample designs. Results indicate homogeneous samples of organizations inflate reported relationships yet leave significant sources of variation uncontrolled in sample selection. an alternative to sampling of homogeneous organizations is suggested by the fact that larger and probabilitistically selected samples are also associated with larger reported relationships among organizational variables.     

Recovering the Metric Structure in Ordinal Data: Linear Versus Nonlinear Principal Components Analysis

Two techniques for data reduction as part of the SPSS package are compared in a Monte Carlo study: principal components analysis (PCA) and nonlinear principal components analysis (NPCA). The relative performance of these techniques in recovering the component scores underlying subjects' scores on observed ordinal variables is studied for two-dimensional spaces. The relative performance is examined as a function of (a) the sample size, (b) the number of categories in the variables, (c) the amount of measurement error, (d) the type of nonlinearity in the data, and (e) the degree of heterogeneity of the marginal distributions of the variables. As expected, when the sample size increases the performance of NPCA improves when compared to PCA. For the range of values considered, there is no effect of the number of categories on the relative performance of PCA and NPCA. For the other factors the effects are more complicated: adding error does not affect PCA as strongly as NPCA, as expected, but not for heterogeneously distributed variables for a particular form of nonlinearity, in which case NPCA becomes more appropriate. PCA appears to outperform NPCA for linear data, but also for a substantial number of nonlinear data sets.     

The Effect of using Household as a Sampling Unit

The effect of sampling people through households is considered. Results on design effects for two stage surveys are reviewed and applied to give design effects of household samples. The main factors that determine the design effect are identified for the designs in which one person, or all people, are selected from each selected household. Within household correlation is one factor. We show that the relationships between household size and the mean and variance within households are also important factors. Census and survey data are used to empirically compare the design effects for a range estimators, variables and designs.     

Objective methods for graphical structural learning

Graphical models are used for expressing conditional independence relationships among variables by the means of graphs, whose structure is typically unknown and must be inferred by the data at hand. We propose a theoretically sound Objective Bayes procedure for graphical model selection. Our method is based on the Expected-Posterior Prior and on the Power-Expected-Posterior Prior. We use as input of the proposed methodology a default improper prior and suggest computationally efficient approximations of Bayes factors and posterior odds. In a variety of simulated scenarios with varying number of nodes and sample sizes, we show that our method is highly competitive with, or better than, current benchmarks. We also discuss an application to protein-signaling data, which wieldy confirms existing results in the scientific literature.     

STRATEGY, STRUCTURE AND ENVIRONMENT: CONTEXT INFLUENCES UPON SOME BIVARIATE ASSOCIATIONS

In studying organizational adaptation most researchers tend to draw sample- wide conclusions about the relationships among strategic, structural, and environmental variables. More often than not, the findings of different investigators are in conflict. Particular variables are said by some to covary positively, while different studies demonstrate a negative relationship. This paper attempts to show that the direction and significance of bivariate product-moment correlations may vary significantly, logically, and systematically according to the adaptive approach used by firms. A sample of firms is segmented into four parts, which are internally homogeneous in the evolutionary patterns among environmental, organizational, and strategy-making variables. The findings from a correlational analysis of each sub-sample indicate how some common discrepancies in the literature show the promise of being resolved when the differences in the contexts of the bivariate relationships are examined.     

THE ORGANIZATIONAL CORRELATES AND CONSEQUENCES OF SUBCONTRACTING: EVIDENCE FROM A SURVEY OF SOUTH WALES BUSINESSES

Recent interest in the growth of subcontracting, as part of a broader trend towards greater ‘flexibility’in forms of employment relationship, has not as yet been matched by a full consideration of its organizational and managerial implications. In particular, the question of whether or not subcontracting has an impact upon, or is affected by, the organization's structural characteristics remains largely unexplored. the aim of this article is to report findings from a detailed study of subcontracting practices across a sample of British industry, which focuses upon the relationship between subcontracting and organizational characteristics. the results indicate several important points about the practice. Firstly, that there is wide variation in the use of subcontracting across industry, although the overall extent is limited. Secondly, that variation in the practice is significantly related to basic firm characteristics - namely, industrial sector, type of production system and pattern of ownership and control. Thirdly, that, on balance, subcontracting is associated with more, not less, structural complexity. Finally, that this greater complexity is found in particular types of firms - notably, outside the manufacturing sector, in mass and process production systems and amongst British independents and subsidiaries, rather than in their foreign competitors.     

基于因子分析法的城市低碳经济实证评价——以太原市为例

综合使用了主成分分析法和因子分析法,对太原市近年的低碳经济发展状况进行分析评价。通过模型的建立和分析,总结出影响太原市低碳经济发展的主要因素,得出的主要结论为:基本经济因子、基础环境因子、居民生活因子和产业结构因子是决定太原市低碳经济发展水平的主要因素,但低碳经济的发展仍是建立在一定的宏观经济水平基础之上的;并提出了该市发展低碳经济的一些政策建议,主要有产业结构升级调整、空间布局设计优化、加大城市基础环境改善的投入、积极提倡低碳办公和低碳生活方式等。     

The analysis of seasonal economic models

Many important insights can be obtained about economic relationships and seasonality when an economic model contains an explicit specification of the mechanism generating seasonality in the endogenous variables. Unfortunately, determining the correct structure is difficult. The purpose of this paper is to outline a methodology for the analysis of seasonal economic models. In particular, several ways of incorporating seasonality into a structural model are considered and the implications for the behavior of the endogenous variables are derived. Finally, a rational expectations version of Cagan's money demand function is analyzed to demonstrate some of the important aspects of the techniques and seasonal economic models in general.     

ENVIRONMENTAL CHARACTERISTICS AND ORGANIZATIONAL CLIMATE: AN EXPLORATORY STUDY

This article is primarily concerned with exploring the relationships between organizational climate and characteristics of organizational environments. Environmental characteristics include dependencies, competition and uncertainty. In addition, the relationship of climate and environments with dimensions of organizational structure and size are examined. Using data from 15 industrial organizations in Britain, the results have shown that different environmental characteristics have different associations with organizational climate. Also, the relationships between organizational environments and climate are not similar to those found between environments and structure. It is suggested that the creation of appropriate climates and structural design as responses to environmental pressures may be considered as complementary strategies in an attempt to maintain administrative control. The results, therefore, provide support for the suggestion that, in order to improve our understanding of the dynamics of organizational climate, characteristics of organizational environments should be incorporated into future research designs.     

Determinants of hierarchical structure in industrial firms: an empirical study

The paper investigates implications of different theoretical models for hierarchical structure. A sample of 6,578 firms in the Brazilian manufacturing industry is considered and explanatory factors pertaining to structural characteristics, network technology, technological innovations, managerial innovations and incentive mechanisms are investigated. Important joint effects are detected for all groups of variables in partial contrast with the related previous literature. Moreover, one detects significant joint effect of the newly considered group of incentive variables. The evidence in terms of individual effects is largely consistent with the predicted effects from the theoretical literature.     

Macroeconomic forecasting using structural factor analysis

The use of a small number of underlying factors to summarize the information from a much larger set of information variables is one of the new frontiers in forecasting. In prior work, the estimated factors have not usually had a structural interpretation and the factors have not been chosen on a theoretical basis. In this paper we propose several variants of a general structural factor forecasting model, and use these to forecast certain key macroeconomic variables. We make the choice of factors more structurally meaningful by estimating factors from subsets of information variables, where these variables can be assigned to subsets on the basis of economic theory. We compare the forecasting performance of the structural factor forecasting model with that of a univariate AR model, a standard VAR model, and some non-structural factor forecasting models. The results suggest that our structural factor forecasting model performs significantly better in forecasting real activity variables, especially at short horizons.     

On estimating the effects of peak demand pricing

Extreme value distributions are seldom used in economics, despite what seems to be a natural application to peak demand pricing. This paper estimates the effect of an individual peak demand charge which is a component of the standard industrial electricity rate structure. The extreme value distribution is used in forming the likelihood function of individual peak demand which is then estimated on a sample of five Ontario cement firms from 1970–1977. Goodness-of-fit tests tend to support the use of the extreme value distribution.     

Asymptotic properties of random extremes under general normalization from nonidentical distributions

In this paper we study the weak convergence of the generally normalized extremes (extremes under nonlinear monotone normalization) of random number of independent (nonidentically distributed) random variables. When the random sample size is assumed to converge in probability and the interrelation between the basic variables and their random size is not restricted, the limit forms as well as the sufficient conditions of convergence are derived. Moreover, when the random sample size is assumed to converge weakly and independent of the basic variables, the necessary and sufficient conditions for the convergence are derived.Acknowledgment. The authors are grateful to a referee for several helpful comments and for pointing out the extensive paper by Galambos (1992) leading to improvement of the representation of the paper.Received January 2002