Allocation Processes in Mergers and Acquisitions: An Organizational Justice Perspective

This paper contributes to the understanding of allocation processes in mergers and acquisitions by applying an organizational justice perspective. This is a perspective that has been neglected in past research on mergers and acquisitions, but which is important in understanding the challenges managers face in allocating human resources and other resources of symbolic importance. In particular, the paper examines the trade-offs between multiple goals in selecting distributive and procedural justice rules, and identifies factors that constrain these choices. Mergers and acquisitions are typically driven by multiple motives. The findings presented in this paper suggest that there are different approaches to applying justice rules in mergers and acquisitions and that these approaches have different implications for meeting multiple goals. The choice of equity in combinations with fair procedural rules is an approach which complies with the two goals of maximizing economic productivity and fostering relationships. However, the choice of this approach rests upon an unbalanced power relationship and relatively low ambiguity. When there is a high degree of ambiguity and a balanced power relationship, management has to make trade-offs between maximizing economic productivity and fostering relationships.     

Educational objectives and measurement of outcomes

Correlation and regression analysis are often used to infer causal relationships in dynamic systems, even though computed on cross-sectional static data. In education these analytic techniques have been used to support assertions that school-controlled variables make little contribution to student learning. Critics of these assertions point to the low quality of the data, but it may be that the techniques themselves are inappropriate for the development of inferences of causality. This study simulated four possible models of dynamic relationships between family and school inputs and achievement outcomes. The models were run for five periods. Data generated were submitted to correlation and regression analysis. Both unique variance and regression coefficient indicators failed to describe reliably causal relationships built into the models. Conclusion: complex systems resist simplistic analyses.     

SIMULATION EXPLORATION OF THE POWER OF MARGINAL REGRESSION ANALYSIS*

In either simple or multivariate regression models, measured coefficients represent a kind of average of co-variant relationships implicit in the data. For time-series data, if co-variant relationships follow no particular pattern over the sample period, use of this average in forecasting is rational. However, if relationships among independent and dependent variables is undergoing change over time, then measured regression coefficients are likely to be poor tools for prediction. This paper is an exploratory study of this general problem area. It develops implications as to the predictive impact of the problem and offers preliminary suggestions as to a method of developing predictions under such circumstances.     

Strategic Orientations in Management Literature: Three Approaches to Understanding the Interaction between Market,Technology, Entrepreneurial and Learning Orientations

Market, technology, entrepreneurial and learning orientations have attracted major scholarly interest within their specific streams of literature for some decades. These strategic orientations are seen as principles that direct and influence the activities of a firm and generate the behaviours intended to ensure its viability and performance. Prior studies have argued that firms should develop and use multiple orientations, yet the relationship between different orientations has received only fragmented attention. This paper presents a systematic review of this literature, covering 67 scholarly articles published between 1987 and 2010 which investigate multiple orientations. The paper contributes first by summarizing the current state of knowledge on the interplay between these orientations. Many of these relationships have not been studied to any great degree, and there are research gaps in the information available on the relationships between entrepreneurial, technology and learning orientation in particular. Secondly, the paper contributes to further theoretical and empirical enquiry by synthesizing the empirical findings into a three‐approach framework. The sequential, alternatives and complementary approaches to perceiving the relationship between orientations all suggest areas for further research. The sequential approach could further contribute by developing better constructs for explaining the orientation of the firm; while the alternatives approach could increase its relevance to management through the exploration of contingency settings and comparative studies. The complementary approach encourages discussion between researchers from the different streams of literature through the investigation of the relationships. It suggests focus on the investigation of both universal‐ and contingency‐dependent‐orientation configurations.     

CYCLICAL BEHAVIOR OF UNEMPLOYMENT AND THE HELP WANTED INDEX: A CROSS SPECTRAL ANALYSIS

The unemployment rate and the help-wanted index are two frequently used indicators of the state of the labor market. Traditional regression techniques used in the analysis of the relationship between these two indicators and their cyclical behavior with business fluctuations have yielded varying results. Spectral analysis is used in this article to examine the cyclical behavior of the labor market indicators with respect to each other and with respect to a measure of aggregate economic activity in the United States. Very strong relationships are found, lending support to the rationale of using such indicators as labor market proxies. This application of spectral analysis also provides an illustration of the potential fruitfulness of spectral analysis in examining the cyclical relationships between economic time series.     

Demographic and Attitudinal Correlates of Employee Satisfaction with an ESOP

A firm's adoption of an employee stock ownership plan (ESOP) has been hypothesized to increase employee productivity. Resulting employee productivity is hypothesized to improve firm profitability and thus ultimately improve stock performance. Most studies to date have tested potential relationships between the mere presence of an ESOP and changes in employee productivity and firm profitability. Few studies have attempted to identify the variables that are associated with employee satisfaction with an ESOP. In order to maximize the productivity gains associated with the adoption of an ESOP, researchers must first identify the relationships and variables most likely to positively affect employee attitudes and subsequently their satisfaction toward an ESOP. The purpose of this paper is to identify the demographic and attitudinal correlates of employee satisfaction with an ESOP. This exploration will provide a more substantive foundation for future research efforts in the area. Correlation and regression results indicated that employees' perceived influence on decision-making, perceived pay equity and perceived influence on stock performance, when examined separately, were each significant correlates of ESOP satisfaction. When combined with the modelled employee demographics in a step-wise regression model, only employees' perceived influence on stock performance, perceived influence on decision-making and age explained a statistically significant amount of variance in ESOP satisfaction.     

THE MESSENGER BIAS: A RELATIONAL MODEL OF KNOWLEDGE VALUATION

How do managers value the knowledge that they encounter in organizations? A rational perspective assumes that managers carefully and accurately cull the best knowledge from their environments, while a random model situates managers in a chaotic organization, filled with preferences and solutions that are temporally matched. This paper develops a third view, a relational perspective, which describes how social relationships between knowledge messengers and knowledge receivers affect the way that managers evaluate new knowledge. We begin by focusing on two key dimensions of relational perception: social identification and threat appraisals. We then use these dimensions to derive a typology of six “relational schemas” that are commonly perceived between knowledge messengers and knowledge receivers at work. Next, we reveal how, holding knowledge content constant, these relational schemas bias the ways in which managers evaluate new knowledge. While network research demonstrates that relationships determine which knowledge managers see, this paper demonstrates that these relationships also have psychological implications by affecting how managers evaluate the knowledge that they see.     

THE EFFECTIVE UNIT OF THE POISSON DISTRIBUTION AND DEMAND FORECASTING

This short paper deals with some of the difficulties which have been found in applying the effective unit concept of estimating the Poisson distribution in order to forecast demand in a major industry. It points to two special cases in which the common formulation produces misleading results, discusses the frequency with which these two special cases could be expected to occur based on this one major industry, and proposes some pragmatic solutions to the problems resulting.     

A novel approach to regression analysis for the classification of quality attributes in the Kano model: an empirical test in the food and beverage industry

Since its introduction in the 1980s, Kano's two-dimensional model has become one of the most popular models with which to evaluate quality, finding a place in a wide range of industries. For decades, various approaches to regression analysis have been applied to explore asymmetric and non-linear relationships in the Kano model. Although a number of authors have questioned the use of these regression methods, there has been a lack of validity testing to evaluate their convergence with the results of the Kano questionnaire in classifying quality attributes. This study proposes a novel approach to regression analysis for the classification of quality attributes, including must-be, one-dimensional, attractive, and indifferent categories, as well as mixed-class distribution. Using popular tools and techniques for the measurement of customer satisfaction, the proposed approach is capable of simplifying the process of collecting data making it far easier to implement than the list of functional and dysfunctional questions initiated by Kano. An empirical study of a food and beverage chain showed that the proposed approach is capable of returning acceptable classification results, compared to the Kano questionnaire. A validity test indicated that the proposed approach significantly outperformed dummy variable regression and the moderated regression. In conclusion, the proposed approach provides a more practical implementation, while maintaining classification power on par with the Kano questionnaire.     

INTERNATIONAL DIFFUSION OF ISO 14000 CERTIFICATION

The ISO 9000 series of quality management systems standards and the more recent ISO 14000 environmental management systems standards have generated much controversy among practitioners. Although ISO 9000 has become a de facto requirement for many firms, its effects are poorly understood, and similarly the value and domain of applicability of ISO 14000 have been questioned. This paper reports on an exploratory study into the global spread of ISO 14000. We interviewed practitioners worldwide to identify factors that they believe explain differences between national ISO 14000 certification counts. We then collected quantititive data for these factors and, using regression analysis, we found that exports, environmental attitudes (combined with economic development), and ISO 9000 certification count were significant. The fact that ISO 9000 appears as an important factor explaining diffusion of ISO 14000 certifications suggests that the drivers behind the two have significant overlap. This indicates that, although ISO 14000 is an environmental standard, many of the factors driving national certification patterns are not at all environmental in nature, and that ISO 14000 therefore needs to be studied from a broader perspective than from a purely environmental point of view.     

AN EMPIRICAL EXAMINATION OF THE UNDERLYING DIMENSIONS OF PURCHASING COMPETENCE

The importance of evaluating the effectiveness of the purchasing function in firms along multiple criteria has attracted considerable attention. However, few studies have identified the defining elements that constitute purchasing competence. This paper introduces the construct of purchasing competence using a second‐order factor structure derived from purchasing practices identified from the literature. The validity of the construct (purchasing competence) is tested using data from a sample of 179 firms. The results indicate (1) the construct validity of purchasing competence and (2) the predictive validity of purchasing competence, which has a significant positive influence on total quality management performance and customer satisfaction. The implications of these findings for additional research are discussed.     

营销渠道成员行为的整合模型

合作、权力和冲突以及它们之间的关系历来为国外营销学者所关注。本文将着重介绍几个行为模型来加深对营销渠道系统管理的认识。合作、权力和冲突都是有助于加深理解营销渠道管理的概念,这三个概念和它们之间的关系为制定渠道管理策略和解决渠道中的问题提供了理论框架。本文首先通过对渠道中的合作、权力和冲突进行详细的阐述,进而推导出渠道成员整合的行为模型,在此基础上着手规划垂直渠道系统的营销策略。     

Risk, Statistical Inference, and the Law of Evidence: The Use of Epidemiological Data in Toxic Tort Cases

Toxic torts are product liability cases dealing with alleged injuries due to chemical or biological hazards such as radiation, thalidomide, or Agent Orange. Toxic tort cases typically rely more heavily than other product liability cases on indirect or statistical proof of injury. There have been numerous theoretical analyses of statistical proof of injury in toxic tort cases. However, there have been only a handful of actual legal decisions regarding the use of such statistical evidence, and most of those decisions have been inconclusive. Recently, a major case from the Fifth Circuit, involving allegations that Benedectin (a morning sickness drug) caused birth defects, was decided entirely on the basis of statistical inference. This paper examines both the conceptual basis of that decision, and also the relationships among statistical inference, scientific evidence, and the rules of product liability in general.     

Cooperation and Knowledge Transfer within Buyer–Supplier Relationships: The Moderating Properties of Trust,Relationship Duration and Supplier Performance*

This paper examines the effect of relational factors on knowledge transfer within strategic buyer–supplier exchange. Prior research examining inter‐firm knowledge transfer has focused almost exclusively on horizontal forms of governance such as strategic alliances and joint ventures, whilst research on vertical forms, such as buyer–supplier relationships, is limited. We test the effect of four important relational properties: cooperation, trust, relationship duration and supplier performance. Quantitative data, gathered from 104 UK manufacturing firms in eight industry sectors, are used to analyse the hypothesized relationships through a moderated hierarchical regression model. Our study provides support for the importance of considering relational factors in the transfer of knowledge at the inter‐organizational level. In particular, the results indicate that knowledge transfer is positively influenced by the extent of cooperation, but that this relationship is moderated by the level of trust and the performance of the supplier firm. Managerial implications for these findings and future directions for research are then offered.     

Female board appointments and firm valuation: short and long-term effects

The presence of women on boards of directors has become a high profile issue in recent years. Several studies, based largely on data from countries with Anglo-Saxon corporate governance systems, have investigated the influence of female board appointments on firm performance. This study focuses on the impact of female directors in Spain, where debate about this topic has been intense for two reasons: the recommendation in 2006 by Spain’s Unified Good Governance Code of positive discrimination in favour of female board appointments and the passing in 2007 of a Gender Equality Act by the Spanish parliament. Our paper analyses the short and long term effect of the appointment of female directors prior to these events. We use an event study to analyze the short term stock market reaction to the appointment of female directors and a multiple regression approach, using the system GMM estimation procedure, to assess the long term influence on firm value of female boardroom appointments. We find that the stock market reacts positively in the short term to the announcement of female board appointments, suggesting that investors on average believe that female directors add value. This belief appears to be confirmed by our regression results which show that female board appointments are positively associated with firm value over a sustained period. These results suggest that the legislative changes in Spain make economic sense as well as advancing the cause of women in Spanish boardrooms.     

The Efficacy of Neural Networks in Predicting Returns on Stock and Bond Indices*

This paper uses two recently developed tests to identify neglected nonlinearity in the relationship between excess returns on four asset classes and several economic and financial variables. Having found some evidence of possible nonlinearity, it was then investigated whether the predictive power of these variables could be enhanced by using neural network models instead of linear regression or GARCH models. Some evidence of nonlinearity in the relationships between the explanatory variables and large stocks and corporate bonds was found. It was also found that the GARCH models are conditionally efficient with respect to neural network models, but the neural network models outperform GARCH models if financial performance measures are used. In resonance with the results reported for the tests for neglected nonlinearity, it was found that the neural network forecasts are conditionally efficient with respect to linear regression models for large stocks and corporate bonds, whereas the evidence is not statistically significant for small stocks and intermediate-term government bonds. This difference persists even when financial performance measures for individual asset classes are used for comparison.     

Sample Splitting and Threshold Estimation

Threshold models have a wide variety of applications in economics. Direct applications include models of separating and multiple equilibria. Other applications include empirical sample splitting when the sample split is based on a continuously‐distributed variable such as firm size. In addition, threshold models may be used as a parsimonious strategy for nonparametric function estimation. For example, the threshold autoregressive model (TAR) is popular in the nonlinear time series literature. Threshold models also emerge as special cases of more complex statistical frameworks, such as mixture models, switching models, Markov switching models, and smooth transition threshold models. It may be important to understand the statistical properties of threshold models as a preliminary step in the development of statistical tools to handle these more complicated structures. Despite the large number of potential applications, the statistical theory of threshold estimation is undeveloped. It is known that threshold estimates are super‐consistent, but a distribution theory useful for testing and inference has yet to be provided. This paper develops a statistical theory for threshold estimation in the regression context. We allow for either cross‐section or time series observations. Least squares estimation of the regression parameters is considered. An asymptotic distribution theory for the regression estimates (the threshold and the regression slopes) is developed. It is found that the distribution of the threshold estimate is nonstandard. A method to construct asymptotic confidence intervals is developed by inverting the likelihood ratio statistic. It is shown that this yields asymptotically conservative confidence regions. Monte Carlo simulations are presented to assess the accuracy of the asymptotic approximations. The empirical relevance of the theory is illustrated through an application to the multiple equilibria growth model of Durlauf and Johnson (1995).     

不同类型转换成本的调节作用机制研究

转换成本对于顾客满意与顾客忠诚之间的关系具有调节作用,这一观点已经得到了普遍的认可。本文以移动通讯业为例,采用多元调节回归(MMP)进一步分析了不同类型转换成本的调节作用,研究结果发现,程序转换成本的调节作用最大,关系转换成本次之,财务转换成本的调节作用最小。     

Combining Forecasts: Multiple Regression versus a Bayesian Approach*

Simple linear combinations of forecasts have consistently been found to be more accurate than individual forecasts. Several recent studies have found that combination forecasts derived by constrained or unconstrained multiple regression are more accurate than a simple average of individual forecasts. This study uses macroeconomic data to compare the accuracy of combination forecasts derived by a Bayesian methodology with the accuracy of composite forecasts derived by multiple regression. Using the forecasts of four macroeconomic variables from five well-known econometric models, the study finds that the Bayesian combination procedure produces more accurate composite forecasts than does the regression combination procedure, based on a version of Theil's U² statistic.     

Universal versus contextual effects on TQM: a triangulation study using neural networks

The objective of this study is to extend previous research on total quality management (TQM)-context-performance relationships and ‘fit’ using multiple methods. We combine artificial neural networks (ANNs) with structural equation modelling (SEM) to analyse several hypotheses and propositions. This is the first study in this area of research that utilises ANNs and a triangulation technique in the presence of several contextual factors. The SEM analyses suggest that company size and industry type may have contingency effects on some of the TQM practices and/or TQM-performance relationships. However, the ANN models have shown that these two contingency factors do not moderate TQM outcomes, implying that all organisations can benefit from TQM regardless of size and type. As well, these models show that formal TQM implementation and/or ISO certifications do not add any predictive power to the ANN models except in one case: TQM implementation and/or ISO certification added to organisational effectiveness and customer results to predict financial and market (F&M) results. The results further indicate that even though implementing TQM alone has a bigger impact on F&M results than obtaining ISO certification alone, combining the two will have an even greater impact on these results. Joint implementation leads to greater improvements in organisational effectiveness, which, in turn, has a positive effect on customer results and consequently F&M results. This is a unique finding within the context of moderator effects on TQM-performance relationships.