Understanding information systems infrastructure in engineering SMEs: A case study

The benefits of integrated enterprise-wide information systems (IS), such as Enterprise Resource Planning (ERP) systems, are widely accepted. The implementation of such systems in large organisations is established with many in their second or third generation. However, in small to medium-sized enterprises (SMEs) the maturity and extent of their IS infrastructure is less clear. In engineering SMEs, this infrastructure needs to support not only business processes but also the activities of design and manufacture.In this case study, our goal is to provide a better understanding of current IS infrastructure, the functional elements of IS infrastructure, integration and expenditures are explored in ten engineering SMEs. A model of IS functionality is developed and used to identify and classify functional elements within the firms and to compare and contrast the IS infrastructures. The findings are contrasted with the SME literature and conclusions and implications developed.     

Estimating the prevalence of sensitive behaviour and cheating with a dual design for direct questioning and randomized response

Randomized response is a misclassification design to estimate the prevalence of sensitive behaviour. Respondents who do not follow the instructions of the design are considered to be cheating. A mixture model is proposed to estimate the prevalence of sensitive behaviour and cheating in the case of a dual sampling scheme with direct questioning and randomized response. The mixing weight is the probability of cheating, where cheating is modelled separately for direct questioning and randomized response. For Bayesian inference, Markov chain Monte Carlo sampling is applied to sample parameter values from the posterior. The model makes it possible to analyse dual sample scheme data in a unified way and to assess cheating for direct questions as well as for randomized response questions. The research is illustrated with randomized response data concerning violations of regulations for social benefit.     

License to fail? How leader group prototypicality moderates the effects of leader performance on perceptions of leadership effectiveness

Leadership often serves as an explanatory category for performance outcomes (i.e., failure and success). This process can strengthen or weaken leadership effectiveness, because contingent on their performance leaders may gain or lose follower endorsement — the basis of leadership. Drawing on the social identity analysis of leadership, we hypothesized that leader group prototypicality and performance information interact to predict followers' perceptions of leadership effectiveness. Because group prototypical leaders are more trusted by their followers, we hypothesized that group prototypical leaders are evaluated as more effective after failure information than non-prototypical leaders. In contrast, we predicted that both prototypical and non-prototypical leaders should receive similar evaluations of leadership effectiveness after success. We found support for our predictions in a scenario experiment, a cross-sectional field study, and a laboratory experiment.     

A Comparison of Ethical Practices of Russian and American Managers

This study examines perception of various business practices of Russian and American managers. Using data collected from 136 Russian managers and 252 American managers we found that cross-national differences account for many differences between perceptions of business practices. Implications for managerial practice are discussed. This revised version was published online in July 2006 with corrections to the Cover Date.     

A new special class of peb designs

A new class of partially efficiency-balanced designs is introduced from a practical point of view. This new design includes all equireplicated incomplete block designs available in literature as special cases. The fundamental properties of the design are clarified with relation to other block designs.     

On a nonparametric test for the mixed-model

Nonparametric tests for the null hypothesis of no treatment effect in the mixed-model experiment which involves n randomly chosen subjects who respond once to each of ρ distinct treatments have been developed by Koch and Sen (1968), These tests were based on the assumption of compound symmetry of the error vectors and on the weaker assumption of diagonal symmetry of the error vectors. This paper considers an alternative (permutationally) distribution-free test under this latter assumption. The new test follows the same type of distribution theory as those in Koch and Sen, but utilizes the inherent invariance structure in a more visable and direct way.     

Univaiuate anu multivariate categorical data analysis for block designs

Analysis for univariate and multivariate categorical data in block designs is given and illustrated through examples. The univariate analysis compares the treatments on the basis of their pooled frequency distributions (pooled over blocks). The test statistic used is called Q after Cochran (1950). The large sample null distribution of Q is a chi-square. Analysis of p-variate categorical data (kth variable having ck classes, K=1,...,p) can be done by treating it as a univariate categorical problem with [d] classes. Very often [d] is large in relation to the size of the experiment. This makes the expected frequencies for some of the cells very small, making the univariate method inapplicable. In these circumstances it may be reasonable to compare the treatments on the basis of marginal distributions up to the mth dimension, 1[d] , which is given in this paper. This method is also illustrated for missing observations     

Prior distribution assessment for a multivariate normal distribution: an experimental study

A variety of methods of eliciting a prior distribution for a multivariate normal (MVN) distribution have recently been proposed. This paper reports an experiment in which 16 meteorologists used the methods to quantify their opinions about climatology variables. Our results compare prior models and show, in particular, that it can be better to assume the mean and variance of an MVN distribution are independent a priori, rather than to model opinion by the conjugate prior distribution. Using a proper scoring rule, different forms of assessment task are examined and alternative ways of estimating parameters are compared. To quantify opinion about means, it proved preferable to ask directly about the means rather than individual observations while, to quantify opinion about the variance matrix, it was best to ask about deviations from the mean. Further results include recommendations for the way parameters of the prior distribution are estimated.