The ability to transfer best practices internally is critical to a firtn's ability to build competitive advantage through the appropriation of rents from scarce internal knowledge. Just as a firm's distinctive competencies tnight be dificult for other firms to imitate, its best prczctices could be dfficult to imitate internnlly. Yet, little systematic attention has been pcrid to such internal stickiness. The author analyzes itlterrzal stickiness of knowledge transfer crnd tests the resulting model using canonical correlation analysis of a data set consisting of 271 observations of 122 best-practice transfers in eight companies. Contrary to corzverztiorzrzl wisdom that blames primarily motivational factors, the study findings show the major barriers to internal knowledge transfer to be knowledge-related factors such as the recipient's lack oj absorptive capacity, causal anzbiguity, and an arciuous relationship between the source and the recipient. The identification and transfer of best practices cally are hindered less by confidentiality and legal is emerging as one of the most important and obstacles than external transfers, they could be widespread practical management issues of the faster and initially less complicated, all other latter half of the 1990s. Armed with meaningful, things being equal. For those reasons, in an era detailed performance data, firms that use fact- when continuous organizational learning and based management methods such as TQM, bench- relentless performance improvement are needed to marking, and process reengineering can regularly remain competitive, companies must increasingly compare the performance of their units along resort to the internal transfer of capabilitie~.~ operational dimensions. Sparse but unequivocal Yet, experience shows that transferring capaevidence suggests that such comparisons often bilities within a firm is far from easy. General reveal surprising performance differences between Motors had great difficulty in transferring manuunits, indicating a need to improve knowledge facturing practices between divisions (Kerwin and utilization within the firm (e.g., Chew, Bresnahan, Woodruff, 1992: 74) and IBM had limited suc

https://onlinelibrary.wiley.com/doi/pdf/10.1002/smj.4250171105

Exploring internal stickiness: Impediments to the transfer of best practice within the firm

The factor analysis literature includes a range of recommendations regarding the minimum sample size necessary to obtain factor solutions that are adequately stable and that correspond closely to population factors. A fundamental misconception about this issue is that the minimum sample size, or the

/pdf/sample-size-in-factor-analysis-3ur0fvq099.pdf

Sample size in factor analysis.

Comparison of five rules for determining the number of components to retain.

This Handbook aims to provide a guide for constructing and using composite indicators for policy makers, academics, the media and other interested parties. While there are several types of composite indicators, this Handbook is concerned with those which compare and rank country performance in areas such as industrial competitiveness, sustainable development, globalisation and innovation. The Handbook aims to contribute to a better understanding of the complexity of composite indicators and to an improvement of the techniques currently used to build them. In particular, it contains a set of technical guidelines that can help constructors of composite indicators to improve the quality of their outputs. It has been prepared jointly by the OECD (the Statistics Directorate and the Directorate for Science, Technology and Industry) and the Applied Statistics and Econometrics Unit of the Joint Research Centre of the European Commission in Ispra, Italy. Primary authors from the JRC are Michela Nardo, Michaela Saisana, Andrea Saltelli and Stefano Tarantola. Primary authors from the OECD are Anders Hoffmann and Enrico Giovannini. Editorial assistance was provided by Candice Stevens, Gunseli Baygan and Karsten Olsen. The research is partly funded by the European Commission, Research Directorate, under the project KEI (Knowledge Economy Indicators), Contract FP6 No. 502529. In the OECD context, the work has benefitted from a grant from the Danish government. The views expressed are those of the authors and should not be regarded as stating an official position of either the European Commission or the OECD.

/pdf/handbook-on-constructing-composite-indicators-methodology-1a4fw776o7.pdf

Handbook on Constructing Composite Indicators: Methodology and User Guide

In medical research data are often collected serially on subjects. The statistical analysis of such data is often inadequate in two ways: it may fail to settle clinically relevant questions and it may be statistically invalid. A commonly used method which compares groups at a series of time points, possibly with t tests, is flawed on both counts. There may, however, be a remedy, which takes the form of a two stage method that uses summary measures. In the first stage a suitable summary of the response in an individual, such as a rate of change or an area under a curve, is identified and calculated for each subject. In the second stage these summary measures are analysed by simple statistical techniques as though they were raw data. The method is statistically valid and likely to be more relevant to the study questions. If this method is borne in mind when the experiment is being planned it should promote studies with enough subjects and sufficient observations at critical times to enable useful conclusions to be drawn. Use of summary measures to analyse serial measurements, though not new, is potentially a useful and simple tool in medical research.

https://www.bmj.com/content/bmj/300/6719/230.full.pdf

Analysis of serial measurements in medical research.

A text designed to make multivariate techniques available to behavioural, social, biological and medical students. Special features include an approach to multivariate inference based on the union-intersection and generalized likelihood ratio principles.

I. G. Evans

Papers

Multivariate Statistical Methods