Knowledge elicitation techniques in a knowledge management context

Gavrilova, Tatiana; Andreeva, Tatiana

doi:10.1108/13673271211246112

Cited by 95 publications

(79 citation statements)

References 50 publications

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“…However, such measures are problematic for two reasons. First, former studies show that employee tenure and expertise are not equal to the ability to share knowledge and might in fact act as a barrier to transferring know-how to peers (Hinds et al, 2001;Gavrilova and Andreeva, 2012). Second, measures such as involvement in the company's training overlap conceptually with measures of HR practices (e.g.…”

Section: Methodsmentioning

confidence: 99%

The more the better … or is it? The contradictory effects of HR practices on knowledge‐sharing motivation and behaviour

Andreeva

2016

Human Res Mgmt Journal

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This article examines how individual‐level antecedents such as motivation and ability to share knowledge mediate the relationship between HR practices and knowledge‐sharing behaviour. The results of a survey of 329 secondary school teachers reveal the contradictory effects of different HR practices on the mediating roles of intrinsic and extrinsic motivation to share knowledge and subsequent knowledge‐sharing behaviour of teachers. The study demonstrates that opportunity‐enhancing HR practices act as a moderating condition that activates either intrinsic or extrinsic motivation to share knowledge and may completely offset the effect of motivation‐enhancing HR practices. The study makes a distinctive contribution by demonstrating how certain combinations of HR practices aimed to enhance knowledge sharing might in fact be a costly solution for organisations, as they activate different mediating mechanisms in the HRM–knowledge‐sharing behaviour link.

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Section: Methodsmentioning

confidence: 99%

The more the better … or is it? The contradictory effects of HR practices on knowledge‐sharing motivation and behaviour

Andreeva

2016

Human Res Mgmt Journal

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“…On the basis of this information for a certain class of dependence models (for class of models of some domain) to which this unknown relationship (between classes and objects) belongs a classification algorithm developments is required. The algorithm using training set builds a decision rule the probability which any new object correct classification is as high as possible [3,4]. The quality of decision rule is evaluated on the basis of the test set, which is different from the training only in the way it is used.…”

Section: Problem and Clustering Problemmentioning

confidence: 99%

The way of inductive formation of medical diagnostic knowledge bases

Kleschev

Smagin

2015

2015 IEEE Seventh International Conference on Intelligent Computing and Information Systems (ICICIS)

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The paper provides an introduction into the area of inductive formation of knowledge bases. It presents traditional definitions of main problems in this area and highlights the current topical questions including the interpretability of the results. For solving of current problems in defined area the method of inductive formation of easily interpretable medical diagnostic knowledge bases is suggested. It includes the new definitions of classification and clustering problems for dependence models with parameters and the learning algorithm (solving mentioned problems in their new definitions) developed for the practically useful and easily interpretable mathematical dependence model with parameters which is a near real-life ontology of medical diagnostics (defined by a system of logical relationships with parameters). Also it includes the software package InForMedKB (INductive FORmation of MEDical Knowledge Bases) which implements above mentioned learning algorithm. InForMedKB allows to create training sets (consisting of clinical histories from various therapeutic areas) and to use them for inductive formation of medical diagnostic knowledge bases. These knowledge bases are presented in form accepted in the medical literature and contain descriptions of diseases (from specified therapeutic areas) as well as an explanation of these knowledge bases based on descriptions of clinical histories from used training sets. The formal representation of medical knowledge bases enables their usage for intelligent systems for medical diagnostics.

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“…It is also necessary to have (i) the ability to integrate this information into the final forecast, and (ii) the motivation to do such integration (Gavrilova & Andreeva, 2012). The review by Webby and O'Connor (1996) showed that experiential knowledge of the cause-effect relationships encountered in the industry may not be a good predictor of superior accuracy.…”

Section: Expertise and Credibility Of System Forecastsmentioning

confidence: 99%

Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting

Alvarado

Barrero

Önkal

et al. 2017

International Journal of Forecasting

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a b s t r a c tExpert knowledge elicitation lies at the core of judgmental forecasting-a domain that relies fully on the power of such knowledge and its integration into forecasting. Using experts in a demand forecasting framework, this work aims to compare the accuracy improvements and forecasting performances of three judgmental integration methods. To do this, a field study was conducted with 31 experts from four companies. The methods compared were the judgmental adjustment, the 50-50 combination, and the divide-and-conquer. Forecaster expertise, the credibility of system forecasts and the need to rectify system forecasts were also assessed, and mechanisms for performing this assessment were considered. When (a) a forecaster's relative expertise was high, (b) the relative credibility of the system forecasts was low, and (c) the system forecasts had a strong need of correction, judgmental adjustment improved the accuracy relative to both the other integration methods and the system forecasts. Experts with higher levels of expertise showed higher adjustment frequencies. Our results suggest that judgmental adjustment promises to be valuable in the long term if adequate conditions of forecaster expertise and the credibility of system forecasts are met.

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Knowledge elicitation techniques in a knowledge management context

Cited by 95 publications

References 50 publications

The more the better … or is it? The contradictory effects of HR practices on knowledge‐sharing motivation and behaviour

The more the better … or is it? The contradictory effects of HR practices on knowledge‐sharing motivation and behaviour

The way of inductive formation of medical diagnostic knowledge bases

Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting

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